diff --git a/invokeai/app/services/model_records/model_records_base.py b/invokeai/app/services/model_records/model_records_base.py
index b5ecd034c3d..4b5afeeca79 100644
--- a/invokeai/app/services/model_records/model_records_base.py
+++ b/invokeai/app/services/model_records/model_records_base.py
@@ -27,6 +27,7 @@
     Flux2VariantType,
     FluxVariantType,
     Krea2VariantType,
+    MiniMaxH3VariantType,
     ModelFormat,
     ModelSourceType,
     ModelType,
@@ -142,6 +143,7 @@ def validate_source_url(cls, v: Any) -> Optional[str]:
         | WanLoRAVariantType
         | Qwen3VariantType
         | Krea2VariantType
+        | MiniMaxH3VariantType
         | PiDDecoderVariantType
     ] = Field(description="The variant of the model.", default=None)
     prediction_type: Optional[SchedulerPredictionType] = Field(
diff --git a/invokeai/backend/minimax_h3/__init__.py b/invokeai/backend/minimax_h3/__init__.py
new file mode 100644
index 00000000000..2500f851e58
--- /dev/null
+++ b/invokeai/backend/minimax_h3/__init__.py
@@ -0,0 +1,25 @@
+"""MiniMax H3 (Hailuo 3.0) model classes, vendored from the in-progress diffusers integration.
+
+Vendored from huggingface/diffusers PR #14355 ("Add MiniMax-H3") at commit
+abc5e9bf71fd38f53cd471bc3acaa84bc5ecbfdc (branch `minimax-h3`), which is not yet
+in any tagged diffusers release. The only local changes are rewriting the
+package-relative imports to absolute `diffusers.*` imports (all referenced
+symbols exist in the pinned diffusers==0.39.0) and ruff import sorting. Keep
+these files otherwise
+byte-identical to upstream: when a diffusers release ships the H3 classes,
+delete this vendoring and import them from diffusers instead.
+
+Apache-2.0, copyright The HuggingFace Team / MiniMax (see file headers).
+"""
+
+from invokeai.backend.minimax_h3.autoencoder_kl_minimax_h3 import AutoencoderKLMiniMaxH3
+from invokeai.backend.minimax_h3.autoencoder_kl_minimax_h3_audio import AutoencoderKLMiniMaxH3Audio
+from invokeai.backend.minimax_h3.scheduling_minimax_h3 import MiniMaxH3Scheduler
+from invokeai.backend.minimax_h3.transformer_minimax_h3 import MiniMaxH3Transformer3DModel
+
+__all__ = [
+    "AutoencoderKLMiniMaxH3",
+    "AutoencoderKLMiniMaxH3Audio",
+    "MiniMaxH3Scheduler",
+    "MiniMaxH3Transformer3DModel",
+]
diff --git a/invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3.py b/invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3.py
new file mode 100644
index 00000000000..45a05fc719e
--- /dev/null
+++ b/invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3.py
@@ -0,0 +1,920 @@
+# Copyright 2026 The MiniMax and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention import AttentionMixin, AttentionModuleMixin, FeedForward
+from diffusers.models.attention_dispatch import dispatch_attention_fn
+from diffusers.models.autoencoders.vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
+from diffusers.models.modeling_outputs import AutoencoderKLOutput
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import logging
+from diffusers.utils.accelerate_utils import apply_forward_hook
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class MiniMaxH3VideoCausalConv3d(nn.Conv3d):
+    r"""
+    3D convolution used throughout the MiniMax-H3 video encoder.
