From dffa75b95270ef3075c0d4660fbbabc71ecbe7cd Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Fri, 12 Jun 2026 15:20:00 +0200
Subject: [PATCH 01/20] .

---
 dequant.py                                    |   3 +
 editor.md                                     |  60 ++
 fp8/transformer/config.json                   |  19 +
 ...usion_pytorch_model.safetensors.index.json |   1 +
 fp8/unconditional_transformer/config.json     |  19 +
 ...usion_pytorch_model.safetensors.index.json |   1 +
 loader.py                                     |  37 +-
 ops.py                                        |  40 +-
 pr.md                                         |  42 +
 tools/convert.py                              |  15 +-
 ui.html                                       | 995 ++++++++++++++++++
 11 files changed, 1222 insertions(+), 10 deletions(-)
 create mode 100644 editor.md
 create mode 100644 fp8/transformer/config.json
 create mode 100644 fp8/transformer/diffusion_pytorch_model.safetensors.index.json
 create mode 100644 fp8/unconditional_transformer/config.json
 create mode 100644 fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json
 create mode 100644 pr.md
 create mode 100644 ui.html

diff --git a/dequant.py b/dequant.py
index 78f5f26..809db16 100644
--- a/dequant.py
+++ b/dequant.py
@@ -18,6 +18,9 @@ def dequantize_tensor(tensor, dtype=None, dequant_dtype=None):
 
     if qtype in TORCH_COMPATIBLE_QTYPES:
         return tensor.to(dtype)
+    elif qtype == gguf.GGMLQuantizationType.BF16:
+        tensor = torch.Tensor(tensor.data.view(torch.bfloat16).reshape(oshape))
+        return tensor if dtype is None or dtype == torch.bfloat16 else tensor.to(dtype)
     elif qtype in dequantize_functions:
         dequant_dtype = dtype if dequant_dtype == "target" else dequant_dtype
         return dequantize(tensor.data, qtype, oshape, dtype=dequant_dtype).to(dtype)
diff --git a/editor.md b/editor.md
new file mode 100644
index 0000000..870afe8
--- /dev/null
+++ b/editor.md
@@ -0,0 +1,60 @@
+# Prompt Canvas Editor
+
+A single-file browser editor for building Ideogram-style structured JSON prompts with canvas-based bounding boxes. The app lives entirely in `ui.html`; there is no build step, package manager, or local server requirement.
+
+## Features
+
+- Set canvas width and height with sliders or by double-clicking the displayed values.
+- Draw, move, resize, delete, and edit bounding boxes directly on the canvas.
+- Cycle through selected boxes with the `<-` and `->` controls when boxes overlap.
+- Edit global prompt fields, including high-level description, aesthetics, lighting, medium, style/photo mode, background, and color palette.
+- Edit per-box mode, description, optional text content, and per-box color palette.
+- Generate formatted JSON from the current canvas and form state.
+- Paste existing prompt JSON into the JSON box and load it back into the editable canvas.
+
+## Usage
+
+Open `ui.html` directly in a modern browser.
+
+The Tailwind design system is loaded from the Tailwind CDN, so the page needs internet access for styling. The editor logic itself is plain HTML, CSS, and JavaScript.
+
+## Basic Workflow
+
+1. Set the canvas size.
+2. Draw boxes on the canvas by clicking and dragging.
+3. Select a box and edit its properties in the right panel.
+4. Fill in the global prompt settings.
+5. Click `Generate JSON` to write the prompt JSON into the textarea.
+6. Copy or save the generated JSON wherever your workflow needs it.
+
+To edit an existing prompt, paste the JSON into the textarea and click `Load JSON`. The editor will rebuild the canvas boxes and form fields from the prompt.
+
+## JSON Shape
+
+The editor expects prompt JSON in this general form:
+
+```json
+{
+  "high_level_description": "",
+  "style_description": {
+    "aesthetics": "",
+    "lighting": "",
+    "medium": "",
+    "art_style": "",
+    "color_palette": []
+  },
+  "compositional_deconstruction": {
+    "background": "",
+    "elements": [
+      {
+        "type": "obj",
+        "bbox": [0, 0, 1000, 1000],
+        "desc": "",
+        "color_palette": []
+      }
+    ]
+  }
+}
+```
+
+Bounding boxes use normalized coordinates from `0` to `1000` in `[y1, x1, y2, x2]` order. The editor converts those coordinates to the current canvas size when loading JSON, then converts them back to normalized coordinates when generating JSON.
\ No newline at end of file
diff --git a/fp8/transformer/config.json b/fp8/transformer/config.json
new file mode 100644
index 0000000..da6e1e8
--- /dev/null
+++ b/fp8/transformer/config.json
@@ -0,0 +1,19 @@
+{
+  "_class_name": "Ideogram4Transformer2DModel",
+  "_diffusers_version": "0.39.0.dev0",
+  "_name_or_path": "/home/jinli/.cache/huggingface/hub/models--ideogram-ai--debug-ideogram-v4/snapshots/41af6183c9fd9b6254864b0720319ef984535bfc/transformer",
+  "adaln_dim": 512,
+  "attention_head_dim": 256,
+  "in_channels": 128,
+  "intermediate_size": 12288,
+  "llm_features_dim": 53248,
+  "mrope_section": [
+    24,
+    20,
+    20
+  ],
+  "norm_eps": 1e-05,
+  "num_attention_heads": 18,
+  "num_layers": 34,
+  "rope_theta": 5000000
+}
diff --git a/fp8/transformer/diffusion_pytorch_model.safetensors.index.json b/fp8/transformer/diffusion_pytorch_model.safetensors.index.json
new file mode 100644
index 0000000..ba1f6d8
--- /dev/null
+++ b/fp8/transformer/diffusion_pytorch_model.safetensors.index.json
@@ -0,0 +1 @@
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diff --git a/fp8/unconditional_transformer/config.json b/fp8/unconditional_transformer/config.json
new file mode 100644
index 0000000..f40a02c
--- /dev/null
+++ b/fp8/unconditional_transformer/config.json
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diff --git a/fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json b/fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json
new file mode 100644
index 0000000..ba1f6d8
--- /dev/null
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\ No newline at end of file
diff --git a/loader.py b/loader.py
index 7cefb11..64177f8 100644
--- a/loader.py
+++ b/loader.py
@@ -9,10 +9,23 @@
 from .ops import GGMLTensor
 from .dequant import is_quantized, dequantize_tensor
 
-IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image"}
+IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram"}
 TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}
 VIS_TYPE_LIST = {"clip-vision", "mmproj"}
 
+def device_supports_bf16():
+    """
+    Return True if the active torch device can run bf16 natively. On devices
+    without native bf16 support, computation silently falls back to fp32 which
+    is very slow, so callers should load tensors as fp16 instead.
+    """
+    try:
+        import comfy.model_management
+        return comfy.model_management.should_use_bf16(comfy.model_management.get_torch_device())
+    except Exception:
+        # If support can't be determined, keep the previous bf16 behavior.
+        return True
+
 def get_orig_shape(reader, tensor_name):
     field_key = f"comfy.gguf.orig_shape.{tensor_name}"
     field = reader.get_field(field_key)
@@ -113,6 +126,9 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
         logging.warning(f"Warning: This gguf model file is loaded in compatibility mode '{compat}' [arch:{arch_str}]")
 
     # main loading loop
+    # Devices without native bf16 fall back to slow fp32 compute, so load the
+    # full-precision BF16 storage tensors as fp16 there instead.
+    bf16_storage_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
     state_dict = {}
     qtype_dict = {}
     for sd_key, tensor in tensors:
@@ -138,9 +154,10 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
             torch_tensor = torch_tensor.view(*shape)
         state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
 
-        # 1D tensors shouldn't be quantized, this is a fix for BF16
-        if len(shape) <= 1 and tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
-            state_dict[sd_key] = dequantize_tensor(state_dict[sd_key], dtype=torch.float32)
+        # BF16 GGUF tensors are full-precision storage, not compressed quants.
+        if tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
+            dtype = torch.float32 if len(shape) <= 1 else bf16_storage_dtype
+            state_dict[sd_key] = dequantize_tensor(state_dict[sd_key], dtype=dtype)
 
         # keep track of loaded tensor types
         tensor_type_str = getattr(tensor.tensor_type, "name", repr(tensor.tensor_type))
@@ -501,6 +518,18 @@ def gguf_clip_loader(path):
         if arch == "qwen2vl":
             vsd = gguf_mmproj_loader(path)
             sd.update(vsd)
+    elif arch == "ideogram":
+        # Dequantize Ideogram model for inference
+        logging.info("Dequantizing Ideogram model for inference...")
+        # Use BF16 to save VRAM while maintaining quality, but fall back to FP16
+        # on devices that don't support bf16 (avoids slow fp32 compute fallback).
+        target_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
+        dequantized_count = 0
+        for key in list(sd.keys()):
+            if is_quantized(sd[key]):
+                sd[key] = dequantize_tensor(sd[key], dtype=target_dtype)
+                dequantized_count += 1
+        logging.info(f"Dequantized {dequantized_count} tensors for Ideogram model ({target_dtype})")
     else:
         pass
     return sd
diff --git a/ops.py b/ops.py
index 88a352e..9c26052 100644
--- a/ops.py
+++ b/ops.py
@@ -8,6 +8,18 @@
 import comfy.model_management
 from .dequant import dequantize_tensor, is_quantized
 
+def _valid_compute_dtype(dtype):
+    return dtype in {torch.float16, torch.bfloat16, torch.float32, torch.float64}
+
+def _infer_compute_dtype(tensor_type, fallback=None):
+    if _valid_compute_dtype(fallback):
+        return fallback
+    if tensor_type == gguf.GGMLQuantizationType.BF16:
+        return torch.bfloat16
+    if tensor_type == gguf.GGMLQuantizationType.F32:
+        return torch.float32
+    return torch.float16
+
 def chained_hasattr(obj, chained_attr):
     probe = obj
     for attr in chained_attr.split('.'):
@@ -45,13 +57,14 @@ class GGMLTensor(torch.Tensor):
     """
     Main tensor-like class for storing quantized weights
     """
-    def __init__(self, *args, tensor_type, tensor_shape, patches=[], **kwargs):
+    def __init__(self, *args, tensor_type, tensor_shape, patches=[], compute_dtype=None, **kwargs):
         super().__init__()
         self.tensor_type = tensor_type
         self.tensor_shape = tensor_shape
         self.patches = patches
+        self.compute_dtype = compute_dtype
 
-    def __new__(cls, *args, tensor_type, tensor_shape, patches=[], **kwargs):
+    def __new__(cls, *args, tensor_type, tensor_shape, patches=[], compute_dtype=None, **kwargs):
         return super().__new__(cls, *args, **kwargs)
 
     def to(self, *args, **kwargs):
@@ -59,6 +72,7 @@ def to(self, *args, **kwargs):
         new.tensor_type = getattr(self, "tensor_type", None)
         new.tensor_shape = getattr(self, "tensor_shape", new.data.shape)
         new.patches = getattr(self, "patches", []).copy()
+        new.compute_dtype = getattr(self, "compute_dtype", None)
         return new
 
     def clone(self, *args, **kwargs):
@@ -81,9 +95,17 @@ def new_empty(self, size, *args, **kwargs):
                 new_tensor,
                 tensor_type = getattr(self, "tensor_type", None),
                 tensor_shape = size,
-                patches = getattr(self, "patches", []).copy()
+                patches = getattr(self, "patches", []).copy(),
+                compute_dtype = getattr(self, "compute_dtype", None),
         )
 
+    @property
+    def dtype(self):
+        qtype = getattr(self, "tensor_type", None)
+        if qtype in GGMLLayer.torch_compatible_tensor_types:
+            return torch.Tensor(self).dtype
+        return _infer_compute_dtype(qtype, getattr(self, "compute_dtype", None))
+
     @property
     def shape(self):
         if not hasattr(self, "tensor_shape"):
@@ -121,8 +143,12 @@ def ggml_load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
         prefix_len = len(prefix)
         for k,v in state_dict.items():
             if k[prefix_len:] == "weight":
+                if isinstance(v, GGMLTensor):
+                    v.compute_dtype = self._ggml_compute_dtype(v, "weight")
                 self.weight = torch.nn.Parameter(v, requires_grad=False)
             elif k[prefix_len:] == "bias" and v is not None:
+                if isinstance(v, GGMLTensor):
+                    v.compute_dtype = self._ggml_compute_dtype(v, "bias")
                 self.bias = torch.nn.Parameter(v, requires_grad=False)
             else:
                 unexpected_keys.append(k)
@@ -137,6 +163,12 @@ def ggml_load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
         if getattr(self.weight, "is_largest_weight", False):
             self.largest_layer = True
 
+    def _ggml_compute_dtype(self, tensor, param_name):
+        if self.dequant_dtype is not None and self.dequant_dtype != "target":
+            return self.dequant_dtype
+        model_dtype = getattr(self, f"{param_name}_comfy_model_dtype", None)
+        return _infer_compute_dtype(getattr(tensor, "tensor_type", None), model_dtype)
+
     def _save_to_state_dict(self, *args, **kwargs):
         if self.is_ggml_quantized():
             return self.ggml_save_to_state_dict(*args, **kwargs)
@@ -238,6 +270,8 @@ def __init__(self, in_features, out_features, bias=True, device=None, dtype=None
             self.out_features = out_features
             self.weight = None
             self.bias = None
+            self.weight_comfy_model_dtype = dtype
+            self.bias_comfy_model_dtype = dtype
 
         def forward_ggml_cast_weights(self, input):
             weight, bias = self.cast_bias_weight(input)
diff --git a/pr.md b/pr.md
new file mode 100644
index 0000000..7ea8184
--- /dev/null
+++ b/pr.md
@@ -0,0 +1,42 @@
+# Summary
+
+This adds support for Ideogram GGUF models.
+
+# What Changed
+
+- Added `ideogram` to the supported image GGUF architectures.
+- Added Ideogram model detection to the converter.
+- Added GGUF dtype handling needed by Ideogram inference.
+- Fixed the Ideogram inference failure where a packed GGUF weight dtype caused a byte tensor to reach CUDA linear.
+- Adjusted BF16 GGUF loading so Ideogram can start inference faster.
+
+# Error Fixed
+
+Before this change, Ideogram GGUF models could load but failed during sampling with:
+
+```text
+"addmm_cuda" not implemented for 'Byte'
+```
+
+# Tests
+
+Ran Python compile checks:
+
+```text
+python -m py_compile dequant.py ops.py loader.py nodes.py tools\convert.py
+```
+
+Checked a synthetic GGUF linear forward:
+
+- packed storage stayed `torch.uint8`
+- reported dtype was `torch.bfloat16`
+- input dtype was `torch.bfloat16`
+- output dtype was `torch.bfloat16`
+
+Checked BF16 loading behavior with finite values.
+
+# Notes
+
+Full ComfyUI inference was not run from this environment.
+
+Other GGUF quant types still use the existing dequant paths.
diff --git a/tools/convert.py b/tools/convert.py
index 5029c87..a9b76ea 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -145,8 +145,18 @@ class ModelLumina2(ModelTemplate):
         ("cap_embedder.1.weight", "context_refiner.0.attention.qkv.weight")
     ]
 
+class ModelIdeogram(ModelTemplate):
+    arch = "ideogram"
+    keys_detect = [
+        (
+            "t_embedding.mlp_in.weight",
+            "layers.0.attention.qkv.weight",
+            "final_layer.linear.weight",
+        )
+    ]
+
 arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2, 
-             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2]
+             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2, ModelIdeogram]
 
 def is_model_arch(model, state_dict):
     # check if model is correct
@@ -361,5 +371,4 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False):
 
 if __name__ == "__main__":
     args = parse_args()
-    convert_file(args.src, args.dst)
-
+    convert_file(args.src, args.dst)
\ No newline at end of file
diff --git a/ui.html b/ui.html
new file mode 100644
index 0000000..740d44a
--- /dev/null
+++ b/ui.html
@@ -0,0 +1,995 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>JSON Prompt Generation Tool</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <style>
+        input[type="range"] {
+            accent-color: #2563eb;
+        }
+
+        #canvas-stage {
+            position: relative;
+            flex: none;
+        }
+
+        #canvas-wrapper {
+            background: repeating-conic-gradient(#eee 0% 25%, transparent 0% 50%) 50% / 20px 20px;
+            background-color: #fff;
+            border: 1px dashed #94a3b8;
+            position: absolute;
+            inset: 0 auto auto 0;
+            transform-origin: top left;
+            overflow: hidden;
+            user-select: none;
+            touch-action: none;
+        }
+
+        .bounding-box {
+            position: absolute;
+            border: 2px solid #ff0044;
+            background: rgba(255, 0, 68, 0.2);
+            cursor: grab;
+            box-sizing: border-box;
+        }
+
+        .bounding-box.selected {
+            border-color: #0066FF;
+            background: rgba(0, 102, 255, 0.2);
+            z-index: 10;
+        }
+
+        .resize-handle {
+            position: absolute;
+            width: 10px;
+            height: 10px;
+            background: white;
+            border: 1px solid black;
+            right: -5px;
+            bottom: -5px;
+            cursor: nwse-resize;
+        }
+
+        .color-list {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 0.375rem;
+            margin-top: 0.5rem;
+        }
+
+        .swatch {
+            width: 2rem;
+            height: 2rem;
+            border: 1px solid #000;
+            border-radius: 0.375rem;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            font-size: 10px;
+            cursor: pointer;
+            color: transparent;
+            transition: 0.2s;
+        }
+
+        .swatch:hover {
+            color: #fff;
+            text-shadow: 0 0 2px #000;
+        }
+    </style>
+
+</head>
+
+<body class="min-h-screen bg-slate-50 text-slate-900 antialiased">
+
+    <main class="mx-auto flex w-full max-w-[1800px] flex-col gap-4 p-4 lg:p-6">
+        <header class="rounded-lg border border-slate-200 bg-white px-4 py-3 shadow-sm">
+            <div class="grid gap-4 lg:grid-cols-[minmax(0,1fr)_minmax(0,1fr)_auto] lg:items-end">
+                <div class="space-y-2">
+                    <div class="flex items-center justify-between gap-3">
+                        <label for="canvas-width" class="text-sm font-semibold text-slate-700">Width</label>
+                        <span id="w-val" class="cursor-text rounded-md bg-slate-100 px-2 py-1 font-mono text-sm text-slate-900" tabindex="0" title="Double-click to edit">1024</span>
+                    </div>
+                    <input type="range" id="canvas-width" min="256" max="4096" step="16" value="1024" class="w-full">
+                </div>
+                <div class="space-y-2">
+                    <div class="flex items-center justify-between gap-3">
+                        <label for="canvas-height" class="text-sm font-semibold text-slate-700">Height</label>
+                        <span id="h-val" class="cursor-text rounded-md bg-slate-100 px-2 py-1 font-mono text-sm text-slate-900" tabindex="0" title="Double-click to edit">1024</span>
+                    </div>
+                    <input type="range" id="canvas-height" min="256" max="4096" step="16" value="1024" class="w-full">
+                </div>
+                <button onclick="initCanvas()" class="inline-flex h-10 items-center justify-center rounded-md bg-blue-600 px-4 text-sm font-semibold text-white shadow-sm hover:bg-blue-700 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2">Reset Canvas</button>
+            </div>
+        </header>
+
+        <div class="grid gap-4 xl:grid-cols-[minmax(0,1fr)_420px]">
+            <section class="flex min-w-0 flex-col gap-4">
+                <section class="overflow-hidden rounded-lg border border-slate-200 bg-white shadow-sm">
+                    <div class="flex items-center justify-between border-b border-slate-200 px-4 py-3">
+                        <h1 class="text-base font-semibold text-slate-900">Prompt Canvas</h1>
+                        <span class="rounded-md bg-slate-100 px-2 py-1 text-xs font-medium text-slate-600" id="canvas-size-label">1024 x 1024</span>
+                    </div>
+                    <div class="bg-slate-100 p-4">
+                        <div id="canvas-viewport" class="mx-auto max-h-[840px] max-w-full overflow-auto rounded-md border border-slate-200 bg-white p-2">
+                            <div id="canvas-stage">
+                                <div id="canvas-wrapper"></div>
+                            </div>
+                        </div>
+                    </div>
+                </section>
+
+                <div class="grid gap-4">
+                    <section class="rounded-lg border border-slate-200 bg-white p-4 shadow-sm">
+                        <div class="mb-3 flex flex-wrap items-center justify-between gap-2">
+                            <h2 class="text-sm font-semibold text-slate-900">Prompt JSON</h2>
+                            <div class="flex gap-2">
+                                <button onclick="parsePromptTextarea()" class="inline-flex h-9 items-center justify-center rounded-md border border-slate-300 bg-white px-3 text-sm font-semibold text-slate-700 shadow-sm hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2">Load JSON</button>
+                                <button onclick="generateJSON()" class="inline-flex h-9 items-center justify-center rounded-md bg-emerald-600 px-3 text-sm font-semibold text-white shadow-sm hover:bg-emerald-700 focus:outline-none focus:ring-2 focus:ring-emerald-500 focus:ring-offset-2">Generate JSON</button>
+                            </div>
+                        </div>
+                        <textarea id="json-output" class="h-[320px] w-full resize-y rounded-md border border-slate-300 bg-white p-3 font-mono text-sm leading-5 text-slate-900 shadow-sm placeholder:text-slate-400 focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500" placeholder="Paste or generate prompt JSON"></textarea>
+                        <div id="json-status" class="mt-2 min-h-5 text-xs text-slate-500"></div>
+                    </section>
+                </div>
+            </section>
+
+            <aside class="space-y-4">
+                <section class="rounded-lg border border-slate-200 bg-white p-4 shadow-sm">
+                    <h2 class="mb-4 border-b border-slate-200 pb-2 text-sm font-semibold text-slate-900">Global Settings</h2>
+                    <div class="mb-4 flex flex-wrap items-center gap-2">
+                        <span class="text-sm font-semibold text-slate-700">Mode</span>
+                        <input type="radio" id="mode_photo" name="art_mode" value="photo" class="peer/photo sr-only" onchange="setPhotoArtstyle(0)">
+                        <label for="mode_photo" class="cursor-pointer rounded-md border border-slate-300 bg-white px-3 py-1.5 text-sm font-semibold text-slate-700 shadow-sm hover:bg-slate-50 peer-checked/photo:bg-blue-600 peer-checked/photo:text-white">photo</label>
+
+                        <input type="radio" id="mode_artstyle" name="art_mode" value="art_style" class="peer/art sr-only" checked onchange="setPhotoArtstyle(1)">
+                        <label for="mode_artstyle" class="cursor-pointer rounded-md border border-slate-300 bg-white px-3 py-1.5 text-sm font-semibold text-slate-700 shadow-sm hover:bg-slate-50 peer-checked/art:bg-blue-600 peer-checked/art:text-white">art_style</label>
+                    </div>
+                    <div class="space-y-3">
+                        <div class="space-y-1.5"><label for="high_level_description" class="text-sm font-semibold text-slate-700">High Level Description</label><textarea id="high_level_description" class="min-h-24 w-full resize-y rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500"></textarea></div>
+                        <div class="space-y-1.5"><label for="aesthetics" class="text-sm font-semibold text-slate-700">Aesthetics</label><input type="text" id="aesthetics" class="w-full rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500"></div>
+                        <div class="space-y-1.5"><label for="lighting" class="text-sm font-semibold text-slate-700">Lighting</label><input type="text" id="lighting" class="w-full rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500"></div>
+                        <div class="space-y-1.5"><label for="medium" class="text-sm font-semibold text-slate-700">Medium</label><input type="text" id="medium" class="w-full rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500 disabled:bg-slate-100 disabled:text-slate-500"></div>
+                        <div class="space-y-1.5"><label id="mode_label" for="art_style" class="text-sm font-semibold text-slate-700">Art Style</label><input type="text" id="art_style" class="w-full rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500"></div>
+                        <div class="space-y-1.5"><label for="background" class="text-sm font-semibold text-slate-700">Background</label><textarea id="background" class="min-h-24 w-full resize-y rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500"></textarea></div>
+                        <div>
+                            <label for="global-color-picker" class="text-sm font-semibold text-slate-700">Global Color Palette (Max 16)</label>
+                            <div class="mt-2 flex items-center gap-2">
+                                <input type="color" id="global-color-picker" class="h-9 w-12 rounded-md border border-slate-300 bg-white p-1">
+                                <button onclick="addColor('global')" class="inline-flex h-9 items-center justify-center rounded-md border border-slate-300 bg-white px-3 text-sm font-semibold text-slate-700 shadow-sm hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2">Add</button>
+                            </div>
+                            <div id="global-colors" class="color-list"></div>
+                        </div>
+                    </div>
+                </section>
+
+                <section class="rounded-lg border border-slate-200 bg-white p-4 shadow-sm" id="box-panel" style="display: none;">
+                    <div class="mb-4 flex items-center justify-between gap-3 border-b border-slate-200 pb-2">
+                        <h2 class="text-sm font-semibold text-slate-900">Selected Box Properties</h2>
+                        <div class="flex items-center gap-1">
+                            <button type="button" onclick="cycleSelectedBox(-1)" class="inline-flex h-7 w-8 items-center justify-center rounded-md border border-slate-300 bg-white text-xs font-semibold text-slate-700 shadow-sm hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2" title="Previous box">&leftarrow;</button>
+                            <button type="button" onclick="cycleSelectedBox(1)" class="inline-flex h-7 w-8 items-center justify-center rounded-md border border-slate-300 bg-white text-xs font-semibold text-slate-700 shadow-sm hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2" title="Next box">&rightarrow;</button>
+                        </div>
+                    </div>
+                    <div class="space-y-3">
+                        <div class="space-y-1.5">
+                            <label for="box-mode" class="text-sm font-semibold text-slate-700">Mode</label>
+                            <select id="box-mode" class="w-full rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500" onchange="updateBoxData()" oninput="updateBoxData()">
+                                <option value="obj">Object (obj)</option>
+                                <option value="text">Text (text)</option>
+                            </select>
+                        </div>
+                        <div class="space-y-1.5" id="text-input-group" style="display: none;">
+                            <label for="box-text" class="text-sm font-semibold text-slate-700">Text Content</label>
+                            <input type="text" id="box-text" class="w-full rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500" onchange="updateBoxData()" oninput="updateBoxData()">
+                        </div>
+                        <div class="space-y-1.5">
+                            <label for="box-desc" class="text-sm font-semibold text-slate-700">Description</label>
+                            <textarea id="box-desc" class="min-h-24 w-full resize-y rounded-md border border-slate-300 bg-white px-3 py-2 text-sm text-slate-900 shadow-sm focus:border-blue-500 focus:outline-none focus:ring-2 focus:ring-blue-500" onchange="updateBoxData()" oninput="updateBoxData()"></textarea>
+                        </div>
+                        <div>
+                            <label for="box-color-picker" class="text-sm font-semibold text-slate-700">Box Color Palette (Max 5)</label>
+                            <div class="mt-2 flex items-center gap-2">
+                                <input type="color" id="box-color-picker" class="h-9 w-12 rounded-md border border-slate-300 bg-white p-1">
+                                <button onclick="addColor('box')" class="inline-flex h-9 items-center justify-center rounded-md border border-slate-300 bg-white px-3 text-sm font-semibold text-slate-700 shadow-sm hover:bg-slate-50 focus:outline-none focus:ring-2 focus:ring-blue-500 focus:ring-offset-2">Add</button>
+                            </div>
+                            <div id="box-colors" class="color-list"></div>
+                        </div>
+                        <button onclick="deleteSelectedBox()" class="inline-flex h-10 w-full items-center justify-center rounded-md bg-rose-600 px-4 text-sm font-semibold text-white shadow-sm hover:bg-rose-700 focus:outline-none focus:ring-2 focus:ring-rose-500 focus:ring-offset-2">Delete Box</button>
+                    </div>
+                </section>
+            </aside>
+        </div>
+    </main>
+
+    <script>
+        var json_prompt = JSON.parse(atob("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"))
+        // State
+        let canvasW = 1024;
+        let canvasH = 1024;
+        let scale = 1;
+        let boxes = [];
+        let globalPalette = [];
+        let selectedBoxId = null;
+        let boxCounter = 0;
+
+        // DOM Elements
+        const canvas = document.getElementById('canvas-wrapper');
+        const canvasStage = document.getElementById('canvas-stage');
+        const canvasWSlider = document.getElementById('canvas-width');
+        const canvasHSlider = document.getElementById('canvas-height');
+        const boxPanel = document.getElementById('box-panel');
+        const jsonOutput = document.getElementById('json-output');
+        const jsonStatus = document.getElementById('json-status');
+        const wValue = document.getElementById('w-val');
+        const hValue = document.getElementById('h-val');
+        const canvasSizeLabel = document.getElementById('canvas-size-label');
+
+        const CANVAS_MIN = parseInt(canvasWSlider.min, 10);
+        const CANVAS_MAX = parseInt(canvasWSlider.max, 10);
+        const CANVAS_STEP = parseInt(canvasWSlider.step, 10) || 1;
+
+        function normalizeDimension(value) {
+            const parsed = Number(value);
+            if (!Number.isFinite(parsed)) return null;
+
+            const snapped = Math.round(parsed / CANVAS_STEP) * CANVAS_STEP;
+            return Math.min(CANVAS_MAX, Math.max(CANVAS_MIN, snapped));
+        }
+
+        function setCanvasControls(width, height) {
+            const nextW = normalizeDimension(width);
+            const nextH = normalizeDimension(height);
+
+            if (nextW !== null) canvasWSlider.value = nextW;
+            if (nextH !== null) canvasHSlider.value = nextH;
+            applyCanvasSize();
+        }
+
+        function syncDimensionLabels() {
+            wValue.innerText = canvasW.toString();
+            hValue.innerText = canvasH.toString();
+            canvasSizeLabel.innerText = `${canvasW} x ${canvasH}`;
+        }
+
+        function applyCanvasSize() {
+            canvasW = parseInt(canvasWSlider.value, 10) || 1024;
+            canvasH = parseInt(canvasHSlider.value, 10) || 1024;
+
+            scale = canvasH > 800 ? 800 / canvasH : 1;
+
+            canvas.style.width = canvasW + 'px';
+            canvas.style.height = canvasH + 'px';
+            canvas.style.transform = `scale(${scale})`;
+            canvasStage.style.width = (canvasW * scale) + 'px';
+            canvasStage.style.height = (canvasH * scale) + 'px';
+            syncDimensionLabels();
+        }
+
+        function editCanvasDimension(axis) {
+            const span = axis === 'width' ? wValue : hValue;
+            const current = axis === 'width' ? canvasW : canvasH;
+            if (span.dataset.editing === 'true') return;
+
+            span.dataset.editing = 'true';
+            const input = document.createElement('input');
+            input.type = 'number';
+            input.min = CANVAS_MIN.toString();
+            input.max = CANVAS_MAX.toString();
+            input.step = CANVAS_STEP.toString();
+            input.value = current.toString();
+            input.className = 'w-24 rounded-md border border-blue-500 bg-white px-2 py-1 font-mono text-sm text-slate-900 shadow-sm outline-none ring-2 ring-blue-500';
+
+            let finished = false;
+            const finish = (commit) => {
+                if (finished) return;
+                finished = true;
+                if (commit) {
+                    const next = normalizeDimension(input.value);
+                    if (next !== null) {
+                        if (axis === 'width') {
+                            canvasWSlider.value = next;
+                        } else {
+                            canvasHSlider.value = next;
+                        }
+                    }
+                }
+                input.replaceWith(span);
+                span.dataset.editing = 'false';
+                applyCanvasSize();
+            };
+
+            input.addEventListener('keydown', (e) => {
+                if (e.key === 'Enter') finish(true);
+                if (e.key === 'Escape') finish(false);
+            });
+            input.addEventListener('blur', () => finish(true));
+            span.replaceWith(input);
+            input.focus();
+            input.select();
+        }
+
+        function clearCanvasBoxes() {
+            canvas.innerHTML = '';
+            boxes = [];
+            boxCounter = 0;
+            selectBox(null);
+        }
+
+        function setJSONStatus(message, type = 'neutral') {
+            jsonStatus.textContent = message;
+            jsonStatus.style.color = type === 'error' ? '#e11d48' : type === 'success' ? '#047857' : '#64748b';
+        }
+
+        // UI Listeners
+        canvasWSlider.addEventListener('input', applyCanvasSize);
+        canvasHSlider.addEventListener('input', applyCanvasSize);
+        wValue.addEventListener('dblclick', () => editCanvasDimension('width'));
+        hValue.addEventListener('dblclick', () => editCanvasDimension('height'));
+        wValue.addEventListener('keydown', e => {
+            if (e.key === 'Enter' || e.key === ' ') {
+                e.preventDefault();
+                editCanvasDimension('width');
+            }
+        });
+        hValue.addEventListener('keydown', e => {
+            if (e.key === 'Enter' || e.key === ' ') {
+                e.preventDefault();
+                editCanvasDimension('height');
+            }
+        });
+        jsonOutput.addEventListener('paste', () => {
+            window.setTimeout(() => parsePromptTextarea({ source: 'paste' }), 0);
+        });
+
+        document.addEventListener('dragover', (e) => {
+            e.preventDefault();
+        });
+        document.addEventListener('drop', (e) => {
+            e.preventDefault();
+
+            if (e.dataTransfer.files.length > 0) {
+                importImage(e.dataTransfer.files[0]); // is async
+            }
+        });
+
+        function initCanvas() {
+            applyCanvasSize();
+
+            canvas.style.backgroundImage = ""
+            canvas.style.backgroundSize = ""
+
+            jsonOutput.value = '';
+            setJSONStatus('');
+            clearCanvasBoxes();
+        }
+
+        // Color Palette Logic
+        function addColor(type) {
+            const picker = document.getElementById(type + '-color-picker');
+            let hex = picker.value.toUpperCase(); // Ideogram4 requires uppercase string
+
+            if (type === 'global') {
+                if (globalPalette.length >= 16) return alert("Max 16 colors allowed.");
+                if (!globalPalette.includes(hex)) {
+                    globalPalette.push(hex);
+                    renderColors('global');
+                }
+            } else if (type === 'box' && selectedBoxId !== null) {
+                const box = boxes.find(b => b.id === selectedBoxId);
+                if (!box) return;
+                box.colors = box.colors || [];
+                if (box.colors.length >= 5) return alert("Max 5 colors allowed per box.");
+                if (!box.colors.includes(hex)) {
+                    box.colors.push(hex);
+                    renderColors('box');
+                }
+            }
+        }
+
+        function removeColor(type, hex) {
+            if (type === 'global') {
+                globalPalette = globalPalette.filter(c => c !== hex);
+                renderColors('global');
+            } else if (type === 'box' && selectedBoxId !== null) {
+                const box = boxes.find(b => b.id === selectedBoxId);
+                box.colors = box.colors.filter(c => c !== hex);
+                renderColors('box');
+            }
+        }
+
+        function renderColors(type) {
+            const container = document.getElementById(type + '-colors');
+            container.innerHTML = '';
+            const list = type === 'global' ? globalPalette : (boxes.find(b => b.id === selectedBoxId)?.colors || []);
+
+            list.forEach(hex => {
+                const swatch = document.createElement('div');
+                swatch.className = 'swatch';
+                swatch.style.backgroundColor = hex;
+                swatch.onclick = () => removeColor(type, hex);
+                swatch.textContent = 'x';
+                container.appendChild(swatch);
+            });
+        }
+
+        function createBoxElement(box, selected = false) {
+            const boxElement = document.createElement('div');
+            boxElement.className = selected ? 'bounding-box selected' : 'bounding-box';
+            boxElement.id = box.id;
+            boxElement.style.left = box.x + 'px';
+            boxElement.style.top = box.y + 'px';
+            boxElement.style.width = box.w + 'px';
+            boxElement.style.height = box.h + 'px';
+
+            const handle = document.createElement('div');
+            handle.className = 'resize-handle';
+            boxElement.appendChild(handle);
+
+            return boxElement;
+        }
+
+        function syncBoxFromDom(boxId) {
+            const box = boxes.find(b => b.id === boxId);
+            const dom = document.getElementById(boxId);
+            if (!box || !dom) return;
+
+            box.x = parseFloat(dom.style.left) || 0;
+            box.y = parseFloat(dom.style.top) || 0;
+            box.w = parseFloat(dom.style.width) || 0;
+            box.h = parseFloat(dom.style.height) || 0;
+        }
+
+        function setBoxPanelHighlight(boxElement) {
+            if (!boxElement) {
+                boxPanel.style.borderColor = '';
+                boxPanel.style.boxShadow = '';
+                return;
+            }
+
+            const borderColor = getComputedStyle(boxElement).borderTopColor;
+            boxPanel.style.borderColor = borderColor;
+            boxPanel.style.boxShadow = `0 0 0 1px ${borderColor}`;
+        }
+
+        // Canvas Drawing Logic
+        let isDrawing = false;
+        let isDragging = false;
+        let isResizing = false;
+        let currentBoxDOM = null;
+        let startX, startY;
+
+        canvas.addEventListener('pointerdown', (e) => {
+            if (e.target === canvas) {
+                isDrawing = true;
+                const rect = canvas.getBoundingClientRect();
+                startX = (e.clientX - rect.left) / scale;
+                startY = (e.clientY - rect.top) / scale;
+
+                const box = {
+                    id: 'box_' + boxCounter++,
+                    x: startX, y: startY, w: 0, h: 0,
+                    mode: 'obj', text: '', desc: '', colors: []
+                };
+                boxes.push(box);
+
+                currentBoxDOM = createBoxElement(box, true);
+                canvas.appendChild(currentBoxDOM);
+                selectBox(box.id);
+            } else if (e.target.classList.contains('resize-handle')) {
+                isResizing = true;
+                currentBoxDOM = e.target.parentElement;
+                selectBox(currentBoxDOM.id);
+                const rect = canvas.getBoundingClientRect();
+                dragStartX = (e.clientX - rect.left) / scale;
+                dragStartY = (e.clientY - rect.top) / scale;
+                initialBoxW = parseFloat(currentBoxDOM.style.width);
+                initialBoxH = parseFloat(currentBoxDOM.style.height);
+                e.stopPropagation();
+            } else if (e.target.classList.contains('bounding-box')) {
+                isDragging = true;
+                currentBoxDOM = e.target;
+                selectBox(currentBoxDOM.id);
+                const rect = canvas.getBoundingClientRect();
+                dragStartX = (e.clientX - rect.left) / scale;
+                dragStartY = (e.clientY - rect.top) / scale;
+                initialBoxX = parseFloat(currentBoxDOM.style.left);
+                initialBoxY = parseFloat(currentBoxDOM.style.top);
+                e.stopPropagation();
+            }
+        });
+
+        canvas.addEventListener('pointermove', (e) => {
+            const rect = canvas.getBoundingClientRect();
+            const currentX = (e.clientX - rect.left) / scale;
+            const currentY = (e.clientY - rect.top) / scale;
+
+            if (isDrawing && currentBoxDOM) {
+                const w = currentX - startX;
+                const h = currentY - startY;
+                currentBoxDOM.style.width = Math.abs(w) + 'px';
+                currentBoxDOM.style.height = Math.abs(h) + 'px';
+                currentBoxDOM.style.left = (w < 0 ? currentX : startX) + 'px';
+                currentBoxDOM.style.top = (h < 0 ? currentY : startY) + 'px';
+            }
+        });
+
+        window.addEventListener('pointerup', () => {
+            if (isDrawing && currentBoxDOM) {
+                // Save final dimensions
+                const box = boxes.find(b => b.id === currentBoxDOM.id);
+                box.w = parseFloat(currentBoxDOM.style.width) || 0;
+                box.h = parseFloat(currentBoxDOM.style.height) || 0;
+                box.x = parseFloat(currentBoxDOM.style.left) || 0;
+                box.y = parseFloat(currentBoxDOM.style.top) || 0;
+
+                if (box.w < 10 || box.h < 10) {
+                    // Ignore tiny accidental clicks
+                    canvas.removeChild(currentBoxDOM);
+                    boxes = boxes.filter(b => b.id !== box.id);
+                    selectBox(null);
+                }
+            } else if ((isDragging || isResizing) && currentBoxDOM) {
+                syncBoxFromDom(currentBoxDOM.id);
+            }
+            isDrawing = false;
+            isDragging = false;
+            isResizing = false;
+            currentBoxDOM = null;
+        });
+
+        // Box Interaction (Select, Drag, Resize)
+        let dragStartX, dragStartY, initialBoxX, initialBoxY, initialBoxW, initialBoxH;
+
+        window.addEventListener('pointermove', (e) => {
+            const rect = canvas.getBoundingClientRect();
+            const currentX = (e.clientX - rect.left) / scale;
+            const currentY = (e.clientY - rect.top) / scale;
+
+            if (isDragging && currentBoxDOM) {
+                const dx = currentX - dragStartX;
+                const dy = currentY - dragStartY;
+                currentBoxDOM.style.left = (initialBoxX + dx) + 'px';
+                currentBoxDOM.style.top = (initialBoxY + dy) + 'px';
+            } else if (isResizing && currentBoxDOM) {
+                const dx = currentX - dragStartX;
+                const dy = currentY - dragStartY;
+                currentBoxDOM.style.width = Math.max(10, initialBoxW + dx) + 'px';
+                currentBoxDOM.style.height = Math.max(10, initialBoxH + dy) + 'px';
+            }
+        });
+
+        // Box Forms logic
+        function selectBox(id) {
+            selectedBoxId = id;
+            document.querySelectorAll('.bounding-box').forEach(el => el.classList.remove('selected'));
+            if (id) {
+                const box = boxes.find(b => b.id === id);
+                const dom = document.getElementById(id);
+                if (!box || !dom) {
+                    selectedBoxId = null;
+                    boxPanel.style.display = 'none';
+                    setBoxPanelHighlight(null);
+                    return;
+                }
+
+                dom.classList.add('selected');
+                boxPanel.style.display = 'block';
+                setBoxPanelHighlight(dom);
+                document.getElementById('box-mode').value = box.mode;
+                document.getElementById('box-text').value = box.text;
+                document.getElementById('box-desc').value = box.desc;
+                document.getElementById('text-input-group').style.display = box.mode === 'text' ? 'block' : 'none';
+                renderColors('box');
+            } else {
+                boxPanel.style.display = 'none';
+                setBoxPanelHighlight(null);
+            }
+        }
+
+        function cycleSelectedBox(direction) {
+            const selectableBoxes = boxes.filter(box => document.getElementById(box.id));
+            if (selectableBoxes.length === 0) {
+                selectBox(null);
+                return;
+            }
+
+            const currentIndex = selectableBoxes.findIndex(box => box.id === selectedBoxId);
+            const startIndex = currentIndex === -1 ? 0 : currentIndex;
+            const nextIndex = (startIndex + direction + selectableBoxes.length) % selectableBoxes.length;
+            selectBox(selectableBoxes[nextIndex].id);
+        }
+
+        function updateBoxData() {
+            if (!selectedBoxId) return;
+            const box = boxes.find(b => b.id === selectedBoxId);
+            if (!box) return;
+            box.mode = document.getElementById('box-mode').value;
+            box.text = document.getElementById('box-text').value;
+            box.desc = document.getElementById('box-desc').value;
+            document.getElementById('text-input-group').style.display = box.mode === 'text' ? 'block' : 'none';
+        }
+
+        function deleteSelectedBox() {
+            if (!selectedBoxId) return;
+            const dom = document.getElementById(selectedBoxId);
+            if (dom) dom.remove();
+            boxes = boxes.filter(b => b.id !== selectedBoxId);
+            selectBox(null);
+        }
+
+        // JSON Generation
+        function generateJSON() {
+            // Save current bounding box positions to state
+            boxes.forEach(box => {
+                const dom = document.getElementById(box.id);
+                if (dom) {
+                    box.x = parseFloat(dom.style.left);
+                    box.y = parseFloat(dom.style.top);
+                    box.w = parseFloat(dom.style.width);
+                    box.h = parseFloat(dom.style.height);
+                }
+            });
+
+            // Normalize Coordinates 0-1000
+            const norm = (val, max) => Math.min(1000, Math.max(0, Math.round((val / max) * 1000)));
+
+            const elements = boxes.map(box => {
+                const x1 = norm(box.x, canvasW);
+                const y1 = norm(box.y, canvasH);
+                const x2 = norm(box.x + box.w, canvasW);
+                const y2 = norm(box.y + box.h, canvasH);
+
+                let el = {
+                    type: box.mode,
+                    bbox: [y1, x1, y2, x2],
+                };
+
+                if (box.mode === 'text') el.text = box.text;
+                el.desc = box.desc;
+                if (box.colors && box.colors.length > 0) el.color_palette = box.colors;
+
+                return el;
+            });
+
+            var output = {
+                high_level_description: document.getElementById('high_level_description').value,
+                style_description: {
+                    aesthetics: document.getElementById('aesthetics').value,
+                    lighting: document.getElementById('lighting').value,
+                    //medium: document.getElementById('medium').value,
+                    //art_style: document.getElementById('art_style').value,
+                    //color_palette: globalPalette
+                },
+                compositional_deconstruction: {
+                    background: document.getElementById('background').value,
+                    elements: elements
+                }
+            };
+
+            // in photo mode, order in prompt should be "photo" then "medium",
+            // while in art_style mode order should be "medium" then "art_style"
+            if(photoArtStyleMode == MODE_PHOTO){
+                output.style_description.photo = document.getElementById('art_style').value;
+                output.style_description.medium = document.getElementById('medium').value;
+            }
+            else{
+                output.style_description.medium = document.getElementById('medium').value;
+                output.style_description.art_style = document.getElementById('art_style').value
+            }
+
+            output.style_description.color_palette = globalPalette
+
+            jsonOutput.value = JSON.stringify(output, null, 2);
+            setJSONStatus(`Generated ${elements.length} element${elements.length === 1 ? '' : 's'}.`, 'success');
+        }
+
+        function setImage(imgURL){
+            canvas.style.backgroundImage = `url("${imgURL}")`
+            canvas.style.backgroundSize = "cover"
+        }
+
+        function extractComfyUIMetadata(arrayBuffer) {
+            const view = new DataView(arrayBuffer);
+
+            // Check valid PNG 8-byte signature
+            if (view.byteLength < 8 || view.getUint32(0) !== 0x89504E47 || view.getUint32(4) !== 0x0D0A1A0A) {
+                throw new Error("Not a valid PNG file.");
+            }
+
+            let offset = 8;
+            const result = {};
+
+            while (offset < view.byteLength) {
+                if (offset + 8 > view.byteLength) break;
+
+                // Read chunk length and type
+                const length = view.getUint32(offset);
+                const chunkType = String.fromCharCode(
+                    view.getUint8(offset + 4),
+                    view.getUint8(offset + 5),
+                    view.getUint8(offset + 6),
+                    view.getUint8(offset + 7)
+                );
+
+                offset += 8; // Move past length and type
+
+                if (chunkType === 'tEXt') {
+                    if (offset + length > view.byteLength) break;
+
+                    const chunkData = new Uint8Array(arrayBuffer, offset, length);
+                    const nullByteIndex = chunkData.indexOf(0); // Keyword and text are separated by a 0x00 byte
+
+                    if (nullByteIndex !== -1) {
+                        const keyword = new TextDecoder('ascii').decode(chunkData.subarray(0, nullByteIndex));
+
+                        if (keyword === 'prompt' || keyword === 'workflow') {
+                            const rawText = new TextDecoder('utf-8').decode(chunkData.subarray(nullByteIndex + 1));
+                            try {
+                                result[keyword] = JSON.parse(rawText);
+                            } catch (e) {
+                                result[keyword] = rawText; // Fallback if JSON parsing fails
+                            }
+                        }
+                    }
+                }
+
+                offset += length + 4; // Skip the data segment and the 4-byte CRC trailing checksum
+            }
+            const found = findPromptJSON(result.prompt) || findPromptJSON(result.workflow);
+            return found?.prompt;
+        }
+
+        function isPromptJSON(value) {
+            return value
+                && typeof value === 'object'
+                && !Array.isArray(value)
+                && Object.prototype.hasOwnProperty.call(value, 'high_level_description')
+                && value.style_description
+                && value.compositional_deconstruction;
+        }
+
+        function tryParseJSON(value) {
+            if (typeof value !== 'string') return value;
+            const trimmed = value.trim();
+            if (!trimmed) return null;
+            return JSON.parse(trimmed);
+        }
+
+        function extractCanvasSizeFromWorkflow(value) {
+            if (!value || typeof value !== 'object' || Array.isArray(value)) return {};
+
+            let width = null;
+            let height = null;
+            Object.values(value).forEach(node => {
+                const title = String(node?._meta?.title || '').toLowerCase();
+                const nodeValue = Number(node?.inputs?.value);
+                if (!Number.isFinite(nodeValue)) return;
+
+                if (title.includes('width')) width = nodeValue;
+                if (title.includes('height')) height = nodeValue;
+            });
+
+            return { width, height };
+        }
+
+        function findPromptJSON(value, seen = new Set()) {
+            let parsed;
+            try {
+                parsed = tryParseJSON(value);
+            } catch (e) {
+                return null;
+            }
+
+            if (!parsed || typeof parsed !== 'object') return null;
+            if (seen.has(parsed)) return null;
+            seen.add(parsed);
+
+            if (isPromptJSON(parsed)) return { prompt: parsed };
+
+            const canvasSize = extractCanvasSizeFromWorkflow(parsed);
+            const withCanvasSize = (result) => {
+                if (!result) return null;
+                return {
+                    prompt: result.prompt,
+                    width: result.width ?? canvasSize.width,
+                    height: result.height ?? canvasSize.height,
+                };
+            };
+
+            if (parsed.prompt) {
+                const result = findPromptJSON(parsed.prompt, seen);
+                if (result) return withCanvasSize(result);
+            }
+
+            for (const item of Object.values(parsed)) {
+                const textInput = item?.inputs?.text;
+                if (typeof textInput === 'string') {
+                    const result = findPromptJSON(textInput, seen);
+                    if (result) return withCanvasSize(result);
+                }
+            }
+
+            for (const item of Object.values(parsed)) {
+                const result = findPromptJSON(item, seen);
+                if (result) return withCanvasSize(result);
+            }
+
+            return null;
+        }
+
+        function sanitizePalette(colors, maxColors) {
+            const seen = new Set();
+            return (Array.isArray(colors) ? colors : [])
+                .map(color => String(color).trim().toUpperCase())
+                .filter(color => /^#[0-9A-F]{6}$/.test(color))
+                .filter(color => {
+                    if (seen.has(color)) return false;
+                    seen.add(color);
+                    return true;
+                })
+                .slice(0, maxColors);
+        }
+
+        function parseBoxesFromJSON(json) {
+            const elements = json?.compositional_deconstruction?.elements || [];
+            const fromNorm = (value, max) => {
+                const number = Number(value);
+                if (!Number.isFinite(number)) return 0;
+                return Math.min(max, Math.max(0, number / 1000 * max));
+            };
+
+            elements.forEach(element => {
+                const bbox = Array.isArray(element?.bbox) ? element.bbox : [0, 0, 0, 0];
+                const y1 = fromNorm(bbox[0], canvasH);
+                const x1 = fromNorm(bbox[1], canvasW);
+                const y2 = fromNorm(bbox[2], canvasH);
+                const x2 = fromNorm(bbox[3], canvasW);
+                const box = {
+                    id: 'box_' + boxCounter++,
+                    x: Math.min(x1, x2),
+                    y: Math.min(y1, y2),
+                    w: Math.abs(x2 - x1),
+                    h: Math.abs(y2 - y1),
+                    mode: element?.type === 'text' ? 'text' : 'obj',
+                    text: element?.text ?? '',
+                    desc: element?.desc ?? '',
+                    colors: sanitizePalette(element?.color_palette, 5),
+                };
+
+                boxes.push(box);
+                canvas.appendChild(createBoxElement(box));
+            });
+
+            if (boxes.length > 0) selectBox(boxes[0].id);
+        }
+
+        function parseParametersFromJSON(json) {
+            const style = json.style_description || {};
+            const composition = json.compositional_deconstruction || {};
+            const isPhotoMode = Object.prototype.hasOwnProperty.call(style, 'photo');
+
+            document.getElementById('high_level_description').value = json.high_level_description ?? '';
+            document.getElementById('aesthetics').value = style.aesthetics ?? '';
+            document.getElementById('lighting').value = style.lighting ?? '';
+            document.getElementById('background').value = composition.background ?? '';
+
+            globalPalette = sanitizePalette(style.color_palette, 16);
+            renderColors('global');
+
+            if (isPhotoMode) {
+                document.getElementById('mode_photo').checked = true;
+                setPhotoArtstyle(MODE_PHOTO);
+                document.getElementById('art_style').value = style.photo ?? '';
+            } else {
+                document.getElementById('mode_artstyle').checked = true;
+                setPhotoArtstyle(MODE_ARTSTYLE);
+                document.getElementById('art_style').value = style.art_style ?? '';
+            }
+
+            document.getElementById('medium').value = style.medium ?? (isPhotoMode ? 'photograph' : '');
+        }
+
+        function loadPromptJSON(json, options = {}) {
+            if (!isPromptJSON(json)) {
+                throw new Error('Unsupported prompt JSON shape.');
+            }
+
+            if (options.width || options.height) {
+                setCanvasControls(options.width ?? canvasW, options.height ?? canvasH);
+            }
+
+            clearCanvasBoxes();
+            parseParametersFromJSON(json);
+            parseBoxesFromJSON(json);
+
+            if (options.updateTextarea !== false) {
+                jsonOutput.value = JSON.stringify(json, null, 2);
+            }
+
+            const count = json?.compositional_deconstruction?.elements?.length || 0;
+            setJSONStatus(`Loaded ${count} editable element${count === 1 ? '' : 's'}.`, 'success');
+        }
+
+        function parsePromptTextarea(options = {}) {
+            const found = findPromptJSON(jsonOutput.value);
+            if (!found?.prompt) {
+                setJSONStatus(options.source === 'paste' ? 'Pasted text is not a supported prompt JSON.' : 'No supported prompt JSON found.', 'error');
+                return false;
+            }
+
+            try {
+                loadPromptJSON(found.prompt, {
+                    width: found.width,
+                    height: found.height,
+                    updateTextarea: true,
+                });
+                return true;
+            } catch (e) {
+                setJSONStatus(e.message, 'error');
+                return false;
+            }
+        }
+
+        async function importImage(image){
+            if(image.type.startsWith("image")){
+                // is image
+                const img = new Image();
+                img.onload = function() {
+                    canvasWSlider.value = this.width.toString();
+                    canvasHSlider.value = this.height.toString();
+                    initCanvas();
+                    setImage(img.src);
+
+                    if(image.type=="image/png"){
+                        // is png. try and find workflow metadata
+                        image.arrayBuffer()
+                        .then(buff => {
+                            const json = extractComfyUIMetadata(buff);
+                            if (json) {
+                                loadPromptJSON(json, { updateTextarea: true });
+                            } else {
+                                setJSONStatus('No supported prompt JSON found in PNG metadata.', 'error');
+                            }
+                        })
+                        .catch(() => setJSONStatus('Could not read PNG metadata.', 'error'));
+                    }
+                };
+                img.src = URL.createObjectURL(image);
+            }
+            else{
+                alert("images only")
+            }
+        }
+
+        var photoArtStyleMode = 1;
+        const MODE_PHOTO = 0;
+        const MODE_ARTSTYLE = 1;
+        function setPhotoArtstyle(mode){
+            console.log(mode)
+            photoArtStyleMode = mode
+            if(mode==MODE_PHOTO){
+                // photo mode
+                document.getElementById("medium").value = "photograph";
+                document.getElementById("medium").disabled = true
+                document.getElementById("mode_label").innerText = "Photo"
+            }
+            else if (mode==MODE_ARTSTYLE){
+                // art_style mode
+                document.getElementById("medium").disabled = false
+                document.getElementById("mode_label").innerText = "Art Style"
+            }
+            else{
+                console.error("invalid mode")
+            }
+        }
+
+        initCanvas();
+        setPhotoArtstyle(1)
+    </script>
+
+</body>
+
+</html>