+
+    Spatial padding is symmetric and uses `spatial_padding_mode` (`"reflect"` in the released checkpoint); temporal
+    padding is causal, i.e. `kernel_size_t - 1` zero frames are prepended and nothing is appended.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int | tuple[int, int, int],
+        stride: int | tuple[int, int, int] = 1,
+        spatial_padding: int = 0,
+        temporal_padding: int = 0,
+        spatial_padding_mode: str = "reflect",
+    ) -> None:
+        super().__init__(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=0)
+        self.spatial_padding = spatial_padding
+        self.temporal_padding = temporal_padding
+        self.spatial_padding_mode = spatial_padding_mode
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.spatial_padding > 0:
+            padding = self.spatial_padding
+            hidden_states = F.pad(
+                hidden_states, (padding, padding, padding, padding, 0, 0), mode=self.spatial_padding_mode
+            )
+        if self.temporal_padding > 0:
+            hidden_states = F.pad(hidden_states, (0, 0, 0, 0, self.temporal_padding, 0), mode="constant")
+        return F.conv3d(hidden_states, self.weight, self.bias, stride=self.stride, padding=0, dilation=self.dilation)
+
+
+class MiniMaxH3VideoGroupNorm(nn.GroupNorm):
+    r"""
+    Group normalization applied to each latent frame in isolation (`use_t_isolated_gn` in the original config): the
+    temporal axis is folded into the batch axis so statistics never mix across frames.
+    """
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).contiguous()
+        hidden_states = hidden_states.view(batch_size * num_frames, num_channels, 1, height, width)
+        hidden_states = super().forward(hidden_states)
+        hidden_states = hidden_states.view(batch_size, num_frames, num_channels, height, width)
+        return hidden_states.permute(0, 2, 1, 3, 4).contiguous()
+
+
+class MiniMaxH3VideoResnetBlock3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-6,
+        spatial_padding_mode: str = "reflect",
+    ) -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.norm1 = MiniMaxH3VideoGroupNorm(norm_num_groups, in_channels, eps=norm_eps, affine=True)
+        self.conv1 = MiniMaxH3VideoCausalConv3d(
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            spatial_padding=1,
+            temporal_padding=2,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+        self.norm2 = MiniMaxH3VideoGroupNorm(norm_num_groups, out_channels, eps=norm_eps, affine=True)
+        self.conv2 = MiniMaxH3VideoCausalConv3d(
+            out_channels,
+            out_channels,
+            kernel_size=3,
+            spatial_padding=1,
+            temporal_padding=2,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = MiniMaxH3VideoCausalConv3d(in_channels, out_channels, kernel_size=1)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = F.silu(self.norm1(hidden_states))
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = F.silu(self.norm2(hidden_states))
+        hidden_states = self.conv2(hidden_states)
+        if self.conv_shortcut is not None:
+            residual = self.conv_shortcut(residual)
+        return residual + hidden_states
+
+
+class MiniMaxH3VideoDownsample3d(nn.Module):
+    r"""
+    Strided 3x3x3 downsampling convolution. A spatial stride of 2 is preceded by an asymmetric bottom/right pad of 1
+    (the convolution itself carries no spatial padding), so the output is exactly `ceil(size / 2)`.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temporal_stride: int = 1,
+        spatial_stride: int = 2,
+        spatial_padding_mode: str = "reflect",
+    ) -> None:
+        super().__init__()
+        self.spatial_stride = spatial_stride
+        self.spatial_padding_mode = spatial_padding_mode
+        self.conv = MiniMaxH3VideoCausalConv3d(
+            in_channels,
+            out_channels,
+            kernel_size=3,
+            stride=(temporal_stride, spatial_stride, spatial_stride),
+            spatial_padding=0,
+            temporal_padding=2,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if self.spatial_stride == 2:
+            hidden_states = F.pad(hidden_states, (0, 1, 0, 1, 0, 0), mode=self.spatial_padding_mode)
+        return self.conv(hidden_states)
+
+
+class MiniMaxH3VideoDownBlock3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int,
+        temporal_downsample_factor: int,
+        spatial_downsample_factor: int,
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-6,
+        spatial_padding_mode: str = "reflect",
+    ) -> None:
+        super().__init__()
+        self.resnets = nn.ModuleList(
+            [
+                MiniMaxH3VideoResnetBlock3d(
+                    in_channels=in_channels if i == 0 else out_channels,
+                    out_channels=out_channels,
+                    norm_num_groups=norm_num_groups,
+                    norm_eps=norm_eps,
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+                for i in range(num_layers)
+            ]
+        )
+        self.downsamplers = None
+        if temporal_downsample_factor * spatial_downsample_factor > 1:
+            self.downsamplers = nn.ModuleList(
+                [
+                    MiniMaxH3VideoDownsample3d(
+                        out_channels,
+                        out_channels,
+                        temporal_stride=temporal_downsample_factor,
+                        spatial_stride=spatial_downsample_factor,
+                        spatial_padding_mode=spatial_padding_mode,
+                    )
+                ]
+            )
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for resnet in self.resnets:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states)
+            else:
+                hidden_states = resnet(hidden_states)
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+        return hidden_states
+
+
+class MiniMaxH3VideoEncoder3d(nn.Module):
+    r"""
+    Causal 3D CNN encoder. `block_out_channels` gives the channel count of every level; the per-level
+    `spatial_downsample_factors` / `temporal_downsample_factors` multiply out to the total compression ratios.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 48,
+        block_out_channels: tuple[int, ...] = (128, 256, 256, 512, 512, 1024),
+        layers_per_block: int = 2,
+        spatial_downsample_factors: tuple[int, ...] = (2, 2, 2, 2, 1, 1),
+        temporal_downsample_factors: tuple[int, ...] = (1, 2, 2, 1, 1, 1),
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-6,
+        spatial_padding_mode: str = "reflect",
+    ) -> None:
+        super().__init__()
+
+        self.conv_in = MiniMaxH3VideoCausalConv3d(
+            in_channels,
+            block_out_channels[0],
+            kernel_size=3,
+            spatial_padding=1,
+            temporal_padding=2,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+        block_in_channels = (block_out_channels[0],) + tuple(block_out_channels[:-1])
+        self.down_blocks = nn.ModuleList(
+            [
+                MiniMaxH3VideoDownBlock3d(
+                    in_channels=block_in_channels[i],
+                    out_channels=block_out_channels[i],
+                    num_layers=layers_per_block,
+                    temporal_downsample_factor=temporal_downsample_factors[i],
+                    spatial_downsample_factor=spatial_downsample_factors[i],
+                    norm_num_groups=norm_num_groups,
+                    norm_eps=norm_eps,
+                    spatial_padding_mode=spatial_padding_mode,
+                )
+                for i in range(len(block_out_channels))
+            ]
+        )
+
+        self.norm_out = MiniMaxH3VideoGroupNorm(norm_num_groups, block_out_channels[-1], eps=norm_eps, affine=True)
+        self.conv_out = MiniMaxH3VideoCausalConv3d(
+            block_out_channels[-1],
+            out_channels,
+            kernel_size=3,
+            spatial_padding=1,
+            temporal_padding=2,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv_in(hidden_states)
+        for down_block in self.down_blocks:
+            hidden_states = down_block(hidden_states)
+        hidden_states = F.silu(self.norm_out(hidden_states))
+        return self.conv_out(hidden_states)
+
+
+class MiniMaxH3VideoRotaryPosEmbed(nn.Module):
+    r"""
+    3-axis rotary embedding for the ViT decoder. Coordinates are length-normalized to `[-1, 1)` per axis and scaled by
+    `2 * pi`, and the resulting `(t, h, w)` angles are concatenated and then duplicated, so the first
+    `rope_dim_ratio * attention_head_dim` channels of every head are rotated.
+    """
+
+    def __init__(self, dim: int, theta: float = 100.0, num_axes: int = 3) -> None:
+        super().__init__()
+        if dim % (2 * num_axes) != 0:
+            raise ValueError(f"`dim` {dim} must be divisible by `2 * num_axes` {2 * num_axes}.")
+        inv_freq = 1.0 / theta ** torch.arange(0, 1, 2 * num_axes / dim, dtype=torch.float32)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        angles = 2.0 * math.pi * position_ids[:, :, :, None] * self.inv_freq[None, None, None, :]
+        angles = angles.flatten(2, 3).tile(2).unsqueeze(2)
+        return angles.cos(), angles.sin()
+
+
+class MiniMaxH3VideoAttnProcessor:
+    _attention_backend = None
+    _parallel_config = None
+
+    def __call__(
+        self,
+        attn: "MiniMaxH3VideoAttention",
+        hidden_states: torch.Tensor,
+        rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+    ) -> torch.Tensor:
+        query = attn.to_q(hidden_states).unflatten(2, (attn.heads, -1))
+        key = attn.to_k(hidden_states).unflatten(2, (attn.heads, -1))
+        value = attn.to_v(hidden_states).unflatten(2, (attn.heads, -1))
+
+        # The reference normalizes Q/K in float32 regardless of the compute dtype.