From 16ddaa43a1a25b66e9ee7f02e9a4d97e759cba63 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Wed, 24 Jun 2026 23:27:17 +0200
Subject: [PATCH 02/20] Add Krea-2 model support and batch converter for GGUF
 files

- Updated IMG_ARCH_LIST to include 'krea2'.
- Introduced ModelKrea2 class with architecture details and tensor handling.
- Enhanced convert_file function to support quantization types.
- Added tools/convert_krea2_gguf.py for batch conversion of Krea-2 models to multiple GGUF quant levels.
---
 loader.py                   |   2 +-
 tools/convert.py            | 117 +++++++++++++++++++++++------
 tools/convert_krea2_gguf.py | 144 ++++++++++++++++++++++++++++++++++++
 tools/krea2_gguf_readme.md  | 100 +++++++++++++++++++++++++
 4 files changed, 339 insertions(+), 24 deletions(-)
 create mode 100644 tools/convert_krea2_gguf.py
 create mode 100644 tools/krea2_gguf_readme.md

diff --git a/loader.py b/loader.py
index 64177f8..88f4713 100644
--- a/loader.py
+++ b/loader.py
@@ -9,7 +9,7 @@
 from .ops import GGMLTensor
 from .dequant import is_quantized, dequantize_tensor
 
-IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram"}
+IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram", "krea2"}
 TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}
 VIS_TYPE_LIST = {"clip-vision", "mmproj"}
 
diff --git a/tools/convert.py b/tools/convert.py
index a9b76ea..a79a395 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -155,8 +155,49 @@ class ModelIdeogram(ModelTemplate):
         )
     ]
 
-arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2, 
-             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2, ModelIdeogram]
+class ModelKrea2(ModelTemplate):
+    """
+    Krea-2 is a novel architecture from krea.ai — NOT Ideogram4.
+    Key structure (verified from Krea2_Turbo_fp8mixed.safetensors header):
+      blocks.N.attn.{wq,wk,wv,wo,gate}  — separate Q/K/V/O projections + gating
+      blocks.N.attn.qknorm.{qnorm,knorm} — Q/K norms
+      blocks.N.mlp.{up,gate,down}        — SwiGLU-style MLP
+      blocks.N.mod.lin                   — per-block modulation
+      blocks.N.{pre,post}norm.scale      — RMSNorm scales
+      txtfusion.layerwise_blocks.N.*     — layerwise text-image cross-attention
+      txtfusion.refiner_blocks.N.*       — refiner text-image cross-attention
+      txtfusion.projector                — text projector
+      first.weight / last.*              — input / output projections
+      tmlp.N / tproj.N                   — timestep MLP / projection
+
+    NOTE: ComfyUI core must have krea2 diffusion model support
+    (i.e. a detection branch for 'blocks.0.attn.wq.weight' in model_detection.py
+    and a matching supported_models entry) for the GGUF to load correctly.
+    Krea-2 was released 2026-06-22; verify your ComfyUI build is up to date.
+    """
+    arch = "krea2"
+    keys_detect = [
+        (
+            "blocks.0.attn.wq.weight",
+            "txtfusion.projector.weight",
+            "first.weight",
+        ),
+        (
+            "blocks.0.attn.wq.weight",
+            "txtfusion.layerwise_blocks.0.attn.wq.weight",
+            "last.linear.weight",
+        ),
+    ]
+    keys_hiprec = [
+        "blocks.0.mod.lin",  # modulation parameters — keep at full precision
+        "last.modulation.lin",
+        "tmlp.",
+        "tproj.",
+    ]
+
+arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2,
+             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2,
+             ModelKrea2, ModelIdeogram]
 
 def is_model_arch(model, state_dict):
     # check if model is correct
@@ -179,10 +220,30 @@ def detect_arch(state_dict):
     assert model_arch is not None, "Unknown model architecture!"
     return model_arch
 
+QUANT_TYPE_MAP = {
+    "F16":  (gguf.GGMLQuantizationType.F16,  gguf.LlamaFileType.MOSTLY_F16),
+    "BF16": (gguf.GGMLQuantizationType.BF16, gguf.LlamaFileType.MOSTLY_BF16),
+    "Q8_0": (gguf.GGMLQuantizationType.Q8_0, gguf.LlamaFileType.MOSTLY_Q8_0),
+    "Q5_1": (gguf.GGMLQuantizationType.Q5_1, gguf.LlamaFileType.MOSTLY_Q5_1),
+    "Q5_0": (gguf.GGMLQuantizationType.Q5_0, gguf.LlamaFileType.MOSTLY_Q5_0),
+    "Q4_1": (gguf.GGMLQuantizationType.Q4_1, gguf.LlamaFileType.MOSTLY_Q4_1),
+    "Q4_0": (gguf.GGMLQuantizationType.Q4_0, gguf.LlamaFileType.MOSTLY_Q4_0),
+}
+
 def parse_args():
-    parser = argparse.ArgumentParser(description="Generate F16 GGUF files from single UNET")
-    parser.add_argument("--src", required=True, help="Source model ckpt file.")
-    parser.add_argument("--dst", help="Output unet gguf file.")
+    parser = argparse.ArgumentParser(
+        description="Convert diffusion model safetensors/ckpt to GGUF."
+        " By default produces an F16/BF16 GGUF; use --quant-type to quantize."
+    )
+    parser.add_argument("--src", required=True, help="Source model ckpt/safetensors file.")
+    parser.add_argument("--dst", help="Output GGUF file path.")
+    parser.add_argument(
+        "--quant-type",
+        choices=list(QUANT_TYPE_MAP.keys()),
+        default=None,
+        help="Target quantization type for eligible 2-D+ tensors "
+             "(1-D biases/scales stay F32). Defaults to F16/BF16 matching the source dtype.",
+    )
     args = parser.parse_args()
 
     if not os.path.isfile(args.src):
@@ -234,7 +295,7 @@ def load_state_dict(path):
 
     return strip_prefix(state_dict)
 
-def handle_tensors(writer, state_dict, model_arch):
+def handle_tensors(writer, state_dict, model_arch, quant_type=None):
     name_lengths = tuple(sorted(
         ((key, len(key)) for key in state_dict.keys()),
         key=lambda item: item[1],
@@ -257,7 +318,7 @@ def handle_tensors(writer, state_dict, model_arch):
             data = data.to(torch.float32).numpy()
         # this is so we don't break torch 2.0.X
         elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]:
-            data = data.to(torch.float16).numpy()
+            data = data.to(torch.float32).numpy()  # cast to f32 for correct downstream quant
         else:
             data = data.numpy()
 
@@ -294,6 +355,10 @@ def handle_tensors(writer, state_dict, model_arch):
                 # tensors that require max precision
                 data_qtype = gguf.GGMLQuantizationType.F32
 
+            elif quant_type is not None:
+                # caller-specified quantization type for all eligible tensors
+                data_qtype = quant_type
+
         if (model_arch.shape_fix                        # NEVER reshape for models such as flux
             and n_dims > 1                              # Skip one-dimensional tensors
             and n_params >= REARRANGE_THRESHOLD         # Only rearrange tensors meeting the size requirement
@@ -318,26 +383,32 @@ def handle_tensors(writer, state_dict, model_arch):
 
         writer.add_tensor(new_name, data, raw_dtype=data_qtype)
 
-def convert_file(path, dst_path=None, interact=True, overwrite=False):
+def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type_name=None):
     # load & run model detection logic
     state_dict = load_state_dict(path)
     model_arch = detect_arch(state_dict)
     logging.info(f"* Architecture detected from input: {model_arch.arch}")
 
-    # detect & set dtype for output file
-    dtypes = [x.dtype for x in state_dict.values()]
-    dtypes = {x:dtypes.count(x) for x in set(dtypes)}
-    main_dtype = max(dtypes, key=dtypes.get)
-
-    if main_dtype == torch.bfloat16:
-        ftype_name = "BF16"
-        ftype_gguf = gguf.LlamaFileType.MOSTLY_BF16
-    # elif main_dtype == torch.float32:
-    #     ftype_name = "F32"
-    #     ftype_gguf = None
+    # resolve quant type from name if provided
+    quant_type = None
+    if quant_type_name is not None and quant_type_name in QUANT_TYPE_MAP:
+        quant_type, ftype_gguf = QUANT_TYPE_MAP[quant_type_name]
+        ftype_name = quant_type_name
     else:
-        ftype_name = "F16"
-        ftype_gguf = gguf.LlamaFileType.MOSTLY_F16
+        # detect & set dtype from source file
+        dtypes = [x.dtype for x in state_dict.values()]
+        dtypes = {x: dtypes.count(x) for x in set(dtypes)}
+        main_dtype = max(dtypes, key=dtypes.get)
+
+        if main_dtype == torch.bfloat16:
+            ftype_name = "BF16"
+            ftype_gguf = gguf.LlamaFileType.MOSTLY_BF16
+        # elif main_dtype == torch.float32:
+        #     ftype_name = "F32"
+        #     ftype_gguf = None
+        else:
+            ftype_name = "F16"
+            ftype_gguf = gguf.LlamaFileType.MOSTLY_F16
 
     if dst_path is None:
         dst_path = f"{os.path.splitext(path)[0]}-{ftype_name}.gguf"
@@ -356,7 +427,7 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False):
     if ftype_gguf is not None:
         writer.add_file_type(ftype_gguf)
 
-    handle_tensors(writer, state_dict, model_arch)
+    handle_tensors(writer, state_dict, model_arch, quant_type=quant_type)
     writer.write_header_to_file(path=dst_path)
     writer.write_kv_data_to_file()
     writer.write_tensors_to_file(progress=True)
@@ -371,4 +442,4 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False):
 
 if __name__ == "__main__":
     args = parse_args()
-    convert_file(args.src, args.dst)
\ No newline at end of file
+    convert_file(args.src, args.dst, quant_type_name=args.quant_type)
\ No newline at end of file
diff --git a/tools/convert_krea2_gguf.py b/tools/convert_krea2_gguf.py
new file mode 100644
index 0000000..8e6fb4b
--- /dev/null
+++ b/tools/convert_krea2_gguf.py
@@ -0,0 +1,144 @@
+"""
+Batch GGUF converter for Krea-2 (Base and Turbo) models.
+
+Produces Q4_0, Q4_1, Q5_0, Q5_1, and Q8_0 GGUF files from a single
+BF16/F16 safetensors source file.  The converter auto-detects architecture
+(ModelKrea2 or ModelIdeogram) from the tensor keys in the source file.
+
+Usage
+-----
+# Convert all five quant levels from a BF16 safetensors:
+python tools/convert_krea2_gguf.py --src krea2_base_bf16.safetensors
+
+# Convert specific quant levels only:
+python tools/convert_krea2_gguf.py --src krea2_turbo_bf16.safetensors --quant Q5_0 Q8_0
+
+# Explicit output directory:
+python tools/convert_krea2_gguf.py --src krea2_base_bf16.safetensors --outdir /path/to/output
+
+Notes
+-----
+- 1-D tensors (biases, norms, scales) are always kept in F32 regardless of
+  the requested quant type.
+- Tensors with fewer than 1024 elements are kept in F32.
+- No model weights are downloaded by this script.  You must supply the source
+  safetensors file yourself.
+- This script calls convert_file() from tools/convert.py; both files must be
+  on the Python path (they are when run from the repo root or from tools/).
+
+Approximate output sizes per quant level (Krea-2 ~24 B parameters):
+  Q4_0 ~ 14 GB   Q4_1 ~ 15 GB   Q5_0 ~ 17 GB   Q5_1 ~ 18 GB   Q8_0 ~ 26 GB
+"""
+
+import os
+import sys
+import logging
+import argparse
+from tqdm import tqdm
+
+# Allow running directly from tools/ or from the repo root.
+_HERE = os.path.dirname(os.path.abspath(__file__))
+_REPO = os.path.dirname(_HERE)
+for _p in (_HERE, _REPO):
+    if _p not in sys.path:
+        sys.path.insert(0, _p)
+
+from convert import convert_file, QUANT_TYPE_MAP  # noqa: E402  (tools/convert.py)
+
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+
+DEFAULT_QUANTS = ["Q4_0", "Q4_1", "Q5_0", "Q5_1", "Q8_0"]
+
+
+def build_dst_path(src: str, quant: str, outdir: str | None) -> str:
+    """Derive an output path like <stem>-Q4_0.gguf next to the source."""
+    stem = os.path.splitext(os.path.basename(src))[0]
+    filename = f"{stem}-{quant}.gguf"
+    directory = outdir if outdir else os.path.dirname(os.path.abspath(src))
+    return os.path.join(directory, filename)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Batch-convert a Krea-2 safetensors file to multiple GGUF quant levels.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument(
+        "--src",
+        required=True,
+        help="Path to the source BF16/F16 safetensors (or ckpt) file.",
+    )
+    parser.add_argument(
+        "--outdir",
+        default=None,
+        help="Directory to write GGUF files into. Defaults to the same directory as --src.",
+    )
+    parser.add_argument(
+        "--quant",
+        nargs="+",
+        choices=list(QUANT_TYPE_MAP.keys()),
+        default=DEFAULT_QUANTS,
+        metavar="QUANT",
+        help=(
+            "One or more quantization levels to produce. "
+            f"Choices: {list(QUANT_TYPE_MAP.keys())}. "
+            f"Default: {DEFAULT_QUANTS}"
+        ),
+    )
+    parser.add_argument(
+        "--overwrite",
+        action="store_true",
+        help="Overwrite existing output files without prompting.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not os.path.isfile(args.src):
+        logging.error(f"Source file not found: {args.src}")
+        sys.exit(1)
+
+    if args.outdir:
+        os.makedirs(args.outdir, exist_ok=True)
+
+    results: list[tuple[str, str, bool]] = []  # (quant, dst_path, success)
+
+    for quant in tqdm(args.quant, desc="GGUF quant levels", unit="quant"):
+        dst = build_dst_path(args.src, quant, args.outdir)
+        logging.info(f"\n{'='*60}")
+        logging.info(f"Converting {os.path.basename(args.src)} → {os.path.basename(dst)}")
+        logging.info(f"Quant type : {quant}")
+        logging.info(f"Output     : {dst}")
+        logging.info(f"{'='*60}")
+
+        try:
+            out_path, model_arch = convert_file(
+                args.src,
+                dst_path=dst,
+                interact=False,
+                overwrite=args.overwrite,
+                quant_type_name=quant,
+            )
+            logging.info(f"[OK] {quant} → {out_path}  (arch={model_arch.arch})")
+            results.append((quant, out_path, True))
+        except Exception as exc:
+            logging.error(f"[FAILED] {quant}: {exc}")
+            results.append((quant, dst, False))
+
+    # Summary
+    print(f"\n{'='*60}")
+    print("Conversion summary:")
+    for quant, path, ok in results:
+        status = "OK  " if ok else "FAIL"
+        print(f"  [{status}] {quant:6s}  {path}")
+    print(f"{'='*60}")
+
+    if not all(ok for _, _, ok in results):
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/krea2_gguf_readme.md b/tools/krea2_gguf_readme.md
new file mode 100644
index 0000000..5321d57
--- /dev/null
+++ b/tools/krea2_gguf_readme.md
@@ -0,0 +1,100 @@
+---
+license: other
+license_name: krea-2-community-license
+license_link: https://huggingface.co/krea/Krea-2-Turbo/blob/main/LICENSE
+pipeline_tag: text-to-image
+tags:
+  - image-generation
+  - diffusion
+  - flow-matching
+  - dit
+  - krea2
+  - quantization
+  - comfyui
+library_name: gguf
+base_model:
+  - krea/Krea-2-Raw
+  - krea/Krea-2-Turbo
+---
+
+# Krea 2 GGUF
+
+Quantized GGUF diffusion transformer weights for [Krea 2](https://huggingface.co/krea/Krea-2-Turbo),
+converted from the original BF16 releases for use with ComfyUI GGUF loader nodes.
+
+This repository provides GGUF files for two checkpoints of the Krea 2 model family:
+
+- `krea2_raw_bf16-*.gguf` — converted from [krea/Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw), the base release checkpoint.
+- `krea2_turbo_bf16-*.gguf` — converted from [krea/Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo), the post-trained checkpoint with additional fine-tuning and distillation.
+
+Krea 2 is a 12-billion parameter Diffusion Transformer with a novel architecture featuring layerwise and refiner text-fusion blocks. It is not based on Flux or any prior open-weight architecture.
+
+These files are not a complete standalone Krea 2 package. Your workflow still needs the text encoder and VAE components.
+
+## ComfyUI Support
+
+Use these models with the ComfyUI nodes from [molbal/ComfyUI-GGUF](https://github.com/molbal/ComfyUI-GGUF).
+Install that custom node repository into your ComfyUI `custom_nodes` folder, then restart ComfyUI.
+
+> **Important:** This repository requires [molbal/ComfyUI-GGUF](https://github.com/molbal/ComfyUI-GGUF),
+> which is a fork of [city96/ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) with added support
+> for the Krea 2 architecture. The original city96 plugin does **not** support these files. (as of 2026-06-24)
+
+Place the downloaded `.gguf` files in one of ComfyUI's diffusion model folders:
+
+```
+ComfyUI/models/diffusion_models/
+ComfyUI/models/unet/
+```
+
+Load the file with `Unet Loader (GGUF)` in a Krea 2 workflow. Krea 2 uses a single transformer
+(unlike Ideogram 4, there is no separate unconditional transformer component).
+
+## Files
+
+| Quant | Raw (base) | Turbo | Size |
+|-------|-----------|-------------------|------|
+| Q4_0 | krea2_raw_bf16-Q4_0.gguf | krea2_turbo_bf16-Q4_0.gguf | 7.74 GB |
+| Q4_1 | krea2_raw_bf16-Q4_1.gguf | krea2_turbo_bf16-Q4_1.gguf | 8.47 GB |
+| Q5_0 | krea2_raw_bf16-Q5_0.gguf | krea2_turbo_bf16-Q5_0.gguf | 9.20 GB |
+| Q5_1 | krea2_raw_bf16-Q5_1.gguf | krea2_turbo_bf16-Q5_1.gguf | 9.93 GB |
+| Q8_0 | krea2_raw_bf16-Q8_0.gguf | krea2_turbo_bf16-Q8_0.gguf | 13.56 GB |
+
+Choose either the Raw or Turbo variant depending on your workflow; they are not paired with each other.
+
+## Which Checkpoint to Use
+
+| Checkpoint | Steps | CFG | Notes |
+|------------|-------|-----|-------|
+| **Turbo** | 4–8 | 0.0 | Distilled; CFG-free. Fast, good for most use cases. |
+| **Raw** | 20–30 | 3.0–7.0 | Full CFG; more controllable, higher inference cost. |
+
+The Turbo checkpoint has been post-trained with distillation and runs well at 8 steps with `CFG=1`. The Raw checkpoint behaves like a standard flow-matching DiT and benefits from more steps and positive CFG.
+
+## When GGUFs Are Worth the Tradeoff
+
+The BF16 source weights for Krea 2 are 26.6 GB each — far beyond what most consumer GPUs can hold entirely in VRAM. GGUFs make sense when:
+
+- **Limited VRAM:** Q4_0 at 7.74 GB fits entirely in an 8 GB GPU; Q5_1 at 9.93 GB targets 10–12 GB cards. Running BF16 on these GPUs would require heavy CPU offloading and become impractically slow.
+- **CPU offload workflows:** If you are already offloading model layers to RAM, GGUF reduces the RAM footprint proportionally alongside VRAM, which is often the actual bottleneck.
+- **Acceptable quality loss at Q5+:** At Q5_0 and above the visual output of Krea 2 is very close to BF16. Q4 levels show mild softening on fine detail but remain usable for most creative tasks.
+
+GGUFs are generally **not** worth it if you have a 24 GB+ GPU and want maximum fidelity — load the FP8 or BF16 source directly in that case.
+
+## Download
+
+Download the file you want from the Files tab, or use the Hugging Face CLI. For example:
+
+```
+huggingface-cli download molbal/krea2-gguf krea2_turbo_bf16-Q5_1.gguf --local-dir ComfyUI/models/diffusion_models
+```
+
+## Compatibility Notes
+
+These are non-K GGUF quantizations intended for PyTorch dequantization in ComfyUI. K-quants are not included because this ComfyUI loading path does not use fused quantized linear kernels.
+
+Krea 2 GGUF support requires ComfyUI to have the `krea2` architecture registered in its model detection system. If your ComfyUI installation does not recognise the checkpoint, update ComfyUI core and [molbal/ComfyUI-GGUF](https://github.com/molbal/ComfyUI-GGUF) to their latest versions.
+
+## License
+
+These files are derived from [krea/Krea-2-Raw](https://huggingface.co/krea/Krea-2-Raw) and [krea/Krea-2-Turbo](https://huggingface.co/krea/Krea-2-Turbo) and follow the [Krea 2 Community License](https://huggingface.co/krea/Krea-2-Turbo/blob/main/LICENSE).

From cd2c9bdca2ee0f83760abd2822d9525b30c4fe6d Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Thu, 25 Jun 2026 11:06:51 +0200
Subject: [PATCH 03/20] Update README and loader.py for Krea-2 and Ideogram 4
 GGUF support

---
 README.md |  5 +++++
 loader.py | 17 +++++++++++++++++
 2 files changed, 22 insertions(+)

diff --git a/README.md b/README.md
index 6915927..db4f912 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,11 @@
 # ComfyUI-GGUF
 GGUF Quantization support for native ComfyUI models
 
+> [!NOTE]  
+> This is a fork of the original nodes, updated to support loading Ideogram 4 GGUFs and Krea 2 GGUFs. 
+> To use it, clone `https://github.com/city96/ComfyUI-GGUF`and not the original repo.
+
+
 This is currently very much WIP. These custom nodes provide support for model files stored in the GGUF format popularized by [llama.cpp](https://github.com/ggerganov/llama.cpp).
 
 While quantization wasn't feasible for regular UNET models (conv2d), transformer/DiT models such as flux seem less affected by quantization. This allows running it in much lower bits per weight variable bitrate quants on low-end GPUs. For further VRAM savings, a node to load a quantized version of the T5 text encoder is also included.
diff --git a/loader.py b/loader.py
index 88f4713..62a00a2 100644
--- a/loader.py
+++ b/loader.py
@@ -518,6 +518,23 @@ def gguf_clip_loader(path):
         if arch == "qwen2vl":
             vsd = gguf_mmproj_loader(path)
             sd.update(vsd)
+        if arch == "qwen3vl" and "model.visual.deepstack_merger_list.0.norm.weight" not in sd:
+            # Standard llama.cpp Qwen3-VL GGUFs omit the visual tower. Without it,
+            # detect_te_model() mis-classifies the state dict as QWEN3_4B/8B (Qwen3 LM)
+            # instead of QWEN3VL_4B/8B, so clip_type=KREA2 never selects the 12-layer
+            # tap encoder and conditioning has shape (B, seq, 2560) instead of (B, seq, 30720).
+            # Inject zero sentinel tensors with shapes that exactly match the model
+            # parameters so that load_state_dict(strict=False) doesn't raise a size
+            # mismatch error while still satisfying detect_te_model()'s key checks.
+            #   deepstack_merger_list.0.norm  -> LayerNorm(vis_hidden * 4)  shape [merge_dim]
+            #   merger.linear_fc2             -> Linear(merge_dim, lm_hidden) shape [lm_hidden, merge_dim]
+            ln_key = "model.layers.0.input_layernorm.weight"
+            lm_hidden = int(sd[ln_key].shape[0]) if ln_key in sd else 2560
+            vis_hidden = 1024 if lm_hidden == 2560 else 1152  # Qwen3-VL-4B vs 8B
+            merge_dim = vis_hidden * 4  # spatial_merge_size=2
+            sd["model.visual.deepstack_merger_list.0.norm.weight"] = torch.zeros(merge_dim)
+            sd["model.visual.merger.linear_fc2.weight"] = torch.zeros(lm_hidden, merge_dim)
+            logging.info(f"qwen3vl GGUF: injected visual marker tensors (lm_hidden={lm_hidden}, merge_dim={merge_dim}) for model type detection")
     elif arch == "ideogram":
         # Dequantize Ideogram model for inference
         logging.info("Dequantizing Ideogram model for inference...")

From e532b7d77ab14860301f93d388105d0587fcf0b7 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Thu, 25 Jun 2026 20:34:23 +0200
Subject: [PATCH 04/20] Update README to include new pre-quantized models and
 clarify quantization support limitations

---
 README.md | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index db4f912..6ff410c 100644
--- a/README.md
+++ b/README.md
@@ -40,15 +40,22 @@ Simply use the GGUF Unet loader found under the `bootleg` category. Place the .g
 
 LoRA loading is experimental but it should work with just the built-in LoRA loader node(s).
 
-Pre-quantized models:
+Pre-quantized models (🍴 icon on ones added by this fork):
 
 - [flux1-dev GGUF](https://huggingface.co/city96/FLUX.1-dev-gguf)
 - [flux1-schnell GGUF](https://huggingface.co/city96/FLUX.1-schnell-gguf)
 - [stable-diffusion-3.5-large GGUF](https://huggingface.co/city96/stable-diffusion-3.5-large-gguf)
 - [stable-diffusion-3.5-large-turbo GGUF](https://huggingface.co/city96/stable-diffusion-3.5-large-turbo-gguf)
+- [Krea 2 (Both Turbo and Raw)](https://huggingface.co/molbal/krea2-gguf) 🍴
+- [Ideogram 4](https://huggingface.co/molbal/ideogram-4-gguff) 🍴
+
+
+> [!IMPORTANT]  
+> Please note, that this fork does not support _K quants on diffusion models, only on text encoders. They may or may not load, but inference speed may be very slow. There may be other forks, or other custom nodes with better support for these quantization types.
 
 Initial support for quantizing T5 has also been added recently, these can be used using the various `*CLIPLoader (gguf)` nodes which can be used inplace of the regular ones. For the CLIP model, use whatever model you were using before for CLIP. The loader can handle both types of files - `gguf` and regular `safetensors`/`bin`.
 
 - [t5_v1.1-xxl GGUF](https://huggingface.co/city96/t5-v1_1-xxl-encoder-gguf)
+- [Qwen3-VL-4B-Instruct-GGUF](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF)🍴
 
 See the instructions in the [tools](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) folder for how to create your own quants.

From f65d0d9e4f048583f740d82b2c8815a57474f760 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Thu, 25 Jun 2026 22:30:26 +0200
Subject: [PATCH 05/20] fix typo

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 6ff410c..9024650 100644
--- a/README.md
+++ b/README.md
@@ -47,7 +47,7 @@ Pre-quantized models (🍴 icon on ones added by this fork):
 - [stable-diffusion-3.5-large GGUF](https://huggingface.co/city96/stable-diffusion-3.5-large-gguf)
 - [stable-diffusion-3.5-large-turbo GGUF](https://huggingface.co/city96/stable-diffusion-3.5-large-turbo-gguf)
 - [Krea 2 (Both Turbo and Raw)](https://huggingface.co/molbal/krea2-gguf) 🍴
-- [Ideogram 4](https://huggingface.co/molbal/ideogram-4-gguff) 🍴
+- [Ideogram 4](https://huggingface.co/molbal/ideogram-4-gguf) 🍴
 
 
 > [!IMPORTANT]  

From 73439d2120c2e465fbdf7eb9e98c17ef592e5da3 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Sat, 27 Jun 2026 23:57:06 +0200
Subject: [PATCH 06/20] Enhance tensor handling in convert.py to drop
 incompatible FP8 scale tensors and skip 0-dim scalars during GGUF conversion

---
 tools/convert.py | 32 ++++++++++++++++++++++++++++++--
 1 file changed, 30 insertions(+), 2 deletions(-)

diff --git a/tools/convert.py b/tools/convert.py
index a79a395..8faa0b2 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -296,6 +296,17 @@ def load_state_dict(path):
     return strip_prefix(state_dict)
 
 def handle_tensors(writer, state_dict, model_arch, quant_type=None):
+    # Pre-collect per-tensor FP8 scales (0-dim float32 tensors named "{key}_scale").
+    # These must be applied to their FP8 weight tensors before GGUF quantization.
+    # The actual weight value is fp8_value * scale; ignoring scale produces wrong magnitudes.
+    fp8_scales = {
+        k[:-len("_scale")]: v.item()
+        for k, v in state_dict.items()
+        if k.endswith("_scale") and len(v.shape) == 0 and v.dtype == torch.float32
+    }
+    if fp8_scales:
+        tqdm.write(f"Found {len(fp8_scales)} FP8 per-tensor scale(s); will apply before quantization.")
+
     name_lengths = tuple(sorted(
         ((key, len(key)) for key in state_dict.keys()),
         key=lambda item: item[1],
@@ -314,11 +325,27 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None):
             tqdm.write(f"Filtering ignored key: '{key}'")
             continue
 
+        # comfy_quant tensors are FP8 scale factors specific to ComfyUI's custom FP8 format.
+        # weight_scale tensors are 0-dim per-tensor FP8 scales (e.g. from torchao/fp8 fine-tunes).
+        # Both are meaningless after GGUF re-quantization and must be dropped so the loader
+        # does not try to apply them to already-GGUF-dequantized weights.
+        if key.endswith(".comfy_quant") or key.endswith("_scale") and len(data.shape) == 0:
+            tqdm.write(f"Dropping FP8 scale tensor: '{key}'")
+            continue
+
+        # 0-dim (scalar) tensors cannot be stored in GGUF and have no meaningful weight data.
+        if len(data.shape) == 0:
+            tqdm.write(f"Skipping 0-dim scalar tensor: '{key}'")
+            continue
+
         if data.dtype == torch.bfloat16:
             data = data.to(torch.float32).numpy()
         # this is so we don't break torch 2.0.X
         elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]:
-            data = data.to(torch.float32).numpy()  # cast to f32 for correct downstream quant
+            data = data.to(torch.float32)
+            if key in fp8_scales:
+                data = data * fp8_scales[key]  # apply per-tensor dequantization scale
+            data = data.numpy()
         else:
             data = data.numpy()
 
@@ -341,7 +368,8 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None):
         for dim_size in data_shape:
             n_params *= dim_size
 
-        if old_dtype in (torch.float32, torch.bfloat16):
+        _FP8_DTYPES = {getattr(torch, "float8_e4m3fn", None), getattr(torch, "float8_e5m2", None)} - {None}
+        if old_dtype in (torch.float32, torch.bfloat16) or old_dtype in _FP8_DTYPES:
             if n_dims == 1:
                 # one-dimensional tensors should be kept in F32
                 # also speeds up inference due to not dequantizing

From 3551793a7b5d4bc203464655d5476c50a686504e Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Sun, 26 Jul 2026 18:18:25 +0200
Subject: [PATCH 07/20] Adds Q8_CR quant type

---
 README.md                                     |  20 +++
 fp8/transformer/config.json                   |  19 ---
 ...usion_pytorch_model.safetensors.index.json |   1 -
 fp8/unconditional_transformer/config.json     |  19 ---
 ...usion_pytorch_model.safetensors.index.json |   1 -
 loader.py                                     |  74 ++++++++-
 nodes.py                                      |  17 +-
 ops.py                                        | 150 ++++++++++++++++++
 tools/convert.py                              | 130 +++++++++++++--
 9 files changed, 371 insertions(+), 60 deletions(-)
 delete mode 100644 fp8/transformer/config.json
 delete mode 100644 fp8/transformer/diffusion_pytorch_model.safetensors.index.json
 delete mode 100644 fp8/unconditional_transformer/config.json
 delete mode 100644 fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json

diff --git a/README.md b/README.md
index 9024650..6e099b5 100644
--- a/README.md
+++ b/README.md
@@ -59,3 +59,23 @@ Initial support for quantizing T5 has also been added recently, these can be use
 - [Qwen3-VL-4B-Instruct-GGUF](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct-GGUF)🍴
 
 See the instructions in the [tools](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) folder for how to create your own quants.
+
+## Native weight-only quantization
+
+The converter supports two custom, global quantization modes for DiT/transformer
+UNets:
+
+- `Q8_CR` stores eligible 2-D Linear weights as per-row INT8 ConvRot. It uses
+  ComfyUI's native `TensorWiseINT8Layout` path, so weights remain INT8 during
+  inference.
+- `Q4_PT` stores eligible 2-D Linear weights as group-size-64 INT4 and uses
+  PyTorch's CUDA `_weight_int4pack_mm` path. The first use of each layer creates
+  the device-specific packed representation; weights are not dequantized.
+
+Both modes keep 1-D, small, and architecture-designated high-precision tensors
+in FP32. Conv2d weights remain FP16 because these modes accelerate Linear
+matrix multiplication only. `Q4_PT` requires CUDA with FP16 or BF16
+activations; it intentionally has no CPU or dequantization fallback.
+
+Reconvert any `Q8_CR` GGUF created before ConvRot weights were marked as
+pre-rotated. Older files load safely with native non-rotated INT8 instead.
diff --git a/fp8/transformer/config.json b/fp8/transformer/config.json
deleted file mode 100644
index da6e1e8..0000000
--- a/fp8/transformer/config.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
-  "_class_name": "Ideogram4Transformer2DModel",
-  "_diffusers_version": "0.39.0.dev0",
-  "_name_or_path": "/home/jinli/.cache/huggingface/hub/models--ideogram-ai--debug-ideogram-v4/snapshots/41af6183c9fd9b6254864b0720319ef984535bfc/transformer",
-  "adaln_dim": 512,
-  "attention_head_dim": 256,
-  "in_channels": 128,
-  "intermediate_size": 12288,
-  "llm_features_dim": 53248,
-  "mrope_section": [
-    24,
-    20,
-    20
-  ],
-  "norm_eps": 1e-05,
-  "num_attention_heads": 18,
-  "num_layers": 34,
-  "rope_theta": 5000000
-}
diff --git a/fp8/transformer/diffusion_pytorch_model.safetensors.index.json b/fp8/transformer/diffusion_pytorch_model.safetensors.index.json
deleted file mode 100644
index ba1f6d8..0000000
--- a/fp8/transformer/diffusion_pytorch_model.safetensors.index.json
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/fp8/unconditional_transformer/config.json b/fp8/unconditional_transformer/config.json
deleted file mode 100644
index f40a02c..0000000
--- a/fp8/unconditional_transformer/config.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
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-  "_diffusers_version": "0.39.0.dev0",
-  "_name_or_path": "/home/jinli/.cache/huggingface/hub/models--ideogram-ai--debug-ideogram-v4/snapshots/41af6183c9fd9b6254864b0720319ef984535bfc/unconditional_transformer",
-  "adaln_dim": 512,
-  "attention_head_dim": 256,
-  "in_channels": 128,
-  "intermediate_size": 12288,
-  "llm_features_dim": 53248,
-  "mrope_section": [
-    24,
-    20,
-    20
-  ],
-  "norm_eps": 1e-05,
-  "num_attention_heads": 18,
-  "num_layers": 34,
-  "rope_theta": 5000000
-}
diff --git a/fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json b/fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json
deleted file mode 100644
index ba1f6d8..0000000
--- a/fp8/unconditional_transformer/diffusion_pytorch_model.safetensors.index.json
+++ /dev/null
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\ No newline at end of file
diff --git a/loader.py b/loader.py
index 62a00a2..657ddd8 100644
--- a/loader.py
+++ b/loader.py
@@ -3,9 +3,9 @@
 import logging
 import torch
 import gguf
+import json
 import re
 import os
-
 from .ops import GGMLTensor
 from .dequant import is_quantized, dequantize_tensor
 
@@ -177,6 +177,78 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
         "arch_str": arch_str,
         "metadata": get_gguf_metadata(reader)
     }
+
+    # Detect custom ComfyUI native quantization metadata
+    warned_unrotated_convrot = False
+    for field_name in reader.fields:
+        if not field_name.startswith("comfy.gguf.quant."):
+            continue
+        key = field_name[len("comfy.gguf.quant."):]
+        field = reader.get_field(field_name)
+        quant_conf = json.loads(str(field.parts[field.data[-1]], "utf-8"))
+        fmt = quant_conf.get("format")
+
+        weight_key = key
+        scale_key = f"{key}_scale"
+        if weight_key not in state_dict or scale_key not in state_dict:
+            logging.warning(f"Missing custom quant tensors for {weight_key}")
+            continue
+
+        weight_ggml = state_dict[weight_key]
+        scale_ggml = state_dict[scale_key]
+
+        if fmt == "int8_tensorwise":
+            if quant_conf.get("convrot") and not quant_conf.get("weight_rotated", False):
+                if not warned_unrotated_convrot:
+                    logging.warning(
+                        "Disabling ConvRot because this GGUF does not mark its weights "
+                        "as pre-rotated. Reconvert with the current converter to enable ConvRot."
+                    )
+                    warned_unrotated_convrot = True
+                quant_conf["convrot"] = False
+                quant_conf.pop("convrot_groupsize", None)
+            elif quant_conf.get("convrot"):
+                groupsize = quant_conf.get("convrot_groupsize", 256)
+                if weight_ggml.tensor_shape[-1] % groupsize != 0:
+                    logging.warning(
+                        "Disabling ConvRot for %s because %d input features are not "
+                        "divisible by group size %d.",
+                        weight_key,
+                        weight_ggml.tensor_shape[-1],
+                        groupsize,
+                    )
+                    quant_conf["convrot"] = False
+                    quant_conf.pop("convrot_groupsize", None)
+
+            # Convert to ComfyUI native state-dict layout
+            weight = weight_ggml.data.view(torch.int8).reshape(weight_ggml.tensor_shape)
+            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
+
+            state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
+            state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
+
+            layer_prefix = weight_key[:weight_key.rfind("weight")]
+            quant_json = json.dumps(quant_conf)
+            state_dict[f"{layer_prefix}comfy_quant"] = torch.nn.Parameter(
+                torch.tensor(list(quant_json.encode("utf-8")), dtype=torch.uint8),
+                requires_grad=False,
+            )
+            extra["gguf_quant_mode"] = "int8_convrot"
+
+        elif fmt == "int4_pytorch":
+            # Keep serialized INT4 weights; custom ops pack them for the CUDA kernel.
+            weight = weight_ggml.data.view(torch.uint8).reshape(weight_ggml.tensor_shape)
+            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
+
+            state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
+            state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
+            layer_prefix = weight_key[:weight_key.rfind("weight")]
+            state_dict[f"{layer_prefix}comfy_quant"] = torch.nn.Parameter(
+                torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8),
+                requires_grad=False,
+            )
+            extra["gguf_quant_mode"] = "int4_pytorch"
+
     return (state_dict, extra)
 
 # for remapping llama.cpp -> original key names
diff --git a/nodes.py b/nodes.py
index 4683514..fab0749 100644
--- a/nodes.py
+++ b/nodes.py
@@ -13,7 +13,7 @@
 import comfy.model_management
 import folder_paths
 
-from .ops import GGMLOps, move_patch_to_device
+from .ops import GGMLOps, GGUFQ4Ops, get_gguf_q8_ops, move_patch_to_device
 from .loader import gguf_sd_loader, gguf_clip_loader
 from .dequant import is_quantized, is_torch_compatible
 
@@ -148,7 +148,18 @@ def INPUT_TYPES(s):
     TITLE = "Unet Loader (GGUF)"
 
     def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
-        ops = GGMLOps()
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        sd, extra = gguf_sd_loader(unet_path)
+
+        mode = extra.get("gguf_quant_mode")
+        if mode == "int8_convrot":
+            # Use ComfyUI native INT8 path (weights stay INT8)
+            ops = get_gguf_q8_ops(compute_dtype=torch.bfloat16)()
+        elif mode == "int4_pytorch":
+            # Use custom INT4 ops class (currently dequant fallback)
+            ops = GGUFQ4Ops()
+        else:
+            ops = GGMLOps()
 
         if dequant_dtype in ("default", None):
             ops.Linear.dequant_dtype = None
@@ -165,8 +176,6 @@ def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_de
             ops.Linear.patch_dtype = getattr(torch, patch_dtype)
 
         # init model
-        unet_path = folder_paths.get_full_path("unet", unet_name)
-        sd, extra = gguf_sd_loader(unet_path)
 
         kwargs = {}
         valid_params = inspect.signature(comfy.sd.load_diffusion_model_state_dict).parameters
diff --git a/ops.py b/ops.py
index 9c26052..5b0c839 100644
--- a/ops.py
+++ b/ops.py
@@ -1,5 +1,6 @@
 # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 import gguf
+import json
 import torch
 import logging
 