+        query = attn.norm_q(query.float()).to(query.dtype)
+        key = attn.norm_k(key.float()).to(key.dtype)
+
+        if rotary_emb is not None:
+            cos, sin = rotary_emb
+            cos = cos.to(query.dtype)
+            sin = sin.to(query.dtype)
+            rotary_dim = cos.shape[-1]
+            query_rotary, query_pass = query[..., :rotary_dim], query[..., rotary_dim:]
+            key_rotary, key_pass = key[..., :rotary_dim], key[..., rotary_dim:]
+            query_first, query_second = query_rotary.chunk(2, dim=-1)
+            key_first, key_second = key_rotary.chunk(2, dim=-1)
+            query_rotated = torch.cat([-query_second, query_first], dim=-1)
+            key_rotated = torch.cat([-key_second, key_first], dim=-1)
+            query = torch.cat([query_rotary * cos + query_rotated * sin, query_pass], dim=-1)
+            key = torch.cat([key_rotary * cos + key_rotated * sin, key_pass], dim=-1)
+
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=None,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+        hidden_states = hidden_states.flatten(2, 3)
+        return attn.to_out[0](hidden_states)
+
+
+class MiniMaxH3VideoAttention(nn.Module, AttentionModuleMixin):
+    _default_processor_cls = MiniMaxH3VideoAttnProcessor
+    _available_processors = [MiniMaxH3VideoAttnProcessor]
+
+    def __init__(self, dim: int, heads: int, dim_head: int, eps: float = 1e-5, bias: bool = True) -> None:
+        super().__init__()
+        self.heads = heads
+        self.dim_head = dim_head
+        self.use_bias = bias
+        inner_dim = heads * dim_head
+
+        self.norm_q = nn.RMSNorm(dim_head, eps=eps, elementwise_affine=False)
+        self.norm_k = nn.RMSNorm(dim_head, eps=eps, elementwise_affine=False)
+        self.to_q = nn.Linear(dim, inner_dim, bias=bias)
+        self.to_k = nn.Linear(dim, inner_dim, bias=bias)
+        self.to_v = nn.Linear(dim, inner_dim, bias=bias)
+        self.to_out = nn.ModuleList([nn.Linear(inner_dim, dim, bias=bias), nn.Dropout(0.0)])
+
+        self.set_processor(MiniMaxH3VideoAttnProcessor())
+
+    def forward(
+        self, hidden_states: torch.Tensor, rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None
+    ) -> torch.Tensor:
+        return self.processor(self, hidden_states, rotary_emb)
+
+
+class MiniMaxH3VideoTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        heads: int,
+        dim_head: int,
+        ffn_mult: int = 4,
+        eps: float = 1e-5,
+        bias: bool = True,
+    ) -> None:
+        super().__init__()
+        self.norm1 = nn.RMSNorm(dim, eps=eps, elementwise_affine=True)
+        self.attn = MiniMaxH3VideoAttention(dim=dim, heads=heads, dim_head=dim_head, eps=eps, bias=bias)
+        self.scale1 = nn.Parameter(torch.zeros(dim))
+        self.norm2 = nn.RMSNorm(dim, eps=eps, elementwise_affine=True)
+        self.ff = FeedForward(dim, mult=ffn_mult, activation_fn="swiglu", bias=bias)
+        self.scale2 = nn.Parameter(torch.zeros(dim))
+
+    def forward(
+        self, hidden_states: torch.Tensor, rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None
+    ) -> torch.Tensor:
+        # The reference normalizes in float32 regardless of the compute dtype.
+        norm_hidden_states = self.norm1(hidden_states.float()).to(hidden_states.dtype)
+        hidden_states = hidden_states + self.attn(norm_hidden_states, rotary_emb) * self.scale1
+        norm_hidden_states = self.norm2(hidden_states.float()).to(hidden_states.dtype)
+        hidden_states = hidden_states + self.ff(norm_hidden_states) * self.scale2
+        return hidden_states
+
+
+class MiniMaxH3VideoViTDecoder3d(nn.Module):
+    r"""
+    Non-causal ViT decoder. Every latent voxel becomes one token; `num_register_tokens` learned register tokens plus a
+    single all-zero token are appended (all at position `0`), attended over with full self-attention, and dropped
+    again before the patch projection expands each token into a `patch_size_t x patch_size x patch_size` pixel block.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 24,
+        out_channels: int = 3,
+        patch_size: int = 16,
+        patch_size_t: int = 4,
+        num_layers: int = 36,
+        num_attention_heads: int = 32,
+        attention_head_dim: int = 64,
+        num_register_tokens: int = 4,
+        ffn_mult: int = 4,
+        rope_theta: float = 100.0,
+        rope_dim_ratio: float = 0.75,
+        norm_eps: float = 1e-5,
+    ) -> None:
+        super().__init__()
+        dim = num_attention_heads * attention_head_dim
+        self.patch_size = patch_size
+        self.patch_size_t = patch_size_t
+        self.out_channels = out_channels
+        self.num_register_tokens = num_register_tokens
+
+        self.rope = MiniMaxH3VideoRotaryPosEmbed(int(attention_head_dim * rope_dim_ratio), theta=rope_theta)
+        self.proj_in = nn.Linear(in_channels, dim)
+        self.register_tokens = nn.Parameter(torch.zeros(1, num_register_tokens, dim))
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MiniMaxH3VideoTransformerBlock(
+                    dim=dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    ffn_mult=ffn_mult,
+                    eps=norm_eps,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.norm_out = nn.LayerNorm(dim, elementwise_affine=True, eps=norm_eps)
+        self.proj_out = nn.Linear(dim, out_channels * patch_size_t * patch_size * patch_size)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+
+        hidden_states = hidden_states.permute(0, 2, 3, 4, 1).reshape(
+            batch_size, num_frames * height * width, num_channels
+        )
+        hidden_states = self.proj_in(hidden_states)
+        num_patches = hidden_states.shape[1]
+
+        register_tokens = self.register_tokens.expand(batch_size, -1, -1)
+        cls_token = torch.zeros_like(hidden_states[:, :1, :])
+        hidden_states = torch.cat([hidden_states, register_tokens, cls_token], dim=1)
+
+        grids = [
+            2.0 * (torch.arange(0.5, size, dtype=torch.float32, device=hidden_states.device) / size) - 1.0
+            for size in (num_frames, height, width)
+        ]
+        position_ids = torch.stack(torch.meshgrid(*grids, indexing="ij"), dim=-1).flatten(0, 2)
+        position_ids = position_ids.unsqueeze(0).expand(batch_size, -1, -1)
+        suffix_ids = position_ids.new_zeros((batch_size, self.num_register_tokens + 1, 3))
+        position_ids = torch.cat([position_ids, suffix_ids], dim=1)
+        rotary_emb = self.rope(position_ids)
+
+        for block in self.transformer_blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(block, hidden_states, rotary_emb)
+            else:
+                hidden_states = block(hidden_states, rotary_emb)
+
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states[:, :num_patches, :]
+
+        patch_size, patch_size_t = self.patch_size, self.patch_size_t
+        hidden_states = hidden_states.view(
+            batch_size,
+            num_frames,
+            height,
+            width,
+            self.out_channels,
+            patch_size_t,
+            patch_size,
+            patch_size,
+        )
+        hidden_states = hidden_states.permute(0, 4, 1, 5, 2, 6, 3, 7).contiguous()
+        return hidden_states.reshape(
+            batch_size,
+            self.out_channels,
+            num_frames * patch_size_t,
+            height * patch_size,
+            width * patch_size,
+        )
+
+
+class AutoencoderKLMiniMaxH3(ModelMixin, ConfigMixin, AttentionMixin, AutoencoderMixin):
+    r"""
+    A VAE model with a causal 3D CNN encoder and a non-causal ViT decoder, used in
+    [MiniMax-H3](https://huggingface.co/MiniMaxAI).