@@ -313,3 +314,152 @@ def move_patch_to_device(item, device):
         return [move_patch_to_device(x, device) for x in item]
     else:
         return item
+
+def get_gguf_q8_ops(compute_dtype=torch.bfloat16, full_precision_mm=False):
+    """
+    Factory for an ops class that uses ComfyUI's native mixed_precision_ops INT8 path.
+    Weights are kept as INT8 and matmul uses comfy_kitchen's TensorWiseINT8Layout.
+    """
+    BaseOps = comfy.ops.mixed_precision_ops(
+        quant_config={},
+        compute_dtype=compute_dtype,
+        full_precision_mm=full_precision_mm,
+    )
+
+    class GGUFQ8Ops(BaseOps):
+        class Linear(BaseOps.Linear):
+            def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
+                # Lazy init: don't allocate weight here; it will be loaded from state dict
+                torch.nn.Module.__init__(self)
+                self.factory_kwargs = {"device": device, "dtype": BaseOps._compute_dtype}
+                self.in_features = in_features
+                self.out_features = out_features
+                self.weight = None
+                if bias:
+                    self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
+                else:
+                    self.register_parameter("bias", None)
+                self._orig_shape = (out_features, in_features)
+                self.tensor_class = None
+                self._full_precision_mm = BaseOps._full_precision_mm
+                self._full_precision_mm_config = False
+
+            def _load_from_state_dict(self, *args):
+                return comfy.ops._load_quantized_module(
+                    self,
+                    torch.nn.Module._load_from_state_dict.__get__(self, type(self)),
+                    *args,
+                    load_extra_params=True,
+                )
+
+    return GGUFQ8Ops
+
+
+class GGUFQ4Ops(comfy.ops.manual_cast):
+    """
+    Ops class for PyTorch's CUDA INT4 matrix multiplication.
+
+    GGUF stores a portable nibble-packed representation. The first forward pass
+    for each layer converts it to PyTorch's hardware-specific packed layout on
+    the active CUDA device, and subsequent forwards reuse that packed tensor.
+    """
+    class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
+        def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
+            torch.nn.Module.__init__(self)
+            self.in_features = in_features
+            self.out_features = out_features
+            self.weight = None
+            self.register_parameter("bias", None)
+            self._orig_shape = (out_features, in_features)
+            self._group_size = None
+            self._pad = 0
+            self._orig_in_features = in_features
+            self._int4_cache = {}
+            self._is_int4 = False
+
+        def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+            weight_key = f"{prefix}weight"
+            scale_key = f"{prefix}weight_scale"
+            quant_key = f"{prefix}comfy_quant"
+
+            weight = state_dict.pop(weight_key, None)
+            scale = state_dict.pop(scale_key, None)
+            quant_raw = state_dict.pop(quant_key, None)
+
+            bias_key = f"{prefix}bias"
+            bias = state_dict.pop(bias_key, None)
+            if quant_raw is None:
+                if weight is None:
+                    missing_keys.append(weight_key)
+                    return
+                self.weight = torch.nn.Parameter(torch.Tensor(weight), requires_grad=False)
+                if bias is not None:
+                    self.bias = torch.nn.Parameter(torch.Tensor(bias), requires_grad=False)
+                self._is_int4 = False
+                return
+
+            if weight is None or scale is None:
+                raise RuntimeError(f"Missing INT4 tensors for {prefix}")
+
+            quant_conf = json.loads(bytes(quant_raw.tolist()).decode("utf-8"))
+            if quant_conf.get("format") != "int4_pytorch":
+                raise ValueError(f"Unsupported INT4 format for {prefix}")
+            self._group_size = quant_conf["group_size"]
+            self._pad = quant_conf.get("pad", 0)
+            orig_shape = tuple(quant_conf["orig_shape"])
+            self._orig_in_features = orig_shape[1]
+            self._orig_shape = orig_shape
+            self.out_features = orig_shape[0]
+
+            self.weight = torch.nn.Parameter(weight, requires_grad=False)
+            self.weight_scale = torch.nn.Parameter(scale, requires_grad=False)
+            self._is_int4 = True
+
+            if bias is not None:
+                self.bias = torch.nn.Parameter(bias, requires_grad=False)
+            for key in (weight_key, scale_key, quant_key, bias_key):
+                if key in missing_keys:
+                    missing_keys.remove(key)
+
+        @staticmethod
+        def _inner_k_tiles():
+            return 2
+
+        def _packed_weight(self, device, dtype):
+            if device.type != "cuda":
+                raise RuntimeError("Q4_PT requires CUDA; no dequantization fallback is available.")
+            if dtype not in (torch.float16, torch.bfloat16):
+                raise RuntimeError(f"Q4_PT requires FP16 or BF16 activations, got {dtype}.")
+
+            cache_key = (device, dtype)
+            cached = self._int4_cache.get(cache_key)
+            if cached is not None:
+                return cached
+
+            serialized = self.weight.to(device=device, dtype=torch.uint8, non_blocking=True)
+            packed = torch._convert_weight_to_int4pack(serialized, self._inner_k_tiles())
+            scale_and_offset = self.weight_scale.to(device=device, dtype=dtype, non_blocking=True)
+            self._int4_cache[cache_key] = (packed, scale_and_offset)
+            return packed, scale_and_offset
+
+        def forward(self, input):
+            if self.weight is None:
+                raise RuntimeError("Q4_PT weight was not loaded.")
+            if not self._is_int4:
+                weight = self.weight.to(device=input.device, dtype=input.dtype)
+                bias = self.bias.to(device=input.device, dtype=input.dtype) if self.bias is not None else None
+                return torch.nn.functional.linear(input, weight, bias)
+            if self.weight_function or self.bias_function:
+                raise RuntimeError("Q4_PT does not support weight patches or LoRAs without dequantization.")
+            input_shape = input.shape
+            input_2d = input.reshape(-1, input_shape[-1])
+            if self._pad:
+                input_2d = torch.nn.functional.pad(input_2d, (0, self._pad))
+
+            packed, scale_and_offset = self._packed_weight(input.device, input.dtype)
+            output = torch._weight_int4pack_mm(
+                input_2d, packed, self._group_size, scale_and_offset
+            )
+            if self.bias is not None:
+                output = output + self.bias.to(device=input.device, dtype=input.dtype)
+            return output.reshape(*input_shape[:-1], self.out_features)
diff --git a/tools/convert.py b/tools/convert.py
index 8faa0b2..f2813c2 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -1,6 +1,7 @@
 # (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
 import os
 import gguf
+import json
 import torch
 import logging
 import argparse
@@ -228,8 +229,84 @@ def detect_arch(state_dict):
     "Q5_0": (gguf.GGMLQuantizationType.Q5_0, gguf.LlamaFileType.MOSTLY_Q5_0),
     "Q4_1": (gguf.GGMLQuantizationType.Q4_1, gguf.LlamaFileType.MOSTLY_Q4_1),
     "Q4_0": (gguf.GGMLQuantizationType.Q4_0, gguf.LlamaFileType.MOSTLY_Q4_0),
+    "Q8_CR": (gguf.GGMLQuantizationType.I8, None),  # INT8 ConvRot (ComfyUI native)
+    "Q4_PT": (gguf.GGMLQuantizationType.I8, None),  # INT4 PyTorch (packed as raw bytes)
 }
 
+
+def quantize_int8_convrot(weight, convrot_groupsize=256):
+    """
+    Quantize a 2D Linear weight to INT8 with ConvRot grouping.
+    Uses per-output-channel scales to match ComfyUI's TensorWiseINT8Layout.
+    """
+    weight = weight.to(torch.float32)
+    orig_shape = tuple(weight.shape)
+    groupsize = next(
+        (
+            size
+            for size in (convrot_groupsize, 64, 16, 4)
+            if size <= weight.shape[1] and weight.shape[1] % size == 0
+        ),
+        None,
+    )
+    if groupsize is not None:
+        from comfy_kitchen.tensor.int8_utils import _build_hadamard, _rotate_weight
+
+        hadamard = _build_hadamard(groupsize, device=weight.device, dtype=weight.dtype)
+        weight = _rotate_weight(weight, hadamard, groupsize)
+
+    scale = weight.abs().amax(dim=1, keepdim=True).clamp_min(1e-9) / 127.0
+    qdata = (weight / scale).round().clamp(-128, 127).to(torch.int8)
+    quant_conf = {
+        "format": "int8_tensorwise",
+        "convrot": groupsize is not None,
+        "weight_rotated": groupsize is not None,
+        "per_row": True,
+    }
+    if groupsize is not None:
+        quant_conf["convrot_groupsize"] = groupsize
+    return qdata, scale, quant_conf, orig_shape
+
+
+def quantize_int4_pytorch(weight, group_size=64):
+    """
+    Quantize a 2D Linear weight for PyTorch's native INT4 kernel.
+    Weights are serialized as packed uint8 [n, k//2]. The runtime converts this
+    portable representation to PyTorch's device-specific INT4 layout.
+    """
+    orig_shape = tuple(weight.shape)
+    n, k = orig_shape
+    weight = weight.to(torch.float32)
+
+    pad = 0
+    if k % group_size != 0:
+        pad = group_size - (k % group_size)
+        weight = torch.nn.functional.pad(weight, (0, pad))
+        k = k + pad
+
+    w_grouped = weight.reshape(n, k // group_size, group_size)
+    w_min = w_grouped.amin(dim=-1, keepdim=True)
+    w_max = w_grouped.amax(dim=-1, keepdim=True)
+    scale = (w_max - w_min) / 15.0
+    scale = scale.clamp_min(1e-9)
+    q = ((w_grouped - w_min) / scale).round().clamp(0, 15).to(torch.uint8)
+
+    q_flat = q.reshape(n, k)
+    packed = ((q_flat[:, 0::2] << 4) | q_flat[:, 1::2]).to(torch.uint8)
+
+    qsz = torch.zeros(k // group_size, n, 2, dtype=torch.float32)
+    qsz[:, :, 0] = scale.reshape(n, k // group_size).t()
+    # _weight_int4pack_mm dequantizes as (q - 8) * scale + offset.
+    qsz[:, :, 1] = (w_min + 8 * scale).reshape(n, k // group_size).t()
+
+    quant_conf = {
+        "format": "int4_pytorch",
+        "group_size": group_size,
+        "orig_shape": orig_shape,
+        "pad": pad,
+    }
+    return packed, qsz, quant_conf, orig_shape
+
 def parse_args():
     parser = argparse.ArgumentParser(
         description="Convert diffusion model safetensors/ckpt to GGUF."
@@ -295,7 +372,7 @@ def load_state_dict(path):
 
     return strip_prefix(state_dict)
 
-def handle_tensors(writer, state_dict, model_arch, quant_type=None):
+def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_name=None):
     # Pre-collect per-tensor FP8 scales (0-dim float32 tensors named "{key}_scale").
     # These must be applied to their FP8 weight tensors before GGUF quantization.
     # The actual weight value is fp8_value * scale; ignoring scale produces wrong magnitudes.
@@ -358,35 +435,58 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None):
         else:
             data_qtype = gguf.GGMLQuantizationType.F16
 
-        # The max no. of dimensions that can be handled by the quantization code is 4
-        if len(data.shape) > MAX_TENSOR_DIMS:
+        # The max no. of dimensions that can be handled by the quantization code is 4.
+        if n_dims > MAX_TENSOR_DIMS:
             model_arch.handle_nd_tensor(key, data)
-            continue # needs to be added back later
+            continue
 
-        # get number of parameters (AKA elements) in this tensor
         n_params = 1
         for dim_size in data_shape:
             n_params *= dim_size
 
         _FP8_DTYPES = {getattr(torch, "float8_e4m3fn", None), getattr(torch, "float8_e5m2", None)} - {None}
-        if old_dtype in (torch.float32, torch.bfloat16) or old_dtype in _FP8_DTYPES:
+        apply_quantization_rules = (
+            quant_type_name in ("Q8_CR", "Q4_PT")
+            or old_dtype in (torch.float32, torch.bfloat16)
+            or old_dtype in _FP8_DTYPES
+        )
+        if apply_quantization_rules:
             if n_dims == 1:
-                # one-dimensional tensors should be kept in F32
-                # also speeds up inference due to not dequantizing
+                # One-dimensional tensors should be kept in F32.
                 data_qtype = gguf.GGMLQuantizationType.F32
-
             elif n_params <= QUANTIZATION_THRESHOLD:
-                # very small tensors
                 data_qtype = gguf.GGMLQuantizationType.F32
-
             elif any(x in key for x in model_arch.keys_hiprec):
-                # tensors that require max precision
                 data_qtype = gguf.GGMLQuantizationType.F32
-
+            elif n_dims == 4 and "conv" in key.lower():
+                # Native quantized paths are Linear-only.
+                data_qtype = gguf.GGMLQuantizationType.F16
             elif quant_type is not None:
-                # caller-specified quantization type for all eligible tensors
                 data_qtype = quant_type
 
+        if quant_type_name in ("Q8_CR", "Q4_PT") and n_dims > 1 and n_dims != 2:
+            # Custom native layouts only represent Linear matrices.
+            data_qtype = gguf.GGMLQuantizationType.F16
+        elif quant_type_name == "Q4_PT" and data_shape[0] % 8 != 0:
+            # PyTorch's CUDA INT4 layout has an eight-output-channel tile.
+            data_qtype = gguf.GGMLQuantizationType.F16
+
+        # Custom quantization paths for Q8_CR and Q4_PT
+        if quant_type_name in ("Q8_CR", "Q4_PT") and data_qtype == quant_type and n_dims == 2:
+            if quant_type_name == "Q8_CR":
+                qdata, scale, quant_conf, orig_shape = quantize_int8_convrot(torch.from_numpy(data))
+                writer.add_tensor(key, qdata.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
+                writer.add_tensor(f"{key}_scale", scale.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
+                writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
+                continue
+
+            elif quant_type_name == "Q4_PT":
+                packed, qsz, quant_conf, orig_shape = quantize_int4_pytorch(torch.from_numpy(data))
+                writer.add_tensor(key, packed.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
+                writer.add_tensor(f"{key}_scale", qsz.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
+                writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
+                continue
+
         if (model_arch.shape_fix                        # NEVER reshape for models such as flux
             and n_dims > 1                              # Skip one-dimensional tensors
             and n_params >= REARRANGE_THRESHOLD         # Only rearrange tensors meeting the size requirement
@@ -455,7 +555,7 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type
     if ftype_gguf is not None:
         writer.add_file_type(ftype_gguf)
 
-    handle_tensors(writer, state_dict, model_arch, quant_type=quant_type)
+    handle_tensors(writer, state_dict, model_arch, quant_type=quant_type, quant_type_name=quant_type_name)
     writer.write_header_to_file(path=dst_path)
     writer.write_kv_data_to_file()
     writer.write_tensors_to_file(progress=True)

From 89f20c88d544cdb8f7f9bf310292344517effe22 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Sun, 26 Jul 2026 22:04:46 +0200
Subject: [PATCH 08/20] Q8_CR follow-up work

---
 README.md        |   36 +-
 loader.py        | 1263 +++++++++++++++++++++++-----------------------
 nodes.py         |  677 ++++++++++++-------------
 ops.py           |  950 +++++++++++++++++-----------------
 tools/convert.py | 1139 +++++++++++++++++++++--------------------
 5 files changed, 2049 insertions(+), 2016 deletions(-)

diff --git a/README.md b/README.md
index 6e099b5..97c152a 100644
--- a/README.md
+++ b/README.md
@@ -1,34 +1,27 @@
 # ComfyUI-GGUF
-GGUF Quantization support for native ComfyUI models
+GGUF Quantization support for native ComfyUI models including the custom Q8_CR
 
 > [!NOTE]  
 > This is a fork of the original nodes, updated to support loading Ideogram 4 GGUFs and Krea 2 GGUFs. 
 > To use it, clone `https://github.com/city96/ComfyUI-GGUF`and not the original repo.
 
-
-This is currently very much WIP. These custom nodes provide support for model files stored in the GGUF format popularized by [llama.cpp](https://github.com/ggerganov/llama.cpp).
-
 While quantization wasn't feasible for regular UNET models (conv2d), transformer/DiT models such as flux seem less affected by quantization. This allows running it in much lower bits per weight variable bitrate quants on low-end GPUs. For further VRAM savings, a node to load a quantized version of the T5 text encoder is also included.
 
-![Comfy_Flux1_dev_Q4_0_GGUF_1024](https://github.com/user-attachments/assets/70d16d97-c522-4ef4-9435-633f128644c8)
-
-Note: The "Force/Set CLIP Device" is **NOT** part of this node pack. Do not install it if you only have one GPU. Do not set it to cuda:0 then complain about OOM errors if you do not undestand what it is for. There is not need to copy the workflow above, just use your own workflow and replace the stock "Load Diffusion Model" with the "Unet Loader (GGUF)" node.
-
 ## Installation
 
 > [!IMPORTANT]  
-> Make sure your ComfyUI is on a recent-enough version to support custom ops when loading the UNET-only.
+> Make sure your ComfyUI is on v0.27.0 or later.
 
 To install the custom node normally, git clone this repository into your custom nodes folder (`ComfyUI/custom_nodes`) and install the only dependency for inference (`pip install --upgrade gguf`)
 
 ```
-git clone https://github.com/city96/ComfyUI-GGUF
+git clone https://github.com/molbal/ComfyUI-GGUF
 ```
 
 To install the custom node on a standalone ComfyUI release, open a CMD inside the "ComfyUI_windows_portable" folder (where your `run_nvidia_gpu.bat` file is) and use the following commands:
 
 ```
-git clone https://github.com/city96/ComfyUI-GGUF ComfyUI/custom_nodes/ComfyUI-GGUF
+git clone https://github.com/molbal/ComfyUI-GGUF ComfyUI/custom_nodes/ComfyUI-GGUF
 .\python_embeded\python.exe -s -m pip install -r .\ComfyUI\custom_nodes\ComfyUI-GGUF\requirements.txt
 ```
 
@@ -68,14 +61,23 @@ UNets:
 - `Q8_CR` stores eligible 2-D Linear weights as per-row INT8 ConvRot. It uses
   ComfyUI's native `TensorWiseINT8Layout` path, so weights remain INT8 during
   inference.
-- `Q4_PT` stores eligible 2-D Linear weights as group-size-64 INT4 and uses
-  PyTorch's CUDA `_weight_int4pack_mm` path. The first use of each layer creates
-  the device-specific packed representation; weights are not dequantized.
 
-Both modes keep 1-D, small, and architecture-designated high-precision tensors
+Q8_CR keeps 1-D, small, and architecture-designated high-precision tensors
 in FP32. Conv2d weights remain FP16 because these modes accelerate Linear
-matrix multiplication only. `Q4_PT` requires CUDA with FP16 or BF16
-activations; it intentionally has no CPU or dequantization fallback.
+matrix multiplication only.
+
+### Q8_CR platform support
+
+Q8_CR does not require CUDA. It uses ComfyUI's `comfy_kitchen` layout backend:
+
+- NVIDIA CUDA uses ComfyUI's optimized native INT8 backend when available.
+- Linux and non-CUDA environments use the `comfy_kitchen` eager backend.
+- CPU Q8_CR loading and inference are supported, but naturally slower than
+  optimized CUDA inference.
+
+Q4_PT is retired from conversion and loading until a performant W4A16 backend
+is available. Its experimental implementation remains in the source for future
+work, but it is no longer selectable or loadable.
 