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Latents are normalized with per-channel `latents_mean` / `latents_std` rather than a `scaling_factor`; a pipeline
+    encodes with `(latent - latents_mean) / latents_std` and decodes with `latent * latents_std + latents_mean`.
+
+    The pixel convention is ImageNet-normalized RGB over a `[0, 1]` base range, not the usual `[-1, 1]`: `encode`
+    expects `(pixel - imagenet_mean) / imagenet_std` and `decode` returns values in that same space, so a pipeline has
+    to apply `sample * imagenet_std + imagenet_mean` (mean `(0.485, 0.456, 0.406)`, std `(0.229, 0.224, 0.225)`) and
+    clamp to `[0, 1]` before postprocessing.
+
+    The temporal geometry is fixed by `clip_length` (17 pixel frames per encoder chunk) and `token_drop` (3 trailing
+    latent frames dropped per encode): `17 * n + 5` pixel frames map to `5 * n + 2` latent frames.
+
+    Unlike most autoencoders in the library, spatial tiling is **on by default**: MiniMax-H3 was released with tiling
+    enabled for both encoding and decoding, and the released frames are the blended-tile ones, so disabling tiling
+    changes the output. Use `enable_tiling` to change the tile geometry, `disable_tiling` to turn it off.
+    """
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["MiniMaxH3VideoResnetBlock3d", "MiniMaxH3VideoTransformerBlock"]
+    _repeated_blocks = ["MiniMaxH3VideoTransformerBlock"]
+    _skip_layerwise_casting_patterns = ["norm"]
+    # The released checkpoint is float32 and the verified decode recipe is float16 *autocast over float32 weights*
+    # (see `decode`). A pipeline-level `torch_dtype=torch.bfloat16` must therefore not downcast the weights, so every
+    # top-level module is pinned, mirroring the transformer's mixed-precision contract.
+    _keep_in_fp32_modules = ["encoder", "decoder", "quant_conv", "post_quant_conv"]
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        latent_channels: int = 24,
+        block_out_channels: tuple[int, ...] = (128, 256, 256, 512, 512, 1024),
+        layers_per_block: int = 2,
+        spatial_downsample_factors: tuple[int, ...] = (2, 2, 2, 2, 1, 1),
+        temporal_downsample_factors: tuple[int, ...] = (1, 2, 2, 1, 1, 1),
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-6,
+        spatial_padding_mode: str = "reflect",
+        decoder_num_layers: int = 36,
+        decoder_num_attention_heads: int = 32,
+        decoder_attention_head_dim: int = 64,
+        decoder_num_register_tokens: int = 4,
+        decoder_ffn_mult: int = 4,
+        decoder_rope_theta: float = 100.0,
+        decoder_rope_dim_ratio: float = 0.75,
+        decoder_norm_eps: float = 1e-5,
+        clip_length: int = 17,
+        token_drop: int = 3,
+        latents_mean: tuple[float, ...] = (0.0,) * 24,
+        latents_std: tuple[float, ...] = (1.0,) * 24,
+    ) -> None:
+        super().__init__()
+
+        self.spatial_compression_ratio = math.prod(spatial_downsample_factors)
+        self.temporal_compression_ratio = math.prod(temporal_downsample_factors)
+
+        self.encoder = MiniMaxH3VideoEncoder3d(
+            in_channels=in_channels,
+            out_channels=2 * latent_channels,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            spatial_downsample_factors=spatial_downsample_factors,
+            temporal_downsample_factors=temporal_downsample_factors,
+            norm_num_groups=norm_num_groups,
+            norm_eps=norm_eps,
+            spatial_padding_mode=spatial_padding_mode,
+        )
+        self.quant_conv = nn.Conv3d(2 * latent_channels, 2 * latent_channels, kernel_size=1)
+        self.post_quant_conv = nn.Conv3d(latent_channels, latent_channels, kernel_size=1)
+        self.decoder = MiniMaxH3VideoViTDecoder3d(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            patch_size=self.spatial_compression_ratio,
+            patch_size_t=self.temporal_compression_ratio,
+            num_layers=decoder_num_layers,
+            num_attention_heads=decoder_num_attention_heads,
+            attention_head_dim=decoder_attention_head_dim,
+            num_register_tokens=decoder_num_register_tokens,
+            ffn_mult=decoder_ffn_mult,
+            rope_theta=decoder_rope_theta,
+            rope_dim_ratio=decoder_rope_dim_ratio,
+            norm_eps=decoder_norm_eps,
+        )
+
+        # Derived temporal-chunking geometry. `clip_length` pixel frames are encoded at a time; because
+        # `clip_length` is not a multiple of `temporal_compression_ratio`, the decoder has to re-derive the
+        # implicit leading pad (`frame_pre_padding`) and the overlap that `token_drop` leaves behind.