 Reconvert any `Q8_CR` GGUF created before ConvRot weights were marked as
 pre-rotated. Older files load safely with native non-rotated INT8 instead.
diff --git a/loader.py b/loader.py
index 657ddd8..e23fef6 100644
--- a/loader.py
+++ b/loader.py
@@ -1,624 +1,639 @@
-# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
-import warnings
-import logging
-import torch
-import gguf
-import json
-import re
-import os
-from .ops import GGMLTensor
-from .dequant import is_quantized, dequantize_tensor
-
-IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram", "krea2"}
-TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}
-VIS_TYPE_LIST = {"clip-vision", "mmproj"}
-
-def device_supports_bf16():
-    """
-    Return True if the active torch device can run bf16 natively. On devices
-    without native bf16 support, computation silently falls back to fp32 which
-    is very slow, so callers should load tensors as fp16 instead.
-    """
-    try:
-        import comfy.model_management
-        return comfy.model_management.should_use_bf16(comfy.model_management.get_torch_device())
-    except Exception:
-        # If support can't be determined, keep the previous bf16 behavior.
-        return True
-
-def get_orig_shape(reader, tensor_name):
-    field_key = f"comfy.gguf.orig_shape.{tensor_name}"
-    field = reader.get_field(field_key)
-    if field is None:
-        return None
-    # Has original shape metadata, so we try to decode it.
-    if len(field.types) != 2 or field.types[0] != gguf.GGUFValueType.ARRAY or field.types[1] != gguf.GGUFValueType.INT32:
-        raise TypeError(f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}")
-    return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data))
-
-def get_field(reader, field_name, field_type):
-    field = reader.get_field(field_name)
-    if field is None:
-        return None
-    elif field_type == str:
-        # extra check here as this is used for checking arch string
-        if len(field.types) != 1 or field.types[0] != gguf.GGUFValueType.STRING:
-            raise TypeError(f"Bad type for GGUF {field_name} key: expected string, got {field.types!r}")
-        return str(field.parts[field.data[-1]], encoding="utf-8")
-    elif field_type in [int, float, bool]:
-        return field_type(field.parts[field.data[-1]].item())
-    else:
-        raise TypeError(f"Unknown field type {field_type}")
-
-def get_list_field(reader, field_name, field_type):
-    field = reader.get_field(field_name)
-    if field is None:
-        return None
-    elif field_type == str:
-        return tuple(str(field.parts[part_idx], encoding="utf-8") for part_idx in field.data)
-    elif field_type in [int, float, bool]:
-        return tuple(field_type(field.parts[part_idx][0]) for part_idx in field.data)
-    else:
-        raise TypeError(f"Unknown field type {field_type}")
-
-def get_gguf_metadata(reader):
-    """Extract all simple metadata fields like safetensors"""
-    metadata = {}
-    for field_name in reader.fields:
-        try:
-            field = reader.get_field(field_name)
-            if len(field.types) == 1:  # Simple scalar fields only
-                if field.types[0] == gguf.GGUFValueType.STRING:
-                    metadata[field_name] = str(field.parts[field.data[-1]], "utf-8")
-                elif field.types[0] == gguf.GGUFValueType.INT32:
-                    metadata[field_name] = int(field.parts[field.data[-1]])
-                elif field.types[0] == gguf.GGUFValueType.F32:
-                    metadata[field_name] = float(field.parts[field.data[-1]])
-                elif field.types[0] == gguf.GGUFValueType.BOOL:
-                    metadata[field_name] = bool(field.parts[field.data[-1]])
-        except:
-            continue
-    return metadata
-
-def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=False):
-    """
-    Read state dict as fake tensors
-    """
-    reader = gguf.GGUFReader(path)
-
-    # filter and strip prefix
-    has_prefix = False
-    if handle_prefix is not None:
-        prefix_len = len(handle_prefix)
-        tensor_names = set(tensor.name for tensor in reader.tensors)
-        has_prefix = any(s.startswith(handle_prefix) for s in tensor_names)
-
-    tensors = []
-    for tensor in reader.tensors:
-        sd_key = tensor_name = tensor.name
-        if has_prefix:
-            if not tensor_name.startswith(handle_prefix):
-                continue
-            sd_key = tensor_name[prefix_len:]
-        tensors.append((sd_key, tensor))
-
-    # detect and verify architecture
-    compat = None
-    arch_str = get_field(reader, "general.architecture", str)
-    type_str = get_field(reader, "general.type", str)
-    if arch_str in [None, "pig", "cow"]:
-        if is_text_model:
-            raise ValueError(f"This gguf file is incompatible with llama.cpp!\nConsider using safetensors or a compatible gguf file\n({path})")
-        compat = "sd.cpp" if arch_str is None else arch_str
-        # import here to avoid changes to convert.py breaking regular models
-        from .tools.convert import detect_arch
-        try:
-            arch_str = detect_arch(set(val[0] for val in tensors)).arch
-        except Exception as e:
-            raise ValueError(f"This model is not currently supported - ({e})")
-    elif arch_str not in TXT_ARCH_LIST and is_text_model:
-        if type_str not in VIS_TYPE_LIST:
-            raise ValueError(f"Unexpected text model architecture type in GGUF file: {arch_str!r}")
-    elif arch_str not in IMG_ARCH_LIST and not is_text_model:
-        raise ValueError(f"Unexpected architecture type in GGUF file: {arch_str!r}")
-
-    if compat:
-        logging.warning(f"Warning: This gguf model file is loaded in compatibility mode '{compat}' [arch:{arch_str}]")
-
-    # main loading loop
-    # Devices without native bf16 fall back to slow fp32 compute, so load the
-    # full-precision BF16 storage tensors as fp16 there instead.
-    bf16_storage_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
-    state_dict = {}
-    qtype_dict = {}
-    for sd_key, tensor in tensors:
-        tensor_name = tensor.name
-        # torch_tensor = torch.from_numpy(tensor.data) # mmap
-
-        # NOTE: line above replaced with this block to avoid persistent numpy warning about mmap
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", message="The given NumPy array is not writable")
-            torch_tensor = torch.from_numpy(tensor.data) # mmap
-
-        shape = get_orig_shape(reader, tensor_name)
-        if shape is None:
-            shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))
-            # Workaround for stable-diffusion.cpp SDXL detection.
-            if compat == "sd.cpp" and arch_str == "sdxl":
-                if any([tensor_name.endswith(x) for x in (".proj_in.weight", ".proj_out.weight")]):
-                    while len(shape) > 2 and shape[-1] == 1:
-                        shape = shape[:-1]
-
-        # add to state dict
-        if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
-            torch_tensor = torch_tensor.view(*shape)
-        state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
-
-        # BF16 GGUF tensors are full-precision storage, not compressed quants.
-        if tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
-            dtype = torch.float32 if len(shape) <= 1 else bf16_storage_dtype
-            state_dict[sd_key] = dequantize_tensor(state_dict[sd_key], dtype=dtype)
-
-        # keep track of loaded tensor types
-        tensor_type_str = getattr(tensor.tensor_type, "name", repr(tensor.tensor_type))
-        qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1
-
-    # print loaded tensor type counts
-    logging.info("gguf qtypes: " + ", ".join(f"{k} ({v})" for k, v in qtype_dict.items()))
-
-    # mark largest tensor for vram estimation
-    qsd = {k:v for k,v in state_dict.items() if is_quantized(v)}
-    if len(qsd) > 0:
-        max_key = max(qsd.keys(), key=lambda k: qsd[k].numel())
-        state_dict[max_key].is_largest_weight = True
-
-    # extra info to return
-    extra = {
-        "arch_str": arch_str,
-        "metadata": get_gguf_metadata(reader)
-    }
-
-    # Detect custom ComfyUI native quantization metadata
-    warned_unrotated_convrot = False
-    for field_name in reader.fields:
-        if not field_name.startswith("comfy.gguf.quant."):
-            continue
-        key = field_name[len("comfy.gguf.quant."):]
-        field = reader.get_field(field_name)
-        quant_conf = json.loads(str(field.parts[field.data[-1]], "utf-8"))
-        fmt = quant_conf.get("format")
-
-        weight_key = key
-        scale_key = f"{key}_scale"
-        if weight_key not in state_dict or scale_key not in state_dict:
-            logging.warning(f"Missing custom quant tensors for {weight_key}")
-            continue
-
-        weight_ggml = state_dict[weight_key]
-        scale_ggml = state_dict[scale_key]
-
-        if fmt == "int8_tensorwise":
-            if quant_conf.get("convrot") and not quant_conf.get("weight_rotated", False):
-                if not warned_unrotated_convrot:
-                    logging.warning(
-                        "Disabling ConvRot because this GGUF does not mark its weights "
-                        "as pre-rotated. Reconvert with the current converter to enable ConvRot."
-                    )
-                    warned_unrotated_convrot = True
-                quant_conf["convrot"] = False
-                quant_conf.pop("convrot_groupsize", None)
-            elif quant_conf.get("convrot"):
-                groupsize = quant_conf.get("convrot_groupsize", 256)
-                if weight_ggml.tensor_shape[-1] % groupsize != 0:
-                    logging.warning(
-                        "Disabling ConvRot for %s because %d input features are not "
-                        "divisible by group size %d.",
-                        weight_key,
-                        weight_ggml.tensor_shape[-1],
-                        groupsize,
-                    )
-                    quant_conf["convrot"] = False
-                    quant_conf.pop("convrot_groupsize", None)
-
-            # Convert to ComfyUI native state-dict layout
-            weight = weight_ggml.data.view(torch.int8).reshape(weight_ggml.tensor_shape)
-            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
-
-            state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
-            state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
-
-            layer_prefix = weight_key[:weight_key.rfind("weight")]
-            quant_json = json.dumps(quant_conf)
-            state_dict[f"{layer_prefix}comfy_quant"] = torch.nn.Parameter(
-                torch.tensor(list(quant_json.encode("utf-8")), dtype=torch.uint8),
-                requires_grad=False,
-            )
-            extra["gguf_quant_mode"] = "int8_convrot"
-
-        elif fmt == "int4_pytorch":
-            # Keep serialized INT4 weights; custom ops pack them for the CUDA kernel.
-            weight = weight_ggml.data.view(torch.uint8).reshape(weight_ggml.tensor_shape)
-            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
-
-            state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
-            state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
-            layer_prefix = weight_key[:weight_key.rfind("weight")]
-            state_dict[f"{layer_prefix}comfy_quant"] = torch.nn.Parameter(
-                torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8),
-                requires_grad=False,
-            )
-            extra["gguf_quant_mode"] = "int4_pytorch"
-
-    return (state_dict, extra)
-
-# for remapping llama.cpp -> original key names
-T5_SD_MAP = {
-    "enc.": "encoder.",
-    ".blk.": ".block.",
-    "token_embd": "shared",
-    "output_norm": "final_layer_norm",
-    "attn_q": "layer.0.SelfAttention.q",
-    "attn_k": "layer.0.SelfAttention.k",
-    "attn_v": "layer.0.SelfAttention.v",
-    "attn_o": "layer.0.SelfAttention.o",
-    "attn_norm": "layer.0.layer_norm",
-    "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias",
-    "ffn_up": "layer.1.DenseReluDense.wi_1",
-    "ffn_down": "layer.1.DenseReluDense.wo",
-    "ffn_gate": "layer.1.DenseReluDense.wi_0",
-    "ffn_norm": "layer.1.layer_norm",
-}
-
-LLAMA_SD_MAP = {
-    "blk.": "model.layers.",
-    "attn_norm": "input_layernorm",
-    "attn_q_norm.": "self_attn.q_norm.",
-    "attn_k_norm.": "self_attn.k_norm.",
-    "attn_v_norm.": "self_attn.v_norm.",
-    "attn_q": "self_attn.q_proj",
-    "attn_k": "self_attn.k_proj",
-    "attn_v": "self_attn.v_proj",
-    "attn_output": "self_attn.o_proj",
-    "ffn_up": "mlp.up_proj",
-    "ffn_down": "mlp.down_proj",
-    "ffn_gate": "mlp.gate_proj",
-    "ffn_norm": "post_attention_layernorm",
-    "token_embd": "model.embed_tokens",
-    "output_norm": "model.norm",
-    "output.weight": "lm_head.weight",
-}
-
-GEMMA3_SD_MAP = LLAMA_SD_MAP.copy()
-GEMMA3_SD_MAP.update({
-    "ffn_norm": "pre_feedforward_layernorm",
-    "post_ffw_norm": "post_feedforward_layernorm",
-    "post_attention_norm": "post_attention_layernorm",
-})
-
-CLIP_VISION_SD_MAP = {
-    "mm.": "visual.merger.mlp.",
-    "v.post_ln.": "visual.merger.ln_q.",
-    "v.patch_embd": "visual.patch_embed.proj",
-    "v.blk.": "visual.blocks.",
-    "ffn_up": "mlp.up_proj",
-    "ffn_down": "mlp.down_proj",
-    "ffn_gate": "mlp.gate_proj",
-    "attn_out.": "attn.proj.",
-    "ln1.": "norm1.",
-    "ln2.": "norm2.",
-}
-
-def sd_map_replace(raw_sd, key_map):
-    sd = {}
-    for k,v in raw_sd.items():
-        for s,d in key_map.items():
-            k = k.replace(s,d)
-        sd[k] = v
-    return sd
-
-def llama_permute(raw_sd, n_head, n_head_kv):
-    # Reverse version of LlamaModel.permute in llama.cpp convert script
-    sd = {}
-    permute = lambda x,h: x.reshape(h, x.shape[0] // h // 2, 2, *x.shape[1:]).swapaxes(1, 2).reshape(x.shape)
-    for k,v in raw_sd.items():
-        if k.endswith(("q_proj.weight", "q_proj.bias")):
-            v.data = permute(v.data, n_head)
-        if k.endswith(("k_proj.weight", "k_proj.bias")):
-            v.data = permute(v.data, n_head_kv)
-        sd[k] = v
-    return sd
-
-def gemma3_norm_corrections(sd):
-    # Reverse change from Gemma3Model modify_tensors in llama.cpp convert script
-    norm_patterns = [
-        "input_layernorm.weight",
-        "post_attention_layernorm.weight",
-        "pre_feedforward_layernorm.weight",
-        "post_feedforward_layernorm.weight",
-        "self_attn.q_norm.weight",
-        "self_attn.k_norm.weight",
-        "model.norm.weight"
-    ]
-    corrected = 0
-    for key in list(sd.keys()):
-        if any(p in key for p in norm_patterns):
-            if is_quantized(sd[key]):
-                sd[key] = dequantize_tensor(sd[key], dtype=torch.float32) - 1.0
-            else:
-                sd[key] = sd[key].float() - 1.0
-            corrected += 1
-    #logging.info(f"Gemma3: Applied -1 norm correction to {corrected} tensors")
-    return sd
-
-def strip_quant_suffix(name):
-    pattern = r"[-_]?(?:ud-)?i?q[0-9]_[a-z0-9_\-]{1,8}$"
-    match = re.search(pattern, name, re.IGNORECASE)
-    if match:
-        name = name[:match.start()]
-    return name
-
-def gguf_mmproj_loader(path):
-    # Reverse version of Qwen2VLVisionModel.modify_tensors
-    logging.info("Attenpting to find mmproj file for text encoder...")
-
-    # get name to match w/o quant suffix
-    tenc_fname = os.path.basename(path)
-    tenc = os.path.splitext(tenc_fname)[0].lower()
-    tenc = strip_quant_suffix(tenc)
-
-    # try and find matching mmproj
-    target = []
-    root = os.path.dirname(path)
-    for fname in os.listdir(root):
-        name, ext = os.path.splitext(fname)
-        if ext.lower() != ".gguf":
-            continue
-        if "mmproj" not in name.lower():
-            continue
-        if tenc in name.lower():
-            target.append(fname)
-
-    if len(target) == 0:
-        logging.error(f"Error: Can't find mmproj file for '{tenc_fname}' (matching:'{tenc}')! Qwen-Image-Edit will be broken!")
-        return {}
-    if len(target) > 1:
-        logging.error(f"Ambiguous mmproj for text encoder '{tenc_fname}', will use first match.")
-
-    logging.info(f"Using mmproj '{target[0]}' for text encoder '{tenc_fname}'.")
-    target = os.path.join(root, target[0])
-    vsd, _ = gguf_sd_loader(target, is_text_model=True)
-
-    # concat 4D to 5D
-    if "v.patch_embd.weight.1" in vsd:
-        w1 = dequantize_tensor(vsd.pop("v.patch_embd.weight"), dtype=torch.float32)
-        w2 = dequantize_tensor(vsd.pop("v.patch_embd.weight.1"), dtype=torch.float32)
-        vsd["v.patch_embd.weight"] = torch.stack([w1, w2], dim=2)
-
-    # run main replacement
-    vsd = sd_map_replace(vsd, CLIP_VISION_SD_MAP)
-
-    # handle split Q/K/V
-    if "visual.blocks.0.attn_q.weight" in vsd:
-        attns = {}
-        # filter out attentions + group
-        for k,v in vsd.items():
-            if any(x in k for x in ["attn_q", "attn_k", "attn_v"]):
-                k_attn, k_name = k.rsplit(".attn_", 1)
-                k_attn += ".attn.qkv." + k_name.split(".")[-1]
-                if k_attn not in attns:
-                    attns[k_attn] = {}
-                attns[k_attn][k_name] = dequantize_tensor(
-                    v, dtype=(torch.bfloat16 if is_quantized(v) else torch.float16)
-                )
-
-        # recombine
-        for k,v in attns.items():
-            suffix = k.split(".")[-1]
-            vsd[k] = torch.cat([
-                v[f"q.{suffix}"],
-                v[f"k.{suffix}"],
-                v[f"v.{suffix}"],
-            ], dim=0)
-        del attns
-
-    return vsd
-
-def gguf_tokenizer_loader(path, temb_shape):
-    # convert gguf tokenizer to spiece
-    logging.info("Attempting to recreate sentencepiece tokenizer from GGUF file metadata...")
-    try:
-        from sentencepiece import sentencepiece_model_pb2 as model
-    except ImportError:
-        raise ImportError("Please make sure sentencepiece and protobuf are installed.\npip install sentencepiece protobuf")
-    spm = model.ModelProto()
-
-    reader = gguf.GGUFReader(path)
-
-    if get_field(reader, "tokenizer.ggml.model", str) == "t5":
-        if temb_shape == (256384, 4096): # probably UMT5
-            spm.trainer_spec.model_type == 1 # Unigram (do we have a T5 w/ BPE?)
-        else:
-            raise NotImplementedError("Unknown model, can't set tokenizer!")
-    else:
-        raise NotImplementedError("Unknown model, can't set tokenizer!")
-
-    spm.normalizer_spec.add_dummy_prefix = get_field(reader, "tokenizer.ggml.add_space_prefix", bool)
-    spm.normalizer_spec.remove_extra_whitespaces = get_field(reader, "tokenizer.ggml.remove_extra_whitespaces", bool)
-
-    tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
-    scores = get_list_field(reader, "tokenizer.ggml.scores", float)
-    toktypes = get_list_field(reader, "tokenizer.ggml.token_type", int)
-
-    for idx, (token, score, toktype) in enumerate(zip(tokens, scores, toktypes)):
-        # # These aren't present in the original?
-        # if toktype == 5 and idx >= temb_shape[0]%1000):
-        #     continue
-
-        piece = spm.SentencePiece()
-        piece.piece = token
-        piece.score = score
-        piece.type = toktype
-        spm.pieces.append(piece)
-
-    # unsure if any of these are correct
-    spm.trainer_spec.byte_fallback = True
-    spm.trainer_spec.vocab_size = len(tokens) # split off unused?
-    spm.trainer_spec.max_sentence_length = 4096
-    spm.trainer_spec.eos_id = get_field(reader, "tokenizer.ggml.eos_token_id", int)
-    spm.trainer_spec.pad_id = get_field(reader, "tokenizer.ggml.padding_token_id", int)
-
-    logging.info(f"Created tokenizer with vocab size of {len(spm.pieces)}")
-    del reader
-    return torch.ByteTensor(list(spm.SerializeToString()))
-
-def gguf_tekken_tokenizer_loader(path, temb_shape):
-    # convert ggml (hf) tokenizer metadata to tekken/comfy data
-    logging.info("Attempting to recreate tekken tokenizer from GGUF file metadata...")
-    import json
-    import base64
-    from transformers.convert_slow_tokenizer import bytes_to_unicode
-
-    reader = gguf.GGUFReader(path)
-
-    model_str = get_field(reader, "tokenizer.ggml.model", str)
-    if model_str == "gpt2":
-        if temb_shape == (131072, 5120): # probably Mistral
-            data = {
-                "config": {"num_vocab_tokens": 150000, "default_vocab_size": 131072},
-                "vocab": [],
-                "special_tokens": [],
-            }
-        else:
-            raise NotImplementedError("Unknown model, can't set tokenizer!")
-    else:
-        raise NotImplementedError("Unknown model, can't set tokenizer!")
-
-    tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
-    toktypes = get_list_field(reader, "tokenizer.ggml.token_type", int)
-
-    decoder = {v: k for k, v in bytes_to_unicode().items()}
-    for idx, (token, toktype) in enumerate(zip(tokens, toktypes)):
-        if toktype == 3:
-            data["special_tokens"].append(
-                {'rank': idx, 'token_str': token, 'is_control': True}
-            )
-        else:
-            tok = bytes([decoder[char] for char in token])
-            data["vocab"].append({
-                "rank": len(data["vocab"]),
-                "token_bytes": base64.b64encode(tok).decode("ascii"),
-                "token_str": tok.decode("utf-8", errors="replace") # ?
-            })
-
-    logging.info(f"Created tekken tokenizer with vocab size of {len(data['vocab'])} (+{len(data['special_tokens'])})")
-    del reader
-    return torch.ByteTensor(list(json.dumps(data).encode('utf-8')))
-
-def gguf_gemma3_tokenizer_loader(path):
-    #TODO: merge into gguf_tokenizer_loader
-    logging.info("Attempting to recreate sentencepiece tokenizer from GGUF file metadata...")
-    try:
-        from sentencepiece import sentencepiece_model_pb2 as model
-    except ImportError:
-        raise ImportError("Please install sentencepiece and protobuf.\npip install sentencepiece protobuf")
-    spm = model.ModelProto()
-    reader = gguf.GGUFReader(path)
-
-    spm.normalizer_spec.name = "identity"
-    spm.normalizer_spec.add_dummy_prefix = False
-    spm.trainer_spec.model_type = 2
-    spm.trainer_spec.input_format = "tsv"
-    spm.trainer_spec.byte_fallback = True
-    spm.trainer_spec.max_sentence_length = 4192
-    spm.trainer_spec.bos_piece = "<bos>"
-
-    tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
-    scores = get_list_field(reader, "tokenizer.ggml.scores", float)
-    toktype = get_list_field(reader, "tokenizer.ggml.token_type", int)
-    
-    if not tokens or not scores or not toktype:
-        raise ValueError("Missing tokenizer metadata")
-    
-    for idx in range(len(tokens)):
-        piece = spm.SentencePiece()
-        piece.piece = tokens[idx]
-        if idx == 3:  # UNK position
-            piece.type = 2  # UNK Token
-            piece.score = 0.0 # UNK Score
-        else:
-            piece.type = toktype[idx]
-            piece.score = scores[idx]
-        spm.pieces.append(piece)
-    
-    spm.trainer_spec.vocab_size = len(spm.pieces)
-    logging.info(f"Created tokenizer with vocab size of {len(spm.pieces)}")
-    
-    del reader
-    return torch.ByteTensor(list(spm.SerializeToString()))
-
-def gguf_clip_loader(path):
-    sd, extra = gguf_sd_loader(path, is_text_model=True)
-    arch = extra.get("arch_str", None)
-    if arch in {"t5", "t5encoder"}:
-        temb_key = "token_embd.weight"
-        if temb_key in sd and sd[temb_key].shape == (256384, 4096):
-            # non-standard Comfy-Org tokenizer
-            sd["spiece_model"] = gguf_tokenizer_loader(path, sd[temb_key].shape)
-            # TODO: dequantizing token embed here is janky but otherwise we OOM due to tensor being massive.
-            logging.warning(f"Dequantizing {temb_key} to prevent runtime OOM.")
-            sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16)
-        sd = sd_map_replace(sd, T5_SD_MAP)
-    elif arch in {"llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}:
-        # TODO: pass model_options["vocab_size"] to loader somehow
-        temb_key = "token_embd.weight"
-        if temb_key in sd and sd[temb_key].shape[0] >= (64 * 1024):
-            if arch == "llama" and sd[temb_key].shape == (131072, 5120):
-                # non-standard Comfy-Org tokenizer
-                sd["tekken_model"] = gguf_tekken_tokenizer_loader(path, sd[temb_key].shape)
-            elif arch == "gemma3":
-                sd["spiece_model"] = gguf_gemma3_tokenizer_loader(path)
-            # See note above for T5.
-            logging.warning(f"Dequantizing {temb_key} to prevent runtime OOM.")
-            sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16)
-        if arch == "gemma3":
-            sd = sd_map_replace(sd, GEMMA3_SD_MAP)
-            sd = gemma3_norm_corrections(sd)
-        else:
-            sd = sd_map_replace(sd, LLAMA_SD_MAP)
-        if arch == "llama":
-            sd = llama_permute(sd, 32, 8) # L3 / Mistral
-        if arch == "qwen2vl":
-            vsd = gguf_mmproj_loader(path)
-            sd.update(vsd)
-        if arch == "qwen3vl" and "model.visual.deepstack_merger_list.0.norm.weight" not in sd:
-            # Standard llama.cpp Qwen3-VL GGUFs omit the visual tower. Without it,
-            # detect_te_model() mis-classifies the state dict as QWEN3_4B/8B (Qwen3 LM)
-            # instead of QWEN3VL_4B/8B, so clip_type=KREA2 never selects the 12-layer
-            # tap encoder and conditioning has shape (B, seq, 2560) instead of (B, seq, 30720).
-            # Inject zero sentinel tensors with shapes that exactly match the model
-            # parameters so that load_state_dict(strict=False) doesn't raise a size
-            # mismatch error while still satisfying detect_te_model()'s key checks.
-            #   deepstack_merger_list.0.norm  -> LayerNorm(vis_hidden * 4)  shape [merge_dim]
-            #   merger.linear_fc2             -> Linear(merge_dim, lm_hidden) shape [lm_hidden, merge_dim]
-            ln_key = "model.layers.0.input_layernorm.weight"
-            lm_hidden = int(sd[ln_key].shape[0]) if ln_key in sd else 2560
-            vis_hidden = 1024 if lm_hidden == 2560 else 1152  # Qwen3-VL-4B vs 8B
-            merge_dim = vis_hidden * 4  # spatial_merge_size=2
-            sd["model.visual.deepstack_merger_list.0.norm.weight"] = torch.zeros(merge_dim)
-            sd["model.visual.merger.linear_fc2.weight"] = torch.zeros(lm_hidden, merge_dim)
-            logging.info(f"qwen3vl GGUF: injected visual marker tensors (lm_hidden={lm_hidden}, merge_dim={merge_dim}) for model type detection")
-    elif arch == "ideogram":
-        # Dequantize Ideogram model for inference
-        logging.info("Dequantizing Ideogram model for inference...")
-        # Use BF16 to save VRAM while maintaining quality, but fall back to FP16
-        # on devices that don't support bf16 (avoids slow fp32 compute fallback).
-        target_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
-        dequantized_count = 0
-        for key in list(sd.keys()):
-            if is_quantized(sd[key]):
-                sd[key] = dequantize_tensor(sd[key], dtype=target_dtype)
-                dequantized_count += 1
-        logging.info(f"Dequantized {dequantized_count} tensors for Ideogram model ({target_dtype})")
-    else:
-        pass
-    return sd
+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import warnings
+import logging
+import torch
+import gguf
+import json
+import re
+import os
+from .ops import GGMLTensor
+from .dequant import is_quantized, dequantize_tensor
+
+IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram", "krea2"}
+TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}
+VIS_TYPE_LIST = {"clip-vision", "mmproj"}
+
+def device_supports_bf16():
+    """
+    Return True if the active torch device can run bf16 natively. On devices
+    without native bf16 support, computation silently falls back to fp32 which
+    is very slow, so callers should load tensors as fp16 instead.
+    """
+    try:
+        import comfy.model_management
+        return comfy.model_management.should_use_bf16(comfy.model_management.get_torch_device())
+    except Exception:
+        # If support can't be determined, keep the previous bf16 behavior.
+        return True
+
+def get_orig_shape(reader, tensor_name):
+    field_key = f"comfy.gguf.orig_shape.{tensor_name}"
+    field = reader.get_field(field_key)
+    if field is None:
+        return None
+    # Has original shape metadata, so we try to decode it.
+    if len(field.types) != 2 or field.types[0] != gguf.GGUFValueType.ARRAY or field.types[1] != gguf.GGUFValueType.INT32:
+        raise TypeError(f"Bad original shape metadata for {field_key}: Expected ARRAY of INT32, got {field.types}")
+    return torch.Size(tuple(int(field.parts[part_idx][0]) for part_idx in field.data))
+
+def get_field(reader, field_name, field_type):
+    field = reader.get_field(field_name)
+    if field is None:
+        return None
+    elif field_type == str:
+        # extra check here as this is used for checking arch string
+        if len(field.types) != 1 or field.types[0] != gguf.GGUFValueType.STRING:
+            raise TypeError(f"Bad type for GGUF {field_name} key: expected string, got {field.types!r}")
+        return str(field.parts[field.data[-1]], encoding="utf-8")
+    elif field_type in [int, float, bool]:
+        return field_type(field.parts[field.data[-1]].item())
+    else:
+        raise TypeError(f"Unknown field type {field_type}")
+
+def get_list_field(reader, field_name, field_type):
+    field = reader.get_field(field_name)
+    if field is None:
+        return None
+    elif field_type == str:
+        return tuple(str(field.parts[part_idx], encoding="utf-8") for part_idx in field.data)
+    elif field_type in [int, float, bool]:
+        return tuple(field_type(field.parts[part_idx][0]) for part_idx in field.data)
+    else:
+        raise TypeError(f"Unknown field type {field_type}")
+
+def get_gguf_metadata(reader):
+    """Extract all simple metadata fields like safetensors"""
+    metadata = {}
+    for field_name in reader.fields:
+        try:
+            field = reader.get_field(field_name)
+            if len(field.types) == 1:  # Simple scalar fields only
+                if field.types[0] == gguf.GGUFValueType.STRING:
+                    metadata[field_name] = str(field.parts[field.data[-1]], "utf-8")
+                elif field.types[0] == gguf.GGUFValueType.INT32:
+                    metadata[field_name] = int(field.parts[field.data[-1]])
+                elif field.types[0] == gguf.GGUFValueType.F32:
+                    metadata[field_name] = float(field.parts[field.data[-1]])
+                elif field.types[0] == gguf.GGUFValueType.BOOL:
+                    metadata[field_name] = bool(field.parts[field.data[-1]])
+        except:
+            continue
+    return metadata
+
+def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=False):
+    """
+    Read state dict as fake tensors
+    """
+    reader = gguf.GGUFReader(path)
+
+    # filter and strip prefix
+    has_prefix = False
+    if handle_prefix is not None:
+        prefix_len = len(handle_prefix)
+        tensor_names = set(tensor.name for tensor in reader.tensors)
+        has_prefix = any(s.startswith(handle_prefix) for s in tensor_names)
+
+    tensors = []
+    for tensor in reader.tensors:
+        sd_key = tensor_name = tensor.name
+        if has_prefix:
+            if not tensor_name.startswith(handle_prefix):
+                continue
+            sd_key = tensor_name[prefix_len:]
+        tensors.append((sd_key, tensor))
+
+    # detect and verify architecture
+    compat = None
+    arch_str = get_field(reader, "general.architecture", str)
+    type_str = get_field(reader, "general.type", str)
+    if arch_str in [None, "pig", "cow"]:
+        if is_text_model:
+            raise ValueError(f"This gguf file is incompatible with llama.cpp!\nConsider using safetensors or a compatible gguf file\n({path})")
+        compat = "sd.cpp" if arch_str is None else arch_str
+        # import here to avoid changes to convert.py breaking regular models
+        from .tools.convert import detect_arch
+        try:
+            arch_str = detect_arch(set(val[0] for val in tensors)).arch
+        except Exception as e:
+            raise ValueError(f"This model is not currently supported - ({e})")
+    elif arch_str not in TXT_ARCH_LIST and is_text_model:
+        if type_str not in VIS_TYPE_LIST:
+            raise ValueError(f"Unexpected text model architecture type in GGUF file: {arch_str!r}")
+    elif arch_str not in IMG_ARCH_LIST and not is_text_model:
+        raise ValueError(f"Unexpected architecture type in GGUF file: {arch_str!r}")
+
+    if compat:
+        logging.warning(f"Warning: This gguf model file is loaded in compatibility mode '{compat}' [arch:{arch_str}]")
+
+    # main loading loop
+    # Devices without native bf16 fall back to slow fp32 compute, so load the
+    # full-precision BF16 storage tensors as fp16 there instead.
+    bf16_storage_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
+    state_dict = {}
+    qtype_dict = {}
+    for sd_key, tensor in tensors:
+        tensor_name = tensor.name
+        # torch_tensor = torch.from_numpy(tensor.data) # mmap
+
+        # NOTE: line above replaced with this block to avoid persistent numpy warning about mmap
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message="The given NumPy array is not writable")
+            torch_tensor = torch.from_numpy(tensor.data) # mmap
+
+        shape = get_orig_shape(reader, tensor_name)
+        if shape is None:
+            shape = torch.Size(tuple(int(v) for v in reversed(tensor.shape)))
+            # Workaround for stable-diffusion.cpp SDXL detection.
+            if compat == "sd.cpp" and arch_str == "sdxl":
+                if any([tensor_name.endswith(x) for x in (".proj_in.weight", ".proj_out.weight")]):
+                    while len(shape) > 2 and shape[-1] == 1:
+                        shape = shape[:-1]
+
+        # add to state dict
+        if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
+            torch_tensor = torch_tensor.view(*shape)
+        state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
+
+        # BF16 GGUF tensors are full-precision storage, not compressed quants.
+        if tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
+            dtype = torch.float32 if len(shape) <= 1 else bf16_storage_dtype
+            state_dict[sd_key] = dequantize_tensor(state_dict[sd_key], dtype=dtype)
+
+        # keep track of loaded tensor types
+        tensor_type_str = getattr(tensor.tensor_type, "name", repr(tensor.tensor_type))
+        qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1
+
+    # print loaded tensor type counts
+    logging.info("gguf qtypes: " + ", ".join(f"{k} ({v})" for k, v in qtype_dict.items()))
+
+    # mark largest tensor for vram estimation
+    qsd = {k:v for k,v in state_dict.items() if is_quantized(v)}
+    if len(qsd) > 0:
+        max_key = max(qsd.keys(), key=lambda k: qsd[k].numel())
+        state_dict[max_key].is_largest_weight = True
+
+    # extra info to return
+    extra = {
+        "arch_str": arch_str,
+        "metadata": get_gguf_metadata(reader)
+    }
+
+    # Detect custom ComfyUI native quantization metadata
+    warned_unrotated_convrot = False
+    for field_name in reader.fields:
+        if not field_name.startswith("comfy.gguf.quant."):
+            continue
+        key = field_name[len("comfy.gguf.quant."):]
+        field = reader.get_field(field_name)
+        quant_conf = json.loads(str(field.parts[field.data[-1]], "utf-8"))
+        fmt = quant_conf.get("format")
+
+        if fmt in {"int4_compact_gemm", "int4_pytorch"}:
+            raise ValueError(
+                "Q4_PT GGUF files are retired because PyTorch's Ampere INT4 "
+                "kernel is not performance-competitive. Reconvert as Q8_CR."
+            )
+
+        weight_key = key
+        scale_key = f"{key}_scale"
+        if weight_key not in state_dict or scale_key not in state_dict:
+            logging.warning(f"Missing custom quant tensors for {weight_key}")
+            continue
+
+        weight_ggml = state_dict[weight_key]
+        scale_ggml = state_dict[scale_key]
+
+        if fmt == "int8_tensorwise":
+            if quant_conf.get("convrot") and not quant_conf.get("weight_rotated", False):
+                if not warned_unrotated_convrot:
+                    logging.warning(
+                        "Disabling ConvRot because this GGUF does not mark its weights "
+                        "as pre-rotated. Reconvert with the current converter to enable ConvRot."
+                    )
+                    warned_unrotated_convrot = True
+                quant_conf["convrot"] = False
+                quant_conf.pop("convrot_groupsize", None)
+            elif quant_conf.get("convrot"):
+                groupsize = quant_conf.get("convrot_groupsize", 256)
+                if weight_ggml.tensor_shape[-1] % groupsize != 0:
+                    logging.warning(
+                        "Disabling ConvRot for %s because %d input features are not "
+                        "divisible by group size %d.",
+                        weight_key,
+                        weight_ggml.tensor_shape[-1],
+                        groupsize,
+                    )
+                    quant_conf["convrot"] = False
+                    quant_conf.pop("convrot_groupsize", None)
+
+            # Convert to ComfyUI native state-dict layout
+            weight = weight_ggml.data.view(torch.int8).reshape(weight_ggml.tensor_shape)
+            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
+
+            state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
+            state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
+
+            layer_prefix = weight_key[:weight_key.rfind("weight")]
+            quant_json = json.dumps(quant_conf)
+            state_dict[f"{layer_prefix}comfy_quant"] = torch.nn.Parameter(
+                torch.tensor(list(quant_json.encode("utf-8")), dtype=torch.uint8),
+                requires_grad=False,
+            )
+            extra["gguf_quant_mode"] = "int8_convrot"
+
+        # Retained loader adaptation for the retired Q4_PT experiment. The
+        # explicit rejection above prevents this branch from being executed.
+        elif fmt in {"int4_compact_gemm", "int4_pytorch"}:
+            # Keep compact INT4 storage while exposing the original shape to
+            # ComfyUI's model detector. It uses first.weight.shape to infer
+            # Krea2's latent channel count before custom ops load the tensor.
+            orig_shape = torch.Size(tuple(quant_conf["orig_shape"]))
+            weight = GGMLTensor(
+                weight_ggml.data.view(torch.uint8).reshape(weight_ggml.tensor_shape),
+                tensor_type=weight_ggml.tensor_type,
+                tensor_shape=orig_shape,
+            )
+            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
+
+            state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
+            state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
+            layer_prefix = weight_key[:weight_key.rfind("weight")]
+            state_dict[f"{layer_prefix}comfy_quant"] = torch.nn.Parameter(
+                torch.tensor(list(json.dumps(quant_conf).encode("utf-8")), dtype=torch.uint8),
+                requires_grad=False,
+            )
+            extra["gguf_quant_mode"] = "int4_pytorch"
+
+    return (state_dict, extra)
+
+# for remapping llama.cpp -> original key names
+T5_SD_MAP = {
+    "enc.": "encoder.",
+    ".blk.": ".block.",
+    "token_embd": "shared",
+    "output_norm": "final_layer_norm",
+    "attn_q": "layer.0.SelfAttention.q",
+    "attn_k": "layer.0.SelfAttention.k",
+    "attn_v": "layer.0.SelfAttention.v",
+    "attn_o": "layer.0.SelfAttention.o",
+    "attn_norm": "layer.0.layer_norm",
+    "attn_rel_b": "layer.0.SelfAttention.relative_attention_bias",
+    "ffn_up": "layer.1.DenseReluDense.wi_1",
+    "ffn_down": "layer.1.DenseReluDense.wo",
+    "ffn_gate": "layer.1.DenseReluDense.wi_0",
+    "ffn_norm": "layer.1.layer_norm",
+}
+
+LLAMA_SD_MAP = {
+    "blk.": "model.layers.",
+    "attn_norm": "input_layernorm",
+    "attn_q_norm.": "self_attn.q_norm.",
+    "attn_k_norm.": "self_attn.k_norm.",
+    "attn_v_norm.": "self_attn.v_norm.",
+    "attn_q": "self_attn.q_proj",
+    "attn_k": "self_attn.k_proj",
+    "attn_v": "self_attn.v_proj",
+    "attn_output": "self_attn.o_proj",
+    "ffn_up": "mlp.up_proj",
+    "ffn_down": "mlp.down_proj",
+    "ffn_gate": "mlp.gate_proj",
+    "ffn_norm": "post_attention_layernorm",
+    "token_embd": "model.embed_tokens",
+    "output_norm": "model.norm",
+    "output.weight": "lm_head.weight",
+}
+
+GEMMA3_SD_MAP = LLAMA_SD_MAP.copy()
+GEMMA3_SD_MAP.update({
+    "ffn_norm": "pre_feedforward_layernorm",
+    "post_ffw_norm": "post_feedforward_layernorm",
+    "post_attention_norm": "post_attention_layernorm",
+})
+
+CLIP_VISION_SD_MAP = {
+    "mm.": "visual.merger.mlp.",
+    "v.post_ln.": "visual.merger.ln_q.",
+    "v.patch_embd": "visual.patch_embed.proj",
+    "v.blk.": "visual.blocks.",
+    "ffn_up": "mlp.up_proj",
+    "ffn_down": "mlp.down_proj",
+    "ffn_gate": "mlp.gate_proj",
+    "attn_out.": "attn.proj.",
+    "ln1.": "norm1.",
+    "ln2.": "norm2.",
+}
+
+def sd_map_replace(raw_sd, key_map):
+    sd = {}
+    for k,v in raw_sd.items():
+        for s,d in key_map.items():
+            k = k.replace(s,d)
+        sd[k] = v
+    return sd
+
+def llama_permute(raw_sd, n_head, n_head_kv):
+    # Reverse version of LlamaModel.permute in llama.cpp convert script
+    sd = {}
+    permute = lambda x,h: x.reshape(h, x.shape[0] // h // 2, 2, *x.shape[1:]).swapaxes(1, 2).reshape(x.shape)
+    for k,v in raw_sd.items():
+        if k.endswith(("q_proj.weight", "q_proj.bias")):
+            v.data = permute(v.data, n_head)
+        if k.endswith(("k_proj.weight", "k_proj.bias")):
+            v.data = permute(v.data, n_head_kv)
+        sd[k] = v
+    return sd
+
+def gemma3_norm_corrections(sd):
+    # Reverse change from Gemma3Model modify_tensors in llama.cpp convert script
+    norm_patterns = [
+        "input_layernorm.weight",
+        "post_attention_layernorm.weight",
+        "pre_feedforward_layernorm.weight",
+        "post_feedforward_layernorm.weight",
+        "self_attn.q_norm.weight",
+        "self_attn.k_norm.weight",
+        "model.norm.weight"
+    ]
+    corrected = 0
+    for key in list(sd.keys()):
+        if any(p in key for p in norm_patterns):
+            if is_quantized(sd[key]):
+                sd[key] = dequantize_tensor(sd[key], dtype=torch.float32) - 1.0
+            else:
+                sd[key] = sd[key].float() - 1.0
+            corrected += 1
+    #logging.info(f"Gemma3: Applied -1 norm correction to {corrected} tensors")
+    return sd
+
+def strip_quant_suffix(name):
+    pattern = r"[-_]?(?:ud-)?i?q[0-9]_[a-z0-9_\-]{1,8}$"
+    match = re.search(pattern, name, re.IGNORECASE)
+    if match:
+        name = name[:match.start()]
+    return name
+
+def gguf_mmproj_loader(path):
+    # Reverse version of Qwen2VLVisionModel.modify_tensors
+    logging.info("Attenpting to find mmproj file for text encoder...")
+
+    # get name to match w/o quant suffix
+    tenc_fname = os.path.basename(path)
+    tenc = os.path.splitext(tenc_fname)[0].lower()
+    tenc = strip_quant_suffix(tenc)
+
+    # try and find matching mmproj
+    target = []
+    root = os.path.dirname(path)
+    for fname in os.listdir(root):
+        name, ext = os.path.splitext(fname)
+        if ext.lower() != ".gguf":
+            continue
+        if "mmproj" not in name.lower():
+            continue
+        if tenc in name.lower():
+            target.append(fname)
+
+    if len(target) == 0:
+        logging.error(f"Error: Can't find mmproj file for '{tenc_fname}' (matching:'{tenc}')! Qwen-Image-Edit will be broken!")
+        return {}
+    if len(target) > 1:
+        logging.error(f"Ambiguous mmproj for text encoder '{tenc_fname}', will use first match.")
+
+    logging.info(f"Using mmproj '{target[0]}' for text encoder '{tenc_fname}'.")
+    target = os.path.join(root, target[0])
+    vsd, _ = gguf_sd_loader(target, is_text_model=True)
+
+    # concat 4D to 5D
+    if "v.patch_embd.weight.1" in vsd:
+        w1 = dequantize_tensor(vsd.pop("v.patch_embd.weight"), dtype=torch.float32)
+        w2 = dequantize_tensor(vsd.pop("v.patch_embd.weight.1"), dtype=torch.float32)
+        vsd["v.patch_embd.weight"] = torch.stack([w1, w2], dim=2)
+
+    # run main replacement
+    vsd = sd_map_replace(vsd, CLIP_VISION_SD_MAP)
+
+    # handle split Q/K/V
+    if "visual.blocks.0.attn_q.weight" in vsd:
+        attns = {}
+        # filter out attentions + group
+        for k,v in vsd.items():
+            if any(x in k for x in ["attn_q", "attn_k", "attn_v"]):
+                k_attn, k_name = k.rsplit(".attn_", 1)
+                k_attn += ".attn.qkv." + k_name.split(".")[-1]
+                if k_attn not in attns:
+                    attns[k_attn] = {}
+                attns[k_attn][k_name] = dequantize_tensor(
+                    v, dtype=(torch.bfloat16 if is_quantized(v) else torch.float16)
+                )
+
+        # recombine
+        for k,v in attns.items():
+            suffix = k.split(".")[-1]
+            vsd[k] = torch.cat([
+                v[f"q.{suffix}"],
+                v[f"k.{suffix}"],
+                v[f"v.{suffix}"],
+            ], dim=0)
+        del attns
+
+    return vsd
+
+def gguf_tokenizer_loader(path, temb_shape):
+    # convert gguf tokenizer to spiece
+    logging.info("Attempting to recreate sentencepiece tokenizer from GGUF file metadata...")
+    try:
+        from sentencepiece import sentencepiece_model_pb2 as model
+    except ImportError:
+        raise ImportError("Please make sure sentencepiece and protobuf are installed.\npip install sentencepiece protobuf")
+    spm = model.ModelProto()
+
+    reader = gguf.GGUFReader(path)
+
+    if get_field(reader, "tokenizer.ggml.model", str) == "t5":
+        if temb_shape == (256384, 4096): # probably UMT5
+            spm.trainer_spec.model_type == 1 # Unigram (do we have a T5 w/ BPE?)
+        else:
+            raise NotImplementedError("Unknown model, can't set tokenizer!")
+    else:
+        raise NotImplementedError("Unknown model, can't set tokenizer!")
+
+    spm.normalizer_spec.add_dummy_prefix = get_field(reader, "tokenizer.ggml.add_space_prefix", bool)
+    spm.normalizer_spec.remove_extra_whitespaces = get_field(reader, "tokenizer.ggml.remove_extra_whitespaces", bool)
+
+    tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
+    scores = get_list_field(reader, "tokenizer.ggml.scores", float)
+    toktypes = get_list_field(reader, "tokenizer.ggml.token_type", int)
+
+    for idx, (token, score, toktype) in enumerate(zip(tokens, scores, toktypes)):
+        # # These aren't present in the original?
+        # if toktype == 5 and idx >= temb_shape[0]%1000):
+        #     continue
+
+        piece = spm.SentencePiece()
+        piece.piece = token
+        piece.score = score
+        piece.type = toktype
+        spm.pieces.append(piece)
+
+    # unsure if any of these are correct
+    spm.trainer_spec.byte_fallback = True
+    spm.trainer_spec.vocab_size = len(tokens) # split off unused?
+    spm.trainer_spec.max_sentence_length = 4096
+    spm.trainer_spec.eos_id = get_field(reader, "tokenizer.ggml.eos_token_id", int)
+    spm.trainer_spec.pad_id = get_field(reader, "tokenizer.ggml.padding_token_id", int)
+
+    logging.info(f"Created tokenizer with vocab size of {len(spm.pieces)}")
+    del reader
+    return torch.ByteTensor(list(spm.SerializeToString()))
+
+def gguf_tekken_tokenizer_loader(path, temb_shape):
+    # convert ggml (hf) tokenizer metadata to tekken/comfy data
+    logging.info("Attempting to recreate tekken tokenizer from GGUF file metadata...")
+    import json
+    import base64
+    from transformers.convert_slow_tokenizer import bytes_to_unicode
+
+    reader = gguf.GGUFReader(path)
+
+    model_str = get_field(reader, "tokenizer.ggml.model", str)
+    if model_str == "gpt2":
+        if temb_shape == (131072, 5120): # probably Mistral
+            data = {
+                "config": {"num_vocab_tokens": 150000, "default_vocab_size": 131072},
+                "vocab": [],
+                "special_tokens": [],
+            }
+        else:
+            raise NotImplementedError("Unknown model, can't set tokenizer!")
+    else:
+        raise NotImplementedError("Unknown model, can't set tokenizer!")
+
+    tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
+    toktypes = get_list_field(reader, "tokenizer.ggml.token_type", int)
+
+    decoder = {v: k for k, v in bytes_to_unicode().items()}
+    for idx, (token, toktype) in enumerate(zip(tokens, toktypes)):
+        if toktype == 3:
+            data["special_tokens"].append(
+                {'rank': idx, 'token_str': token, 'is_control': True}
+            )
+        else:
+            tok = bytes([decoder[char] for char in token])
+            data["vocab"].append({
+                "rank": len(data["vocab"]),
+                "token_bytes": base64.b64encode(tok).decode("ascii"),
+                "token_str": tok.decode("utf-8", errors="replace") # ?
+            })
+
+    logging.info(f"Created tekken tokenizer with vocab size of {len(data['vocab'])} (+{len(data['special_tokens'])})")
+    del reader
+    return torch.ByteTensor(list(json.dumps(data).encode('utf-8')))
+
+def gguf_gemma3_tokenizer_loader(path):
+    #TODO: merge into gguf_tokenizer_loader
+    logging.info("Attempting to recreate sentencepiece tokenizer from GGUF file metadata...")
+    try:
+        from sentencepiece import sentencepiece_model_pb2 as model
+    except ImportError:
+        raise ImportError("Please install sentencepiece and protobuf.\npip install sentencepiece protobuf")
+    spm = model.ModelProto()
+    reader = gguf.GGUFReader(path)
+
+    spm.normalizer_spec.name = "identity"
+    spm.normalizer_spec.add_dummy_prefix = False
+    spm.trainer_spec.model_type = 2
+    spm.trainer_spec.input_format = "tsv"
+    spm.trainer_spec.byte_fallback = True
+    spm.trainer_spec.max_sentence_length = 4192
+    spm.trainer_spec.bos_piece = "<bos>"
+
+    tokens = get_list_field(reader, "tokenizer.ggml.tokens", str)
+    scores = get_list_field(reader, "tokenizer.ggml.scores", float)
+    toktype = get_list_field(reader, "tokenizer.ggml.token_type", int)
+
+    if not tokens or not scores or not toktype:
+        raise ValueError("Missing tokenizer metadata")
+
+    for idx in range(len(tokens)):
+        piece = spm.SentencePiece()
+        piece.piece = tokens[idx]
+        if idx == 3:  # UNK position
+            piece.type = 2  # UNK Token
+            piece.score = 0.0 # UNK Score
+        else:
+            piece.type = toktype[idx]
+            piece.score = scores[idx]
+        spm.pieces.append(piece)
+
+    spm.trainer_spec.vocab_size = len(spm.pieces)
+    logging.info(f"Created tokenizer with vocab size of {len(spm.pieces)}")
+
+    del reader
+    return torch.ByteTensor(list(spm.SerializeToString()))
+
+def gguf_clip_loader(path):
+    sd, extra = gguf_sd_loader(path, is_text_model=True)
+    arch = extra.get("arch_str", None)
+    if arch in {"t5", "t5encoder"}:
+        temb_key = "token_embd.weight"
+        if temb_key in sd and sd[temb_key].shape == (256384, 4096):
+            # non-standard Comfy-Org tokenizer
+            sd["spiece_model"] = gguf_tokenizer_loader(path, sd[temb_key].shape)
+            # TODO: dequantizing token embed here is janky but otherwise we OOM due to tensor being massive.
+            logging.warning(f"Dequantizing {temb_key} to prevent runtime OOM.")
+            sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16)
+        sd = sd_map_replace(sd, T5_SD_MAP)
+    elif arch in {"llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}:
+        # TODO: pass model_options["vocab_size"] to loader somehow
+        temb_key = "token_embd.weight"
+        if temb_key in sd and sd[temb_key].shape[0] >= (64 * 1024):
+            if arch == "llama" and sd[temb_key].shape == (131072, 5120):
+                # non-standard Comfy-Org tokenizer
+                sd["tekken_model"] = gguf_tekken_tokenizer_loader(path, sd[temb_key].shape)
+            elif arch == "gemma3":
+                sd["spiece_model"] = gguf_gemma3_tokenizer_loader(path)
+            # See note above for T5.
+            logging.warning(f"Dequantizing {temb_key} to prevent runtime OOM.")
+            sd[temb_key] = dequantize_tensor(sd[temb_key], dtype=torch.float16)
+        if arch == "gemma3":
+            sd = sd_map_replace(sd, GEMMA3_SD_MAP)
+            sd = gemma3_norm_corrections(sd)
+        else:
+            sd = sd_map_replace(sd, LLAMA_SD_MAP)
+        if arch == "llama":
+            sd = llama_permute(sd, 32, 8) # L3 / Mistral
+        if arch == "qwen2vl":
+            vsd = gguf_mmproj_loader(path)
+            sd.update(vsd)
+        if arch == "qwen3vl" and "model.visual.deepstack_merger_list.0.norm.weight" not in sd:
+            # Standard llama.cpp Qwen3-VL GGUFs omit the visual tower. Without it,
+            # detect_te_model() mis-classifies the state dict as QWEN3_4B/8B (Qwen3 LM)
+            # instead of QWEN3VL_4B/8B, so clip_type=KREA2 never selects the 12-layer
+            # tap encoder and conditioning has shape (B, seq, 2560) instead of (B, seq, 30720).
+            # Inject zero sentinel tensors with shapes that exactly match the model
+            # parameters so that load_state_dict(strict=False) doesn't raise a size
+            # mismatch error while still satisfying detect_te_model()'s key checks.
+            #   deepstack_merger_list.0.norm  -> LayerNorm(vis_hidden * 4)  shape [merge_dim]
+            #   merger.linear_fc2             -> Linear(merge_dim, lm_hidden) shape [lm_hidden, merge_dim]
+            ln_key = "model.layers.0.input_layernorm.weight"
+            lm_hidden = int(sd[ln_key].shape[0]) if ln_key in sd else 2560
+            vis_hidden = 1024 if lm_hidden == 2560 else 1152  # Qwen3-VL-4B vs 8B
+            merge_dim = vis_hidden * 4  # spatial_merge_size=2
+            sd["model.visual.deepstack_merger_list.0.norm.weight"] = torch.zeros(merge_dim)
+            sd["model.visual.merger.linear_fc2.weight"] = torch.zeros(lm_hidden, merge_dim)
+            logging.info(f"qwen3vl GGUF: injected visual marker tensors (lm_hidden={lm_hidden}, merge_dim={merge_dim}) for model type detection")
+    elif arch == "ideogram":
+        # Dequantize Ideogram model for inference
+        logging.info("Dequantizing Ideogram model for inference...")
+        # Use BF16 to save VRAM while maintaining quality, but fall back to FP16
+        # on devices that don't support bf16 (avoids slow fp32 compute fallback).
+        target_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
+        dequantized_count = 0
+        for key in list(sd.keys()):
+            if is_quantized(sd[key]):
+                sd[key] = dequantize_tensor(sd[key], dtype=target_dtype)
+                dequantized_count += 1
+        logging.info(f"Dequantized {dequantized_count} tensors for Ideogram model ({target_dtype})")
+    else:
+        pass
+    return sd
diff --git a/nodes.py b/nodes.py
index fab0749..ce30888 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1,338 +1,339 @@
-# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
-import torch
-import logging
-import inspect
-import collections
-
-import nodes
-import comfy.sd
-import comfy.lora
-import comfy.float
-import comfy.utils
-import comfy.model_patcher
-import comfy.model_management
-import folder_paths
-
-from .ops import GGMLOps, GGUFQ4Ops, get_gguf_q8_ops, move_patch_to_device
-from .loader import gguf_sd_loader, gguf_clip_loader
-from .dequant import is_quantized, is_torch_compatible
-
-def update_folder_names_and_paths(key, targets=[]):
-    # check for existing key
-    base = folder_paths.folder_names_and_paths.get(key, ([], {}))
-    base = base[0] if isinstance(base[0], (list, set, tuple)) else []
-    # find base key & add w/ fallback, sanity check + warning
-    target = next((x for x in targets if x in folder_paths.folder_names_and_paths), targets[0])
-    orig, _ = folder_paths.folder_names_and_paths.get(target, ([], {}))
-    folder_paths.folder_names_and_paths[key] = (orig or base, {".gguf"})
-    if base and base != orig:
-        logging.warning(f"Unknown file list already present on key {key}: {base}")
-
-# Add a custom keys for files ending in .gguf
-update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"])
-update_folder_names_and_paths("clip_gguf", ["text_encoders", "clip"])
-
-class GGUFModelPatcher(comfy.model_patcher.ModelPatcher):
-    patch_on_device = False
-
-    def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
-        if key not in self.patches:
-            return
-        weight = comfy.utils.get_attr(self.model, key)
-
-        patches = self.patches[key]
-        if is_quantized(weight):
-            out_weight = weight.to(device_to)
-            patches = move_patch_to_device(patches, self.load_device if self.patch_on_device else self.offload_device)
-            # TODO: do we ever have legitimate duplicate patches? (i.e. patch on top of patched weight)
-            out_weight.patches = [(patches, key)]
-        else:
-            inplace_update = self.weight_inplace_update or inplace_update
-            if key not in self.backup:
-                self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(
-                    weight.to(device=self.offload_device, copy=inplace_update), inplace_update
-                )
-
-            if device_to is not None:
-                temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
-            else:
-                temp_weight = weight.to(torch.float32, copy=True)
-
-            out_weight = comfy.lora.calculate_weight(patches, temp_weight, key)
-            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
-
-        if inplace_update:
-            comfy.utils.copy_to_param(self.model, key, out_weight)
-        else:
-            comfy.utils.set_attr_param(self.model, key, out_weight)
-
-    def unpatch_model(self, device_to=None, unpatch_weights=True):
-        if unpatch_weights:
-            for p in self.model.parameters():
-                if is_torch_compatible(p):
-                    continue
-                patches = getattr(p, "patches", [])
-                if len(patches) > 0:
-                    p.patches = []
-        # TODO: Find another way to not unload after patches
-        return super().unpatch_model(device_to=device_to, unpatch_weights=unpatch_weights)
-
-
-    def pin_weight_to_device(self, key):
-        op_key = key.rsplit('.', 1)[0]
-        if not self.mmap_released and op_key in self.named_modules_to_munmap:
-            # TODO: possible to OOM, find better way to detach
-            self.named_modules_to_munmap[op_key].to(self.load_device).to(self.offload_device)
-            del self.named_modules_to_munmap[op_key]
-        super().pin_weight_to_device(key)
-
-    mmap_released = False
-    named_modules_to_munmap = {}
-
-    def load(self, *args, force_patch_weights=False, **kwargs):
-        if not self.mmap_released:
-            self.named_modules_to_munmap = dict(self.model.named_modules())
-
-        # always call `patch_weight_to_device` even for lowvram
-        super().load(*args, force_patch_weights=True, **kwargs)
-
-        # make sure nothing stays linked to mmap after first load
-        if not self.mmap_released:
-            linked = []
-            if kwargs.get("lowvram_model_memory", 0) > 0:
-                for n, m in self.named_modules_to_munmap.items():
-                    if hasattr(m, "weight"):
-                        device = getattr(m.weight, "device", None)
-                        if device == self.offload_device:
-                            linked.append((n, m))
-                            continue
-                    if hasattr(m, "bias"):
-                        device = getattr(m.bias, "device", None)
-                        if device == self.offload_device:
-                            linked.append((n, m))
-                            continue
-            if linked and self.load_device != self.offload_device:
-                logging.info(f"Attempting to release mmap ({len(linked)})")
-                for n, m in linked:
-                    # TODO: possible to OOM, find better way to detach
-                    m.to(self.load_device).to(self.offload_device)
-            self.mmap_released = True
-            self.named_modules_to_munmap = {}
-
-    def clone(self, *args, **kwargs):
-        src_cls = self.__class__
-        self.__class__ = GGUFModelPatcher
-        n = super().clone(*args, **kwargs)
-        n.__class__ = GGUFModelPatcher
-        self.__class__ = src_cls
-        # GGUF specific clone values below
-        n.patch_on_device = getattr(self, "patch_on_device", False)
-        n.mmap_released = getattr(self, "mmap_released", False)
-        if src_cls != GGUFModelPatcher:
-            n.size = 0 # force recalc
-        return n
-
-class UnetLoaderGGUF:
-    @classmethod
-    def INPUT_TYPES(s):
-        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
-        return {
-            "required": {
-                "unet_name": (unet_names,),
-            }
-        }
-
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "load_unet"
-    CATEGORY = "bootleg"
-    TITLE = "Unet Loader (GGUF)"
-
-    def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
-        unet_path = folder_paths.get_full_path("unet", unet_name)
-        sd, extra = gguf_sd_loader(unet_path)
-
-        mode = extra.get("gguf_quant_mode")
-        if mode == "int8_convrot":
-            # Use ComfyUI native INT8 path (weights stay INT8)
-            ops = get_gguf_q8_ops(compute_dtype=torch.bfloat16)()
-        elif mode == "int4_pytorch":
-            # Use custom INT4 ops class (currently dequant fallback)
-            ops = GGUFQ4Ops()
-        else:
-            ops = GGMLOps()
-
-        if dequant_dtype in ("default", None):
-            ops.Linear.dequant_dtype = None
-        elif dequant_dtype in ["target"]:
-            ops.Linear.dequant_dtype = dequant_dtype
-        else:
-            ops.Linear.dequant_dtype = getattr(torch, dequant_dtype)
-
-        if patch_dtype in ("default", None):
-            ops.Linear.patch_dtype = None
-        elif patch_dtype in ["target"]:
-            ops.Linear.patch_dtype = patch_dtype
-        else:
-            ops.Linear.patch_dtype = getattr(torch, patch_dtype)
-
-        # init model
-
-        kwargs = {}
-        valid_params = inspect.signature(comfy.sd.load_diffusion_model_state_dict).parameters
-        if "metadata" in valid_params:
-            kwargs["metadata"] = extra.get("metadata", {})
-
-        model = comfy.sd.load_diffusion_model_state_dict(
-            sd, model_options={"custom_operations": ops}, **kwargs,
-        )
-        if model is None:
-            logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
-            raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
-        model = GGUFModelPatcher.clone(model)
-        model.patch_on_device = patch_on_device
-        return (model,)
-
-class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
-    @classmethod
-    def INPUT_TYPES(s):
-        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
-        return {
-            "required": {
-                "unet_name": (unet_names,),
-                "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
-                "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
-                "patch_on_device": ("BOOLEAN", {"default": False}),
-            }
-        }
-    TITLE = "Unet Loader (GGUF/Advanced)"
-
-class CLIPLoaderGGUF:
-    @classmethod
-    def INPUT_TYPES(s):
-        base = nodes.CLIPLoader.INPUT_TYPES()
-        return {
-            "required": {
-                "clip_name": (s.get_filename_list(),),
-                "type": base["required"]["type"],
-            }
-        }
-
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "load_clip"
-    CATEGORY = "bootleg"
-    TITLE = "CLIPLoader (GGUF)"
-
-    @classmethod
-    def get_filename_list(s):
-        files = []
-        files += folder_paths.get_filename_list("clip")
-        files += folder_paths.get_filename_list("clip_gguf")
-        return sorted(files)
-
-    def load_data(self, ckpt_paths):
-        clip_data = []
-        for p in ckpt_paths:
-            if p.endswith(".gguf"):
-                sd = gguf_clip_loader(p)
-            else:
-                sd = comfy.utils.load_torch_file(p, safe_load=True)
-                if "scaled_fp8" in sd: # NOTE: Scaled FP8 would require different custom ops, but only one can be active
-                    raise NotImplementedError(f"Mixing scaled FP8 with GGUF is not supported! Use regular CLIP loader or switch model(s)\n({p})")
-            clip_data.append(sd)
-        return clip_data
-
-    def load_patcher(self, clip_paths, clip_type, clip_data):
-        clip = comfy.sd.load_text_encoder_state_dicts(
-            clip_type = clip_type,
-            state_dicts = clip_data,
-            model_options = {
-                "custom_operations": GGMLOps,
-                "initial_device": comfy.model_management.text_encoder_offload_device()
-            },
-            embedding_directory = folder_paths.get_folder_paths("embeddings"),
-        )
-        clip.patcher = GGUFModelPatcher.clone(clip.patcher)
-        return clip
-
-    def load_clip(self, clip_name, type="stable_diffusion"):
-        clip_path = folder_paths.get_full_path("clip", clip_name)
-        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
-        return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),)
-
-class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
-    @classmethod
-    def INPUT_TYPES(s):
-        base = nodes.DualCLIPLoader.INPUT_TYPES()
-        file_options = (s.get_filename_list(), )
-        return {
-            "required": {
-                "clip_name1": file_options,
-                "clip_name2": file_options,
-                "type": base["required"]["type"],
-            }
-        }
-
-    TITLE = "DualCLIPLoader (GGUF)"
-
-    def load_clip(self, clip_name1, clip_name2, type):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
-        clip_paths = (clip_path1, clip_path2)
-        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
-        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
-
-class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
-    @classmethod
-    def INPUT_TYPES(s):
-        file_options = (s.get_filename_list(), )
-        return {
-            "required": {
-                "clip_name1": file_options,
-                "clip_name2": file_options,
-                "clip_name3": file_options,
-            }
-        }
-
-    TITLE = "TripleCLIPLoader (GGUF)"
-
-    def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
-        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
-        clip_paths = (clip_path1, clip_path2, clip_path3)
-        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
-        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
-
-class QuadrupleCLIPLoaderGGUF(CLIPLoaderGGUF):
-    @classmethod
-    def INPUT_TYPES(s):
-        file_options = (s.get_filename_list(), )
-        return {
-            "required": {
-            "clip_name1": file_options,
-            "clip_name2": file_options,
-            "clip_name3": file_options,
-            "clip_name4": file_options,
-        }
-    }
-
-    TITLE = "QuadrupleCLIPLoader (GGUF)"
-
-    def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable_diffusion"):
-        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
-        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
-        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
-        clip_path4 = folder_paths.get_full_path("clip", clip_name4)
-        clip_paths = (clip_path1, clip_path2, clip_path3, clip_path4)
-        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
-        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
-
-NODE_CLASS_MAPPINGS = {
-    "UnetLoaderGGUF": UnetLoaderGGUF,
-    "CLIPLoaderGGUF": CLIPLoaderGGUF,
-    "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
-    "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
-    "QuadrupleCLIPLoaderGGUF": QuadrupleCLIPLoaderGGUF,
-    "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
-}
-
+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import torch
+import logging
+import inspect
+import collections
+
+import nodes
+import comfy.sd
+import comfy.lora
+import comfy.float
+import comfy.utils
+import comfy.model_patcher
+import comfy.model_management
+import folder_paths
+
+from .ops import GGMLOps, get_gguf_q8_ops, move_patch_to_device
+from .loader import gguf_sd_loader, gguf_clip_loader
+from .dequant import is_quantized, is_torch_compatible
+
+def update_folder_names_and_paths(key, targets=[]):
+    # check for existing key
+    base = folder_paths.folder_names_and_paths.get(key, ([], {}))
+    base = base[0] if isinstance(base[0], (list, set, tuple)) else []
+    # find base key & add w/ fallback, sanity check + warning
+    target = next((x for x in targets if x in folder_paths.folder_names_and_paths), targets[0])
+    orig, _ = folder_paths.folder_names_and_paths.get(target, ([], {}))
+    folder_paths.folder_names_and_paths[key] = (orig or base, {".gguf"})
+    if base and base != orig:
+        logging.warning(f"Unknown file list already present on key {key}: {base}")
+
+# Add a custom keys for files ending in .gguf
+update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"])
+update_folder_names_and_paths("clip_gguf", ["text_encoders", "clip"])
+
+class GGUFModelPatcher(comfy.model_patcher.ModelPatcher):
+    patch_on_device = False
+
+    def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
+        if key not in self.patches:
+            return
+        weight = comfy.utils.get_attr(self.model, key)
+
+        patches = self.patches[key]
+        if is_quantized(weight):
+            out_weight = weight.to(device_to)
+            patches = move_patch_to_device(patches, self.load_device if self.patch_on_device else self.offload_device)
+            # TODO: do we ever have legitimate duplicate patches? (i.e. patch on top of patched weight)
+            out_weight.patches = [(patches, key)]
+        else:
+            inplace_update = self.weight_inplace_update or inplace_update
+            if key not in self.backup:
+                self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(
+                    weight.to(device=self.offload_device, copy=inplace_update), inplace_update
+                )
+
+            if device_to is not None:
+                temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+            else:
+                temp_weight = weight.to(torch.float32, copy=True)
+
+            out_weight = comfy.lora.calculate_weight(patches, temp_weight, key)
+            out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
+
+        if inplace_update:
+            comfy.utils.copy_to_param(self.model, key, out_weight)
+        else:
+            comfy.utils.set_attr_param(self.model, key, out_weight)
+
+    def unpatch_model(self, device_to=None, unpatch_weights=True):
+        if unpatch_weights:
+            for p in self.model.parameters():
+                if is_torch_compatible(p):
+                    continue
+                patches = getattr(p, "patches", [])
+                if len(patches) > 0:
+                    p.patches = []
+        # TODO: Find another way to not unload after patches
+        return super().unpatch_model(device_to=device_to, unpatch_weights=unpatch_weights)
+
+
+    def pin_weight_to_device(self, key):
+        op_key = key.rsplit('.', 1)[0]
+        if not self.mmap_released and op_key in self.named_modules_to_munmap:
+            # TODO: possible to OOM, find better way to detach
+            self.named_modules_to_munmap[op_key].to(self.load_device).to(self.offload_device)
+            del self.named_modules_to_munmap[op_key]
+        super().pin_weight_to_device(key)
+
+    mmap_released = False
+    named_modules_to_munmap = {}
+
+    def load(self, *args, force_patch_weights=False, **kwargs):
+        if not self.mmap_released:
+            self.named_modules_to_munmap = dict(self.model.named_modules())
+
+        # always call `patch_weight_to_device` even for lowvram
+        super().load(*args, force_patch_weights=True, **kwargs)
+
+        # make sure nothing stays linked to mmap after first load
+        if not self.mmap_released:
+            linked = []
+            if kwargs.get("lowvram_model_memory", 0) > 0:
+                for n, m in self.named_modules_to_munmap.items():
+                    if hasattr(m, "weight"):
+                        device = getattr(m.weight, "device", None)
+                        if device == self.offload_device:
+                            linked.append((n, m))
+                            continue
+                    if hasattr(m, "bias"):
+                        device = getattr(m.bias, "device", None)
+                        if device == self.offload_device:
+                            linked.append((n, m))
+                            continue
+            if linked and self.load_device != self.offload_device:
+                logging.info(f"Attempting to release mmap ({len(linked)})")
+                for n, m in linked:
+                    # TODO: possible to OOM, find better way to detach
+                    m.to(self.load_device).to(self.offload_device)
+            self.mmap_released = True
+            self.named_modules_to_munmap = {}
+
+    def clone(self, *args, **kwargs):
+        src_cls = self.__class__
+        self.__class__ = GGUFModelPatcher
+        n = super().clone(*args, **kwargs)
+        n.__class__ = GGUFModelPatcher
+        self.__class__ = src_cls
+        # GGUF specific clone values below
+        n.patch_on_device = getattr(self, "patch_on_device", False)
+        n.mmap_released = getattr(self, "mmap_released", False)
+        if src_cls != GGUFModelPatcher:
+            n.size = 0 # force recalc
+        return n
+
+class UnetLoaderGGUF:
+    @classmethod
+    def INPUT_TYPES(s):
+        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
+        return {
+            "required": {
+                "unet_name": (unet_names,),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "load_unet"
+    CATEGORY = "bootleg"
+    TITLE = "Unet Loader (GGUF)"
+
+    def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        sd, extra = gguf_sd_loader(unet_path)
+
+        mode = extra.get("gguf_quant_mode")
+        if mode == "int8_convrot":
+            # Use ComfyUI native INT8 path (weights stay INT8)
+            ops = get_gguf_q8_ops(compute_dtype=torch.bfloat16)()
+        elif mode == "int4_pytorch":
+            raise RuntimeError(
+                "Q4_PT is retired because PyTorch's Ampere INT4 kernel is not "
+                "performance-competitive. Reconvert the model as Q8_CR."
+            )
+        else:
+            ops = GGMLOps()
+
+        if dequant_dtype in ("default", None):
+            ops.Linear.dequant_dtype = None
+        elif dequant_dtype in ["target"]:
+            ops.Linear.dequant_dtype = dequant_dtype
+        else:
+            ops.Linear.dequant_dtype = getattr(torch, dequant_dtype)
+
+        if patch_dtype in ("default", None):
+            ops.Linear.patch_dtype = None
+        elif patch_dtype in ["target"]:
+            ops.Linear.patch_dtype = patch_dtype
+        else:
+            ops.Linear.patch_dtype = getattr(torch, patch_dtype)
+
+        # init model
+
+        kwargs = {}
+        valid_params = inspect.signature(comfy.sd.load_diffusion_model_state_dict).parameters
+        if "metadata" in valid_params:
+            kwargs["metadata"] = extra.get("metadata", {})
+
+        model = comfy.sd.load_diffusion_model_state_dict(
+            sd, model_options={"custom_operations": ops}, **kwargs,
+        )
+        if model is None:
+            logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
+            raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+        model = GGUFModelPatcher.clone(model)
+        model.patch_on_device = patch_on_device
+        return (model,)
+
+class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
+        return {
+            "required": {
+                "unet_name": (unet_names,),
+                "dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
+                "patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
+                "patch_on_device": ("BOOLEAN", {"default": False}),
+            }
+        }
+    TITLE = "Unet Loader (GGUF/Advanced)"
+
+class CLIPLoaderGGUF:
+    @classmethod
+    def INPUT_TYPES(s):
+        base = nodes.CLIPLoader.INPUT_TYPES()
+        return {
+            "required": {
+                "clip_name": (s.get_filename_list(),),
+                "type": base["required"]["type"],
+            }
+        }
+
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+    CATEGORY = "bootleg"
+    TITLE = "CLIPLoader (GGUF)"
+
+    @classmethod
+    def get_filename_list(s):
+        files = []
+        files += folder_paths.get_filename_list("clip")
+        files += folder_paths.get_filename_list("clip_gguf")
+        return sorted(files)
+
+    def load_data(self, ckpt_paths):
+        clip_data = []
+        for p in ckpt_paths:
+            if p.endswith(".gguf"):
+                sd = gguf_clip_loader(p)
+            else:
+                sd = comfy.utils.load_torch_file(p, safe_load=True)
+                if "scaled_fp8" in sd: # NOTE: Scaled FP8 would require different custom ops, but only one can be active
+                    raise NotImplementedError(f"Mixing scaled FP8 with GGUF is not supported! Use regular CLIP loader or switch model(s)\n({p})")
+            clip_data.append(sd)
+        return clip_data
+
+    def load_patcher(self, clip_paths, clip_type, clip_data):
+        clip = comfy.sd.load_text_encoder_state_dicts(
+            clip_type = clip_type,
+            state_dicts = clip_data,
+            model_options = {
+                "custom_operations": GGMLOps,
+                "initial_device": comfy.model_management.text_encoder_offload_device()
+            },
+            embedding_directory = folder_paths.get_folder_paths("embeddings"),
+        )
+        clip.patcher = GGUFModelPatcher.clone(clip.patcher)
+        return clip
+
+    def load_clip(self, clip_name, type="stable_diffusion"):
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),)
+
+class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        base = nodes.DualCLIPLoader.INPUT_TYPES()
+        file_options = (s.get_filename_list(), )
+        return {
+            "required": {
+                "clip_name1": file_options,
+                "clip_name2": file_options,
+                "type": base["required"]["type"],
+            }
+        }
+
+    TITLE = "DualCLIPLoader (GGUF)"
+
+    def load_clip(self, clip_name1, clip_name2, type):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_paths = (clip_path1, clip_path2)
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
+
+class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        file_options = (s.get_filename_list(), )
+        return {
+            "required": {
+                "clip_name1": file_options,
+                "clip_name2": file_options,
+                "clip_name3": file_options,
+            }
+        }
+
+    TITLE = "TripleCLIPLoader (GGUF)"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip_paths = (clip_path1, clip_path2, clip_path3)
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
+
+class QuadrupleCLIPLoaderGGUF(CLIPLoaderGGUF):
+    @classmethod
+    def INPUT_TYPES(s):
+        file_options = (s.get_filename_list(), )
+        return {
+            "required": {
+            "clip_name1": file_options,
+            "clip_name2": file_options,
+            "clip_name3": file_options,
+            "clip_name4": file_options,
+        }
+    }
+
+    TITLE = "QuadrupleCLIPLoader (GGUF)"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable_diffusion"):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip_path4 = folder_paths.get_full_path("clip", clip_name4)
+        clip_paths = (clip_path1, clip_path2, clip_path3, clip_path4)
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
+
+NODE_CLASS_MAPPINGS = {
+    "UnetLoaderGGUF": UnetLoaderGGUF,
+    "CLIPLoaderGGUF": CLIPLoaderGGUF,
+    "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
+    "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
+    "QuadrupleCLIPLoaderGGUF": QuadrupleCLIPLoaderGGUF,
+    "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
+}
diff --git a/ops.py b/ops.py
index 5b0c839..5904e62 100644
--- a/ops.py
+++ b/ops.py
@@ -1,465 +1,485 @@
-# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
-import gguf
-import json
-import torch
-import logging
-
-import comfy.ops
-import comfy.lora
-import comfy.model_management
-from .dequant import dequantize_tensor, is_quantized
-
-def _valid_compute_dtype(dtype):
-    return dtype in {torch.float16, torch.bfloat16, torch.float32, torch.float64}
-
-def _infer_compute_dtype(tensor_type, fallback=None):
-    if _valid_compute_dtype(fallback):
-        return fallback
-    if tensor_type == gguf.GGMLQuantizationType.BF16:
-        return torch.bfloat16
-    if tensor_type == gguf.GGMLQuantizationType.F32:
-        return torch.float32
-    return torch.float16
-
-def chained_hasattr(obj, chained_attr):
-    probe = obj
-    for attr in chained_attr.split('.'):
-        if hasattr(probe, attr):
-            probe = getattr(probe, attr)
-        else:
-            return False
-    return True
-
-# A bakcward and forward compatible way to get `torch.compiler.disable`.
-def get_torch_compiler_disable_decorator():
-    def dummy_decorator(*args, **kwargs):
-        def noop(x):
-            return x
-        return noop
-
-    from packaging import version
-
-    if not chained_hasattr(torch, "compiler.disable"):
-        logging.info("ComfyUI-GGUF: Torch too old for torch.compile - bypassing")
-        return dummy_decorator # torch too old
-    elif version.parse(torch.__version__) >= version.parse("2.8"):
-        logging.info("ComfyUI-GGUF: Allowing full torch compile")
-        return dummy_decorator # torch compile works
-    if chained_hasattr(torch, "_dynamo.config.nontraceable_tensor_subclasses"):
-        logging.info("ComfyUI-GGUF: Allowing full torch compile (nightly)")
-        return dummy_decorator # torch compile works, nightly before 2.8 release
-    else:
-        logging.info("ComfyUI-GGUF: Partial torch compile only, consider updating pytorch")
-        return torch.compiler.disable
-
-torch_compiler_disable = get_torch_compiler_disable_decorator()
-
-class GGMLTensor(torch.Tensor):
-    """
-    Main tensor-like class for storing quantized weights
-    """
-    def __init__(self, *args, tensor_type, tensor_shape, patches=[], compute_dtype=None, **kwargs):
-        super().__init__()
-        self.tensor_type = tensor_type
-        self.tensor_shape = tensor_shape
-        self.patches = patches
-        self.compute_dtype = compute_dtype
-
-    def __new__(cls, *args, tensor_type, tensor_shape, patches=[], compute_dtype=None, **kwargs):
-        return super().__new__(cls, *args, **kwargs)
-
-    def to(self, *args, **kwargs):
-        new = super().to(*args, **kwargs)
-        new.tensor_type = getattr(self, "tensor_type", None)