+        self.frame_pre_padding = (-clip_length) % self.temporal_compression_ratio
+        self.tokens_chunk_size = math.ceil(clip_length / self.temporal_compression_ratio)
+        self.token_overlap = (-token_drop) % self.tokens_chunk_size
+        self.frame_overlap = max(self.token_overlap * self.temporal_compression_ratio - self.frame_pre_padding, 0)
+
+        # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
+        # to perform decoding of a single video latent at a time.
+        self.use_slicing = False
+
+        # When encoding/decoding spatially large videos, the memory requirement is very high. By splitting the frames
+        # into smaller tiles, running the encoder/decoder per tile and blending the overlaps, the memory requirement
+        # can be lowered. MiniMax-H3 ships with tiling enabled.
+        self.use_tiling = True
+
+        # The tile size in pixel space, and the minimum overlap between two neighbouring tiles. The actual overlaps are
+        # widened (in multiples of `spatial_compression_ratio`) so that the tiles cover the frame exactly.
+        self.tile_sample_min_height = 256
+        self.tile_sample_min_width = 256
+        self.tile_sample_min_overlap_height = 64
+        self.tile_sample_min_overlap_width = 64
+
+    def enable_tiling(
+        self,
+        tile_sample_min_height: int | None = None,
+        tile_sample_min_width: int | None = None,
+        tile_sample_min_overlap_height: int | None = None,
+        tile_sample_min_overlap_width: int | None = None,
+    ) -> None:
+        r"""
+        Enable tiled VAE encoding/decoding. When this option is enabled, the VAE splits the frames into tiles, encodes
+        or decodes each tile separately and linearly blends the overlaps back together. This lowers the memory
+        requirement and allows processing larger frames.
+
+        Args:
+            tile_sample_min_height (`int`, *optional*):
+                The tile height in pixel space. Frames taller than this are split along the height dimension.
+            tile_sample_min_width (`int`, *optional*):
+                The tile width in pixel space. Frames wider than this are split along the width dimension.
+            tile_sample_min_overlap_height (`int`, *optional*):
+                The minimum overlap, in pixels, between two consecutive vertical tiles.
+            tile_sample_min_overlap_width (`int`, *optional*):
+                The minimum overlap, in pixels, between two consecutive horizontal tiles.
+        """
+        self.use_tiling = True
+        self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
+        self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
+        self.tile_sample_min_overlap_height = tile_sample_min_overlap_height or self.tile_sample_min_overlap_height
+        self.tile_sample_min_overlap_width = tile_sample_min_overlap_width or self.tile_sample_min_overlap_width
+
+    def _split_tiles(self, length: int, tile_size: int, min_overlap: int) -> tuple[list[int], list[int], list[int]]:
+        r"""
+        Lay `tile_size`-wide tiles over `length` pixels. The number of tiles is the smallest one whose union can cover
+        `length` while keeping every overlap at least `min_overlap`; the slack is then distributed round-robin over the
+        overlaps in whole `spatial_compression_ratio` steps so that every tile boundary stays latent-aligned.