-        new.tensor_shape = getattr(self, "tensor_shape", new.data.shape)
-        new.patches = getattr(self, "patches", []).copy()
-        new.compute_dtype = getattr(self, "compute_dtype", None)
-        return new
-
-    def clone(self, *args, **kwargs):
-        return self
-
-    def detach(self, *args, **kwargs):
-        return self
-
-    def copy_(self, *args, **kwargs):
-        # fixes .weight.copy_ in comfy/clip_model/CLIPTextModel
-        try:
-            return super().copy_(*args, **kwargs)
-        except Exception as e:
-            logging.warning(f"ignoring 'copy_' on tensor: {e}")
-
-    def new_empty(self, size, *args, **kwargs):
-        # Intel Arc fix, ref#50
-        new_tensor = super().new_empty(size, *args, **kwargs)
-        return GGMLTensor(
-                new_tensor,
-                tensor_type = getattr(self, "tensor_type", None),
-                tensor_shape = size,
-                patches = getattr(self, "patches", []).copy(),
-                compute_dtype = getattr(self, "compute_dtype", None),
-        )
-
-    @property
-    def dtype(self):
-        qtype = getattr(self, "tensor_type", None)
-        if qtype in GGMLLayer.torch_compatible_tensor_types:
-            return torch.Tensor(self).dtype
-        return _infer_compute_dtype(qtype, getattr(self, "compute_dtype", None))
-
-    @property
-    def shape(self):
-        if not hasattr(self, "tensor_shape"):
-            self.tensor_shape = self.size()
-        return self.tensor_shape
-
-class GGMLLayer(torch.nn.Module):
-    """
-    This (should) be responsible for de-quantizing on the fly
-    """
-    comfy_cast_weights = True
-    dequant_dtype = None
-    patch_dtype = None
-    largest_layer = False
-    torch_compatible_tensor_types = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}
-
-    def is_ggml_quantized(self, *, weight=None, bias=None):
-        if weight is None:
-            weight = self.weight
-        if bias is None:
-            bias = self.bias
-        return is_quantized(weight) or is_quantized(bias)
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        weight, bias = state_dict.get(f"{prefix}weight"), state_dict.get(f"{prefix}bias")
-        # NOTE: using modified load for linear due to not initializing on creation, see GGMLOps todo
-        if self.is_ggml_quantized(weight=weight, bias=bias) or isinstance(self, torch.nn.Linear):
-            return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs)
-        # Not strictly required, but fixes embedding shape mismatch. Threshold set in loader.py
-        if isinstance(self, torch.nn.Embedding) and self.weight.shape[0] >= (64 * 1024):
-            return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs)
-        return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-    def ggml_load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
-        prefix_len = len(prefix)
-        for k,v in state_dict.items():
-            if k[prefix_len:] == "weight":
-                if isinstance(v, GGMLTensor):
-                    v.compute_dtype = self._ggml_compute_dtype(v, "weight")
-                self.weight = torch.nn.Parameter(v, requires_grad=False)
-            elif k[prefix_len:] == "bias" and v is not None:
-                if isinstance(v, GGMLTensor):
-                    v.compute_dtype = self._ggml_compute_dtype(v, "bias")
-                self.bias = torch.nn.Parameter(v, requires_grad=False)
-            else:
-                unexpected_keys.append(k)
-
-        # For Linear layer with missing weight
-        if self.weight is None and isinstance(self, torch.nn.Linear):
-            v = torch.zeros(self.in_features, self.out_features)
-            self.weight = torch.nn.Parameter(v, requires_grad=False)
-            missing_keys.append(prefix+"weight")
-
-        # for vram estimation (TODO: less fragile logic?)
-        if getattr(self.weight, "is_largest_weight", False):
-            self.largest_layer = True
-
-    def _ggml_compute_dtype(self, tensor, param_name):
-        if self.dequant_dtype is not None and self.dequant_dtype != "target":
-            return self.dequant_dtype
-        model_dtype = getattr(self, f"{param_name}_comfy_model_dtype", None)
-        return _infer_compute_dtype(getattr(tensor, "tensor_type", None), model_dtype)
-
-    def _save_to_state_dict(self, *args, **kwargs):
-        if self.is_ggml_quantized():
-            return self.ggml_save_to_state_dict(*args, **kwargs)
-        return super()._save_to_state_dict(*args, **kwargs)
-
-    def ggml_save_to_state_dict(self, destination, prefix, keep_vars):
-        # This is a fake state dict for vram estimation
-        weight = torch.zeros_like(self.weight, device=torch.device("meta"))
-        destination[prefix + "weight"] = weight
-        if self.bias is not None:
-            bias = torch.zeros_like(self.bias, device=torch.device("meta"))
-            destination[prefix + "bias"] = bias
-
-        # Take into account space required for dequantizing the largest tensor
-        if self.largest_layer:
-            shape = getattr(self.weight, "tensor_shape", self.weight.shape)
-            dtype = self.dequant_dtype if self.dequant_dtype and self.dequant_dtype != "target" else torch.float16
-            temp = torch.empty(*shape, device=torch.device("meta"), dtype=dtype)
-            destination[prefix + "temp.weight"] = temp
-
-        return
-        # This would return the dequantized state dict
-        destination[prefix + "weight"] = self.get_weight(self.weight)
-        if bias is not None:
-            destination[prefix + "bias"] = self.get_weight(self.bias)
-
-    def get_weight(self, tensor, dtype):
-        if tensor is None:
-            return
-
-        # consolidate and load patches to GPU in async
-        patch_list = []
-        device = tensor.device
-        for patches, key in getattr(tensor, "patches", []):
-            patch_list += move_patch_to_device(patches, device)
-
-        # dequantize tensor while patches load
-        weight = dequantize_tensor(tensor, dtype, self.dequant_dtype)
-
-        # prevent propagating custom tensor class
-        if isinstance(weight, GGMLTensor):
-            weight = torch.Tensor(weight)
-
-        # apply patches
-        if len(patch_list) > 0:
-            if self.patch_dtype is None:
-                weight = comfy.lora.calculate_weight(patch_list, weight, key)
-            else:
-                # for testing, may degrade image quality
-                patch_dtype = dtype if self.patch_dtype == "target" else self.patch_dtype
-                weight = comfy.lora.calculate_weight(patch_list, weight, key, patch_dtype)
-        return weight
-
-    @torch_compiler_disable()
-    def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
-        if input is not None:
-            if dtype is None:
-                dtype = getattr(input, "dtype", torch.float32)
-            if bias_dtype is None:
-                bias_dtype = dtype
-            if device is None:
-                device = input.device
-
-        bias = None
-        non_blocking = comfy.model_management.device_supports_non_blocking(device)
-        if s.bias is not None:
-            bias = s.get_weight(s.bias.to(device), dtype)
-            bias = comfy.ops.cast_to(bias, bias_dtype, device, non_blocking=non_blocking, copy=False)
-
-        weight = s.get_weight(s.weight.to(device), dtype)
-        weight = comfy.ops.cast_to(weight, dtype, device, non_blocking=non_blocking, copy=False)
-        return weight, bias
-
-    def forward_comfy_cast_weights(self, input, *args, **kwargs):
-        if self.is_ggml_quantized():
-            out = self.forward_ggml_cast_weights(input, *args, **kwargs)
-        else:
-            out = super().forward_comfy_cast_weights(input, *args, **kwargs)
-
-        # non-ggml forward might still propagate custom tensor class
-        if isinstance(out, GGMLTensor):
-            out = torch.Tensor(out)
-        return out
-
-    def forward_ggml_cast_weights(self, input):
-        raise NotImplementedError
-
-class GGMLOps(comfy.ops.manual_cast):
-    """
-    Dequantize weights on the fly before doing the compute
-    """
-    class Linear(GGMLLayer, comfy.ops.manual_cast.Linear):
-        def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            torch.nn.Module.__init__(self)
-            # TODO: better workaround for reserved memory spike on windows
-            # Issue is with `torch.empty` still reserving the full memory for the layer
-            # Windows doesn't over-commit memory so without this 24GB+ of pagefile is used
-            self.in_features = in_features
-            self.out_features = out_features
-            self.weight = None
-            self.bias = None
-            self.weight_comfy_model_dtype = dtype
-            self.bias_comfy_model_dtype = dtype
-
-        def forward_ggml_cast_weights(self, input):
-            weight, bias = self.cast_bias_weight(input)
-            return torch.nn.functional.linear(input, weight, bias)
-
-    class Conv2d(GGMLLayer, comfy.ops.manual_cast.Conv2d):
-        def forward_ggml_cast_weights(self, input):
-            weight, bias = self.cast_bias_weight(input)
-            return self._conv_forward(input, weight, bias)
-
-    class Embedding(GGMLLayer, comfy.ops.manual_cast.Embedding):
-        def forward_ggml_cast_weights(self, input, out_dtype=None):
-            output_dtype = out_dtype
-            if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
-                out_dtype = None
-            weight, _bias = self.cast_bias_weight(self, device=input.device, dtype=out_dtype)
-            return torch.nn.functional.embedding(
-                input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse
-            ).to(dtype=output_dtype)
-
-    class LayerNorm(GGMLLayer, comfy.ops.manual_cast.LayerNorm):
-        def forward_ggml_cast_weights(self, input):
-            if self.weight is None:
-                return super().forward_comfy_cast_weights(input)
-            weight, bias = self.cast_bias_weight(input)
-            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
-
-    class GroupNorm(GGMLLayer, comfy.ops.manual_cast.GroupNorm):
-        def forward_ggml_cast_weights(self, input):
-            weight, bias = self.cast_bias_weight(input)
-            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
-
-def move_patch_to_device(item, device):
-    if isinstance(item, torch.Tensor):
-        return item.to(device, non_blocking=True)
-    elif isinstance(item, tuple):
-        return tuple(move_patch_to_device(x, device) for x in item)
-    elif isinstance(item, list):
-        return [move_patch_to_device(x, device) for x in item]
-    else:
-        return item
-
-def get_gguf_q8_ops(compute_dtype=torch.bfloat16, full_precision_mm=False):
-    """
-    Factory for an ops class that uses ComfyUI's native mixed_precision_ops INT8 path.
-    Weights are kept as INT8 and matmul uses comfy_kitchen's TensorWiseINT8Layout.
-    """
-    BaseOps = comfy.ops.mixed_precision_ops(
-        quant_config={},
-        compute_dtype=compute_dtype,
-        full_precision_mm=full_precision_mm,
-    )
-
-    class GGUFQ8Ops(BaseOps):
-        class Linear(BaseOps.Linear):
-            def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-                # Lazy init: don't allocate weight here; it will be loaded from state dict
-                torch.nn.Module.__init__(self)
-                self.factory_kwargs = {"device": device, "dtype": BaseOps._compute_dtype}
-                self.in_features = in_features
-                self.out_features = out_features
-                self.weight = None
-                if bias:
-                    self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
-                else:
-                    self.register_parameter("bias", None)
-                self._orig_shape = (out_features, in_features)
-                self.tensor_class = None
-                self._full_precision_mm = BaseOps._full_precision_mm
-                self._full_precision_mm_config = False
-
-            def _load_from_state_dict(self, *args):
-                return comfy.ops._load_quantized_module(
-                    self,
-                    torch.nn.Module._load_from_state_dict.__get__(self, type(self)),
-                    *args,
-                    load_extra_params=True,
-                )
-
-    return GGUFQ8Ops
-
-
-class GGUFQ4Ops(comfy.ops.manual_cast):
-    """
-    Ops class for PyTorch's CUDA INT4 matrix multiplication.
-
-    GGUF stores a portable nibble-packed representation. The first forward pass
-    for each layer converts it to PyTorch's hardware-specific packed layout on
-    the active CUDA device, and subsequent forwards reuse that packed tensor.
-    """
-    class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
-        def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
-            torch.nn.Module.__init__(self)
-            self.in_features = in_features
-            self.out_features = out_features
-            self.weight = None
-            self.register_parameter("bias", None)
-            self._orig_shape = (out_features, in_features)
-            self._group_size = None
-            self._pad = 0
-            self._orig_in_features = in_features
-            self._int4_cache = {}
-            self._is_int4 = False
-
-        def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
-            weight_key = f"{prefix}weight"
-            scale_key = f"{prefix}weight_scale"
-            quant_key = f"{prefix}comfy_quant"
-
-            weight = state_dict.pop(weight_key, None)
-            scale = state_dict.pop(scale_key, None)
-            quant_raw = state_dict.pop(quant_key, None)
-
-            bias_key = f"{prefix}bias"
-            bias = state_dict.pop(bias_key, None)
-            if quant_raw is None:
-                if weight is None:
-                    missing_keys.append(weight_key)
-                    return
-                self.weight = torch.nn.Parameter(torch.Tensor(weight), requires_grad=False)
-                if bias is not None:
-                    self.bias = torch.nn.Parameter(torch.Tensor(bias), requires_grad=False)
-                self._is_int4 = False
-                return
-
-            if weight is None or scale is None:
-                raise RuntimeError(f"Missing INT4 tensors for {prefix}")
-
-            quant_conf = json.loads(bytes(quant_raw.tolist()).decode("utf-8"))
-            if quant_conf.get("format") != "int4_pytorch":
-                raise ValueError(f"Unsupported INT4 format for {prefix}")
-            self._group_size = quant_conf["group_size"]
-            self._pad = quant_conf.get("pad", 0)
-            orig_shape = tuple(quant_conf["orig_shape"])
-            self._orig_in_features = orig_shape[1]
-            self._orig_shape = orig_shape
-            self.out_features = orig_shape[0]
-
-            self.weight = torch.nn.Parameter(weight, requires_grad=False)
-            self.weight_scale = torch.nn.Parameter(scale, requires_grad=False)
-            self._is_int4 = True
-
-            if bias is not None:
-                self.bias = torch.nn.Parameter(bias, requires_grad=False)
-            for key in (weight_key, scale_key, quant_key, bias_key):
-                if key in missing_keys:
-                    missing_keys.remove(key)
-
-        @staticmethod
-        def _inner_k_tiles():
-            return 2
-
-        def _packed_weight(self, device, dtype):
-            if device.type != "cuda":
-                raise RuntimeError("Q4_PT requires CUDA; no dequantization fallback is available.")
-            if dtype not in (torch.float16, torch.bfloat16):
-                raise RuntimeError(f"Q4_PT requires FP16 or BF16 activations, got {dtype}.")
-
-            cache_key = (device, dtype)
-            cached = self._int4_cache.get(cache_key)
-            if cached is not None:
-                return cached
-
-            serialized = self.weight.to(device=device, dtype=torch.uint8, non_blocking=True)
-            packed = torch._convert_weight_to_int4pack(serialized, self._inner_k_tiles())
-            scale_and_offset = self.weight_scale.to(device=device, dtype=dtype, non_blocking=True)
-            self._int4_cache[cache_key] = (packed, scale_and_offset)
-            return packed, scale_and_offset
-
-        def forward(self, input):
-            if self.weight is None:
-                raise RuntimeError("Q4_PT weight was not loaded.")
-            if not self._is_int4:
-                weight = self.weight.to(device=input.device, dtype=input.dtype)
-                bias = self.bias.to(device=input.device, dtype=input.dtype) if self.bias is not None else None
-                return torch.nn.functional.linear(input, weight, bias)
-            if self.weight_function or self.bias_function:
-                raise RuntimeError("Q4_PT does not support weight patches or LoRAs without dequantization.")
-            input_shape = input.shape
-            input_2d = input.reshape(-1, input_shape[-1])
-            if self._pad:
-                input_2d = torch.nn.functional.pad(input_2d, (0, self._pad))
-
-            packed, scale_and_offset = self._packed_weight(input.device, input.dtype)
-            output = torch._weight_int4pack_mm(
-                input_2d, packed, self._group_size, scale_and_offset
-            )
-            if self.bias is not None:
-                output = output + self.bias.to(device=input.device, dtype=input.dtype)
-            return output.reshape(*input_shape[:-1], self.out_features)
+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import gguf
+import json
+import torch
+import logging
+
+import comfy.ops
+import comfy.lora
+import comfy.model_management
+from .dequant import dequantize_tensor, is_quantized
+
+def _valid_compute_dtype(dtype):
+    return dtype in {torch.float16, torch.bfloat16, torch.float32, torch.float64}
+
+def _infer_compute_dtype(tensor_type, fallback=None):
+    if _valid_compute_dtype(fallback):
+        return fallback
+    if tensor_type == gguf.GGMLQuantizationType.BF16:
+        return torch.bfloat16
+    if tensor_type == gguf.GGMLQuantizationType.F32:
+        return torch.float32
+    return torch.float16
+
+def chained_hasattr(obj, chained_attr):
+    probe = obj
+    for attr in chained_attr.split('.'):
+        if hasattr(probe, attr):
+            probe = getattr(probe, attr)
+        else:
+            return False
+    return True
+
+# A bakcward and forward compatible way to get `torch.compiler.disable`.
+def get_torch_compiler_disable_decorator():
+    def dummy_decorator(*args, **kwargs):
+        def noop(x):
+            return x
+        return noop
+
+    from packaging import version
+
+    if not chained_hasattr(torch, "compiler.disable"):
+        logging.info("ComfyUI-GGUF: Torch too old for torch.compile - bypassing")
+        return dummy_decorator # torch too old
+    elif version.parse(torch.__version__) >= version.parse("2.8"):
+        logging.info("ComfyUI-GGUF: Allowing full torch compile")
+        return dummy_decorator # torch compile works
+    if chained_hasattr(torch, "_dynamo.config.nontraceable_tensor_subclasses"):
+        logging.info("ComfyUI-GGUF: Allowing full torch compile (nightly)")
+        return dummy_decorator # torch compile works, nightly before 2.8 release
+    else:
+        logging.info("ComfyUI-GGUF: Partial torch compile only, consider updating pytorch")
+        return torch.compiler.disable
+
+torch_compiler_disable = get_torch_compiler_disable_decorator()
+
+class GGMLTensor(torch.Tensor):
+    """
+    Main tensor-like class for storing quantized weights
+    """
+    def __init__(self, *args, tensor_type, tensor_shape, patches=[], compute_dtype=None, **kwargs):
+        super().__init__()
+        self.tensor_type = tensor_type
+        self.tensor_shape = tensor_shape
+        self.patches = patches
+        self.compute_dtype = compute_dtype
+
+    def __new__(cls, *args, tensor_type, tensor_shape, patches=[], compute_dtype=None, **kwargs):
+        return super().__new__(cls, *args, **kwargs)
+
+    def to(self, *args, **kwargs):
+        new = super().to(*args, **kwargs)
+        new.tensor_type = getattr(self, "tensor_type", None)
+        new.tensor_shape = getattr(self, "tensor_shape", new.data.shape)
+        new.patches = getattr(self, "patches", []).copy()
+        new.compute_dtype = getattr(self, "compute_dtype", None)
+        return new
+
+    def clone(self, *args, **kwargs):
+        return self
+
+    def detach(self, *args, **kwargs):
+        return self
+
+    def copy_(self, *args, **kwargs):
+        # fixes .weight.copy_ in comfy/clip_model/CLIPTextModel
+        try:
+            return super().copy_(*args, **kwargs)
+        except Exception as e:
+            logging.warning(f"ignoring 'copy_' on tensor: {e}")
+
+    def new_empty(self, size, *args, **kwargs):
+        # Intel Arc fix, ref#50
+        new_tensor = super().new_empty(size, *args, **kwargs)
+        return GGMLTensor(
+                new_tensor,
+                tensor_type = getattr(self, "tensor_type", None),
+                tensor_shape = size,
+                patches = getattr(self, "patches", []).copy(),
+                compute_dtype = getattr(self, "compute_dtype", None),
+        )
+
+    @property
+    def dtype(self):
+        qtype = getattr(self, "tensor_type", None)
+        if qtype in GGMLLayer.torch_compatible_tensor_types:
+            return torch.Tensor(self).dtype
+        return _infer_compute_dtype(qtype, getattr(self, "compute_dtype", None))
+
+    @property
+    def shape(self):
+        if not hasattr(self, "tensor_shape"):
+            self.tensor_shape = self.size()
+        return self.tensor_shape
+
+class GGMLLayer(torch.nn.Module):
+    """
+    This (should) be responsible for de-quantizing on the fly
+    """
+    comfy_cast_weights = True
+    dequant_dtype = None
+    patch_dtype = None
+    largest_layer = False
+    torch_compatible_tensor_types = {None, gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}
+
+    def is_ggml_quantized(self, *, weight=None, bias=None):
+        if weight is None:
+            weight = self.weight
+        if bias is None:
+            bias = self.bias
+        return is_quantized(weight) or is_quantized(bias)
+
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        weight, bias = state_dict.get(f"{prefix}weight"), state_dict.get(f"{prefix}bias")
+        # NOTE: using modified load for linear due to not initializing on creation, see GGMLOps todo
+        if self.is_ggml_quantized(weight=weight, bias=bias) or isinstance(self, torch.nn.Linear):
+            return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs)
+        # Not strictly required, but fixes embedding shape mismatch. Threshold set in loader.py
+        if isinstance(self, torch.nn.Embedding) and self.weight.shape[0] >= (64 * 1024):
+            return self.ggml_load_from_state_dict(state_dict, prefix, *args, **kwargs)
+        return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
+
+    def ggml_load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        prefix_len = len(prefix)
+        for k,v in state_dict.items():
+            if k[prefix_len:] == "weight":
+                if isinstance(v, GGMLTensor):
+                    v.compute_dtype = self._ggml_compute_dtype(v, "weight")
+                self.weight = torch.nn.Parameter(v, requires_grad=False)
+            elif k[prefix_len:] == "bias" and v is not None:
+                if isinstance(v, GGMLTensor):
+                    v.compute_dtype = self._ggml_compute_dtype(v, "bias")
+                self.bias = torch.nn.Parameter(v, requires_grad=False)
+            else:
+                unexpected_keys.append(k)
+
+        # For Linear layer with missing weight
+        if self.weight is None and isinstance(self, torch.nn.Linear):
+            v = torch.zeros(self.in_features, self.out_features)
+            self.weight = torch.nn.Parameter(v, requires_grad=False)
+            missing_keys.append(prefix+"weight")
+
+        # for vram estimation (TODO: less fragile logic?)
+        if getattr(self.weight, "is_largest_weight", False):
+            self.largest_layer = True
+
+    def _ggml_compute_dtype(self, tensor, param_name):
+        if self.dequant_dtype is not None and self.dequant_dtype != "target":
+            return self.dequant_dtype
+        model_dtype = getattr(self, f"{param_name}_comfy_model_dtype", None)
+        return _infer_compute_dtype(getattr(tensor, "tensor_type", None), model_dtype)
+
+    def _save_to_state_dict(self, *args, **kwargs):
+        if self.is_ggml_quantized():
+            return self.ggml_save_to_state_dict(*args, **kwargs)
+        return super()._save_to_state_dict(*args, **kwargs)
+
+    def ggml_save_to_state_dict(self, destination, prefix, keep_vars):
+        # This is a fake state dict for vram estimation
+        weight = torch.zeros_like(self.weight, device=torch.device("meta"))
+        destination[prefix + "weight"] = weight
+        if self.bias is not None:
+            bias = torch.zeros_like(self.bias, device=torch.device("meta"))
+            destination[prefix + "bias"] = bias
+
+        # Take into account space required for dequantizing the largest tensor
+        if self.largest_layer:
+            shape = getattr(self.weight, "tensor_shape", self.weight.shape)
+            dtype = self.dequant_dtype if self.dequant_dtype and self.dequant_dtype != "target" else torch.float16
+            temp = torch.empty(*shape, device=torch.device("meta"), dtype=dtype)
+            destination[prefix + "temp.weight"] = temp
+
+        return
+        # This would return the dequantized state dict
+        destination[prefix + "weight"] = self.get_weight(self.weight)
+        if bias is not None:
+            destination[prefix + "bias"] = self.get_weight(self.bias)
+
+    def get_weight(self, tensor, dtype):
+        if tensor is None:
+            return
+
+        # consolidate and load patches to GPU in async
+        patch_list = []
+        device = tensor.device
+        for patches, key in getattr(tensor, "patches", []):
+            patch_list += move_patch_to_device(patches, device)
+
+        # dequantize tensor while patches load
+        weight = dequantize_tensor(tensor, dtype, self.dequant_dtype)
+
+        # prevent propagating custom tensor class
+        if isinstance(weight, GGMLTensor):
+            weight = torch.Tensor(weight)
+
+        # apply patches
+        if len(patch_list) > 0:
+            if self.patch_dtype is None:
+                weight = comfy.lora.calculate_weight(patch_list, weight, key)
+            else:
+                # for testing, may degrade image quality
+                patch_dtype = dtype if self.patch_dtype == "target" else self.patch_dtype
+                weight = comfy.lora.calculate_weight(patch_list, weight, key, patch_dtype)
+        return weight
+
+    @torch_compiler_disable()
+    def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+        if input is not None:
+            if dtype is None:
+                dtype = getattr(input, "dtype", torch.float32)
+            if bias_dtype is None:
+                bias_dtype = dtype
+            if device is None:
+                device = input.device
+
+        bias = None
+        non_blocking = comfy.model_management.device_supports_non_blocking(device)
+        if s.bias is not None:
+            bias = s.get_weight(s.bias.to(device), dtype)
+            bias = comfy.ops.cast_to(bias, bias_dtype, device, non_blocking=non_blocking, copy=False)
+
+        weight = s.get_weight(s.weight.to(device), dtype)
+        weight = comfy.ops.cast_to(weight, dtype, device, non_blocking=non_blocking, copy=False)
+        return weight, bias
+
+    def forward_comfy_cast_weights(self, input, *args, **kwargs):
+        if self.is_ggml_quantized():
+            out = self.forward_ggml_cast_weights(input, *args, **kwargs)
+        else:
+            out = super().forward_comfy_cast_weights(input, *args, **kwargs)
+
+        # non-ggml forward might still propagate custom tensor class
+        if isinstance(out, GGMLTensor):
+            out = torch.Tensor(out)
+        return out
+
+    def forward_ggml_cast_weights(self, input):
+        raise NotImplementedError
+
+class GGMLOps(comfy.ops.manual_cast):
+    """
+    Dequantize weights on the fly before doing the compute
+    """
+    class Linear(GGMLLayer, comfy.ops.manual_cast.Linear):
+        def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
+            torch.nn.Module.__init__(self)
+            # TODO: better workaround for reserved memory spike on windows
+            # Issue is with `torch.empty` still reserving the full memory for the layer
+            # Windows doesn't over-commit memory so without this 24GB+ of pagefile is used
+            self.in_features = in_features
+            self.out_features = out_features
+            self.weight = None
+            self.bias = None
+            self.weight_comfy_model_dtype = dtype
+            self.bias_comfy_model_dtype = dtype
+
+        def forward_ggml_cast_weights(self, input):
+            weight, bias = self.cast_bias_weight(input)
+            return torch.nn.functional.linear(input, weight, bias)
+
+    class Conv2d(GGMLLayer, comfy.ops.manual_cast.Conv2d):
+        def forward_ggml_cast_weights(self, input):
+            weight, bias = self.cast_bias_weight(input)
+            return self._conv_forward(input, weight, bias)
+
+    class Embedding(GGMLLayer, comfy.ops.manual_cast.Embedding):
+        def forward_ggml_cast_weights(self, input, out_dtype=None):
+            output_dtype = out_dtype
+            if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
+                out_dtype = None
+            weight, _bias = self.cast_bias_weight(self, device=input.device, dtype=out_dtype)
+            return torch.nn.functional.embedding(
+                input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse
+            ).to(dtype=output_dtype)
+
+    class LayerNorm(GGMLLayer, comfy.ops.manual_cast.LayerNorm):
+        def forward_ggml_cast_weights(self, input):
+            if self.weight is None:
+                return super().forward_comfy_cast_weights(input)
+            weight, bias = self.cast_bias_weight(input)
+            return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+
+    class GroupNorm(GGMLLayer, comfy.ops.manual_cast.GroupNorm):
+        def forward_ggml_cast_weights(self, input):
+            weight, bias = self.cast_bias_weight(input)
+            return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+
+def move_patch_to_device(item, device):
+    if isinstance(item, torch.Tensor):
+        return item.to(device, non_blocking=True)
+    elif isinstance(item, tuple):
+        return tuple(move_patch_to_device(x, device) for x in item)
+    elif isinstance(item, list):
+        return [move_patch_to_device(x, device) for x in item]
+    else:
+        return item
+
+def get_gguf_q8_ops(compute_dtype=torch.bfloat16, full_precision_mm=False):
+    """
+    Factory for an ops class that uses ComfyUI's native mixed_precision_ops INT8 path.
+    Weights are kept as INT8 and matmul uses comfy_kitchen's TensorWiseINT8Layout.
+    """
+    BaseOps = comfy.ops.mixed_precision_ops(
+        quant_config={},
+        compute_dtype=compute_dtype,
+        full_precision_mm=full_precision_mm,
+    )
+
+    class GGUFQ8Ops(BaseOps):
+        class Linear(BaseOps.Linear):
+            def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
+                # Lazy init: don't allocate weight here; it will be loaded from state dict
+                torch.nn.Module.__init__(self)
+                self.factory_kwargs = {"device": device, "dtype": BaseOps._compute_dtype}
+                self.in_features = in_features
+                self.out_features = out_features
+                self.weight = None
+                if bias:
+                    self.bias = torch.nn.Parameter(torch.empty(out_features, **self.factory_kwargs))
+                else:
+                    self.register_parameter("bias", None)
+                self._orig_shape = (out_features, in_features)
+                self.tensor_class = None
+                self._full_precision_mm = BaseOps._full_precision_mm
+                self._full_precision_mm_config = False
+
+            def _load_from_state_dict(self, *args):
+                return comfy.ops._load_quantized_module(
+                    self,
+                    torch.nn.Module._load_from_state_dict.__get__(self, type(self)),
+                    *args,
+                    load_extra_params=True,
+                )
+
+    return GGUFQ8Ops
+
+
+# Retired experimental Q4_PT implementation. No loader or node runtime path
+# references this class until a performant W4A16 backend is available.
+class RetiredGGUFQ4Ops(comfy.ops.manual_cast):
+    """
+    Ops class for PyTorch's native compact INT4 GEMM.
+
+    Packed weights are created transiently at invocation time. This keeps
+    low-VRAM offloading viable without retaining a second copy of all weights.
+    """
+    class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
+        comfy_cast_weights = True
+
+        def __init__(self, in_features, out_features, bias=True, device=None, dtype=None):
+            torch.nn.Module.__init__(self)
+            self.in_features = in_features
+            self.out_features = out_features
+            self.weight = None
+            self.register_parameter("bias", None)
+            self._orig_shape = (out_features, in_features)
+            self._group_size = None
+            self._pad = 0
+            self._orig_in_features = in_features
+            self._is_int4 = False
+
+        def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+            weight_key = f"{prefix}weight"
+            scale_key = f"{prefix}weight_scale"
+            quant_key = f"{prefix}comfy_quant"
+
+            weight = state_dict.pop(weight_key, None)
+            scale = state_dict.pop(scale_key, None)
+            quant_raw = state_dict.pop(quant_key, None)
+
+            bias_key = f"{prefix}bias"
+            bias = state_dict.pop(bias_key, None)
+            if quant_raw is None:
+                if weight is None:
+                    missing_keys.append(weight_key)
+                    return
+                self.weight = torch.nn.Parameter(torch.Tensor(weight), requires_grad=False)
+                if bias is not None:
+                    self.bias = torch.nn.Parameter(torch.Tensor(bias), requires_grad=False)
+                self._is_int4 = False
+                return
+
+            if weight is None or scale is None:
+                raise RuntimeError(f"Missing INT4 tensors for {prefix}")
+
+            quant_conf = json.loads(bytes(quant_raw.tolist()).decode("utf-8"))
+            if quant_conf.get("format") not in {"int4_compact_gemm", "int4_pytorch"}:
+                raise ValueError(f"Unsupported INT4 format for {prefix}")
+            self._group_size = quant_conf["group_size"]
+            self._pad = quant_conf.get("pad", 0)
+            orig_shape = tuple(quant_conf["orig_shape"])
+            self._orig_in_features = orig_shape[1]
+            self._orig_shape = orig_shape
+            self.out_features = orig_shape[0]
+
+            self.weight = torch.nn.Parameter(weight, requires_grad=False)
+            self.weight_scale = torch.nn.Parameter(scale, requires_grad=False)
+            self._is_int4 = True
+
+            if bias is not None:
+                self.bias = torch.nn.Parameter(bias, requires_grad=False)
+            for key in (weight_key, scale_key, quant_key, bias_key):
+                if key in missing_keys:
+                    missing_keys.remove(key)
+
+        def forward(self, input):
+            if self.weight is None:
+                raise RuntimeError("Q4_PT weight was not loaded.")
+            if not self._is_int4:
+                weight = self.weight.to(device=input.device, dtype=input.dtype)
+                bias = self.bias.to(device=input.device, dtype=input.dtype) if self.bias is not None else None
+                return torch.nn.functional.linear(input, weight, bias)
+            if self.weight_function or self.bias_function:
+                raise RuntimeError("Q4_PT does not support weight patches or LoRAs without dequantization.")
+            input_shape = input.shape
+            input_2d = input.reshape(-1, input_shape[-1])
+            if self._pad:
+                input_2d = torch.nn.functional.pad(input_2d, (0, self._pad))
+
+            if self._group_size != 64:
+                raise RuntimeError(
+                    f"Q4_PT requires PyTorch's group-size-64 INT4 operator, got {self._group_size}."
+                )
+            if input_2d.device.type != "cuda":
+                raise RuntimeError("Q4_PT requires a CUDA device.")
+            if input_2d.dtype != torch.bfloat16:
+                raise RuntimeError(
+                    f"Q4_PT requires BF16 activations for PyTorch's native INT4 operator, got {input_2d.dtype}."
+                )
+
+            weight = torch.Tensor(
+                self.weight.to(device=input.device, dtype=torch.uint8, non_blocking=True)
+            )
+            scale_and_offset = self.weight_scale.to(
+                device=input.device,
+                dtype=torch.bfloat16,
+                non_blocking=True,
+            )
+            packed_features = weight.size(-1) * 2
+            native_padding = (-packed_features) % 128
+            if native_padding:
+                # PyTorch's INT4 packer needs K to be a multiple of 128.
+                # A zero-valued group and zero input features preserve the
+                # original result for K=64 projections such as Krea2's input.
+                if native_padding % self._group_size:
+                    raise RuntimeError(
+                        f"Cannot pad Q4_PT input width {packed_features} to PyTorch's INT4 tile size."
+                    )
+                input_2d = torch.nn.functional.pad(input_2d, (0, native_padding))
+                weight = torch.nn.functional.pad(weight, (0, native_padding // 2))
+                scale_and_offset = torch.nn.functional.pad(
+                    scale_and_offset,
+                    (0, 0, 0, 0, 0, native_padding // self._group_size),
+                )
+
+            packed_weight = torch._convert_weight_to_int4pack(weight, 8)
+            output = torch._weight_int4pack_mm(
+                input_2d,
+                packed_weight,
+                self._group_size,
+                scale_and_offset,
+            )
+            if self.bias is not None:
+                output = output + self.bias.to(device=input.device, dtype=input.dtype)
+            return output.reshape(*input_shape[:-1], self.out_features)
diff --git a/tools/convert.py b/tools/convert.py
index f2813c2..2bd2993 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -1,573 +1,568 @@
-# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
-import os
-import gguf
-import json
-import torch
-import logging
-import argparse
-from tqdm import tqdm
-from safetensors.torch import load_file, save_file
-
-QUANTIZATION_THRESHOLD = 1024
-REARRANGE_THRESHOLD = 512
-MAX_TENSOR_NAME_LENGTH = 127
-MAX_TENSOR_DIMS = 4
-
-class ModelTemplate:
-    arch = "invalid"  # string describing architecture
-    shape_fix = False # whether to reshape tensors
-    keys_detect = []  # list of lists to match in state dict
-    keys_banned = []  # list of keys that should mark model as invalid for conversion
-    keys_hiprec = []  # list of keys that need to be kept in fp32 for some reason
-    keys_ignore = []  # list of strings to ignore keys by when found
-
-    def handle_nd_tensor(self, key, data):
-        raise NotImplementedError(f"Tensor detected that exceeds dims supported by C++ code! ({key} @ {data.shape})")
-
-class ModelFlux(ModelTemplate):
-    arch = "flux"
-    keys_detect = [
-        ("transformer_blocks.0.attn.norm_added_k.weight",),
-        ("double_blocks.0.img_attn.proj.weight",),
-    ]
-    keys_banned = ["transformer_blocks.0.attn.norm_added_k.weight",]
-
-class ModelSD3(ModelTemplate):
-    arch = "sd3"
-    keys_detect = [
-        ("transformer_blocks.0.attn.add_q_proj.weight",),
-        ("joint_blocks.0.x_block.attn.qkv.weight",),
-    ]
-    keys_banned = ["transformer_blocks.0.attn.add_q_proj.weight",]
-
-class ModelAura(ModelTemplate):
-    arch = "aura"
-    keys_detect = [
-        ("double_layers.3.modX.1.weight",),
-        ("joint_transformer_blocks.3.ff_context.out_projection.weight",),
-    ]
-    keys_banned = ["joint_transformer_blocks.3.ff_context.out_projection.weight",]
-
-class ModelHiDream(ModelTemplate):
-    arch = "hidream"
-    keys_detect = [
-        (
-            "caption_projection.0.linear.weight",
-            "double_stream_blocks.0.block.ff_i.shared_experts.w3.weight"
-        )
-    ]
-    keys_hiprec = [
-        # nn.parameter, can't load from BF16 ver
-        ".ff_i.gate.weight",
-        "img_emb.emb_pos"
-    ]
-
-class CosmosPredict2(ModelTemplate):
-    arch = "cosmos"
-    keys_detect = [
-        (
-            "blocks.0.mlp.layer1.weight",
-            "blocks.0.adaln_modulation_cross_attn.1.weight",
-        )
-    ]
-    keys_hiprec = ["pos_embedder"]
-    keys_ignore = ["_extra_state", "accum_"]
-
-class ModelHyVid(ModelTemplate):
-    arch = "hyvid"
-    keys_detect = [
-        (
-            "double_blocks.0.img_attn_proj.weight",
-            "txt_in.individual_token_refiner.blocks.1.self_attn_qkv.weight",
-        )
-    ]
-
-    def handle_nd_tensor(self, key, data):
-        # hacky but don't have any better ideas
-        path = f"./fix_5d_tensors_{self.arch}.safetensors" # TODO: somehow get a path here??
-        if os.path.isfile(path):
-            raise RuntimeError(f"5D tensor fix file already exists! {path}")
-        fsd = {key: torch.from_numpy(data)}
-        tqdm.write(f"5D key found in state dict! Manual fix required! - {key} {data.shape}")
-        save_file(fsd, path)
-
-class ModelWan(ModelHyVid):
-    arch = "wan"
-    keys_detect = [
-        (
-            "blocks.0.self_attn.norm_q.weight",
-            "text_embedding.2.weight",
-            "head.modulation",
-        )
-    ]
-    keys_hiprec = [
-        ".modulation" # nn.parameter, can't load from BF16 ver
-    ]
-
-class ModelLTXV(ModelTemplate):
-    arch = "ltxv"
-    keys_detect = [
-        (
-            "adaln_single.emb.timestep_embedder.linear_2.weight",
-            "transformer_blocks.27.scale_shift_table",
-            "caption_projection.linear_2.weight",
-        )
-    ]
-    keys_hiprec = [
-        "scale_shift_table" # nn.parameter, can't load from BF16 base quant
-    ]
-
-class ModelSDXL(ModelTemplate):
-    arch = "sdxl"
-    shape_fix = True
-    keys_detect = [
-        ("down_blocks.0.downsamplers.0.conv.weight", "add_embedding.linear_1.weight",),
-        (
-            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight",
-            "output_blocks.2.2.conv.weight", "output_blocks.5.2.conv.weight",
-        ), # Non-diffusers
-        ("label_emb.0.0.weight",),
-    ]
-
-class ModelSD1(ModelTemplate):
-    arch = "sd1"
-    shape_fix = True
-    keys_detect = [
-        ("down_blocks.0.downsamplers.0.conv.weight",),
-        (
-            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight", "input_blocks.9.0.op.weight",
-            "output_blocks.2.1.conv.weight", "output_blocks.5.2.conv.weight", "output_blocks.8.2.conv.weight"
-        ), # Non-diffusers
-    ]
-
-class ModelLumina2(ModelTemplate):
-    arch = "lumina2"
-    keys_detect = [
-        ("cap_embedder.1.weight", "context_refiner.0.attention.qkv.weight")
-    ]
-
-class ModelIdeogram(ModelTemplate):
-    arch = "ideogram"
-    keys_detect = [
-        (
-            "t_embedding.mlp_in.weight",
-            "layers.0.attention.qkv.weight",
-            "final_layer.linear.weight",
-        )
-    ]
-
-class ModelKrea2(ModelTemplate):
-    """
-    Krea-2 is a novel architecture from krea.ai — NOT Ideogram4.
-    Key structure (verified from Krea2_Turbo_fp8mixed.safetensors header):
-      blocks.N.attn.{wq,wk,wv,wo,gate}  — separate Q/K/V/O projections + gating
-      blocks.N.attn.qknorm.{qnorm,knorm} — Q/K norms
-      blocks.N.mlp.{up,gate,down}        — SwiGLU-style MLP
-      blocks.N.mod.lin                   — per-block modulation
-      blocks.N.{pre,post}norm.scale      — RMSNorm scales
-      txtfusion.layerwise_blocks.N.*     — layerwise text-image cross-attention
-      txtfusion.refiner_blocks.N.*       — refiner text-image cross-attention
-      txtfusion.projector                — text projector
-      first.weight / last.*              — input / output projections
-      tmlp.N / tproj.N                   — timestep MLP / projection
-
-    NOTE: ComfyUI core must have krea2 diffusion model support
-    (i.e. a detection branch for 'blocks.0.attn.wq.weight' in model_detection.py
-    and a matching supported_models entry) for the GGUF to load correctly.
-    Krea-2 was released 2026-06-22; verify your ComfyUI build is up to date.
-    """
-    arch = "krea2"
-    keys_detect = [
-        (
-            "blocks.0.attn.wq.weight",
-            "txtfusion.projector.weight",
-            "first.weight",
-        ),
-        (
-            "blocks.0.attn.wq.weight",
-            "txtfusion.layerwise_blocks.0.attn.wq.weight",
-            "last.linear.weight",
-        ),
-    ]
-    keys_hiprec = [
-        "blocks.0.mod.lin",  # modulation parameters — keep at full precision
-        "last.modulation.lin",
-        "tmlp.",
-        "tproj.",
-    ]
-
-arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2,
-             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2,
-             ModelKrea2, ModelIdeogram]
-
-def is_model_arch(model, state_dict):
-    # check if model is correct
-    matched = False
-    invalid = False
-    for match_list in model.keys_detect:
-        if all(key in state_dict for key in match_list):
-            matched = True
-            invalid = any(key in state_dict for key in model.keys_banned)
-            break
-    assert not invalid, "Model architecture not allowed for conversion! (i.e. reference VS diffusers format)"
-    return matched
-
-def detect_arch(state_dict):
-    model_arch = None
-    for arch in arch_list:
-        if is_model_arch(arch, state_dict):
-            model_arch = arch()
-            break
-    assert model_arch is not None, "Unknown model architecture!"
-    return model_arch
-
-QUANT_TYPE_MAP = {
-    "F16":  (gguf.GGMLQuantizationType.F16,  gguf.LlamaFileType.MOSTLY_F16),
-    "BF16": (gguf.GGMLQuantizationType.BF16, gguf.LlamaFileType.MOSTLY_BF16),
-    "Q8_0": (gguf.GGMLQuantizationType.Q8_0, gguf.LlamaFileType.MOSTLY_Q8_0),
-    "Q5_1": (gguf.GGMLQuantizationType.Q5_1, gguf.LlamaFileType.MOSTLY_Q5_1),
-    "Q5_0": (gguf.GGMLQuantizationType.Q5_0, gguf.LlamaFileType.MOSTLY_Q5_0),
-    "Q4_1": (gguf.GGMLQuantizationType.Q4_1, gguf.LlamaFileType.MOSTLY_Q4_1),
-    "Q4_0": (gguf.GGMLQuantizationType.Q4_0, gguf.LlamaFileType.MOSTLY_Q4_0),
-    "Q8_CR": (gguf.GGMLQuantizationType.I8, None),  # INT8 ConvRot (ComfyUI native)
-    "Q4_PT": (gguf.GGMLQuantizationType.I8, None),  # INT4 PyTorch (packed as raw bytes)
-}
-
-
-def quantize_int8_convrot(weight, convrot_groupsize=256):
-    """
-    Quantize a 2D Linear weight to INT8 with ConvRot grouping.
-    Uses per-output-channel scales to match ComfyUI's TensorWiseINT8Layout.
-    """
-    weight = weight.to(torch.float32)
-    orig_shape = tuple(weight.shape)
-    groupsize = next(
-        (
-            size
-            for size in (convrot_groupsize, 64, 16, 4)
-            if size <= weight.shape[1] and weight.shape[1] % size == 0
-        ),
-        None,
-    )
-    if groupsize is not None:
-        from comfy_kitchen.tensor.int8_utils import _build_hadamard, _rotate_weight
-
-        hadamard = _build_hadamard(groupsize, device=weight.device, dtype=weight.dtype)
-        weight = _rotate_weight(weight, hadamard, groupsize)
-
-    scale = weight.abs().amax(dim=1, keepdim=True).clamp_min(1e-9) / 127.0
-    qdata = (weight / scale).round().clamp(-128, 127).to(torch.int8)
-    quant_conf = {
-        "format": "int8_tensorwise",
-        "convrot": groupsize is not None,
-        "weight_rotated": groupsize is not None,
-        "per_row": True,
-    }
-    if groupsize is not None:
-        quant_conf["convrot_groupsize"] = groupsize
-    return qdata, scale, quant_conf, orig_shape
-
-
-def quantize_int4_pytorch(weight, group_size=64):
-    """
-    Quantize a 2D Linear weight for PyTorch's native INT4 kernel.
-    Weights are serialized as packed uint8 [n, k//2]. The runtime converts this
-    portable representation to PyTorch's device-specific INT4 layout.
-    """
-    orig_shape = tuple(weight.shape)
-    n, k = orig_shape
-    weight = weight.to(torch.float32)
-
-    pad = 0
-    if k % group_size != 0:
-        pad = group_size - (k % group_size)
-        weight = torch.nn.functional.pad(weight, (0, pad))
-        k = k + pad
-
-    w_grouped = weight.reshape(n, k // group_size, group_size)
-    w_min = w_grouped.amin(dim=-1, keepdim=True)
-    w_max = w_grouped.amax(dim=-1, keepdim=True)
-    scale = (w_max - w_min) / 15.0
-    scale = scale.clamp_min(1e-9)
-    q = ((w_grouped - w_min) / scale).round().clamp(0, 15).to(torch.uint8)
-
-    q_flat = q.reshape(n, k)
-    packed = ((q_flat[:, 0::2] << 4) | q_flat[:, 1::2]).to(torch.uint8)
-
-    qsz = torch.zeros(k // group_size, n, 2, dtype=torch.float32)
-    qsz[:, :, 0] = scale.reshape(n, k // group_size).t()
-    # _weight_int4pack_mm dequantizes as (q - 8) * scale + offset.
-    qsz[:, :, 1] = (w_min + 8 * scale).reshape(n, k // group_size).t()
-
-    quant_conf = {
-        "format": "int4_pytorch",
-        "group_size": group_size,
-        "orig_shape": orig_shape,
-        "pad": pad,
-    }
-    return packed, qsz, quant_conf, orig_shape
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description="Convert diffusion model safetensors/ckpt to GGUF."
-        " By default produces an F16/BF16 GGUF; use --quant-type to quantize."
-    )
-    parser.add_argument("--src", required=True, help="Source model ckpt/safetensors file.")
-    parser.add_argument("--dst", help="Output GGUF file path.")
-    parser.add_argument(
-        "--quant-type",
-        choices=list(QUANT_TYPE_MAP.keys()),
-        default=None,
-        help="Target quantization type for eligible 2-D+ tensors "
-             "(1-D biases/scales stay F32). Defaults to F16/BF16 matching the source dtype.",
-    )
-    args = parser.parse_args()
-
-    if not os.path.isfile(args.src):
-        parser.error("No input provided!")
-
-    return args
-
-def strip_prefix(state_dict):
-    # prefix for mixed state dict
-    prefix = None
-    for pfx in ["model.diffusion_model.", "model."]:
-        if any([x.startswith(pfx) for x in state_dict.keys()]):
-            prefix = pfx
-            break
-
-    # prefix for uniform state dict
-    if prefix is None:
-        for pfx in ["net."]:
-            if all([x.startswith(pfx) for x in state_dict.keys()]):
-                prefix = pfx
-                break
-
-    # strip prefix if found
-    if prefix is not None:
-        logging.info(f"State dict prefix found: '{prefix}'")
-        sd = {}
-        for k, v in state_dict.items():
-            if prefix not in k:
-                continue
-            k = k.replace(prefix, "")
-            sd[k] = v
-    else:
-        logging.debug("State dict has no prefix")
-        sd = state_dict
-
-    return sd
-
-def load_state_dict(path):
-    if any(path.endswith(x) for x in [".ckpt", ".pt", ".bin", ".pth"]):
-        state_dict = torch.load(path, map_location="cpu", weights_only=True)
-        for subkey in ["model", "module"]:
-            if subkey in state_dict:
-                state_dict = state_dict[subkey]
-                break
-        if len(state_dict) < 20:
-            raise RuntimeError(f"pt subkey load failed: {state_dict.keys()}")
-    else:
-        state_dict = load_file(path)
-
-    return strip_prefix(state_dict)
-
-def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_name=None):
-    # Pre-collect per-tensor FP8 scales (0-dim float32 tensors named "{key}_scale").
-    # These must be applied to their FP8 weight tensors before GGUF quantization.
-    # The actual weight value is fp8_value * scale; ignoring scale produces wrong magnitudes.
-    fp8_scales = {
-        k[:-len("_scale")]: v.item()
-        for k, v in state_dict.items()
-        if k.endswith("_scale") and len(v.shape) == 0 and v.dtype == torch.float32
-    }
-    if fp8_scales:
-        tqdm.write(f"Found {len(fp8_scales)} FP8 per-tensor scale(s); will apply before quantization.")
-
-    name_lengths = tuple(sorted(
-        ((key, len(key)) for key in state_dict.keys()),
-        key=lambda item: item[1],
-        reverse=True,
-    ))
-    if not name_lengths:
-        return
-    max_name_len = name_lengths[0][1]
-    if max_name_len > MAX_TENSOR_NAME_LENGTH:
-        bad_list = ", ".join(f"{key!r} ({namelen})" for key, namelen in name_lengths if namelen > MAX_TENSOR_NAME_LENGTH)
-        raise ValueError(f"Can only handle tensor names up to {MAX_TENSOR_NAME_LENGTH} characters. Tensors exceeding the limit: {bad_list}")
-    for key, data in tqdm(state_dict.items()):
-        old_dtype = data.dtype
-
-        if any(x in key for x in model_arch.keys_ignore):
-            tqdm.write(f"Filtering ignored key: '{key}'")
-            continue
-
-        # comfy_quant tensors are FP8 scale factors specific to ComfyUI's custom FP8 format.
-        # weight_scale tensors are 0-dim per-tensor FP8 scales (e.g. from torchao/fp8 fine-tunes).
-        # Both are meaningless after GGUF re-quantization and must be dropped so the loader
-        # does not try to apply them to already-GGUF-dequantized weights.
-        if key.endswith(".comfy_quant") or key.endswith("_scale") and len(data.shape) == 0:
-            tqdm.write(f"Dropping FP8 scale tensor: '{key}'")
-            continue
-
-        # 0-dim (scalar) tensors cannot be stored in GGUF and have no meaningful weight data.
-        if len(data.shape) == 0:
-            tqdm.write(f"Skipping 0-dim scalar tensor: '{key}'")
-            continue
-
-        if data.dtype == torch.bfloat16:
-            data = data.to(torch.float32).numpy()
-        # this is so we don't break torch 2.0.X
-        elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]:
-            data = data.to(torch.float32)
-            if key in fp8_scales:
-                data = data * fp8_scales[key]  # apply per-tensor dequantization scale
-            data = data.numpy()
-        else:
-            data = data.numpy()
-
-        n_dims = len(data.shape)
-        data_shape = data.shape
-        if old_dtype == torch.bfloat16:
-            data_qtype = gguf.GGMLQuantizationType.BF16
-        # elif old_dtype == torch.float32:
-        #     data_qtype = gguf.GGMLQuantizationType.F32
-        else:
-            data_qtype = gguf.GGMLQuantizationType.F16
-
-        # The max no. of dimensions that can be handled by the quantization code is 4.
-        if n_dims > MAX_TENSOR_DIMS:
-            model_arch.handle_nd_tensor(key, data)
-            continue
-
-        n_params = 1
-        for dim_size in data_shape:
-            n_params *= dim_size
-
-        _FP8_DTYPES = {getattr(torch, "float8_e4m3fn", None), getattr(torch, "float8_e5m2", None)} - {None}
-        apply_quantization_rules = (
-            quant_type_name in ("Q8_CR", "Q4_PT")
-            or old_dtype in (torch.float32, torch.bfloat16)
-            or old_dtype in _FP8_DTYPES
-        )
-        if apply_quantization_rules:
-            if n_dims == 1:
-                # One-dimensional tensors should be kept in F32.
-                data_qtype = gguf.GGMLQuantizationType.F32
-            elif n_params <= QUANTIZATION_THRESHOLD:
-                data_qtype = gguf.GGMLQuantizationType.F32
-            elif any(x in key for x in model_arch.keys_hiprec):
-                data_qtype = gguf.GGMLQuantizationType.F32
-            elif n_dims == 4 and "conv" in key.lower():
-                # Native quantized paths are Linear-only.
-                data_qtype = gguf.GGMLQuantizationType.F16
-            elif quant_type is not None:
-                data_qtype = quant_type
-
-        if quant_type_name in ("Q8_CR", "Q4_PT") and n_dims > 1 and n_dims != 2:
-            # Custom native layouts only represent Linear matrices.
-            data_qtype = gguf.GGMLQuantizationType.F16
-        elif quant_type_name == "Q4_PT" and data_shape[0] % 8 != 0:
-            # PyTorch's CUDA INT4 layout has an eight-output-channel tile.
-            data_qtype = gguf.GGMLQuantizationType.F16
-
-        # Custom quantization paths for Q8_CR and Q4_PT
-        if quant_type_name in ("Q8_CR", "Q4_PT") and data_qtype == quant_type and n_dims == 2:
-            if quant_type_name == "Q8_CR":
-                qdata, scale, quant_conf, orig_shape = quantize_int8_convrot(torch.from_numpy(data))
-                writer.add_tensor(key, qdata.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
-                writer.add_tensor(f"{key}_scale", scale.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
-                writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
-                continue
-
-            elif quant_type_name == "Q4_PT":
-                packed, qsz, quant_conf, orig_shape = quantize_int4_pytorch(torch.from_numpy(data))
-                writer.add_tensor(key, packed.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
-                writer.add_tensor(f"{key}_scale", qsz.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
-                writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
-                continue
-
-        if (model_arch.shape_fix                        # NEVER reshape for models such as flux
-            and n_dims > 1                              # Skip one-dimensional tensors
-            and n_params >= REARRANGE_THRESHOLD         # Only rearrange tensors meeting the size requirement
-            and (n_params / 256).is_integer()           # Rearranging only makes sense if total elements is divisible by 256
-            and not (data.shape[-1] / 256).is_integer() # Only need to rearrange if the last dimension is not divisible by 256
-        ):
-            orig_shape = data.shape
-            data = data.reshape(n_params // 256, 256)
-            writer.add_array(f"comfy.gguf.orig_shape.{key}", tuple(int(dim) for dim in orig_shape))
-
-        try:
-            data = gguf.quants.quantize(data, data_qtype)
-        except (AttributeError, gguf.QuantError) as e:
-            tqdm.write(f"falling back to F16: {e}")
-            data_qtype = gguf.GGMLQuantizationType.F16
-            data = gguf.quants.quantize(data, data_qtype)
-
-        new_name = key # do we need to rename?
-
-        shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
-        tqdm.write(f"{f'%-{max_name_len + 4}s' % f'{new_name}'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
-
-        writer.add_tensor(new_name, data, raw_dtype=data_qtype)
-
-def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type_name=None):
-    # load & run model detection logic
-    state_dict = load_state_dict(path)
-    model_arch = detect_arch(state_dict)
-    logging.info(f"* Architecture detected from input: {model_arch.arch}")
-
-    # resolve quant type from name if provided
-    quant_type = None
-    if quant_type_name is not None and quant_type_name in QUANT_TYPE_MAP:
-        quant_type, ftype_gguf = QUANT_TYPE_MAP[quant_type_name]
-        ftype_name = quant_type_name
-    else:
-        # detect & set dtype from source file
-        dtypes = [x.dtype for x in state_dict.values()]
-        dtypes = {x: dtypes.count(x) for x in set(dtypes)}
-        main_dtype = max(dtypes, key=dtypes.get)
-
-        if main_dtype == torch.bfloat16:
-            ftype_name = "BF16"
-            ftype_gguf = gguf.LlamaFileType.MOSTLY_BF16
-        # elif main_dtype == torch.float32:
-        #     ftype_name = "F32"
-        #     ftype_gguf = None
-        else:
-            ftype_name = "F16"
-            ftype_gguf = gguf.LlamaFileType.MOSTLY_F16
-
-    if dst_path is None:
-        dst_path = f"{os.path.splitext(path)[0]}-{ftype_name}.gguf"
-    elif "{ftype}" in dst_path: # lcpp logic
-        dst_path = dst_path.replace("{ftype}", ftype_name)
-
-    if os.path.isfile(dst_path) and not overwrite:
-        if interact:
-            input("Output exists enter to continue or ctrl+c to abort!")
-        else:
-            raise OSError("Output exists and overwriting is disabled!")
-
-    # handle actual file
-    writer = gguf.GGUFWriter(path=None, arch=model_arch.arch)
-    writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
-    if ftype_gguf is not None:
-        writer.add_file_type(ftype_gguf)
-
-    handle_tensors(writer, state_dict, model_arch, quant_type=quant_type, quant_type_name=quant_type_name)
-    writer.write_header_to_file(path=dst_path)
-    writer.write_kv_data_to_file()
-    writer.write_tensors_to_file(progress=True)
-    writer.close()
-
-    fix = f"./fix_5d_tensors_{model_arch.arch}.safetensors"
-    if os.path.isfile(fix):
-        logging.warning(f"\n### Warning! Fix file found at '{fix}'")
-        logging.warning(" you most likely need to run 'fix_5d_tensors.py' after quantization.")
-
-    return dst_path, model_arch
-
-if __name__ == "__main__":
-    args = parse_args()
+# (c) City96 || Apache-2.0 (apache.org/licenses/LICENSE-2.0)
+import os
+import gguf
+import json
+import torch
+import logging
+import argparse
+from tqdm import tqdm
+from safetensors.torch import load_file, save_file
+
+QUANTIZATION_THRESHOLD = 1024
+REARRANGE_THRESHOLD = 512
+MAX_TENSOR_NAME_LENGTH = 127
+MAX_TENSOR_DIMS = 4
+
+class ModelTemplate:
+    arch = "invalid"  # string describing architecture
+    shape_fix = False # whether to reshape tensors
+    keys_detect = []  # list of lists to match in state dict
+    keys_banned = []  # list of keys that should mark model as invalid for conversion
+    keys_hiprec = []  # list of keys that need to be kept in fp32 for some reason
+    keys_ignore = []  # list of strings to ignore keys by when found
+
+    def handle_nd_tensor(self, key, data):
+        raise NotImplementedError(f"Tensor detected that exceeds dims supported by C++ code! ({key} @ {data.shape})")
+
+class ModelFlux(ModelTemplate):
+    arch = "flux"
+    keys_detect = [
+        ("transformer_blocks.0.attn.norm_added_k.weight",),
+        ("double_blocks.0.img_attn.proj.weight",),
+    ]
+    keys_banned = ["transformer_blocks.0.attn.norm_added_k.weight",]
+
+class ModelSD3(ModelTemplate):
+    arch = "sd3"
+    keys_detect = [
+        ("transformer_blocks.0.attn.add_q_proj.weight",),
+        ("joint_blocks.0.x_block.attn.qkv.weight",),
+    ]
+    keys_banned = ["transformer_blocks.0.attn.add_q_proj.weight",]
+
+class ModelAura(ModelTemplate):
+    arch = "aura"
+    keys_detect = [
+        ("double_layers.3.modX.1.weight",),
+        ("joint_transformer_blocks.3.ff_context.out_projection.weight",),
+    ]
+    keys_banned = ["joint_transformer_blocks.3.ff_context.out_projection.weight",]
+
+class ModelHiDream(ModelTemplate):
+    arch = "hidream"
+    keys_detect = [
+        (
+            "caption_projection.0.linear.weight",
+            "double_stream_blocks.0.block.ff_i.shared_experts.w3.weight"
+        )
+    ]
+    keys_hiprec = [
+        # nn.parameter, can't load from BF16 ver
+        ".ff_i.gate.weight",
+        "img_emb.emb_pos"
+    ]
+
+class CosmosPredict2(ModelTemplate):
+    arch = "cosmos"
+    keys_detect = [
+        (
+            "blocks.0.mlp.layer1.weight",
+            "blocks.0.adaln_modulation_cross_attn.1.weight",
+        )
+    ]
+    keys_hiprec = ["pos_embedder"]
+    keys_ignore = ["_extra_state", "accum_"]
+
+class ModelHyVid(ModelTemplate):
+    arch = "hyvid"
+    keys_detect = [
+        (
+            "double_blocks.0.img_attn_proj.weight",
+            "txt_in.individual_token_refiner.blocks.1.self_attn_qkv.weight",
+        )
+    ]
+
+    def handle_nd_tensor(self, key, data):
+        # hacky but don't have any better ideas
+        path = f"./fix_5d_tensors_{self.arch}.safetensors" # TODO: somehow get a path here??
+        if os.path.isfile(path):
+            raise RuntimeError(f"5D tensor fix file already exists! {path}")
+        fsd = {key: torch.from_numpy(data)}
+        tqdm.write(f"5D key found in state dict! Manual fix required! - {key} {data.shape}")
+        save_file(fsd, path)
+
+class ModelWan(ModelHyVid):
+    arch = "wan"
+    keys_detect = [
+        (
+            "blocks.0.self_attn.norm_q.weight",
+            "text_embedding.2.weight",
+            "head.modulation",
+        )
+    ]
+    keys_hiprec = [
+        ".modulation" # nn.parameter, can't load from BF16 ver
+    ]
+
+class ModelLTXV(ModelTemplate):
+    arch = "ltxv"
+    keys_detect = [
+        (
+            "adaln_single.emb.timestep_embedder.linear_2.weight",
+            "transformer_blocks.27.scale_shift_table",
+            "caption_projection.linear_2.weight",
+        )
+    ]
+    keys_hiprec = [
+        "scale_shift_table" # nn.parameter, can't load from BF16 base quant
+    ]
+
+class ModelSDXL(ModelTemplate):
+    arch = "sdxl"
+    shape_fix = True
+    keys_detect = [
+        ("down_blocks.0.downsamplers.0.conv.weight", "add_embedding.linear_1.weight",),
+        (
+            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight",
+            "output_blocks.2.2.conv.weight", "output_blocks.5.2.conv.weight",
+        ), # Non-diffusers
+        ("label_emb.0.0.weight",),
+    ]
+
+class ModelSD1(ModelTemplate):
+    arch = "sd1"
+    shape_fix = True
+    keys_detect = [
+        ("down_blocks.0.downsamplers.0.conv.weight",),
+        (
+            "input_blocks.3.0.op.weight", "input_blocks.6.0.op.weight", "input_blocks.9.0.op.weight",
+            "output_blocks.2.1.conv.weight", "output_blocks.5.2.conv.weight", "output_blocks.8.2.conv.weight"
+        ), # Non-diffusers
+    ]
+
+class ModelLumina2(ModelTemplate):
+    arch = "lumina2"
+    keys_detect = [
+        ("cap_embedder.1.weight", "context_refiner.0.attention.qkv.weight")
+    ]
+
+class ModelIdeogram(ModelTemplate):
+    arch = "ideogram"
+    keys_detect = [
+        (
+            "t_embedding.mlp_in.weight",
+            "layers.0.attention.qkv.weight",
+            "final_layer.linear.weight",
+        )
+    ]
+
+class ModelKrea2(ModelTemplate):
+    """
+    Krea-2 is a novel architecture from krea.ai — NOT Ideogram4.
+    Key structure (verified from Krea2_Turbo_fp8mixed.safetensors header):
+      blocks.N.attn.{wq,wk,wv,wo,gate}  — separate Q/K/V/O projections + gating
+      blocks.N.attn.qknorm.{qnorm,knorm} — Q/K norms
+      blocks.N.mlp.{up,gate,down}        — SwiGLU-style MLP
+      blocks.N.mod.lin                   — per-block modulation
+      blocks.N.{pre,post}norm.scale      — RMSNorm scales
+      txtfusion.layerwise_blocks.N.*     — layerwise text-image cross-attention
+      txtfusion.refiner_blocks.N.*       — refiner text-image cross-attention
+      txtfusion.projector                — text projector
+      first.weight / last.*              — input / output projections
+      tmlp.N / tproj.N                   — timestep MLP / projection
+
+    NOTE: ComfyUI core must have krea2 diffusion model support
+    (i.e. a detection branch for 'blocks.0.attn.wq.weight' in model_detection.py
+    and a matching supported_models entry) for the GGUF to load correctly.
+    Krea-2 was released 2026-06-22; verify your ComfyUI build is up to date.
+    """
+    arch = "krea2"
+    keys_detect = [
+        (
+            "blocks.0.attn.wq.weight",
+            "txtfusion.projector.weight",
+            "first.weight",
+        ),
+        (
+            "blocks.0.attn.wq.weight",
+            "txtfusion.layerwise_blocks.0.attn.wq.weight",
+            "last.linear.weight",
+        ),
+    ]
+    keys_hiprec = [
+        "blocks.0.mod.lin",  # modulation parameters — keep at full precision
+        "last.modulation.lin",
+        "tmlp.",
+        "tproj.",
+    ]
+
+arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2,
+             ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2,
+             ModelKrea2, ModelIdeogram]
+
+def is_model_arch(model, state_dict):
+    # check if model is correct
+    matched = False
+    invalid = False
+    for match_list in model.keys_detect:
+        if all(key in state_dict for key in match_list):
+            matched = True
+            invalid = any(key in state_dict for key in model.keys_banned)
+            break
+    assert not invalid, "Model architecture not allowed for conversion! (i.e. reference VS diffusers format)"
+    return matched
+
+def detect_arch(state_dict):
+    model_arch = None
+    for arch in arch_list:
+        if is_model_arch(arch, state_dict):
+            model_arch = arch()
+            break
+    assert model_arch is not None, "Unknown model architecture!"
+    return model_arch
+
+QUANT_TYPE_MAP = {
+    "F16":  (gguf.GGMLQuantizationType.F16,  gguf.LlamaFileType.MOSTLY_F16),
+    "BF16": (gguf.GGMLQuantizationType.BF16, gguf.LlamaFileType.MOSTLY_BF16),
+    "Q8_0": (gguf.GGMLQuantizationType.Q8_0, gguf.LlamaFileType.MOSTLY_Q8_0),
+    "Q5_1": (gguf.GGMLQuantizationType.Q5_1, gguf.LlamaFileType.MOSTLY_Q5_1),
+    "Q5_0": (gguf.GGMLQuantizationType.Q5_0, gguf.LlamaFileType.MOSTLY_Q5_0),
+    "Q4_1": (gguf.GGMLQuantizationType.Q4_1, gguf.LlamaFileType.MOSTLY_Q4_1),
+    "Q4_0": (gguf.GGMLQuantizationType.Q4_0, gguf.LlamaFileType.MOSTLY_Q4_0),
+    "Q8_CR": (gguf.GGMLQuantizationType.I8, None),  # INT8 ConvRot (ComfyUI native)
+    # Q4_PT is retired pending a performant Ampere W4A16 backend.
+    # "Q4_PT": (gguf.GGMLQuantizationType.I8, None),
+}
+
+
+def quantize_int8_convrot(weight, convrot_groupsize=256):
+    """
+    Quantize a 2D Linear weight to INT8 with ConvRot grouping.
+    Uses per-output-channel scales to match ComfyUI's TensorWiseINT8Layout.
+    """
+    weight = weight.to(torch.float32)
+    orig_shape = tuple(weight.shape)
+    groupsize = next(
+        (
+            size
+            for size in (convrot_groupsize, 64, 16, 4)
+            if size <= weight.shape[1] and weight.shape[1] % size == 0
+        ),
+        None,
+    )
+    if groupsize is not None:
+        from comfy_kitchen.tensor.int8_utils import _build_hadamard, _rotate_weight
+
+        hadamard = _build_hadamard(groupsize, device=weight.device, dtype=weight.dtype)
+        weight = _rotate_weight(weight, hadamard, groupsize)
+
+    scale = weight.abs().amax(dim=1, keepdim=True).clamp_min(1e-9) / 127.0
+    qdata = (weight / scale).round().clamp(-128, 127).to(torch.int8)
+    quant_conf = {
+        "format": "int8_tensorwise",
+        "convrot": groupsize is not None,
+        "weight_rotated": groupsize is not None,
+        "per_row": True,
+    }
+    if groupsize is not None:
+        quant_conf["convrot_groupsize"] = groupsize
+    return qdata, scale, quant_conf, orig_shape
+
+
+def retired_quantize_int4_pytorch(weight, group_size=64):
+    """
+    Quantize a 2D Linear weight for PyTorch's native INT4 kernel.
+    Weights are serialized as packed uint8 [n, k//2]. The runtime converts this
+    portable representation to PyTorch's device-specific INT4 layout.
+    """
+    orig_shape = tuple(weight.shape)
+    n, k = orig_shape
+    weight = weight.to(torch.float32)
+
+    pad = 0
+    if k % group_size != 0:
+        pad = group_size - (k % group_size)
+        weight = torch.nn.functional.pad(weight, (0, pad))
+        k = k + pad
+
+    w_grouped = weight.reshape(n, k // group_size, group_size)
+    w_min = w_grouped.amin(dim=-1, keepdim=True)
+    w_max = w_grouped.amax(dim=-1, keepdim=True)
+    scale = (w_max - w_min) / 15.0
+    scale = scale.clamp_min(1e-9)
+    q = ((w_grouped - w_min) / scale).round().clamp(0, 15).to(torch.uint8)
+
+    q_flat = q.reshape(n, k)
+    packed = ((q_flat[:, 0::2] << 4) | q_flat[:, 1::2]).to(torch.uint8)
+
+    qsz = torch.zeros(k // group_size, n, 2, dtype=torch.float32)
+    qsz[:, :, 0] = scale.reshape(n, k // group_size).t()
+    # _weight_int4pack_mm dequantizes as (q - 8) * scale + offset.
+    qsz[:, :, 1] = (w_min + 8 * scale).reshape(n, k // group_size).t()
+
+    quant_conf = {
+        "format": "int4_compact_gemm",
+        "group_size": group_size,
+        "orig_shape": orig_shape,
+        "pad": pad,
+    }
+    return packed, qsz, quant_conf, orig_shape
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Convert diffusion model safetensors/ckpt to GGUF."
+        " By default produces an F16/BF16 GGUF; use --quant-type to quantize."
+    )
+    parser.add_argument("--src", required=True, help="Source model ckpt/safetensors file.")
+    parser.add_argument("--dst", help="Output GGUF file path.")
+    parser.add_argument(
+        "--quant-type",
+        choices=list(QUANT_TYPE_MAP.keys()),
+        default=None,
+        help="Target quantization type for eligible 2-D+ tensors "
+             "(1-D biases/scales stay F32). Defaults to F16/BF16 matching the source dtype.",
+    )
+    args = parser.parse_args()
+
+    if not os.path.isfile(args.src):
+        parser.error("No input provided!")
+
+    return args
+
+def strip_prefix(state_dict):
+    # prefix for mixed state dict
+    prefix = None
+    for pfx in ["model.diffusion_model.", "model."]:
+        if any([x.startswith(pfx) for x in state_dict.keys()]):
+            prefix = pfx
+            break
+
+    # prefix for uniform state dict
+    if prefix is None:
+        for pfx in ["net."]:
+            if all([x.startswith(pfx) for x in state_dict.keys()]):
+                prefix = pfx
+                break
+
+    # strip prefix if found
+    if prefix is not None:
+        logging.info(f"State dict prefix found: '{prefix}'")
+        sd = {}
+        for k, v in state_dict.items():
+            if prefix not in k:
+                continue
+            k = k.replace(prefix, "")
+            sd[k] = v
+    else:
+        logging.debug("State dict has no prefix")
+        sd = state_dict
+
+    return sd
+
+def load_state_dict(path):
+    if any(path.endswith(x) for x in [".ckpt", ".pt", ".bin", ".pth"]):
+        state_dict = torch.load(path, map_location="cpu", weights_only=True)
+        for subkey in ["model", "module"]:
+            if subkey in state_dict:
+                state_dict = state_dict[subkey]
+                break
+        if len(state_dict) < 20:
+            raise RuntimeError(f"pt subkey load failed: {state_dict.keys()}")
+    else:
+        state_dict = load_file(path)
+
+    return strip_prefix(state_dict)
+
+def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_name=None):
+    # Pre-collect per-tensor FP8 scales (0-dim float32 tensors named "{key}_scale").
+    # These must be applied to their FP8 weight tensors before GGUF quantization.
+    # The actual weight value is fp8_value * scale; ignoring scale produces wrong magnitudes.
+    fp8_scales = {
+        k[:-len("_scale")]: v.item()
+        for k, v in state_dict.items()
+        if k.endswith("_scale") and len(v.shape) == 0 and v.dtype == torch.float32
+    }
+    if fp8_scales:
+        tqdm.write(f"Found {len(fp8_scales)} FP8 per-tensor scale(s); will apply before quantization.")
+
+    name_lengths = tuple(sorted(
+        ((key, len(key)) for key in state_dict.keys()),
+        key=lambda item: item[1],
+        reverse=True,
+    ))
+    if not name_lengths:
+        return
+    max_name_len = name_lengths[0][1]
+    if max_name_len > MAX_TENSOR_NAME_LENGTH:
+        bad_list = ", ".join(f"{key!r} ({namelen})" for key, namelen in name_lengths if namelen > MAX_TENSOR_NAME_LENGTH)
+        raise ValueError(f"Can only handle tensor names up to {MAX_TENSOR_NAME_LENGTH} characters. Tensors exceeding the limit: {bad_list}")
+    for key, data in tqdm(state_dict.items()):
+        old_dtype = data.dtype
+
+        if any(x in key for x in model_arch.keys_ignore):
+            tqdm.write(f"Filtering ignored key: '{key}'")
+            continue
+
+        # comfy_quant tensors are FP8 scale factors specific to ComfyUI's custom FP8 format.
+        # weight_scale tensors are 0-dim per-tensor FP8 scales (e.g. from torchao/fp8 fine-tunes).
+        # Both are meaningless after GGUF re-quantization and must be dropped so the loader
+        # does not try to apply them to already-GGUF-dequantized weights.
+        if key.endswith(".comfy_quant") or key.endswith("_scale") and len(data.shape) == 0:
+            tqdm.write(f"Dropping FP8 scale tensor: '{key}'")
+            continue
+
+        # 0-dim (scalar) tensors cannot be stored in GGUF and have no meaningful weight data.
+        if len(data.shape) == 0:
+            tqdm.write(f"Skipping 0-dim scalar tensor: '{key}'")
+            continue
+
+        if data.dtype == torch.bfloat16:
+            data = data.to(torch.float32).numpy()
+        # this is so we don't break torch 2.0.X
+        elif data.dtype in [getattr(torch, "float8_e4m3fn", "_invalid"), getattr(torch, "float8_e5m2", "_invalid")]:
+            data = data.to(torch.float32)
+            if key in fp8_scales:
+                data = data * fp8_scales[key]  # apply per-tensor dequantization scale
+            data = data.numpy()
+        else:
+            data = data.numpy()
+
+        n_dims = len(data.shape)
+        data_shape = data.shape
+        if old_dtype == torch.bfloat16:
+            data_qtype = gguf.GGMLQuantizationType.BF16
+        # elif old_dtype == torch.float32:
+        #     data_qtype = gguf.GGMLQuantizationType.F32
+        else:
+            data_qtype = gguf.GGMLQuantizationType.F16
+
+        # The max no. of dimensions that can be handled by the quantization code is 4.
+        if n_dims > MAX_TENSOR_DIMS:
+            model_arch.handle_nd_tensor(key, data)
+            continue
+
+        n_params = 1
+        for dim_size in data_shape:
+            n_params *= dim_size
+
+        _FP8_DTYPES = {getattr(torch, "float8_e4m3fn", None), getattr(torch, "float8_e5m2", None)} - {None}
+        apply_quantization_rules = (
+            quant_type_name == "Q8_CR"
+            or old_dtype in (torch.float32, torch.bfloat16)
+            or old_dtype in _FP8_DTYPES
+        )
+        if apply_quantization_rules:
+            if n_dims == 1:
+                # One-dimensional tensors should be kept in F32.
+                data_qtype = gguf.GGMLQuantizationType.F32
+            elif n_params <= QUANTIZATION_THRESHOLD:
+                data_qtype = gguf.GGMLQuantizationType.F32
+            elif any(x in key for x in model_arch.keys_hiprec):
+                data_qtype = gguf.GGMLQuantizationType.F32
+            elif n_dims == 4 and "conv" in key.lower():
+                # Native quantized paths are Linear-only.
+                data_qtype = gguf.GGMLQuantizationType.F16
+            elif quant_type is not None:
+                data_qtype = quant_type
+
+        if quant_type_name == "Q8_CR" and n_dims > 1 and n_dims != 2:
+            # Custom native layouts only represent Linear matrices.
+            data_qtype = gguf.GGMLQuantizationType.F16
+        # Q4_PT layout restrictions are retained with
+        # retired_quantize_int4_pytorch and are intentionally not selectable.
+
+        # Q8_CR is the supported custom quantization path.
+        if quant_type_name == "Q8_CR" and data_qtype == quant_type and n_dims == 2:
+            if quant_type_name == "Q8_CR":
+                qdata, scale, quant_conf, orig_shape = quantize_int8_convrot(torch.from_numpy(data))
+                writer.add_tensor(key, qdata.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
+                writer.add_tensor(f"{key}_scale", scale.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
+                writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
+                continue
+
+            # Q4_PT emission is retired with its runtime backend.
+
+        if (model_arch.shape_fix                        # NEVER reshape for models such as flux
+            and n_dims > 1                              # Skip one-dimensional tensors
+            and n_params >= REARRANGE_THRESHOLD         # Only rearrange tensors meeting the size requirement
+            and (n_params / 256).is_integer()           # Rearranging only makes sense if total elements is divisible by 256
+            and not (data.shape[-1] / 256).is_integer() # Only need to rearrange if the last dimension is not divisible by 256
+        ):
+            orig_shape = data.shape
+            data = data.reshape(n_params // 256, 256)
+            writer.add_array(f"comfy.gguf.orig_shape.{key}", tuple(int(dim) for dim in orig_shape))
+
+        try:
+            data = gguf.quants.quantize(data, data_qtype)
+        except (AttributeError, gguf.QuantError) as e:
+            tqdm.write(f"falling back to F16: {e}")
+            data_qtype = gguf.GGMLQuantizationType.F16
+            data = gguf.quants.quantize(data, data_qtype)
+
+        new_name = key # do we need to rename?
+
+        shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
+        tqdm.write(f"{f'%-{max_name_len + 4}s' % f'{new_name}'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+
+        writer.add_tensor(new_name, data, raw_dtype=data_qtype)
+
+def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type_name=None):
+    # load & run model detection logic
+    state_dict = load_state_dict(path)
+    model_arch = detect_arch(state_dict)
+    logging.info(f"* Architecture detected from input: {model_arch.arch}")
+
+    # resolve quant type from name if provided
+    quant_type = None
+    if quant_type_name is not None and quant_type_name in QUANT_TYPE_MAP:
+        quant_type, ftype_gguf = QUANT_TYPE_MAP[quant_type_name]
+        ftype_name = quant_type_name
+    else:
+        # detect & set dtype from source file
+        dtypes = [x.dtype for x in state_dict.values()]
+        dtypes = {x: dtypes.count(x) for x in set(dtypes)}
+        main_dtype = max(dtypes, key=dtypes.get)
+
+        if main_dtype == torch.bfloat16:
+            ftype_name = "BF16"
+            ftype_gguf = gguf.LlamaFileType.MOSTLY_BF16
+        # elif main_dtype == torch.float32:
+        #     ftype_name = "F32"
+        #     ftype_gguf = None
+        else:
+            ftype_name = "F16"
+            ftype_gguf = gguf.LlamaFileType.MOSTLY_F16
+
+    if dst_path is None:
+        dst_path = f"{os.path.splitext(path)[0]}-{ftype_name}.gguf"
+    elif "{ftype}" in dst_path: # lcpp logic
+        dst_path = dst_path.replace("{ftype}", ftype_name)
+
+    if os.path.isfile(dst_path) and not overwrite:
+        if interact:
+            input("Output exists enter to continue or ctrl+c to abort!")
+        else:
+            raise OSError("Output exists and overwriting is disabled!")
+
+    # handle actual file
+    writer = gguf.GGUFWriter(path=None, arch=model_arch.arch)
+    writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
+    if ftype_gguf is not None:
+        writer.add_file_type(ftype_gguf)
+
+    handle_tensors(writer, state_dict, model_arch, quant_type=quant_type, quant_type_name=quant_type_name)
+    writer.write_header_to_file(path=dst_path)
+    writer.write_kv_data_to_file()
+    writer.write_tensors_to_file(progress=True)
+    writer.close()
+
+    fix = f"./fix_5d_tensors_{model_arch.arch}.safetensors"
+    if os.path.isfile(fix):
+        logging.warning(f"\n### Warning! Fix file found at '{fix}'")
+        logging.warning(" you most likely need to run 'fix_5d_tensors.py' after quantization.")
+
+    return dst_path, model_arch
+
+if __name__ == "__main__":
+    args = parse_args()
     convert_file(args.src, args.dst, quant_type_name=args.quant_type)
\ No newline at end of file

From 297672043c4b6e211aef47a160b08530f62af12f Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Sun, 26 Jul 2026 22:25:22 +0200
Subject: [PATCH 09/20] Adds docs

---
 README.md                           | 97 ++++++++++++++++++++++++++++-
 __init__.py                         |  4 +-
 web/docs/CLIPLoaderGGUF.md          | 18 ++++++
 web/docs/DualCLIPLoaderGGUF.md      | 18 ++++++
 web/docs/QuadrupleCLIPLoaderGGUF.md | 18 ++++++
 web/docs/TripleCLIPLoaderGGUF.md    | 17 +++++
 web/docs/UnetLoaderGGUF.md          | 20 ++++++
 web/docs/UnetLoaderGGUFAdvanced.md  | 26 ++++++++
 8 files changed, 215 insertions(+), 3 deletions(-)
 create mode 100644 web/docs/CLIPLoaderGGUF.md
 create mode 100644 web/docs/DualCLIPLoaderGGUF.md
 create mode 100644 web/docs/QuadrupleCLIPLoaderGGUF.md
 create mode 100644 web/docs/TripleCLIPLoaderGGUF.md
 create mode 100644 web/docs/UnetLoaderGGUF.md
 create mode 100644 web/docs/UnetLoaderGGUFAdvanced.md

diff --git a/README.md b/README.md
index 97c152a..cd2307e 100644
--- a/README.md
+++ b/README.md
@@ -53,10 +53,89 @@ Initial support for quantizing T5 has also been added recently, these can be use
 
 See the instructions in the [tools](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) folder for how to create your own quants.
 
+## Converting Krea 2 and Ideogram 4 models
+
+The converter detects supported Krea 2 and Ideogram 4 checkpoints directly.
+Provide an existing `.safetensors`, `.ckpt`, `.pt`, `.pth`, or `.bin` diffusion
+model file; no model-specific conversion script is required.
+
+Run these commands from the `ComfyUI-GGUF` directory, replacing the source and
+destination paths with your model names:
+
+```bash
+# Compact standard GGUF
+python tools/convert.py --src /path/to/krea2_or_ideogram.safetensors \
+  --dst /path/to/model-Q4_0.gguf --quant-type Q4_0
+
+# Higher-quality standard GGUF
+python tools/convert.py --src /path/to/krea2_or_ideogram.safetensors \
+  --dst /path/to/model-Q8_0.gguf --quant-type Q8_0
+
+# Recommended for RTX 30-series NVIDIA GPUs
+python tools/convert.py --src /path/to/krea2_or_ideogram.safetensors \
+  --dst /path/to/model-Q8_CR.gguf --quant-type Q8_CR
+```
+
+For the portable Windows distribution, use its embedded Python executable:
+
+```bat
+.\python_embeded\python.exe .\ComfyUI\custom_nodes\ComfyUI-GGUF\tools\convert.py ^
+  --src C:\path\to\krea2_or_ideogram.safetensors ^
+  --dst C:\path\to\model-Q8_CR.gguf --quant-type Q8_CR
+```
+
+Place the resulting GGUF in `ComfyUI/models/unet` or
+`ComfyUI/models/diffusion_models`, then load it with **Unet Loader (GGUF)**.
+
+## Supported conversion formats
+
+The standard GGUF formats use the package's normal GGML loader path. `Q8_CR`
+is a custom ComfyUI-native INT8 layout for eligible Linear weights.
+
+| Format | Storage / execution | Recommended use |
+| --- | --- | --- |
+| `F16` | FP16 GGUF | Maximum compatibility with half-precision storage. |
+| `BF16` | BF16 GGUF | Preserve BF16 source models where the target supports BF16. |
+| `Q8_0` | Standard GGML 8-bit quantization | Excellent general-quality 8-bit GGUF; portable and straightforward to offload. |
+| `Q5_1` | Standard GGML 5-bit quantization | Lower storage with a quality-oriented 5-bit format. |
+| `Q5_0` | Standard GGML 5-bit quantization | Lower storage alternative to `Q5_1`. |
+| `Q4_1` | Standard GGML 4-bit quantization | Smaller files when VRAM or RAM is constrained. |
+| `Q4_0` | Standard GGML 4-bit quantization | Smallest supported standard diffusion-model format; expect the largest quality trade-off. |
+| `Q8_CR` | Per-row INT8 ConvRot through ComfyUI native ops | **Maintainer recommendation for NVIDIA RTX 30-series systems.** |
+
+
+> [!WARNING]
+> `_K` quant formats are not supported for diffusion models; they are supported only for text encoders.
+
 ## Native weight-only quantization
 
-The converter supports two custom, global quantization modes for DiT/transformer
-UNets:
+The converter supports one custom global quantization mode for DiT/transformer
+UNets: `Q8_CR`.
+
+### What Q8_CR is, why it exists, and how it works
+
+`Q8_CR` is an INT8 weight-only format designed for transformer-style diffusion
+models. Its purpose is to reduce GGUF model storage and VRAM pressure while
+preserving the fast native INT8 Linear operations available on supported NVIDIA
+GPUs. It is especially useful when the full diffusion model does not fit in
+VRAM and ComfyUI needs to offload weights to CPU memory.
+
+During conversion, the converter:
+
+1. Selects eligible 2-D Linear weights. One-dimensional tensors, small
+   tensors, and architecture-designated sensitive tensors stay FP32; Conv2d
+   weights stay FP16.
+2. Applies the compatible ConvRot/Hadamard rotation to each eligible weight
+   matrix.
+3. Quantizes the rotated weights to INT8 using an FP32 scale for every output
+   row.
+4. Stores the INT8 payload, scales, and ConvRot metadata in the GGUF file.
+
+During loading, the GGUF loader recognizes this metadata and passes the raw
+INT8 weights and row scales to ComfyUI's `TensorWiseINT8Layout`. On supported
+CUDA systems, ComfyUI executes the native INT8/ConvRot Linear path directly;
+it does not first expand the weight matrix to FP16 or BF16. The ordinary GGUF
+container still provides memory-mapped loading and CPU offload behavior.
 
 - `Q8_CR` stores eligible 2-D Linear weights as per-row INT8 ConvRot. It uses
   ComfyUI's native `TensorWiseINT8Layout` path, so weights remain INT8 during
@@ -66,6 +145,20 @@ Q8_CR keeps 1-D, small, and architecture-designated high-precision tensors
 in FP32. Conv2d weights remain FP16 because these modes accelerate Linear
 matrix multiplication only.
 
+### Maintainer recommendation: Q8_CR on RTX 30-series
+
+For NVIDIA RTX 30-series systems, the maintainer recommends `Q8_CR` for Krea 2
+and Ideogram 4. It combines:
+
+- Fast native INT8 operations on these GPUs through ComfyUI's ConvRot backend.
+- GGUF's convenient CPU offload and memory-mapped model storage behavior.
+- The generally excellent image quality expected from 8-bit GGUF
+  quantization, while retaining selected sensitive tensors in higher precision.
+
+Use `Q8_0` instead when you need the conventional portable GGML 8-bit format.
+Use `Q4_0` primarily when the smaller model footprint matters more than
+quality or sampling speed.
+
 ### Q8_CR platform support
 
 Q8_CR does not require CUDA. It uses ComfyUI's `comfy_kitchen` layout backend:
diff --git a/__init__.py b/__init__.py
index a03726e..9cd3727 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,3 +1,5 @@
+WEB_DIRECTORY = "./web"
+
 # only import if running as a custom node
 try:
     import comfy.utils
@@ -6,4 +8,4 @@
 else:
     from .nodes import NODE_CLASS_MAPPINGS
     NODE_DISPLAY_NAME_MAPPINGS = {k:v.TITLE for k,v in NODE_CLASS_MAPPINGS.items()}
-    __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+    __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', 'WEB_DIRECTORY']
diff --git a/web/docs/CLIPLoaderGGUF.md b/web/docs/CLIPLoaderGGUF.md
new file mode 100644
index 0000000..940b4a1
--- /dev/null
+++ b/web/docs/CLIPLoaderGGUF.md
@@ -0,0 +1,18 @@
+# CLIP Loader (GGUF)
+
+Loads one text encoder from a GGUF model file and returns a ComfyUI `CLIP`.
+
+## Parameters
+
+- **clip_name**: A GGUF text encoder from `models/clip` or
+  `models/text_encoders`. Regular compatible checkpoint files are also listed.
+- **type**: Select the model family expected by the workflow, such as
+  `stable_diffusion`, `sd3`, or `krea2`.
+
+## Usage
+
+Connect **CLIP** to `CLIP Text Encode` nodes. The selected **type** must match
+the diffusion model; for example, Krea 2 workflows require the `krea2` type.
+
+GGUF text encoders are loaded with the package's GGML operations and may use
+less VRAM than full-precision checkpoints.
diff --git a/web/docs/DualCLIPLoaderGGUF.md b/web/docs/DualCLIPLoaderGGUF.md
new file mode 100644
index 0000000..c55803f
--- /dev/null
+++ b/web/docs/DualCLIPLoaderGGUF.md
@@ -0,0 +1,18 @@
+# Dual CLIP Loader (GGUF)
+
+Loads two text encoder files and returns one combined ComfyUI `CLIP` object.
+
+## Parameters
+
+- **clip_name1**: First encoder file.
+- **clip_name2**: Second encoder file.
+- **type**: The model family required by the downstream diffusion model.
+
+Files can be GGUF or compatible regular text-encoder checkpoints. Select the
+same pair and model type normally used by the equivalent built-in Dual CLIP
+loader.
+
+## Usage
+
+Connect the output to the workflow's text-encoding nodes. Both encoders must
+match the selected model family.
diff --git a/web/docs/QuadrupleCLIPLoaderGGUF.md b/web/docs/QuadrupleCLIPLoaderGGUF.md
new file mode 100644
index 0000000..eb84fa6
--- /dev/null
+++ b/web/docs/QuadrupleCLIPLoaderGGUF.md
@@ -0,0 +1,18 @@
+# Quadruple CLIP Loader (GGUF)
+
+Loads four text encoder files and returns one combined ComfyUI `CLIP` object.
+
+## Parameters
+
+- **clip_name1**
+- **clip_name2**
+- **clip_name3**
+- **clip_name4**
+
+Each input accepts a GGUF text encoder or a compatible regular checkpoint.
+The default model type is `stable_diffusion`.
+
+## Usage
+
+Use this node only for workflows whose model family requires four text
+encoders. Connect **CLIP** to the workflow's normal text-encoding node.
diff --git a/web/docs/TripleCLIPLoaderGGUF.md b/web/docs/TripleCLIPLoaderGGUF.md
new file mode 100644
index 0000000..e229f3c
--- /dev/null
+++ b/web/docs/TripleCLIPLoaderGGUF.md
@@ -0,0 +1,17 @@
+# Triple CLIP Loader (GGUF)
+
+Loads three text encoder files and returns one combined ComfyUI `CLIP` object.
+
+## Parameters
+
+- **clip_name1**
+- **clip_name2**
+- **clip_name3**
+
+Each input accepts a GGUF text encoder or a compatible regular checkpoint.
+This node uses the `sd3` model type by default.
+
+## Usage
+
+Use this node where an SD3-style workflow requires three text encoders. Connect
+the resulting **CLIP** output to the normal text-encoding node.
diff --git a/web/docs/UnetLoaderGGUF.md b/web/docs/UnetLoaderGGUF.md
new file mode 100644
index 0000000..41e73f2
--- /dev/null
+++ b/web/docs/UnetLoaderGGUF.md
@@ -0,0 +1,20 @@
+# Unet Loader (GGUF)
+
+Loads a diffusion model stored in GGUF format and returns a ComfyUI `MODEL`.
+
+Place GGUF diffusion models in `ComfyUI/models/unet` or
+`ComfyUI/models/diffusion_models`, then select the file with **unet_name**.
+
+## Q8_CR models
+
+`Q8_CR` GGUFs use ComfyUI's native INT8 ConvRot path. Eligible Linear weights
+remain INT8 during inference; 1-D, small, and designated high-precision tensors
+are retained in FP32, while convolution weights remain FP16.
+
+CUDA is optional. CUDA uses the optimized ComfyUI backend when available;
+non-CUDA environments use the eager backend and are slower.
+
+## Usage
+
+Connect **MODEL** to a sampler such as `KSampler`. Use the normal model-family
+workflow inputs, including the matching text encoder and VAE.
\ No newline at end of file
diff --git a/web/docs/UnetLoaderGGUFAdvanced.md b/web/docs/UnetLoaderGGUFAdvanced.md
new file mode 100644
index 0000000..de2beb1
--- /dev/null
+++ b/web/docs/UnetLoaderGGUFAdvanced.md
@@ -0,0 +1,26 @@
+# Unet Loader (GGUF/Advanced)
+
+Loads a GGUF diffusion model with the same behavior as **Unet Loader (GGUF)**,
+with additional controls for dequantization and LoRA patch handling.
+
+## Parameters
+
+- **unet_name**: GGUF diffusion model in `models/unet` or
+  `models/diffusion_models`.
+- **dequant_dtype**: Dtype used when a conventional GGUF quant needs
+  dequantization. Use **default** unless troubleshooting a model.
+- **patch_dtype**: Dtype used when applying weight patches such as LoRAs. Use
+  **default** unless a specific workflow requires another dtype.
+- **patch_on_device**: Applies patches on the model load device. Leave this off
+  for the usual lower-VRAM behavior.
+
+## Q8_CR
+
+For `Q8_CR` files, the loader selects ComfyUI's native INT8 ConvRot path.
+The advanced dtype controls do not convert native INT8 weights to floating
+point.
+
+## Usage
+
+Connect **MODEL** to the same downstream nodes as the standard GGUF UNet loader.
+Start with all advanced options set to **default**.

From bcaa7545512a53c2c6f7b0729c1686cd51e06ce5 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Wed, 29 Jul 2026 23:23:23 +0200
Subject: [PATCH 10/20] Add Dynamic VRAM support for GGUF loaders

Add DynamicVRAM-aware GGUF loading and node variants. loader.py: attach mmap-backed file slices, preserve custom GGUF quant configs, and switch to dynamic handling (including BF16/quant paths). nodes.py: add DynamicVRAM loaders and GGUFModelPatcherDynamic, plus helpers to load Unet/CLIP via DynamicVRAM. quant_ops.py: implement GGML quantized tensor layout for Comfy's QuantizedTensor system. tools/convert.py: preserve safetensors metadata, introduce keys_noquant, and avoid quantizing specified small tensors. Add documentation pages for the new Dynamic VRAM loader nodes.
---
 loader.py                                     |  96 +++++++--
 nodes.py                                      | 183 ++++++++++++++++++
 quant_ops.py                                  |  70 +++++++
 tools/convert.py                              |  36 +++-
 web/docs/CLIPLoaderGGUFDynamicVRAM.md         |   5 +
 web/docs/DualCLIPLoaderGGUFDynamicVRAM.md     |   5 +
 .../QuadrupleCLIPLoaderGGUFDynamicVRAM.md     |   5 +
 web/docs/TripleCLIPLoaderGGUFDynamicVRAM.md   |   5 +
 web/docs/UnetLoaderGGUFDynamicVRAM.md         |   7 +
 9 files changed, 398 insertions(+), 14 deletions(-)
 create mode 100644 quant_ops.py
 create mode 100644 web/docs/CLIPLoaderGGUFDynamicVRAM.md
 create mode 100644 web/docs/DualCLIPLoaderGGUFDynamicVRAM.md
 create mode 100644 web/docs/QuadrupleCLIPLoaderGGUFDynamicVRAM.md
 create mode 100644 web/docs/TripleCLIPLoaderGGUFDynamicVRAM.md
 create mode 100644 web/docs/UnetLoaderGGUFDynamicVRAM.md

diff --git a/loader.py b/loader.py
index e23fef6..5dd8d8f 100644
--- a/loader.py
+++ b/loader.py
@@ -6,8 +6,11 @@
 import json
 import re
 import os
+import threading
+import comfy.memory_management
 from .ops import GGMLTensor
 from .dequant import is_quantized, dequantize_tensor
+from .quant_ops import make_quantized
 
 IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram", "krea2"}
 TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}
@@ -26,6 +29,32 @@ def device_supports_bf16():
         # If support can't be determined, keep the previous bf16 behavior.
         return True
 
+
+def dynamic_gguf_file_slice(path):
+    """
+    Create the file handle metadata used by DynamicVRAM to transfer a GGUF
+    tensor directly from its mmap-backed file to GPU memory.
+    """
+    if not comfy.memory_management.aimdo_enabled:
+        return None
+
+    import comfy_aimdo.model_mmap
+
+    model_mmap = comfy_aimdo.model_mmap.ModelMMAP(path)
+    return model_mmap, threading.Lock()
+
+
+def attach_dynamic_file_slice(torch_tensor, model_mmap, file_lock, offset, size):
+    storage = torch_tensor.untyped_storage()
+    storage._comfy_tensor_file_slice = comfy.memory_management.TensorFileSlice(
+        model_mmap.get_file_handle(),
+        file_lock,
+        offset,
+        size,
+    )
+    # Keep the native mmap alive for the storage lifetime.
+    storage._comfy_tensor_mmap_refs = (model_mmap,)
+
 def get_orig_shape(reader, tensor_name):
     field_key = f"comfy.gguf.orig_shape.{tensor_name}"
     field = reader.get_field(field_key)
@@ -80,11 +109,12 @@ def get_gguf_metadata(reader):
             continue
     return metadata
 
-def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=False):
+def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=False, dynamic=False):
     """
     Read state dict as fake tensors
     """
     reader = gguf.GGUFReader(path)
+    dynamic_file_slice = dynamic_gguf_file_slice(path) if dynamic else None
 
     # filter and strip prefix
     has_prefix = False
@@ -125,6 +155,22 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
     if compat:
         logging.warning(f"Warning: This gguf model file is loaded in compatibility mode '{compat}' [arch:{arch_str}]")
 
+    # Q8_CR weights must use ComfyUI's native TensorWiseINT8Layout rather than
+    # the generic GGML layout so DynamicVRAM retains native INT8 ConvRot kernels.
+    custom_quant_configs = {}
+    for field_name in reader.fields:
+        if field_name.startswith("comfy.gguf.quant."):
+            key = field_name[len("comfy.gguf.quant."):]
+            field = reader.get_field(field_name)
+            custom_quant_configs[key] = json.loads(str(field.parts[field.data[-1]], "utf-8"))
+
+    custom_quant_tensor_names = {
+        tensor_name
+        for key, quant_conf in custom_quant_configs.items()
+        if quant_conf.get("format") == "int8_tensorwise"
+        for tensor_name in (key, f"{key}_scale")
+    }
+
     # main loading loop
     # Devices without native bf16 fall back to slow fp32 compute, so load the
     # full-precision BF16 storage tensors as fp16 there instead.
@@ -139,6 +185,15 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", message="The given NumPy array is not writable")
             torch_tensor = torch.from_numpy(tensor.data) # mmap
+        if dynamic_file_slice is not None:
+            model_mmap, file_lock = dynamic_file_slice
+            attach_dynamic_file_slice(
+                torch_tensor,
+                model_mmap,
+                file_lock,
+                tensor.data_offset,
+                tensor.n_bytes,
+            )
 
         shape = get_orig_shape(reader, tensor_name)
         if shape is None:
@@ -150,12 +205,26 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
                         shape = shape[:-1]
 
         # add to state dict
-        if tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
+        if dynamic and sd_key not in custom_quant_tensor_names:
+            if tensor.tensor_type in {
+                gguf.GGMLQuantizationType.F32,
+                gguf.GGMLQuantizationType.F16,
+            }:
+                state_dict[sd_key] = torch_tensor.view(*shape)
+            elif tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
+                state_dict[sd_key] = torch_tensor.view(torch.bfloat16).reshape(shape).to(
+                    dtype=torch.float32 if len(shape) <= 1 else bf16_storage_dtype,
+                )
+            else:
+                state_dict[sd_key] = make_quantized(torch_tensor, tensor.tensor_type, shape)
+        elif tensor.tensor_type in {gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16}:
             torch_tensor = torch_tensor.view(*shape)
-        state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
+            state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
+        else:
+            state_dict[sd_key] = GGMLTensor(torch_tensor, tensor_type=tensor.tensor_type, tensor_shape=shape)
 
         # BF16 GGUF tensors are full-precision storage, not compressed quants.
-        if tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
+        if not dynamic and tensor.tensor_type == gguf.GGMLQuantizationType.BF16:
             dtype = torch.float32 if len(shape) <= 1 else bf16_storage_dtype
             state_dict[sd_key] = dequantize_tensor(state_dict[sd_key], dtype=dtype)
 
@@ -185,7 +254,7 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
             continue
         key = field_name[len("comfy.gguf.quant."):]
         field = reader.get_field(field_name)
-        quant_conf = json.loads(str(field.parts[field.data[-1]], "utf-8"))
+        quant_conf = custom_quant_configs[key]
         fmt = quant_conf.get("format")
 
         if fmt in {"int4_compact_gemm", "int4_pytorch"}:
@@ -215,20 +284,25 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
                 quant_conf.pop("convrot_groupsize", None)
             elif quant_conf.get("convrot"):
                 groupsize = quant_conf.get("convrot_groupsize", 256)
-                if weight_ggml.tensor_shape[-1] % groupsize != 0:
+                weight_shape = weight_ggml.shape if dynamic else weight_ggml.tensor_shape
+                if weight_shape[-1] % groupsize != 0:
                     logging.warning(
                         "Disabling ConvRot for %s because %d input features are not "
                         "divisible by group size %d.",
                         weight_key,
-                        weight_ggml.tensor_shape[-1],
+                        weight_shape[-1],
                         groupsize,
                     )
                     quant_conf["convrot"] = False
                     quant_conf.pop("convrot_groupsize", None)
 
             # Convert to ComfyUI native state-dict layout
-            weight = weight_ggml.data.view(torch.int8).reshape(weight_ggml.tensor_shape)
-            scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
+            if dynamic:
+                weight = weight_ggml.view(torch.int8).reshape(weight_ggml.shape)
+                scale = scale_ggml.view(torch.float32).reshape(scale_ggml.shape)
+            else:
+                weight = weight_ggml.data.view(torch.int8).reshape(weight_ggml.tensor_shape)
+                scale = scale_ggml.data.view(torch.float32).reshape(scale_ggml.tensor_shape)
 
             state_dict[weight_key] = torch.nn.Parameter(weight, requires_grad=False)
             state_dict[scale_key] = torch.nn.Parameter(scale, requires_grad=False)
@@ -571,8 +645,8 @@ def gguf_gemma3_tokenizer_loader(path):
     del reader
     return torch.ByteTensor(list(spm.SerializeToString()))
 
-def gguf_clip_loader(path):
-    sd, extra = gguf_sd_loader(path, is_text_model=True)
+def gguf_clip_loader(path, dynamic=False):
+    sd, extra = gguf_sd_loader(path, is_text_model=True, dynamic=dynamic)
     arch = extra.get("arch_str", None)
     if arch in {"t5", "t5encoder"}:
         temb_key = "token_embd.weight"
diff --git a/nodes.py b/nodes.py
index ce30888..0c672d7 100644
--- a/nodes.py
+++ b/nodes.py
@@ -11,6 +11,7 @@
 import comfy.utils
 import comfy.model_patcher
 import comfy.model_management
+import comfy.memory_management
 import folder_paths
 
 from .ops import GGMLOps, get_gguf_q8_ops, move_patch_to_device
@@ -194,6 +195,95 @@ def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_de
         model.patch_on_device = patch_on_device
         return (model,)
 
+
+def _require_dynamic_vram():
+    if not comfy.memory_management.aimdo_enabled:
+        raise RuntimeError(
+            "Dynamic VRAM is not enabled in this ComfyUI installation. "
+            "Start ComfyUI without --disable-dynamic-vram and use a build that supports DynamicVRAM."
+        )
+
+
+def _clone_as_dynamic_gguf_patcher(model_patcher):
+    source_class = model_patcher.__class__
+    model_patcher.__class__ = GGUFModelPatcherDynamic
+    cloned = model_patcher.clone()
+    model_patcher.__class__ = source_class
+    return cloned
+
+
+def _legacy_gguf_ops(extra):
+    mode = extra.get("gguf_quant_mode")
+    if mode == "int8_convrot":
+        return get_gguf_q8_ops(compute_dtype=torch.bfloat16)()
+    if mode == "int4_pytorch":
+        raise RuntimeError(
+            "Q4_PT is retired because PyTorch's Ampere INT4 kernel is not "
+            "performance-competitive. Reconvert the model as Q8_CR."
+        )
+    return GGMLOps()
+
+
+def _load_dynamic_gguf_unet(unet_path, disable_dynamic=False):
+    if not disable_dynamic:
+        _require_dynamic_vram()
+    sd, extra = gguf_sd_loader(unet_path, dynamic=not disable_dynamic)
+
+    kwargs = {}
+    valid_params = inspect.signature(comfy.sd.load_diffusion_model_state_dict).parameters
+    if "metadata" in valid_params:
+        kwargs["metadata"] = extra.get("metadata", {})
+
+    model_options = {} if not disable_dynamic else {
+        "custom_operations": _legacy_gguf_ops(extra),
+    }
+    model = comfy.sd.load_diffusion_model_state_dict(
+        sd,
+        model_options=model_options,
+        disable_dynamic=disable_dynamic,
+        **kwargs,
+    )
+    if model is None:
+        logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
+        raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
+    model = GGUFModelPatcher.clone(model) if disable_dynamic else _clone_as_dynamic_gguf_patcher(model)
+    model.cached_patcher_init = (_load_dynamic_gguf_unet, (unet_path,))
+    return model
+
+
+class GGUFModelPatcherDynamic(comfy.model_patcher.ModelPatcherDynamic):
+    def load(self, *args, **kwargs):
+        super().load(*args, **kwargs)
+        # GGML weights cannot be requantized after applying a LoRA patch.
+        for _, module in self.model.named_modules():
+            for param_key in ("weight", "bias"):
+                attr = f"{param_key}_lowvram_function"
+                lowvram_function = getattr(module, attr, None)
+                if lowvram_function is not None:
+                    setattr(module, attr, None)
+                    functions = getattr(module, f"{param_key}_function", [])
+                    functions.append(lowvram_function)
+                    setattr(module, f"{param_key}_function", functions)
+
+    def clone(self, disable_dynamic=False, model_override=None):
+        if disable_dynamic:
+            if model_override is None:
+                fallback = self.cached_patcher_init[0](
+                    *self.cached_patcher_init[1],
+                    disable_dynamic=True,
+                )
+                model_override = fallback.get_clone_model_override()
+            return GGUFModelPatcher.clone(self, model_override=model_override)
+        return super().clone(disable_dynamic=disable_dynamic, model_override=model_override)
+
+
+class UnetLoaderGGUFDynamicVRAM(UnetLoaderGGUF):
+    TITLE = "Unet Loader (Dynamic VRAM)"
+
+    def load_unet(self, unet_name, **kwargs):
+        unet_path = folder_paths.get_full_path("unet", unet_name)
+        return (_load_dynamic_gguf_unet(unet_path),)
+
 class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
     @classmethod
     def INPUT_TYPES(s):
@@ -261,6 +351,55 @@ def load_clip(self, clip_name, type="stable_diffusion"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
         return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),)
 
+
+def _load_dynamic_gguf_clip(clip_paths, clip_type, disable_dynamic=False):
+    if not disable_dynamic:
+        _require_dynamic_vram()
+    clip_data = []
+    for path in clip_paths:
+        if path.endswith(".gguf"):
+            clip_data.append(gguf_clip_loader(path, dynamic=not disable_dynamic))
+        else:
+            clip_data.append(comfy.utils.load_torch_file(path, safe_load=True))
+
+    model_options = {
+        "initial_device": comfy.model_management.text_encoder_offload_device(),
+    }
+    if disable_dynamic:
+        model_options["custom_operations"] = GGMLOps
+
+    clip = comfy.sd.load_text_encoder_state_dicts(
+        clip_type=clip_type,
+        state_dicts=clip_data,
+        model_options=model_options,
+        embedding_directory=folder_paths.get_folder_paths("embeddings"),
+        disable_dynamic=disable_dynamic,
+    )
+    clip.patcher = (
+        GGUFModelPatcher.clone(clip.patcher)
+        if disable_dynamic
+        else _clone_as_dynamic_gguf_patcher(clip.patcher)
+    )
+    clip.patcher.cached_patcher_init = (_load_dynamic_gguf_clip_patcher, (clip_paths, clip_type))
+    return clip
+
+
+def _load_dynamic_gguf_clip_patcher(clip_paths, clip_type, disable_dynamic=False):
+    return _load_dynamic_gguf_clip(
+        clip_paths,
+        clip_type,
+        disable_dynamic=disable_dynamic,
+    ).patcher
+
+
+class CLIPLoaderGGUFDynamicVRAM(CLIPLoaderGGUF):
+    TITLE = "CLIPLoader (Dynamic VRAM)"
+
+    def load_clip(self, clip_name, type="stable_diffusion"):
+        clip_path = folder_paths.get_full_path("clip", clip_name)
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (_load_dynamic_gguf_clip([clip_path], clip_type),)
+
 class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
     @classmethod
     def INPUT_TYPES(s):
@@ -283,6 +422,18 @@ def load_clip(self, clip_name1, clip_name2, type):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
         return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
 
+
+class DualCLIPLoaderGGUFDynamicVRAM(DualCLIPLoaderGGUF):
+    TITLE = "DualCLIPLoader (Dynamic VRAM)"
+
+    def load_clip(self, clip_name1, clip_name2, type):
+        clip_paths = (
+            folder_paths.get_full_path("clip", clip_name1),
+            folder_paths.get_full_path("clip", clip_name2),
+        )
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (_load_dynamic_gguf_clip(clip_paths, clip_type),)
+
 class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
     @classmethod
     def INPUT_TYPES(s):
@@ -305,6 +456,19 @@ def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
         return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
 
+
+class TripleCLIPLoaderGGUFDynamicVRAM(TripleCLIPLoaderGGUF):
+    TITLE = "TripleCLIPLoader (Dynamic VRAM)"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
+        clip_paths = (
+            folder_paths.get_full_path("clip", clip_name1),
+            folder_paths.get_full_path("clip", clip_name2),
+            folder_paths.get_full_path("clip", clip_name3),
+        )
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (_load_dynamic_gguf_clip(clip_paths, clip_type),)
+
 class QuadrupleCLIPLoaderGGUF(CLIPLoaderGGUF):
     @classmethod
     def INPUT_TYPES(s):
@@ -329,6 +493,20 @@ def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
         return (self.load_patcher(clip_paths, clip_type, self.load_data(clip_paths)),)
 
+
+class QuadrupleCLIPLoaderGGUFDynamicVRAM(QuadrupleCLIPLoaderGGUF):
+    TITLE = "QuadrupleCLIPLoader (Dynamic VRAM)"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable_diffusion"):
+        clip_paths = (
+            folder_paths.get_full_path("clip", clip_name1),
+            folder_paths.get_full_path("clip", clip_name2),
+            folder_paths.get_full_path("clip", clip_name3),
+            folder_paths.get_full_path("clip", clip_name4),
+        )
+        clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
+        return (_load_dynamic_gguf_clip(clip_paths, clip_type),)
+
 NODE_CLASS_MAPPINGS = {
     "UnetLoaderGGUF": UnetLoaderGGUF,
     "CLIPLoaderGGUF": CLIPLoaderGGUF,
@@ -336,4 +514,9 @@ def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable
     "TripleCLIPLoaderGGUF": TripleCLIPLoaderGGUF,
     "QuadrupleCLIPLoaderGGUF": QuadrupleCLIPLoaderGGUF,
     "UnetLoaderGGUFAdvanced": UnetLoaderGGUFAdvanced,
+    "UnetLoaderGGUFDynamicVRAM": UnetLoaderGGUFDynamicVRAM,
+    "CLIPLoaderGGUFDynamicVRAM": CLIPLoaderGGUFDynamicVRAM,
+    "DualCLIPLoaderGGUFDynamicVRAM": DualCLIPLoaderGGUFDynamicVRAM,
+    "TripleCLIPLoaderGGUFDynamicVRAM": TripleCLIPLoaderGGUFDynamicVRAM,
+    "QuadrupleCLIPLoaderGGUFDynamicVRAM": QuadrupleCLIPLoaderGGUFDynamicVRAM,
 }
diff --git a/quant_ops.py b/quant_ops.py
new file mode 100644
index 0000000..80d1adb
--- /dev/null
+++ b/quant_ops.py
@@ -0,0 +1,70 @@
+# GGML QuantizedTensor support for ComfyUI DynamicVRAM loading.
+from dataclasses import dataclass
+
+import gguf
+import torch
+
+from comfy_kitchen.tensor import (
+    BaseLayoutParams,
+    QuantizedLayout,
+    QuantizedTensor,
+    register_layout_class,
+)
+
+from .dequant import TORCH_COMPATIBLE_QTYPES, dequantize_functions
+
+
+@dataclass(frozen=True)
+class GGMLLayoutParams(BaseLayoutParams):
+    tensor_type: int
+
+
+class GGMLLayout(QuantizedLayout):
+    Params = GGMLLayoutParams
+
+    @classmethod
+    def quantize(cls, tensor, **kwargs):
+        raise NotImplementedError("Quantization to GGML format is not supported")
+
+    @classmethod
+    def dequantize(cls, qdata, params):
+        qtype = gguf.GGMLQuantizationType(params.tensor_type)
+        orig_shape = params.orig_shape
+
+        if qtype in TORCH_COMPATIBLE_QTYPES:
+            return qdata.reshape(orig_shape).to(params.orig_dtype)
+
+        if qtype not in dequantize_functions:
+            dequantized = gguf.quants.dequantize(qdata.cpu().numpy(), qtype)
+            return torch.from_numpy(dequantized).reshape(orig_shape).to(
+                device=qdata.device,
+                dtype=params.orig_dtype,
+            )
+
+        _, type_size = gguf.GGML_QUANT_SIZES[qtype]
+        raw = qdata.reshape(-1).view(torch.uint8)
+        blocks = raw.reshape((raw.numel() // type_size, type_size))
+        return dequantize_functions[qtype](blocks, *gguf.GGML_QUANT_SIZES[qtype], None).reshape(
+            orig_shape
+        ).to(params.orig_dtype)
+
+    @classmethod
+    def get_plain_tensors(cls, qtensor):
+        return (qtensor._qdata,)
+
+    @classmethod
+    def state_dict_tensors(cls, qdata, params):
+        return {"weight": qdata}
+
+
+register_layout_class("GGMLLayout", GGMLLayout)
+
+
+def make_quantized(qdata, tensor_type, tensor_shape, orig_dtype=torch.float16):
+    params = GGMLLayoutParams(
+        scale=torch.ones((), dtype=torch.float32),
+        orig_dtype=orig_dtype,
+        orig_shape=tuple(tensor_shape),
+        tensor_type=tensor_type.value if not isinstance(tensor_type, int) else tensor_type,
+    )
+    return QuantizedTensor(qdata, "GGMLLayout", params)
diff --git a/tools/convert.py b/tools/convert.py
index 2bd2993..96d07ca 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -6,6 +6,7 @@
 import logging
 import argparse
 from tqdm import tqdm
+from safetensors import safe_open
 from safetensors.torch import load_file, save_file
 
 QUANTIZATION_THRESHOLD = 1024
@@ -19,6 +20,7 @@ class ModelTemplate:
     keys_detect = []  # list of lists to match in state dict
     keys_banned = []  # list of keys that should mark model as invalid for conversion
     keys_hiprec = []  # list of keys that need to be kept in fp32 for some reason
+    keys_noquant = [] # list of keys that must retain their source precision
     keys_ignore = []  # list of strings to ignore keys by when found
 
     def handle_nd_tensor(self, key, data):
@@ -111,10 +113,27 @@ class ModelLTXV(ModelTemplate):
             "adaln_single.emb.timestep_embedder.linear_2.weight",
             "transformer_blocks.27.scale_shift_table",
             "caption_projection.linear_2.weight",
-        )
+        ),
+        # LTX 2.3 audio-video checkpoints replace the video-only caption
+        # projection with audio/video connector modules.
+        (
+            "adaln_single.emb.timestep_embedder.linear_2.weight",
+            "transformer_blocks.27.scale_shift_table",
+            "audio_adaln_single.linear.weight",
+        ),
     ]
     keys_hiprec = [
-        "scale_shift_table" # nn.parameter, can't load from BF16 base quant
+        "scale_shift_table", # nn.Parameter, can't load from BF16 base quant
+        "learnable_registers", # Connector nn.Parameter, not a Linear weight
+    ]
+    # LTX's native INT8 ConvRot checkpoints intentionally leave these
+    # projections in BF16. The many 32-output gate logits are particularly
+    # small, so ConvRot dispatch overhead outweighs their INT8 benefit.
+    keys_noquant = [
+        "adaln_single",
+        "patchify_proj",
+        "proj_out",
+        "to_gate_logits",
     ]
 
 class ModelSDXL(ModelTemplate):
@@ -373,6 +392,12 @@ def load_state_dict(path):
 
     return strip_prefix(state_dict)
 
+def load_safetensors_metadata(path):
+    if not path.endswith(".safetensors"):
+        return {}
+    with safe_open(path, framework="pt", device="cpu") as checkpoint:
+        return checkpoint.metadata() or {}
+
 def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_name=None):
     # Pre-collect per-tensor FP8 scales (0-dim float32 tensors named "{key}_scale").
     # These must be applied to their FP8 weight tensors before GGUF quantization.
@@ -452,7 +477,9 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
             or old_dtype in _FP8_DTYPES
         )
         if apply_quantization_rules:
-            if n_dims == 1:
+            if any(x in key for x in model_arch.keys_noquant):
+                pass
+            elif n_dims == 1:
                 # One-dimensional tensors should be kept in F32.
                 data_qtype = gguf.GGMLQuantizationType.F32
             elif n_params <= QUANTIZATION_THRESHOLD:
@@ -509,6 +536,7 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
 def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type_name=None):
     # load & run model detection logic
     state_dict = load_state_dict(path)
+    source_metadata = load_safetensors_metadata(path)
     model_arch = detect_arch(state_dict)
     logging.info(f"* Architecture detected from input: {model_arch.arch}")
 
@@ -549,6 +577,8 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type
     writer.add_quantization_version(gguf.GGML_QUANT_VERSION)
     if ftype_gguf is not None:
         writer.add_file_type(ftype_gguf)
+    if "config" in source_metadata:
+        writer.add_string("config", source_metadata["config"])
 
     handle_tensors(writer, state_dict, model_arch, quant_type=quant_type, quant_type_name=quant_type_name)
     writer.write_header_to_file(path=dst_path)
diff --git a/web/docs/CLIPLoaderGGUFDynamicVRAM.md b/web/docs/CLIPLoaderGGUFDynamicVRAM.md
new file mode 100644
index 0000000..6bf6bbe
--- /dev/null
+++ b/web/docs/CLIPLoaderGGUFDynamicVRAM.md
@@ -0,0 +1,5 @@
+# CLIPLoader (Dynamic VRAM)
+
+Loads one text encoder through ComfyUI's DynamicVRAM path.
+
+Use this node when DynamicVRAM is enabled and the text encoder benefits from demand loading. It accepts GGUF and regular text-encoder files.
diff --git a/web/docs/DualCLIPLoaderGGUFDynamicVRAM.md b/web/docs/DualCLIPLoaderGGUFDynamicVRAM.md
new file mode 100644
index 0000000..db6baaa
--- /dev/null
+++ b/web/docs/DualCLIPLoaderGGUFDynamicVRAM.md
@@ -0,0 +1,5 @@
+# DualCLIPLoader (Dynamic VRAM)
+
+Loads two text encoders through ComfyUI's DynamicVRAM path.
+
+Use this node when DynamicVRAM is enabled and your workflow requires two CLIP/text-encoder inputs.
diff --git a/web/docs/QuadrupleCLIPLoaderGGUFDynamicVRAM.md b/web/docs/QuadrupleCLIPLoaderGGUFDynamicVRAM.md
new file mode 100644
index 0000000..1fd2a8c
--- /dev/null
+++ b/web/docs/QuadrupleCLIPLoaderGGUFDynamicVRAM.md
@@ -0,0 +1,5 @@
+# QuadrupleCLIPLoader (Dynamic VRAM)
+
+Loads four text encoders through ComfyUI's DynamicVRAM path.
+
+Use this node when DynamicVRAM is enabled and your workflow requires four CLIP/text-encoder inputs.
diff --git a/web/docs/TripleCLIPLoaderGGUFDynamicVRAM.md b/web/docs/TripleCLIPLoaderGGUFDynamicVRAM.md
new file mode 100644
index 0000000..c946c26
--- /dev/null
+++ b/web/docs/TripleCLIPLoaderGGUFDynamicVRAM.md
@@ -0,0 +1,5 @@
+# TripleCLIPLoader (Dynamic VRAM)
+
+Loads three text encoders through ComfyUI's DynamicVRAM path.
+
+Use this node when DynamicVRAM is enabled and your workflow requires three CLIP/text-encoder inputs.
diff --git a/web/docs/UnetLoaderGGUFDynamicVRAM.md b/web/docs/UnetLoaderGGUFDynamicVRAM.md
new file mode 100644
index 0000000..042b97b
--- /dev/null
+++ b/web/docs/UnetLoaderGGUFDynamicVRAM.md
@@ -0,0 +1,7 @@
+# Unet Loader (Dynamic VRAM)
+
+Loads a GGUF diffusion model through ComfyUI's DynamicVRAM path.
+
+DynamicVRAM keeps quantized GGUF weights in host memory and loads them on demand. It requires a ComfyUI build with DynamicVRAM enabled; use the regular GGUF loader when DynamicVRAM is unavailable.
+
+For Q8_CR models, supported linear layers retain ComfyUI's native INT8 ConvRot layout.

From 340da950c69aa094c9921d5a2d2e625c3466de61 Mon Sep 17 00:00:00 2001
From: boessu <boessu@users.noreply.github.com>
Date: Fri, 31 Jul 2026 18:19:15 +0200
Subject: [PATCH 11/20] Update tensor handling for Krea2 architecture

## `lcpp.patch` changes (llama.cpp)
All additions follow the existing pattern used for the other image-model architectures (Flux, SD3, Aura, LTXV, HyVid, Wan, HiDream, Cosmos, Lumina2) --five/six standard insertion points plus two architecture-specific refinements (one a required correctness fix, one an optional quality tuning knob).

### Standard architecture registration (5 places in `src/llama.cpp`)

1. **`enum llm_arch`**: add `LLM_ARCH_KREA2,`
2. **`LLM_ARCH_NAMES` map**: add `{ LLM_ARCH_KREA2, "krea2" },`
3. **`LLM_TENSOR_NAMES` map**: add `{ LLM_ARCH_KREA2, {}},`
4. **`llm_load_hparams`**, "disable LLM metadata for image models" switch: add
   `case LLM_ARCH_KREA2:` alongside the other image archs (skips LLM-style
   hparam parsing, since this isn't a language model).
5. **`llama_model_quantize_internal`**, "rules for image models" section: add
   a `LLM_ARCH_KREA2` block excluding the small/non-repeating tensors from
   quantization (kept at their original F32/F16 precision):

```cpp
if (model.arch == LLM_ARCH_KREA2) {
    image_model = true;
    quantize &= name.find("first.") == std::string::npos;
    quantize &= name.find("last.") == std::string::npos;
    quantize &= name.find("tproj.") == std::string::npos;
    quantize &= name.find("tmlp.") == std::string::npos;
    quantize &= name.find("txtmlp.") == std::string::npos;
    quantize &= name.find("txtfusion.projector.") == std::string::npos;
}
```

### The one architecture-specific fix: `txtfusion.projector.weight`

**Symptom:** ComfyUI failed to load the *quantized* GGUF (worked fine for the
BF16 intermediate file) with:

```
File ".../comfy/model_detection.py", line 917, in detect_unet_config
    dit_config["txtlayers"] = state_dict['{}txtfusion.projector.weight'.format(key_prefix)].shape[1]
IndexError: tuple index out of range
```

**Root cause:** `txtfusion.projector.weight` has shape `(1, 12)` in the original checkpoint. When `llama-quantize` loads this into an actual `ggml_tensor` and writes it back out via `gguf_add_tensor()`, the tensor's dimensionality is derived from `ggml_n_dims()`, which scans the `ne[]` array from the highest index downward and drops trailing 1s. Since GGML's `ne` order is the reverse of the original torch shape, `(1, 12)` becomes `ne = [12, 1, 1, 1]` -- and `ne[1] == 1` gets trimmed away, collapsing the
tensor to 1D. The BF16 GGUF written directly by `convert.py`'s Python `gguf` library does *not* have this problem (it just serializes the given shape faithfully); the collapse is specific to the C++ `llama-quantize` step.

This is the same class of bug already handled for other architectures (SD3's `pos_embed`, AuraFlow's `positional_encoding`/`register_tokens`, Wan's `.modulation`/`img_emb.emb_pos`), all of which use a helper added to GGML for exactly this purpose: `gguf_set_tensor_ndim()`.

**Fix**, added to the same tensor-writing loop in `llama_model_quantize_internal` where the other archs' fixes live (right after the `LLM_ARCH_WAN` block):

```cpp
// Krea2's txtfusion.projector.weight has shape (1, 12) -- the leading
// dim of 1 becomes a trailing `ne` entry and gets truncated by
// ggml_n_dims() unless corrected explicitly.
if (model.arch == LLM_ARCH_KREA2) {
    const std::string name = ggml_get_name(tensor);
    if (name == "txtfusion.projector.weight" && tensor->ne[1] == 1) {
        const int n_dim = 2;
        gguf_set_tensor_ndim(ctx_outs[i_split], "txtfusion.projector.weight", n_dim);
        LLAMA_LOG_INFO("\n%s: Correcting txtfusion.projector.weight shape for Krea2: [key:%s]\n", __func__, tensor->name);
    }
}
```

Note the difference from the SD3/Aura/Wan precedents: those check `tensor->ne[2] == 1` and restore `n_dim = 3` because their tensors were originally 3D (e.g. `(1, H, W)`). Krea2's `txtfusion.projector.weight` is only 2D (`(1, 12)`), so the check is `tensor->ne[1] == 1` and the restored dimension is `n_dim = 2`.

**No other Krea2 tensors needed this fix.** All other tensors are either purely 1D, or 2D with no leading-1 dimension (verified against actual shapes pulled from the checkpoint).

### Optional refinement: `to_v` attention quant-type rules

llama.cpp's `img_tensor_get_type()` has a block of special-case rules that give the attention value projection ("to_v") preferential K-quant subtype treatment at certain `ftype`s, already applied to the other image archs (Flux, SD3, etc. -- matched via names like `.to_v.weight`, `.attn.w1v.weight`, `.attn.w2v.weight`). Krea2's equivalent tensor, `.attn.wv.weight`, wasn't originally in that match list. Added:

```cpp
if ( // Rules for to_v attention
        (name.find("attn_v.weight") != std::string::npos) ||
        (name.find(".to_v.weight") != std::string::npos) ||
        (name.find(".v.weight") != std::string::npos) ||
        (name.find(".attn.w1v.weight") != std::string::npos) ||
        (name.find(".attn.w2v.weight") != std::string::npos) ||
+       (name.find(".attn.wv.weight") != std::string::npos) ||
        (name.find("_attn.v_proj.weight") != std::string::npos)
    ){
```

Unlike the `txtfusion.projector.weight` fix above, this isn't a correctness issue -- without it, Krea2 quantizes and runs fine, `.attn.wv.weight` just gets the generic type-selection path instead of the to_v-specific one. It's a size/quality tuning knob, initially left out to keep the first working version minimal, then added and verified once the base support was confirmed stable.
---
 tools/lcpp.patch | 49 +++++++++++++++++++++++++++++++++++-------------
 1 file changed, 36 insertions(+), 13 deletions(-)

diff --git a/tools/lcpp.patch b/tools/lcpp.patch
index 92396e1..aad4c61 100644
--- a/tools/lcpp.patch
+++ b/tools/lcpp.patch
@@ -1,5 +1,5 @@
 diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
-index de3c706f..0267c1fa 100644
+index de3c706fc..0267c1faa 100644
 --- a/ggml/include/ggml.h
 +++ b/ggml/include/ggml.h
 @@ -223,7 +223,7 @@
@@ -20,7 +20,7 @@ index de3c706f..0267c1fa 100644
      GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
  
 diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
-index b16c462f..6d1568f1 100644
+index b16c462fa..6d1568f1b 100644
 --- a/ggml/src/ggml.c
 +++ b/ggml/src/ggml.c
 @@ -22960,6 +22960,14 @@ void gguf_add_tensor(
@@ -39,10 +39,10 @@ index b16c462f..6d1568f1 100644
      const int idx = gguf_find_tensor(ctx, name);
      if (idx < 0) {
 diff --git a/src/llama.cpp b/src/llama.cpp
-index 24e1f1f0..25db4c69 100644
+index 24e1f1f01..23df89523 100644
 --- a/src/llama.cpp
 +++ b/src/llama.cpp
-@@ -205,6 +205,17 @@ enum llm_arch {
+@@ -205,6 +205,18 @@ enum llm_arch {
      LLM_ARCH_GRANITE,
      LLM_ARCH_GRANITE_MOE,
      LLM_ARCH_CHAMELEON,
@@ -57,10 +57,11 @@ index 24e1f1f0..25db4c69 100644
 +    LLM_ARCH_HIDREAM,
 +    LLM_ARCH_COSMOS,
 +    LLM_ARCH_LUMINA2,
++    LLM_ARCH_KREA2,
      LLM_ARCH_UNKNOWN,
  };
  
-@@ -258,6 +269,17 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+@@ -258,6 +270,18 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
      { LLM_ARCH_GRANITE,         "granite"      },
      { LLM_ARCH_GRANITE_MOE,     "granitemoe"   },
      { LLM_ARCH_CHAMELEON,       "chameleon"    },
@@ -75,10 +76,11 @@ index 24e1f1f0..25db4c69 100644
 +    { LLM_ARCH_HIDREAM,         "hidream"      },
 +    { LLM_ARCH_COSMOS,          "cosmos"       },
 +    { LLM_ARCH_LUMINA2,         "lumina2"      },
++    { LLM_ARCH_KREA2,           "krea2"        },
      { LLM_ARCH_UNKNOWN,         "(unknown)"    },
  };
  
-@@ -1531,6 +1553,17 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
+@@ -1531,6 +1555,18 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
              { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
          },
      },
@@ -93,10 +95,11 @@ index 24e1f1f0..25db4c69 100644
 +    { LLM_ARCH_HIDREAM, {}},
 +    { LLM_ARCH_COSMOS,  {}},
 +    { LLM_ARCH_LUMINA2, {}},
++    { LLM_ARCH_KREA2,   {}},
      {
          LLM_ARCH_UNKNOWN,
          {
-@@ -5403,6 +5436,25 @@ static void llm_load_hparams(
+@@ -5403,6 +5439,26 @@ static void llm_load_hparams(
      // get general kv
      ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
  
@@ -113,6 +116,7 @@ index 24e1f1f0..25db4c69 100644
 +        case LLM_ARCH_HIDREAM:
 +        case LLM_ARCH_COSMOS:
 +        case LLM_ARCH_LUMINA2:
++        case LLM_ARCH_KREA2:
 +            model.ftype = ml.ftype;
 +            return;
 +        default:
@@ -122,7 +126,7 @@ index 24e1f1f0..25db4c69 100644
      // get hparams kv
      ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
  
-@@ -18016,6 +18068,134 @@ static void llama_tensor_dequantize_internal(
+@@ -18016,6 +18072,135 @@ static void llama_tensor_dequantize_internal(
      workers.clear();
  }
  
@@ -158,6 +162,7 @@ index 24e1f1f0..25db4c69 100644
 +            (name.find(".v.weight") != std::string::npos) ||
 +            (name.find(".attn.w1v.weight") != std::string::npos) ||
 +            (name.find(".attn.w2v.weight") != std::string::npos) ||
++            (name.find(".attn.wv.weight") != std::string::npos) ||
 +            (name.find("_attn.v_proj.weight") != std::string::npos)
 +        ){
 +            if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
@@ -257,7 +262,7 @@ index 24e1f1f0..25db4c69 100644
  static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
      const std::string name = ggml_get_name(tensor);
  
-@@ -18513,7 +18693,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+@@ -18513,7 +18698,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
          if (llama_model_has_encoder(&model)) {
              n_attn_layer *= 3;
          }
@@ -268,7 +273,7 @@ index 24e1f1f0..25db4c69 100644
      }
  
      size_t total_size_org = 0;
-@@ -18547,6 +18729,51 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+@@ -18547,6 +18734,60 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
              ctx_outs[i_split] = gguf_init_empty();
          }
          gguf_add_tensor(ctx_outs[i_split], tensor);
@@ -316,11 +321,20 @@ index 24e1f1f0..25db4c69 100644
 +                gguf_set_tensor_ndim(ctx_outs[i_split], tensor->name, n_dim);
 +                LLAMA_LOG_INFO("\n%s: Correcting shape for Wan FLF2V: [key:%s]\n", __func__, tensor->name);
 +            }
++        }
++        // Krea2's txtfusion.projector.weight has shape (1, 12) — leading dim of 1 gets truncated
++        if (model.arch == LLM_ARCH_KREA2) {
++            const std::string name = ggml_get_name(tensor);
++            if (name == "txtfusion.projector.weight" && tensor->ne[1] == 1) {
++                const int n_dim = 2;
++                gguf_set_tensor_ndim(ctx_outs[i_split], "txtfusion.projector.weight", n_dim);
++                LLAMA_LOG_INFO("\n%s: Correcting txtfusion.projector.weight shape for Krea2: [key:%s]\n", __func__, tensor->name);
++            }
 +        }
      }
  
      // Set split info if needed
-@@ -18647,6 +18874,110 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+@@ -18647,6 +18888,119 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
          // do not quantize relative position bias (T5)
          quantize &= name.find("attn_rel_b.weight") == std::string::npos;
  
@@ -423,6 +437,15 @@ index 24e1f1f0..25db4c69 100644
 +            quantize &= name.find("context_refiner.") == std::string::npos;
 +            quantize &= name.find("noise_refiner.") == std::string::npos;
 +        }
++        if (model.arch == LLM_ARCH_KREA2) {
++            image_model = true;
++            quantize &= name.find("first.") == std::string::npos;
++            quantize &= name.find("last.") == std::string::npos;
++            quantize &= name.find("tproj.") == std::string::npos;
++            quantize &= name.find("tmlp.") == std::string::npos;
++            quantize &= name.find("txtmlp.") == std::string::npos;
++            quantize &= name.find("txtfusion.projector.") == std::string::npos;
++        }
 +        // ignore 3D/4D tensors for image models as the code was never meant to handle these
 +        if (image_model) {
 +            quantize &= ggml_n_dims(tensor) == 2;
@@ -431,7 +454,7 @@ index 24e1f1f0..25db4c69 100644
          enum ggml_type new_type;
          void * new_data;
          size_t new_size;
-@@ -18655,6 +18986,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+@@ -18655,6 +19009,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
              new_type = default_type;
  
              // get more optimal quantization type based on the tensor shape, layer, etc.
@@ -441,7 +464,7 @@ index 24e1f1f0..25db4c69 100644
              if (!params->pure && ggml_is_quantized(default_type)) {
                  new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
              }
-@@ -18664,6 +18998,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
+@@ -18664,6 +19021,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
              if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
                  new_type = params->output_tensor_type;
              }

From 4534f96a9cae5cbc1515d37c5c682170e0914de4 Mon Sep 17 00:00:00 2001
From: boessu <boessu@users.noreply.github.com>
Date: Fri, 31 Jul 2026 19:33:43 +0200
Subject: [PATCH 12/20] Enhance tensor reading and comparison functionality

Updated read_tensors.py to support tensor comparison and improved shape checking.
---
 tools/read_tensors.py | 190 +++++++++++++++++++++++++++++++++++++++---
 1 file changed, 177 insertions(+), 13 deletions(-)

diff --git a/tools/read_tensors.py b/tools/read_tensors.py
index 1bdff02..08554e7 100644
--- a/tools/read_tensors.py
+++ b/tools/read_tensors.py
@@ -1,21 +1,185 @@
 #!/usr/bin/python3
+"""
+read_tensors.py - list tensors in a GGUF file, optionally comparing two files.
+
+Single-file mode:
+    python read_tensors.py model.gguf [--quantized-only]
+
+    Lists every tensor with its quant type and shape. By default this
+    INCLUDES F32 tensors (unlike the original version), since those are
+    exactly the ones that can silently suffer a dimension collapse when
+    quantizing (see below). Pass --quantized-only to restore the old
+    behavior of skipping F32 tensors. Tensors with a suspicious shape
+    (see is_suspicious_shape) are flagged inline.
+
+Compare mode (recommended when you have both files):
+    python read_tensors.py before.gguf after.gguf
+
+    Compares two GGUF files tensor-by-tensor (e.g. the pre-quantize
+    BF16/F16 GGUF from convert.py vs. the output of llama-quantize).
+    Flags any tensor whose number of dimensions changed between the two
+    files -- this is the exact, unambiguous signature of a GGML
+    ggml_n_dims() collapse (a tensor with a trailing `ne` dim of 1 gets
+    silently truncated to fewer dims when llama.cpp reloads it into a
+    ggml_tensor and writes it back out) -- e.g. SD3's pos_embed,
+    AuraFlow's positional_encoding/register_tokens, Wan's .modulation
+    tensors, or Krea2's txtfusion.projector.weight. This mode doesn't
+    rely on any heuristic, so prefer it whenever you have both files.
+
+    Any tensor flagged here is a candidate for a gguf_set_tensor_ndim()
+    fix in lcpp.patch, inside the llama_model_quantize_internal()
+    tensor-writing loop.
+
+Batch/log-friendly compare mode:
+    python read_tensors.py --verify before.gguf after.gguf
+
+    Same check as compare mode, but prints exactly one line -- "OK" or
+    "FAIL" plus a compact issue list -- instead of the full per-tensor
+    table. No --quantized-only equivalent needed here since the ndim
+    check inherently only concerns itself with what actually differs.
+    Skips the interactive "press enter to close" pause, and sets the
+    process exit code (0 = OK, 1 = FAIL) so it plays well in scripts.
+    Intended for looping over several quant types and collecting one
+    line per run into a shared log file, e.g.:
+
+        for QUANT in Q4_0 Q5_0 Q8_0 Q2_K Q5_K_M Q6_K; do
+            ./llama-quantize model-BF16.gguf model-${QUANT}.gguf ${QUANT}
+            python read_tensors.py --verify model-BF16.gguf model-${QUANT}.gguf \\
+                >> quant_test_results.log
+        done
+"""
 import os
 import sys
 import gguf
 
-def read_tensors(path):
+
+def shape_str(tensor):
+    return "x".join(str(d) for d in tensor.shape)
+
+
+def is_suspicious_shape(shape):
+    """
+    Flags shapes with a dimension of size 1 anywhere except shape[0].
+
+    gguf.GGUFReader reports tensor.shape in GGML `ne` order, i.e.
+    reversed relative to the original torch/numpy shape. For example
+    txtfusion.projector.weight was (1, 12) in the safetensors file, but
+    shows up here as (12, 1) -- the "1" moved to the end. llama.cpp's
+    C++ quantizer determines a tensor's dimensionality via ggml_n_dims(),
+    which scans the `ne` array from the highest index downward and drops
+    trailing 1s -- so a 1 anywhere in shape[1:] is at risk of being
+    silently collapsed once the tensor passes through llama-quantize,
+    even though the GGUF written by convert.py still has the correct
+    shape. A 1 at shape[0] is safe, since the scan stops there.
+
+    Only usable as an early warning on a single file (typically the
+    *-BF16.gguf straight out of convert.py); prefer compare mode when
+    you have both the pre- and post-quantize files, since that checks
+    the actual outcome instead of guessing.
+    """
+    dims = list(shape)
+    if len(dims) < 2:
+        return False
+    return any(d == 1 for d in dims[1:])
+
+
+def print_single(path, quantized_only):
     reader = gguf.GGUFReader(path)
+    n_flagged = 0
     for tensor in reader.tensors:
-        if tensor.tensor_type == gguf.GGMLQuantizationType.F32:
+        if quantized_only and tensor.tensor_type == gguf.GGMLQuantizationType.F32:
             continue
-        print(f"{str(tensor.tensor_type):32}: {tensor.name}")
-
-try:
-    path = sys.argv[1]
-    assert os.path.isfile(path), "Invalid path"
-    print(f"input: {path}")
-except Exception as e:
-    input(f"failed: {e}")
-else:
-    read_tensors(path)
-    input()
+        suspicious = is_suspicious_shape(tensor.shape)
+        flag = ""
+        if suspicious:
+            flag = "  <-- dim other than shape[0] is 1, check for ndim collapse risk"
+            n_flagged += 1
+        print(f"{str(tensor.tensor_type):26} {shape_str(tensor):18}: {tensor.name}{flag}")
+    if n_flagged:
+        print(f"\n{n_flagged} tensor(s) flagged as at risk of a ggml_n_dims() collapse.")
+
+
+def print_compare(path_a, path_b, verify_only=False):
+    tensors_a = {t.name: t for t in gguf.GGUFReader(path_a).tensors}
+    tensors_b = {t.name: t for t in gguf.GGUFReader(path_b).tensors}
+
+    names = sorted(set(tensors_a) | set(tensors_b))
+    ndim_mismatches = []
+    missing = []
+
+    for name in names:
+        a = tensors_a.get(name)
+        b = tensors_b.get(name)
+
+        if a is None:
+            missing.append(f"{name} (only in B)")
+            if not verify_only:
+                print(f"only in B ({path_b}): {name}")
+            continue
+        if b is None:
+            missing.append(f"{name} (only in A)")
+            if not verify_only:
+                print(f"only in A ({path_a}): {name}")
+            continue
+
+        flag = ""
+        if len(a.shape) != len(b.shape):
+            flag = "  <-- DIM COUNT CHANGED (ggml_n_dims collapse!)"
+            ndim_mismatches.append(f"{name} (dim {len(a.shape)}->{len(b.shape)})")
+
+        if not verify_only:
+            print(
+                f"{name:55} A: {str(a.tensor_type):10} {shape_str(a):14}  "
+                f"B: {str(b.tensor_type):10} {shape_str(b):14}{flag}"
+            )
+
+    issues = ndim_mismatches + missing
+
+    if verify_only:
+        # One compact line per run -- meant to be piped/redirected into a
+        # shared log file across a batch of quantization types, e.g.:
+        #   python read_tensors.py --verify base.gguf quant.gguf >> quant_test.log
+        status = "OK  " if not issues else "FAIL"
+        summary = f"{status} {os.path.basename(path_b):40} ({len(names)} tensors, {len(issues)} issue(s))"
+        if issues:
+            summary += ": " + "; ".join(issues)
+        print(summary)
+        return not issues
+
+    print()
+    if ndim_mismatches:
+        print(f"!!! {len(ndim_mismatches)} tensor(s) changed dimensionality between the two files:")
+        for name in ndim_mismatches:
+            print(f"  - {name}")
+        print("These are prime suspects for needing gguf_set_tensor_ndim() in lcpp.patch.")
+    else:
+        print("No dimensionality changes detected between the two files.")
+    return not issues
+
+
+if __name__ == "__main__":
+    raw_args = sys.argv[1:]
+    quantized_only = "--quantized-only" in raw_args
+    verify_only = "--verify" in raw_args
+    paths = [a for a in raw_args if not a.startswith("--")]
+
+    try:
+        assert len(paths) in (1, 2), "Usage: read_tensors.py <file.gguf> [file_to_compare.gguf] [--quantized-only] [--verify]"
+        assert not (verify_only and len(paths) != 2), "--verify requires two files to compare"
+        for p in paths:
+            assert os.path.isfile(p), f"Invalid path: {p}"
+    except Exception as e:
+        input(f"failed: {e}")
+        sys.exit(1)
+    else:
+        if len(paths) == 1:
+            print(f"input: {paths[0]}")
+            print_single(paths[0], quantized_only)
+            input()
+        else:
+            if verify_only:
+                ok = print_compare(paths[0], paths[1], verify_only=True)
+                sys.exit(0 if ok else 1)
+            print(f"comparing:\n  A: {paths[0]}\n  B: {paths[1]}\n")
+            print_compare(paths[0], paths[1])
+            input()

From 937051b8a2e8d1d2230cbca199ef02e34d580590 Mon Sep 17 00:00:00 2001
From: boessu <boessu@users.noreply.github.com>
Date: Sun, 2 Aug 2026 02:01:50 +0200
Subject: [PATCH 13/20] Implement key_matches function for tensor name
 validation

Added key_matches function to match tensor names against patterns and validate key patterns for model architectures.
---
 tools/convert.py | 95 ++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 83 insertions(+), 12 deletions(-)

diff --git a/tools/convert.py b/tools/convert.py
index 96d07ca..5ea3164 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -26,6 +26,29 @@ class ModelTemplate:
     def handle_nd_tensor(self, key, data):
         raise NotImplementedError(f"Tensor detected that exceeds dims supported by C++ code! ({key} @ {data.shape})")
 
+def key_matches(key, patterns):
+    """
+    Match a tensor name against a list of patterns.
+
+    Plain patterns match anywhere in the key (today's behavior, unchanged --
+    e.g. "pos_embedder", "scale_shift_table", ".modulation").
+
+    A pattern prefixed with '^' matches only at the START of the key, e.g.
+    "^tmlp." matches "tmlp.0.weight" but NOT "blocks.5.txtmlp.0.weight" --
+    use this for short/generic fragments that would otherwise collide with
+    an unrelated, similarly-named submodule elsewhere in the tensor name
+    (see ModelKrea2: bare "tmlp."/"tproj." also matched every per-block
+    "txtmlp."/"txtproj." tensor, silently forcing far more of the model to
+    F32 than intended).
+    """
+    for pattern in patterns:
+        if pattern.startswith("^"):
+            if key.startswith(pattern[1:]):
+                return True
+        elif pattern in key:
+            return True
+    return False
+
 class ModelFlux(ModelTemplate):
     arch = "flux"
     keys_detect = [
@@ -209,10 +232,12 @@ class ModelKrea2(ModelTemplate):
         ),
     ]
     keys_hiprec = [
-        "blocks.0.mod.lin",  # modulation parameters — keep at full precision
-        "last.modulation.lin",
-        "tmlp.",
-        "tproj.",
+        "^first.",
+        "^last.",
+        "^tproj.",
+        "^tmlp.",
+        "^txtmlp.",
+        "^txtfusion.projector.",
     ]
 
 arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2,
@@ -223,10 +248,16 @@ def is_model_arch(model, state_dict):
     # check if model is correct
     matched = False
     invalid = False
-    for match_list in model.keys_detect:
+    for match_idx, match_list in enumerate(model.keys_detect):
         if all(key in state_dict for key in match_list):
             matched = True
             invalid = any(key in state_dict for key in model.keys_banned)
+            if len(model.keys_detect) > 1:
+                # Multiple detect variants usually mean multiple known checkpoint
+                # exports of the same architecture (e.g. different key subsets
+                # across releases). Logging which one matched makes it obvious
+                # which variant you're actually converting.
+                logging.info(f"* Matched keys_detect variant #{match_idx} for '{model.arch}': {match_list}")
             break
     assert not invalid, "Model architecture not allowed for conversion! (i.e. reference VS diffusers format)"
     return matched
@@ -240,6 +271,34 @@ def detect_arch(state_dict):
     assert model_arch is not None, "Unknown model architecture!"
     return model_arch
 
+def validate_key_patterns(model_arch, state_dict):
+    """
+    Warn if a configured key pattern (keys_hiprec / keys_noquant / keys_ignore)
+    matches no tensor at all in this checkpoint.
+
+    These patterns are substring matches against tensor names (`any(x in key ...)`),
+    hand-written against one specific checkpoint export. If the upstream model
+    renames a layer in a later release, the pattern silently stops firing --
+    no error, no warning, just quietly reduced precision/behavior on tensors
+    that were meant to be protected. This check surfaces that case early,
+    at conversion time, instead of relying on someone noticing degraded output
+    later.
+
+    This is intentionally a warning, not an assert: a pattern legitimately
+    matching nothing can happen for known reasons too, e.g. a checkpoint
+    variant that simply doesn't include that sub-module (see ModelKrea2's
+    two keys_detect alternatives, which exist for exactly this reason).
+    """
+    for attr in ("keys_hiprec", "keys_noquant", "keys_ignore"):
+        for pattern in getattr(model_arch, attr, []):
+            if not any(key_matches(key, [pattern]) for key in state_dict.keys()):
+                logging.warning(
+                    f"[{model_arch.arch}] '{pattern}' in {attr} matched no tensor in this "
+                    f"checkpoint -- possible naming drift in the source model, or an "
+                    f"intentionally absent sub-module for this checkpoint variant. "
+                    f"Verify this is expected before trusting the output precision."
+                )
+
 QUANT_TYPE_MAP = {
     "F16":  (gguf.GGMLQuantizationType.F16,  gguf.LlamaFileType.MOSTLY_F16),
     "BF16": (gguf.GGMLQuantizationType.BF16, gguf.LlamaFileType.MOSTLY_BF16),
@@ -424,7 +483,7 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
     for key, data in tqdm(state_dict.items()):
         old_dtype = data.dtype
 
-        if any(x in key for x in model_arch.keys_ignore):
+        if key_matches(key, model_arch.keys_ignore):
             tqdm.write(f"Filtering ignored key: '{key}'")
             continue
 
@@ -477,15 +536,26 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
             or old_dtype in _FP8_DTYPES
         )
         if apply_quantization_rules:
-            if any(x in key for x in model_arch.keys_noquant):
-                pass
-            elif n_dims == 1:
-                # One-dimensional tensors should be kept in F32.
+            if n_dims == 1:
+                # One-dimensional tensors should be kept in F32. This is a
+                # universal safety net and must take priority over
+                # keys_noquant -- a broad keys_noquant prefix (e.g. Krea2's
+                # "^last.") would otherwise also match that submodule's 1D
+                # bias/scale tensors and silently downgrade them from the
+                # F32 they'd normally always get to whatever the generic
+                # default happens to be (F16/BF16).
                 data_qtype = gguf.GGMLQuantizationType.F32
             elif n_params <= QUANTIZATION_THRESHOLD:
                 data_qtype = gguf.GGMLQuantizationType.F32
-            elif any(x in key for x in model_arch.keys_hiprec):
+            elif key_matches(key, model_arch.keys_hiprec):
+                # More specific than keys_noquant by design: keys_hiprec
+                # forces F32 even when a broader keys_noquant pattern for
+                # the same submodule would also match (e.g. Krea2's
+                # "last.modulation.lin" needs full F32, even though the
+                # broader "^last." keys_noquant entry also matches it).
                 data_qtype = gguf.GGMLQuantizationType.F32
+            elif key_matches(key, model_arch.keys_noquant):
+                pass
             elif n_dims == 4 and "conv" in key.lower():
                 # Native quantized paths are Linear-only.
                 data_qtype = gguf.GGMLQuantizationType.F16
@@ -539,6 +609,7 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type
     source_metadata = load_safetensors_metadata(path)
     model_arch = detect_arch(state_dict)
     logging.info(f"* Architecture detected from input: {model_arch.arch}")
+    validate_key_patterns(model_arch, state_dict)
 
     # resolve quant type from name if provided
     quant_type = None
@@ -595,4 +666,4 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type
 
 if __name__ == "__main__":
     args = parse_args()
-    convert_file(args.src, args.dst, quant_type_name=args.quant_type)
\ No newline at end of file
+    convert_file(args.src, args.dst, quant_type_name=args.quant_type)

From a731dc38ab6eb9601efc41c6663a85d5f67362a7 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Mon, 3 Aug 2026 21:14:04 +0200
Subject: [PATCH 14/20] Update README and pyproject.toml for repository
 details; modify quantization defaults in conversion script

---
 README.md                   |  6 +----
 pyproject.toml              | 44 +++++++++++++++++++++++++------------
 tools/convert_krea2_gguf.py |  2 +-
 tools/lcpp.patch            |  2 +-
 4 files changed, 33 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index cd2307e..f0f6465 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@ GGUF Quantization support for native ComfyUI models including the custom Q8_CR
 
 > [!NOTE]  
 > This is a fork of the original nodes, updated to support loading Ideogram 4 GGUFs and Krea 2 GGUFs. 
-> To use it, clone `https://github.com/city96/ComfyUI-GGUF`and not the original repo.
+> To use this maintained fork, clone `https://github.com/molbal/ComfyUI-GGUF`.
 
 While quantization wasn't feasible for regular UNET models (conv2d), transformer/DiT models such as flux seem less affected by quantization. This allows running it in much lower bits per weight variable bitrate quants on low-end GPUs. For further VRAM savings, a node to load a quantized version of the T5 text encoder is also included.
 
@@ -168,9 +168,5 @@ Q8_CR does not require CUDA. It uses ComfyUI's `comfy_kitchen` layout backend:
 - CPU Q8_CR loading and inference are supported, but naturally slower than
   optimized CUDA inference.
 
-Q4_PT is retired from conversion and loading until a performant W4A16 backend
-is available. Its experimental implementation remains in the source for future
-work, but it is no longer selectable or loadable.
-
 Reconvert any `Q8_CR` GGUF created before ConvRot weights were marked as
 pre-rotated. Older files load safely with native non-rotated INT8 instead.
diff --git a/pyproject.toml b/pyproject.toml
index 8b8cf53..246c2cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,14 +1,30 @@
-[project]
-name = "comfyui-gguf"
-description = "GGUF Quantization support for native ComfyUI models."
-version = "2.0.0" # 2.0.0 = GitHub main, 1.X.X = ComfyUI Registry
-license = { file = "LICENSE" }
-dependencies = ["gguf>=0.13.0", "sentencepiece", "protobuf"]
-
-[project.urls]
-Repository = "https://github.com/city96/ComfyUI-GGUF"
-
-[tool.comfy]
-PublisherId = "city96"
-DisplayName = "ComfyUI-GGUF"
-Icon = ""
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "comfyui-gguf-reboot"
+version = "1.0.0"
+description = "Actively maintained fork of ComfyUI-GGUF node pack with support for new models, INT8 GGUFs and optionally ComfyUI Dynamic VRAM"
+readme = "README.md"
+license = { file = "LICENSE" }
+authors = [
+    { name = "molbal" }
+]
+dependencies = [
+    "gguf>=0.13.0",
+    "torch",
+    "numpy",
+    "sentencepiece",
+    "protobuf",
+    "tqdm",
+]
+
+[project.urls]
+Repository = "https://github.com/molbal/ComfyUI-GGUF"
+Documentation = "https://github.com/molbal/ComfyUI-GGUF#readme"
+
+[tool.comfy]
+PublisherId = "molbal"
+DisplayName = "ComfyUI GGUF (molbal's fork)"
+Icon = ""
diff --git a/tools/convert_krea2_gguf.py b/tools/convert_krea2_gguf.py
index 8e6fb4b..5a4afce 100644
--- a/tools/convert_krea2_gguf.py
+++ b/tools/convert_krea2_gguf.py
@@ -47,7 +47,7 @@
 
 logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
 
-DEFAULT_QUANTS = ["Q4_0", "Q4_1", "Q5_0", "Q5_1", "Q8_0"]
+DEFAULT_QUANTS = ["Q4_0", "Q8_CR", "Q8_0"]
 
 
 def build_dst_path(src: str, quant: str, outdir: str | None) -> str:
diff --git a/tools/lcpp.patch b/tools/lcpp.patch
index aad4c61..9018f5a 100644
--- a/tools/lcpp.patch
+++ b/tools/lcpp.patch
@@ -471,4 +471,4 @@ index 24e1f1f01..23df89523 100644
 +            }
  
              // If we've decided to quantize to the same type the tensor is already
-             // in then there's nothing to do.
+             // in then there's nothing to do.
\ No newline at end of file

From c7bd78f8051f1e7882243f4fea89eb11266cc665 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Mon, 3 Aug 2026 21:14:36 +0200
Subject: [PATCH 15/20] Add icon and update pyproject Icon URL

Add icon.png to the repository and update pyproject.toml's [tool.comfy] Icon entry to point to the raw GitHub URL for the icon (refs/heads/main). This enables ComfyUI to display the package icon.
---
 icon.png       | Bin 0 -> 750661 bytes
 pyproject.toml |   2 +-
 2 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 icon.png

diff --git a/icon.png b/icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..3cc47f8c7ac8415d3bd42db5559f28d5aadc4aea
GIT binary patch
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diff --git a/pyproject.toml b/pyproject.toml
index 246c2cb..c3aca51 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,4 +27,4 @@ Documentation = "https://github.com/molbal/ComfyUI-GGUF#readme"
 [tool.comfy]
 PublisherId = "molbal"
 DisplayName = "ComfyUI GGUF (molbal's fork)"
-Icon = ""
+Icon = "https://raw.githubusercontent.com/molbal/ComfyUI-GGUF/refs/heads/main/icon.png"

From f010b7de27c71f0ad00eabf99583f6cb5688a587 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Mon, 3 Aug 2026 22:49:59 +0200
Subject: [PATCH 16/20] Add targeted GGUF quantization and progress

Introduce TARGET_SIZE-based GGUF quantization and progress reporting. Added planning and conversion logic (tools/convert.py) to select per-tensor quant types to meet a target size, CLI --max-size-mb support, and unit tests for targeted quantization. Propagate progress callbacks into safetensors/pt readers and GGUF loaders (loader.py, nodes.py) and add GGUFLoadProgress to coordinate ComfyUI progress bars for multi-file loads. Make ops compatible with mixed Q8_CR/Q4_0 GGUFs by materializing GGML weights before mixed-precision loading (ops.py). Update README and register a TargetedQuantizationGGUF node; include node.zip and tests.
---
 README.md                           |  20 +++
 loader.py                           |  19 ++-
 node.zip                            | Bin 0 -> 822119 bytes
 nodes.py                            | 163 ++++++++++++++++++--
 ops.py                              |  17 +++
 tests/test_targeted_quantization.py |  49 ++++++
 tools/convert.py                    | 222 ++++++++++++++++++++++++----
 7 files changed, 451 insertions(+), 39 deletions(-)
 create mode 100644 node.zip
 create mode 100644 tests/test_targeted_quantization.py

diff --git a/README.md b/README.md
index f0f6465..002f088 100644
--- a/README.md
+++ b/README.md
@@ -168,5 +168,25 @@ Q8_CR does not require CUDA. It uses ComfyUI's `comfy_kitchen` layout backend:
 - CPU Q8_CR loading and inference are supported, but naturally slower than
   optimized CUDA inference.
 
+All GGUF UNET and CLIP loader nodes, including Dynamic VRAM and multi-CLIP
+variants, report their tensor-loading progress through ComfyUI's global
+progress bar. A multi-CLIP loader uses one bar for every selected file.
+
+### Target-size quantization
+
+Use `tools/convert.py --max-size-mb <MiB>` to create the best supported mixed
+quantization below a maximum output size. The converter starts with core 2-D
+Linear weights in Q8_CR while preserving 1-D and architecture-sensitive tensors
+in FP32. It then changes core matrices closest to the model's center to Q4_0
+until the target is met, retaining the beginning and end in Q8_CR for as long as
+possible. If every Q4_0-compatible core matrix is already Q4_0, ordinary 1-D
+tensors are reduced to BF16; protected tensors remain FP32.
+
+`Q4_0` is the smallest supported core quantization. A target below the minimum
+attainable size raises an error that reports that minimum; Q3 and lower are not
+used. The **Targeted Quantization (GGUF)** ComfyUI node exposes the same source,
+destination, quantization, target-size, and overwrite options, reports loading
+and conversion progress, and outputs both the GGUF path and output details.
+
 Reconvert any `Q8_CR` GGUF created before ConvRot weights were marked as
 pre-rotated. Older files load safely with native non-rotated INT8 instead.
diff --git a/loader.py b/loader.py
index 5dd8d8f..967ee47 100644
--- a/loader.py
+++ b/loader.py
@@ -109,7 +109,11 @@ def get_gguf_metadata(reader):
             continue
     return metadata
 
-def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=False, dynamic=False):
+def gguf_tensor_count(path):
+    return len(gguf.GGUFReader(path).tensors)
+
+
+def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=False, dynamic=False, progress_callback=None):
     """
     Read state dict as fake tensors
     """
@@ -177,7 +181,7 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
     bf16_storage_dtype = torch.bfloat16 if device_supports_bf16() else torch.float16
     state_dict = {}
     qtype_dict = {}
-    for sd_key, tensor in tensors:
+    for tensor_index, (sd_key, tensor) in enumerate(tensors, start=1):
         tensor_name = tensor.name
         # torch_tensor = torch.from_numpy(tensor.data) # mmap
 
@@ -231,6 +235,8 @@ def gguf_sd_loader(path, handle_prefix="model.diffusion_model.", is_text_model=F
         # keep track of loaded tensor types
         tensor_type_str = getattr(tensor.tensor_type, "name", repr(tensor.tensor_type))
         qtype_dict[tensor_type_str] = qtype_dict.get(tensor_type_str, 0) + 1
+        if progress_callback is not None:
+            progress_callback(tensor_index, len(tensors))
 
     # print loaded tensor type counts
     logging.info("gguf qtypes: " + ", ".join(f"{k} ({v})" for k, v in qtype_dict.items()))
@@ -645,8 +651,13 @@ def gguf_gemma3_tokenizer_loader(path):
     del reader
     return torch.ByteTensor(list(spm.SerializeToString()))
 
-def gguf_clip_loader(path, dynamic=False):
-    sd, extra = gguf_sd_loader(path, is_text_model=True, dynamic=dynamic)
+def gguf_clip_loader(path, dynamic=False, progress_callback=None):
+    sd, extra = gguf_sd_loader(
+        path,
+        is_text_model=True,
+        dynamic=dynamic,
+        progress_callback=progress_callback,
+    )
     arch = extra.get("arch_str", None)
     if arch in {"t5", "t5encoder"}:
         temb_key = "token_embd.weight"
diff --git a/node.zip b/node.zip
new file mode 100644
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diff --git a/nodes.py b/nodes.py
index 0c672d7..9f46814 100644
--- a/nodes.py
+++ b/nodes.py
@@ -3,6 +3,7 @@
 import logging
 import inspect
 import collections
+import os
 
 import nodes
 import comfy.sd
@@ -15,8 +16,9 @@
 import folder_paths
 
 from .ops import GGMLOps, get_gguf_q8_ops, move_patch_to_device
-from .loader import gguf_sd_loader, gguf_clip_loader
+from .loader import gguf_sd_loader, gguf_clip_loader, gguf_tensor_count
 from .dequant import is_quantized, is_torch_compatible
+from .tools.convert import QUANT_TYPE_MAP, TARGET_SIZE_QUANT_TYPE, convert_file
 
 def update_folder_names_and_paths(key, targets=[]):
     # check for existing key
@@ -33,6 +35,32 @@ def update_folder_names_and_paths(key, targets=[]):
 update_folder_names_and_paths("unet_gguf", ["diffusion_models", "unet"])
 update_folder_names_and_paths("clip_gguf", ["text_encoders", "clip"])
 
+
+class GGUFLoadProgress:
+    """Coordinate one ComfyUI progress bar across one or more model files."""
+
+    def __init__(self, paths):
+        self.path_totals = {
+            path: gguf_tensor_count(path) if path.endswith(".gguf") else 1
+            for path in paths
+        }
+        self.total = sum(self.path_totals.values())
+        self.pbar = comfy.utils.ProgressBar(self.total)
+        self.completed = 0
+
+    def callback_for(self, path):
+        offset = self.completed
+        total = self.path_totals[path]
+
+        def update(current, _loader_total):
+            self.pbar.update_absolute(offset + current, self.total)
+
+        return update
+
+    def complete_file(self, path):
+        self.completed += self.path_totals[path]
+        self.pbar.update_absolute(self.completed, self.total)
+
 class GGUFModelPatcher(comfy.model_patcher.ModelPatcher):
     patch_on_device = False
 
@@ -150,7 +178,9 @@ def INPUT_TYPES(s):
 
     def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        sd, extra = gguf_sd_loader(unet_path)
+        progress = GGUFLoadProgress([unet_path])
+        sd, extra = gguf_sd_loader(unet_path, progress_callback=progress.callback_for(unet_path))
+        progress.complete_file(unet_path)
 
         mode = extra.get("gguf_quant_mode")
         if mode == "int8_convrot":
@@ -196,6 +226,98 @@ def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_de
         return (model,)
 
 
+class TargetedQuantizationGGUF:
+    """Convert a source checkpoint to GGUF from a ComfyUI workflow."""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "source_path": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": "Absolute path to a .safetensors, .ckpt, .pt, .bin, or .pth source model.",
+                    },
+                ),
+                "destination_path": (
+                    "STRING",
+                    {
+                        "default": "",
+                        "tooltip": "Output .gguf path. Leave empty to derive it from the source path.",
+                    },
+                ),
+                "quantization": (
+                    [TARGET_SIZE_QUANT_TYPE, *QUANT_TYPE_MAP.keys()],
+                    {
+                        "default": TARGET_SIZE_QUANT_TYPE,
+                        "tooltip": (
+                            "TARGET_SIZE starts at Q8_CR, reduces central core matrices to Q4_0, "
+                            "then ordinary 1-D tensors to BF16 only when necessary."
+                        ),
+                    },
+                ),
+                "max_size_mb": (
+                    "FLOAT",
+                    {
+                        "default": 0.0,
+                        "min": 0.0,
+                        "max": 1000000.0,
+                        "step": 1.0,
+                        "tooltip": "Maximum output size in MiB. Required only for TARGET_SIZE.",
+                    },
+                ),
+                "overwrite": ("BOOLEAN", {"default": False}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING", "STRING")
+    RETURN_NAMES = ("gguf_path", "quantization_info")
+    FUNCTION = "quantize"
+    CATEGORY = "bootleg/quantization"
+    TITLE = "Targeted Quantization (GGUF)"
+
+    def quantize(self, source_path, destination_path, quantization, max_size_mb, overwrite):
+        source_path = os.path.abspath(os.path.expanduser(source_path))
+        if not os.path.isfile(source_path):
+            raise FileNotFoundError(f"Source model does not exist: {source_path}")
+
+        if quantization == TARGET_SIZE_QUANT_TYPE and max_size_mb <= 0:
+            raise ValueError("TARGET_SIZE requires max_size_mb greater than zero.")
+
+        progress = {"bar": None, "read_total": None, "total": None}
+
+        def report_progress(stage, current, total):
+            if stage == "read":
+                if progress["bar"] is None:
+                    progress["read_total"] = total
+                    progress["total"] = total * 2 + 1
+                    progress["bar"] = comfy.utils.ProgressBar(progress["total"])
+                progress["bar"].update_absolute(current, progress["total"])
+            elif stage == "quantize":
+                if progress["bar"] is None:
+                    progress["read_total"] = 0
+                    progress["total"] = total + 1
+                    progress["bar"] = comfy.utils.ProgressBar(progress["total"])
+                progress["bar"].update_absolute(progress["read_total"] + current, progress["total"])
+
+        output_path, _ = convert_file(
+            source_path,
+            dst_path=destination_path or None,
+            interact=False,
+            overwrite=overwrite,
+            quant_type_name=None if quantization == TARGET_SIZE_QUANT_TYPE else quantization,
+            max_size_mb=max_size_mb if quantization == TARGET_SIZE_QUANT_TYPE else None,
+            progress_callback=report_progress,
+        )
+        if progress["bar"] is not None:
+            progress["bar"].update_absolute(progress["total"], progress["total"])
+
+        output_size_mb = os.path.getsize(output_path) / (1024 * 1024)
+        info = f"{quantization}: {output_size_mb:.2f} MiB written to {output_path}"
+        return (output_path, info)
+
+
 def _require_dynamic_vram():
     if not comfy.memory_management.aimdo_enabled:
         raise RuntimeError(
@@ -224,17 +346,27 @@ def _legacy_gguf_ops(extra):
     return GGMLOps()
 
 
-def _load_dynamic_gguf_unet(unet_path, disable_dynamic=False):
+def _load_dynamic_gguf_unet(unet_path, disable_dynamic=False, progress=None):
     if not disable_dynamic:
         _require_dynamic_vram()
-    sd, extra = gguf_sd_loader(unet_path, dynamic=not disable_dynamic)
+    progress = progress or GGUFLoadProgress([unet_path])
+    sd, extra = gguf_sd_loader(
+        unet_path,
+        dynamic=not disable_dynamic,
+        progress_callback=progress.callback_for(unet_path),
+    )
+    progress.complete_file(unet_path)
 
     kwargs = {}
     valid_params = inspect.signature(comfy.sd.load_diffusion_model_state_dict).parameters
     if "metadata" in valid_params:
         kwargs["metadata"] = extra.get("metadata", {})
 
-    model_options = {} if not disable_dynamic else {
+    # Target-size models combine native Q8_CR layers with standard GGML Q4_0
+    # layers. Dynamic VRAM's default mixed-precision Linear cannot serialize a
+    # bare GGML Q4_0 weight, so use the GGUF Q8 ops for both paths. Those ops
+    # retain Q8_CR metadata and materialize only standard GGML layers as needed.
+    model_options = {
         "custom_operations": _legacy_gguf_ops(extra),
     }
     model = comfy.sd.load_diffusion_model_state_dict(
@@ -282,7 +414,7 @@ class UnetLoaderGGUFDynamicVRAM(UnetLoaderGGUF):
 
     def load_unet(self, unet_name, **kwargs):
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        return (_load_dynamic_gguf_unet(unet_path),)
+        return (_load_dynamic_gguf_unet(unet_path, progress=GGUFLoadProgress([unet_path])),)
 
 class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
     @classmethod
@@ -323,14 +455,16 @@ def get_filename_list(s):
 
     def load_data(self, ckpt_paths):
         clip_data = []
+        progress = GGUFLoadProgress(ckpt_paths)
         for p in ckpt_paths:
             if p.endswith(".gguf"):
-                sd = gguf_clip_loader(p)
+                sd = gguf_clip_loader(p, progress_callback=progress.callback_for(p))
             else:
                 sd = comfy.utils.load_torch_file(p, safe_load=True)
                 if "scaled_fp8" in sd: # NOTE: Scaled FP8 would require different custom ops, but only one can be active
                     raise NotImplementedError(f"Mixing scaled FP8 with GGUF is not supported! Use regular CLIP loader or switch model(s)\n({p})")
             clip_data.append(sd)
+            progress.complete_file(p)
         return clip_data
 
     def load_patcher(self, clip_paths, clip_type, clip_data):
@@ -352,15 +486,23 @@ def load_clip(self, clip_name, type="stable_diffusion"):
         return (self.load_patcher([clip_path], clip_type, self.load_data([clip_path])),)
 
 
-def _load_dynamic_gguf_clip(clip_paths, clip_type, disable_dynamic=False):
+def _load_dynamic_gguf_clip(clip_paths, clip_type, disable_dynamic=False, progress=None):
     if not disable_dynamic:
         _require_dynamic_vram()
+    progress = progress or GGUFLoadProgress(clip_paths)
     clip_data = []
     for path in clip_paths:
         if path.endswith(".gguf"):
-            clip_data.append(gguf_clip_loader(path, dynamic=not disable_dynamic))
+            clip_data.append(
+                gguf_clip_loader(
+                    path,
+                    dynamic=not disable_dynamic,
+                    progress_callback=progress.callback_for(path),
+                )
+            )
         else:
             clip_data.append(comfy.utils.load_torch_file(path, safe_load=True))
+        progress.complete_file(path)
 
     model_options = {
         "initial_device": comfy.model_management.text_encoder_offload_device(),
@@ -398,7 +540,7 @@ class CLIPLoaderGGUFDynamicVRAM(CLIPLoaderGGUF):
     def load_clip(self, clip_name, type="stable_diffusion"):
         clip_path = folder_paths.get_full_path("clip", clip_name)
         clip_type = getattr(comfy.sd.CLIPType, type.upper(), comfy.sd.CLIPType.STABLE_DIFFUSION)
-        return (_load_dynamic_gguf_clip([clip_path], clip_type),)
+        return (_load_dynamic_gguf_clip([clip_path], clip_type, progress=GGUFLoadProgress([clip_path])),)
 
 class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
     @classmethod
@@ -508,6 +650,7 @@ def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4, type="stable
         return (_load_dynamic_gguf_clip(clip_paths, clip_type),)
 
 NODE_CLASS_MAPPINGS = {
+    "TargetedQuantizationGGUF": TargetedQuantizationGGUF,
     "UnetLoaderGGUF": UnetLoaderGGUF,
     "CLIPLoaderGGUF": CLIPLoaderGGUF,
     "DualCLIPLoaderGGUF": DualCLIPLoaderGGUF,
diff --git a/ops.py b/ops.py
index 5904e62..7ffa244 100644
--- a/ops.py
+++ b/ops.py
@@ -345,6 +345,23 @@ def __init__(self, in_features, out_features, bias=True, device=None, dtype=None
                 self._full_precision_mm_config = False
 
             def _load_from_state_dict(self, *args):
+                state_dict, prefix = args[:2]
+                weight_key = f"{prefix}weight"
+                # Target-size GGUFs combine native Q8_CR weights with standard
+                # Q4_0 weights. The mixed-precision loader only understands
+                # the former's comfy_quant metadata, so materialize standard
+                # GGML weights before handing them to that loader.
+                if weight_key in state_dict and f"{prefix}comfy_quant" not in state_dict:
+                    weight = state_dict[weight_key]
+                    if is_quantized(weight):
+                        state_dict[weight_key] = dequantize_tensor(
+                            weight,
+                            dtype=self.factory_kwargs["dtype"],
+                        )
+                    elif hasattr(weight, "dequantize"):
+                        state_dict[weight_key] = weight.dequantize().to(
+                            dtype=self.factory_kwargs["dtype"],
+                        )
                 return comfy.ops._load_quantized_module(
                     self,
                     torch.nn.Module._load_from_state_dict.__get__(self, type(self)),
diff --git a/tests/test_targeted_quantization.py b/tests/test_targeted_quantization.py
new file mode 100644
index 0000000..c2d630b
--- /dev/null
+++ b/tests/test_targeted_quantization.py
@@ -0,0 +1,49 @@
+import unittest
+from collections import OrderedDict
+
+import gguf
+import torch
+
+from tools.convert import (
+    MEBIBYTE,
+    ModelTemplate,
+    plan_target_size_quantization,
+)
+
+
+class TargetSizeQuantizationTests(unittest.TestCase):
+    def setUp(self):
+        self.model_arch = ModelTemplate()
+        self.state_dict = OrderedDict(
+            (f"blocks.{index}.weight", torch.ones((4096, 32), dtype=torch.float32))
+            for index in range(3)
+        )
+        self.state_dict["normalization.weight"] = torch.ones((4096,), dtype=torch.float32)
+
+    def test_reduces_center_core_layers_before_outer_layers(self):
+        plan, _, selected_size = plan_target_size_quantization(
+            self.state_dict, self.model_arch, 0.38
+        )
+
+        self.assertEqual(plan["blocks.1.weight"], gguf.GGMLQuantizationType.Q4_0)
+        self.assertEqual(plan["blocks.0.weight"], gguf.GGMLQuantizationType.I8)
+        self.assertEqual(plan["blocks.2.weight"], gguf.GGMLQuantizationType.I8)
+        self.assertLessEqual(selected_size, int(0.38 * MEBIBYTE))
+
+    def test_reduces_one_dimensional_weights_only_after_all_core_layers(self):
+        plan, _, selected_size = plan_target_size_quantization(
+            self.state_dict, self.model_arch, 0.222
+        )
+
+        for index in range(3):
+            self.assertEqual(plan[f"blocks.{index}.weight"], gguf.GGMLQuantizationType.Q4_0)
+        self.assertEqual(plan["normalization.weight"], gguf.GGMLQuantizationType.BF16)
+        self.assertLessEqual(selected_size, int(0.222 * MEBIBYTE))
+
+    def test_reports_minimum_when_target_is_unsupported(self):
+        with self.assertRaisesRegex(ValueError, "smallest supported TARGET_SIZE output"):
+            plan_target_size_quantization(self.state_dict, self.model_arch, 0.1)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tools/convert.py b/tools/convert.py
index 5ea3164..ab258ea 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -7,7 +7,7 @@
 import argparse
 from tqdm import tqdm
 from safetensors import safe_open
-from safetensors.torch import load_file, save_file
+from safetensors.torch import save_file
 
 QUANTIZATION_THRESHOLD = 1024
 REARRANGE_THRESHOLD = 512
@@ -312,6 +312,132 @@ def validate_key_patterns(model_arch, state_dict):
     # "Q4_PT": (gguf.GGMLQuantizationType.I8, None),
 }
 
+TARGET_SIZE_QUANT_TYPE = "TARGET_SIZE"
+MEBIBYTE = 1024 * 1024
+
+
+def _tensor_size_bytes(shape, quant_type):
+    """Return the GGUF payload size for a tensor with the given quantization."""
+    n_params = 1
+    for dim_size in shape:
+        n_params *= dim_size
+
+    if quant_type == gguf.GGMLQuantizationType.I8:
+        return n_params
+
+    block_size, type_size = gguf.constants.GGML_QUANT_SIZES[quant_type]
+    if n_params % block_size:
+        raise ValueError(
+            f"{quant_type.name} requires a tensor size divisible by {block_size}, "
+            f"got shape {tuple(shape)} ({n_params} elements)."
+        )
+    return n_params // block_size * type_size
+
+
+def _default_qtype(data_or_dtype):
+    return (
+        gguf.GGMLQuantizationType.BF16
+        if getattr(data_or_dtype, "dtype", data_or_dtype) == torch.bfloat16
+        else gguf.GGMLQuantizationType.F16
+    )
+
+
+def _is_target_core_tensor(key, data, model_arch):
+    if len(data.shape) != 2:
+        return False
+    if data.numel() <= QUANTIZATION_THRESHOLD:
+        return False
+    if key_matches(key, model_arch.keys_hiprec) or key_matches(key, model_arch.keys_noquant):
+        return False
+    return True
+
+
+def _can_use_q4_0(data):
+    block_size, _ = gguf.constants.GGML_QUANT_SIZES[gguf.GGMLQuantizationType.Q4_0]
+    return data.shape[-1] % block_size == 0
+
+
+def plan_target_size_quantization(state_dict, model_arch, max_size_mb):
+    """
+    Select per-tensor types that fit a maximum serialized payload size.
+
+    Core 2-D tensors start in Q8_CR. The center of their checkpoint order is
+    downgraded first, leaving the beginning and end at Q8_CR as long as possible.
+    Once every Q4-compatible core tensor is Q4_0, ordinary 1-D tensors may be
+    reduced to BF16. Protected tensors always remain F32.
+    """
+    if max_size_mb <= 0:
+        raise ValueError("--max-size-mb must be greater than zero.")
+
+    plan = {}
+    core_tensors = []
+    one_dimensional_tensors = []
+
+    for key, data in state_dict.items():
+        if key_matches(key, model_arch.keys_ignore):
+            continue
+        if key.endswith(".comfy_quant") or key.endswith("_scale") and len(data.shape) == 0:
+            continue
+        if len(data.shape) == 0 or len(data.shape) > MAX_TENSOR_DIMS:
+            continue
+
+        n_params = data.numel()
+        if len(data.shape) == 1 or n_params <= QUANTIZATION_THRESHOLD or key_matches(key, model_arch.keys_hiprec):
+            plan[key] = gguf.GGMLQuantizationType.F32
+            if len(data.shape) == 1 and not key_matches(key, model_arch.keys_hiprec):
+                one_dimensional_tensors.append((key, data))
+        elif key_matches(key, model_arch.keys_noquant):
+            plan[key] = _default_qtype(data)
+        elif len(data.shape) == 4 and "conv" in key.lower():
+            plan[key] = gguf.GGMLQuantizationType.F16
+        elif _is_target_core_tensor(key, data, model_arch):
+            plan[key] = gguf.GGMLQuantizationType.I8
+            if _can_use_q4_0(data):
+                core_tensors.append((key, data))
+        else:
+            plan[key] = _default_qtype(data)
+
+    def plan_size():
+        total = 0
+        for key, data in state_dict.items():
+            if key not in plan:
+                continue
+            qtype = plan[key]
+            total += _tensor_size_bytes(data.shape, qtype)
+            if qtype == gguf.GGMLQuantizationType.I8:
+                # Q8_CR stores a F32 scale for every output row.
+                total += data.shape[0] * 4
+        return total
+
+    target_size = int(max_size_mb * MEBIBYTE)
+    maximum_size = plan_size()
+    if maximum_size <= target_size:
+        return plan, maximum_size, maximum_size
+
+    center = (len(core_tensors) - 1) / 2
+    for index, (key, _) in sorted(
+        enumerate(core_tensors),
+        key=lambda item: (abs(item[0] - center), item[0]),
+    ):
+        plan[key] = gguf.GGMLQuantizationType.Q4_0
+        current_size = plan_size()
+        if current_size <= target_size:
+            return plan, maximum_size, current_size
+
+    for key, _ in one_dimensional_tensors:
+        plan[key] = gguf.GGMLQuantizationType.BF16
+        current_size = plan_size()
+        if current_size <= target_size:
+            return plan, maximum_size, current_size
+
+    minimum_size = plan_size()
+    raise ValueError(
+        f"Cannot shrink this model to {max_size_mb:g} MiB. "
+        f"The smallest supported TARGET_SIZE output is {minimum_size / MEBIBYTE:.2f} MiB "
+        f"(all Q4_0-compatible core matrices at Q4_0 and ordinary 1-D tensors at BF16). "
+        "Q3 and lower quantization are not supported."
+    )
+
 
 def quantize_int8_convrot(weight, convrot_groupsize=256):
     """
@@ -400,6 +526,16 @@ def parse_args():
         help="Target quantization type for eligible 2-D+ tensors "
              "(1-D biases/scales stay F32). Defaults to F16/BF16 matching the source dtype.",
     )
+    parser.add_argument(
+        "--max-size-mb",
+        type=float,
+        default=None,
+        help=(
+            "Maximum output payload size in MiB. Selects TARGET_SIZE quantization: "
+            "Q8_CR core weights are progressively changed to Q4_0 from the model "
+            "center outward, then ordinary 1-D tensors to BF16 if necessary."
+        ),
+    )
     args = parser.parse_args()
 
     if not os.path.isfile(args.src):
@@ -437,9 +573,13 @@ def strip_prefix(state_dict):
 
     return sd
 
-def load_state_dict(path):
+def load_state_dict(path, progress_callback=None):
     if any(path.endswith(x) for x in [".ckpt", ".pt", ".bin", ".pth"]):
-        state_dict = torch.load(path, map_location="cpu", weights_only=True)
+        with tqdm(total=1, desc="Reading checkpoint", unit="file") as progress:
+            state_dict = torch.load(path, map_location="cpu", weights_only=True)
+            progress.update()
+        if progress_callback is not None:
+            progress_callback("read", 1, 1)
         for subkey in ["model", "module"]:
             if subkey in state_dict:
                 state_dict = state_dict[subkey]
@@ -447,7 +587,13 @@ def load_state_dict(path):
         if len(state_dict) < 20:
             raise RuntimeError(f"pt subkey load failed: {state_dict.keys()}")
     else:
-        state_dict = load_file(path)
+        with safe_open(path, framework="pt", device="cpu") as checkpoint:
+            keys = list(checkpoint.keys())
+            state_dict = {}
+            for index, key in enumerate(tqdm(keys, desc="Reading tensors", unit="tensor"), start=1):
+                state_dict[key] = checkpoint.get_tensor(key)
+                if progress_callback is not None:
+                    progress_callback("read", index, len(keys))
 
     return strip_prefix(state_dict)
 
@@ -457,7 +603,7 @@ def load_safetensors_metadata(path):
     with safe_open(path, framework="pt", device="cpu") as checkpoint:
         return checkpoint.metadata() or {}
 
-def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_name=None):
+def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_name=None, quantization_plan=None, progress_callback=None):
     # Pre-collect per-tensor FP8 scales (0-dim float32 tensors named "{key}_scale").
     # These must be applied to their FP8 weight tensors before GGUF quantization.
     # The actual weight value is fp8_value * scale; ignoring scale produces wrong magnitudes.
@@ -480,7 +626,7 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
     if max_name_len > MAX_TENSOR_NAME_LENGTH:
         bad_list = ", ".join(f"{key!r} ({namelen})" for key, namelen in name_lengths if namelen > MAX_TENSOR_NAME_LENGTH)
         raise ValueError(f"Can only handle tensor names up to {MAX_TENSOR_NAME_LENGTH} characters. Tensors exceeding the limit: {bad_list}")
-    for key, data in tqdm(state_dict.items()):
+    for tensor_index, (key, data) in enumerate(tqdm(state_dict.items()), start=1):
         old_dtype = data.dtype
 
         if key_matches(key, model_arch.keys_ignore):
@@ -513,12 +659,7 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
 
         n_dims = len(data.shape)
         data_shape = data.shape
-        if old_dtype == torch.bfloat16:
-            data_qtype = gguf.GGMLQuantizationType.BF16
-        # elif old_dtype == torch.float32:
-        #     data_qtype = gguf.GGMLQuantizationType.F32
-        else:
-            data_qtype = gguf.GGMLQuantizationType.F16
+        data_qtype = _default_qtype(old_dtype)
 
         # The max no. of dimensions that can be handled by the quantization code is 4.
         if n_dims > MAX_TENSOR_DIMS:
@@ -535,7 +676,9 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
             or old_dtype in (torch.float32, torch.bfloat16)
             or old_dtype in _FP8_DTYPES
         )
-        if apply_quantization_rules:
+        if quantization_plan is not None and key in quantization_plan:
+            data_qtype = quantization_plan[key]
+        elif apply_quantization_rules:
             if n_dims == 1:
                 # One-dimensional tensors should be kept in F32. This is a
                 # universal safety net and must take priority over
@@ -569,13 +712,14 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
         # retired_quantize_int4_pytorch and are intentionally not selectable.
 
         # Q8_CR is the supported custom quantization path.
-        if quant_type_name == "Q8_CR" and data_qtype == quant_type and n_dims == 2:
-            if quant_type_name == "Q8_CR":
-                qdata, scale, quant_conf, orig_shape = quantize_int8_convrot(torch.from_numpy(data))
-                writer.add_tensor(key, qdata.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
-                writer.add_tensor(f"{key}_scale", scale.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
-                writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
-                continue
+        if data_qtype == gguf.GGMLQuantizationType.I8 and n_dims == 2:
+            qdata, scale, quant_conf, orig_shape = quantize_int8_convrot(torch.from_numpy(data))
+            writer.add_tensor(key, qdata.numpy(), raw_dtype=gguf.GGMLQuantizationType.I8)
+            writer.add_tensor(f"{key}_scale", scale.numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
+            writer.add_string(f"comfy.gguf.quant.{key}", json.dumps(quant_conf))
+            if progress_callback is not None:
+                progress_callback("quantize", tensor_index, len(state_dict))
+            continue
 
             # Q4_PT emission is retired with its runtime backend.
 
@@ -602,18 +746,38 @@ def handle_tensors(writer, state_dict, model_arch, quant_type=None, quant_type_n
         tqdm.write(f"{f'%-{max_name_len + 4}s' % f'{new_name}'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
 
         writer.add_tensor(new_name, data, raw_dtype=data_qtype)
+        if progress_callback is not None:
+            progress_callback("quantize", tensor_index, len(state_dict))
 
-def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type_name=None):
+def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type_name=None, max_size_mb=None, progress_callback=None):
     # load & run model detection logic
-    state_dict = load_state_dict(path)
+    state_dict = load_state_dict(path, progress_callback=progress_callback)
     source_metadata = load_safetensors_metadata(path)
     model_arch = detect_arch(state_dict)
     logging.info(f"* Architecture detected from input: {model_arch.arch}")
     validate_key_patterns(model_arch, state_dict)
 
+    if max_size_mb is not None and quant_type_name not in (None, TARGET_SIZE_QUANT_TYPE):
+        raise ValueError("--max-size-mb cannot be combined with --quant-type.")
+
+    quantization_plan = None
+    if max_size_mb is not None:
+        quant_type_name = TARGET_SIZE_QUANT_TYPE
+        quantization_plan, maximum_size, selected_size = plan_target_size_quantization(
+            state_dict, model_arch, max_size_mb
+        )
+        logging.info(
+            "TARGET_SIZE selected %.2f MiB from a Q8_CR baseline of %.2f MiB.",
+            selected_size / MEBIBYTE,
+            maximum_size / MEBIBYTE,
+        )
+
     # resolve quant type from name if provided
     quant_type = None
-    if quant_type_name is not None and quant_type_name in QUANT_TYPE_MAP:
+    if quantization_plan is not None:
+        ftype_name = TARGET_SIZE_QUANT_TYPE
+        ftype_gguf = None
+    elif quant_type_name is not None and quant_type_name in QUANT_TYPE_MAP:
         quant_type, ftype_gguf = QUANT_TYPE_MAP[quant_type_name]
         ftype_name = quant_type_name
     else:
@@ -651,7 +815,15 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type
     if "config" in source_metadata:
         writer.add_string("config", source_metadata["config"])
 
-    handle_tensors(writer, state_dict, model_arch, quant_type=quant_type, quant_type_name=quant_type_name)
+    handle_tensors(
+        writer,
+        state_dict,
+        model_arch,
+        quant_type=quant_type,
+        quant_type_name=quant_type_name,
+        quantization_plan=quantization_plan,
+        progress_callback=progress_callback,
+    )
     writer.write_header_to_file(path=dst_path)
     writer.write_kv_data_to_file()
     writer.write_tensors_to_file(progress=True)
@@ -666,4 +838,4 @@ def convert_file(path, dst_path=None, interact=True, overwrite=False, quant_type
 
 if __name__ == "__main__":
     args = parse_args()
-    convert_file(args.src, args.dst, quant_type_name=args.quant_type)
+    convert_file(args.src, args.dst, quant_type_name=args.quant_type, max_size_mb=args.max_size_mb)

From 17901fa8def03fc654c0ba78a8e830bb9510429e Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Mon, 3 Aug 2026 23:07:49 +0200
Subject: [PATCH 17/20] Add Minimax H3 support

---
 README.md                           | 12 ++++-
 loader.py                           |  2 +-
 tests/test_targeted_quantization.py | 68 +++++++++++++++++++++++++++++
 tools/convert.py                    | 18 +++++++-
 4 files changed, 96 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 002f088..43f5855 100644
--- a/README.md
+++ b/README.md
@@ -53,9 +53,10 @@ Initial support for quantizing T5 has also been added recently, these can be use
 
 See the instructions in the [tools](https://github.com/city96/ComfyUI-GGUF/tree/main/tools) folder for how to create your own quants.
 
-## Converting Krea 2 and Ideogram 4 models
+## Converting Krea 2, Ideogram 4, and Minimax M3 models
 
-The converter detects supported Krea 2 and Ideogram 4 checkpoints directly.
+The converter detects supported Krea 2, Ideogram 4, and native Minimax M3
+(`minimax_h3`) checkpoints directly.
 Provide an existing `.safetensors`, `.ckpt`, `.pt`, `.pth`, or `.bin` diffusion
 model file; no model-specific conversion script is required.
 
@@ -76,6 +77,13 @@ python tools/convert.py --src /path/to/krea2_or_ideogram.safetensors \
   --dst /path/to/model-Q8_CR.gguf --quant-type Q8_CR
 ```
 
+For a native Minimax M3 checkpoint, use the same command:
+
+```bash
+python tools/convert.py --src /path/to/minimax_m3.safetensors \
+  --dst /path/to/minimax_m3-Q8_CR.gguf --quant-type Q8_CR
+```
+
 For the portable Windows distribution, use its embedded Python executable:
 
 ```bat
diff --git a/loader.py b/loader.py
index 967ee47..4684b69 100644
--- a/loader.py
+++ b/loader.py
@@ -12,7 +12,7 @@
 from .dequant import is_quantized, dequantize_tensor
 from .quant_ops import make_quantized
 
-IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram", "krea2"}
+IMG_ARCH_LIST = {"flux", "sd1", "sdxl", "sd3", "aura", "hidream", "cosmos", "ltxv", "hyvid", "wan", "lumina2", "qwen_image", "ideogram", "krea2", "minimax_h3"}
 TXT_ARCH_LIST = {"t5", "t5encoder", "llama", "qwen2vl", "qwen3", "qwen3vl", "gemma3"}
 VIS_TYPE_LIST = {"clip-vision", "mmproj"}
 
diff --git a/tests/test_targeted_quantization.py b/tests/test_targeted_quantization.py
index c2d630b..5cd8b84 100644
--- a/tests/test_targeted_quantization.py
+++ b/tests/test_targeted_quantization.py
@@ -1,12 +1,18 @@
 import unittest
 from collections import OrderedDict
+from pathlib import Path
+from tempfile import TemporaryDirectory
 
 import gguf
 import torch
+from safetensors.torch import save_file
 
 from tools.convert import (
     MEBIBYTE,
+    ModelMinimaxH3,
     ModelTemplate,
+    convert_file,
+    detect_arch,
     plan_target_size_quantization,
 )
 
@@ -45,5 +51,67 @@ def test_reports_minimum_when_target_is_unsupported(self):
             plan_target_size_quantization(self.state_dict, self.model_arch, 0.1)
 
 
+class MinimaxH3DetectionTests(unittest.TestCase):
+    def test_detects_native_minimax_h3_checkpoint_layout(self):
+        checkpoint_keys = {
+            "video_patch_proj.weight",
+            "audio_patch_proj.weight",
+            "blocks.0.attn.qkv_proj.weight",
+            "final_layer.video_out.weight",
+        }
+
+        model_arch = detect_arch(checkpoint_keys)
+
+        self.assertIsInstance(model_arch, ModelMinimaxH3)
+        self.assertEqual(model_arch.arch, "minimax_h3")
+
+    def test_keeps_adaln_curve_table_in_full_precision(self):
+        model_arch = ModelMinimaxH3()
+
+        self.assertIn("adaln_t_table", model_arch.keys_hiprec)
+
+    def test_converts_to_minimax_h3_gguf_with_full_precision_adaln_table(self):
+        state_dict = {
+            "video_patch_proj.weight": torch.ones((32, 32), dtype=torch.float32),
+            "audio_patch_proj.weight": torch.ones((32, 32), dtype=torch.float32),
+            "blocks.0.attn.qkv_proj.weight": torch.ones((96, 32), dtype=torch.float32),
+            "final_layer.video_out.weight": torch.ones((96, 32), dtype=torch.float32),
+            "adaln_t_table": torch.ones((32, 32), dtype=torch.float32),
+        }
+
+        with TemporaryDirectory() as temp_dir:
+            source_path = Path(temp_dir) / "minimax_h3.safetensors"
+            output_path = Path(temp_dir) / "minimax_h3-Q8_0.gguf"
+            save_file(state_dict, str(source_path))
+
+            converted_path, model_arch = convert_file(
+                str(source_path),
+                str(output_path),
+                interact=False,
+                quant_type_name="Q8_0",
+            )
+
+            reader = gguf.GGUFReader(converted_path)
+            tensor_types = {tensor.name: tensor.tensor_type for tensor in reader.tensors}
+            architecture = reader.get_field("general.architecture")
+            architecture_name = str(architecture.parts[architecture.data[-1]], "utf-8")
+            del architecture
+            reader.tensors.clear()
+            reader.fields.clear()
+            reader.data._mmap.close()
+            del reader
+
+        self.assertEqual(model_arch.arch, "minimax_h3")
+        self.assertEqual(architecture_name, "minimax_h3")
+        self.assertEqual(
+            tensor_types["blocks.0.attn.qkv_proj.weight"],
+            gguf.GGMLQuantizationType.Q8_0,
+        )
+        self.assertEqual(
+            tensor_types["adaln_t_table"],
+            gguf.GGMLQuantizationType.F32,
+        )
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/tools/convert.py b/tools/convert.py
index ab258ea..172163f 100644
--- a/tools/convert.py
+++ b/tools/convert.py
@@ -240,9 +240,25 @@ class ModelKrea2(ModelTemplate):
         "^txtfusion.projector.",
     ]
 
+
+class ModelMinimaxH3(ModelTemplate):
+    arch = "minimax_h3"
+    keys_detect = [
+        (
+            "video_patch_proj.weight",
+            "audio_patch_proj.weight",
+            "blocks.0.attn.qkv_proj.weight",
+            "final_layer.video_out.weight",
+        )
+    ]
+    # This is a model buffer used to interpolate timestep embeddings, rather
+    # than a Linear weight. Keep it in FP32 for the native interpolation path.
+    keys_hiprec = ["adaln_t_table"]
+
+
 arch_list = [ModelFlux, ModelSD3, ModelAura, ModelHiDream, CosmosPredict2,
              ModelLTXV, ModelHyVid, ModelWan, ModelSDXL, ModelSD1, ModelLumina2,
-             ModelKrea2, ModelIdeogram]
+             ModelKrea2, ModelIdeogram, ModelMinimaxH3]
 
 def is_model_arch(model, state_dict):
     # check if model is correct

From 8070200d93bdfc3e74ad57c60edb165f10d756e7 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Tue, 4 Aug 2026 00:05:17 +0200
Subject: [PATCH 18/20] Add GitHub Actions workflow for publishing to Comfy
 Registry

---
 .github/workflows/publish.yml | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 .github/workflows/publish.yml

diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
new file mode 100644
index 0000000..e8525ad
--- /dev/null
+++ b/.github/workflows/publish.yml
@@ -0,0 +1,18 @@
+name: Publish to Comfy Registry
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  publish-node:
+    name: Publish Custom Node
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

From 26b506cb189e60c939be9fb203f46c8828d01196 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Tue, 4 Aug 2026 00:08:10 +0200
Subject: [PATCH 19/20] Update pyproject.toml

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index c3aca51..1e85677 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "comfyui-gguf-reboot"
-version = "1.0.0"
+version = "v26-08-04"
 description = "Actively maintained fork of ComfyUI-GGUF node pack with support for new models, INT8 GGUFs and optionally ComfyUI Dynamic VRAM"
 readme = "README.md"
 license = { file = "LICENSE" }

From 80b7ab622b68894de8bf533bfa730a16b58d62b9 Mon Sep 17 00:00:00 2001
From: molbal <molbal@outlook.com>
Date: Tue, 4 Aug 2026 00:12:09 +0200
Subject: [PATCH 20/20] Add workflow_dispatch trigger to publish.yml

---
 .github/workflows/publish.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index e8525ad..9b8932f 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -1,6 +1,7 @@
 name: Publish to Comfy Registry
 
 on:
+  workflow_dispatch:
   release:
     types: [published]