+        """
+        if tile_size >= length:
+            return [0], [length], []
+
+        num_tiles = math.ceil(length / tile_size)
+        while tile_size * num_tiles - min_overlap * (num_tiles - 1) - length < 0:
+            num_tiles += 1
+
+        overlaps = [min_overlap] * (num_tiles - 1)
+        remaining = tile_size * num_tiles - sum(overlaps) - length
+        for i in range(remaining // self.spatial_compression_ratio):
+            overlaps[i % (num_tiles - 1)] += self.spatial_compression_ratio
+
+        tile_start_indices = [0]
+        for i in range(num_tiles - 1):
+            tile_start_indices.append(tile_start_indices[-1] + tile_size - overlaps[i])
+        return tile_start_indices, [tile_size] * num_tiles, overlaps
+
+    def _blend(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int, dim: int) -> torch.Tensor:
+        blend_extent = min(a.shape[dim], b.shape[dim], blend_extent)
+        positions = torch.arange(blend_extent, device=b.device, dtype=b.dtype)
+        shape = [1] * a.ndim
+        shape[dim] = blend_extent
+        weight_a = (1 - positions / blend_extent).view(shape)
+        weight_b = (positions / blend_extent).view(shape)
+
+        slice_a = [slice(None)] * a.ndim
+        slice_a[dim] = slice(-blend_extent, None)
+        slice_b = [slice(None)] * b.ndim
+        slice_b[dim] = slice(0, blend_extent)
+        blended = a[tuple(slice_a)] * weight_a + b[tuple(slice_b)] * weight_b
+
+        if blend_extent == b.shape[dim]:
+            return blended
+        slice_rest = [slice(None)] * b.ndim
+        slice_rest[dim] = slice(blend_extent, None)
+        return torch.cat([blended, b[tuple(slice_rest)]], dim=dim)
+
+    def _stitch_tiles(
+        self,
+        tiles: list[list[torch.Tensor]],
+        height_overlaps: list[int],
+        width_overlaps: list[int],
+    ) -> torch.Tensor:
+        result_rows = []
+        for i, row in enumerate(tiles):
+            result_row = []
+            for j, tile in enumerate(row):
+                if i > 0:
+                    tile = self._blend(tiles[i - 1][j], tile, height_overlaps[i - 1], dim=-2)
+                if j > 0:
+                    tile = self._blend(row[j - 1], tile, width_overlaps[j - 1], dim=-1)
+                if i < len(tiles) - 1:
+                    tile = tile[..., : -height_overlaps[i], :]
+                if j < len(row) - 1:
+                    tile = tile[..., :, : -width_overlaps[j]]
+                result_row.append(tile)
+            result_rows.append(torch.cat(result_row, dim=-1))
+        return torch.cat(result_rows, dim=-2)
+
+    @apply_forward_hook
+    def _encode_clip(self, x: torch.Tensor) -> torch.Tensor:
+        r"""
+        Encode one temporal clip, spatially tiled when tiling is enabled.
+
+        MiniMax-H3 encodes a keyframe or an image reference through this method rather than through [`~encode`],
+        because a single frame must not go through the temporal chunking, so it carries the offload hook too.
+        """
+        if not self.use_tiling:
+            return self.quant_conv(self.encoder(x))
+
+        height, width = x.shape[-2], x.shape[-1]
+        y_indices, y_lengths, y_overlaps = self._split_tiles(
+            height, self.tile_sample_min_height, self.tile_sample_min_overlap_height
+        )
+        x_indices, x_lengths, x_overlaps = self._split_tiles(
+            width, self.tile_sample_min_width, self.tile_sample_min_overlap_width
+        )
+
+        rows = []
+        for i_pos, i_len in zip(y_indices, y_lengths):
+            row = []
+            for j_pos, j_len in zip(x_indices, x_lengths):
+                tile = x[..., i_pos : i_pos + i_len, j_pos : j_pos + j_len]
+                row.append(self.quant_conv(self.encoder(tile)))
+            rows.append(row)
+
+        latent_y_overlaps = [overlap // self.spatial_compression_ratio for overlap in y_overlaps]
+        latent_x_overlaps = [overlap // self.spatial_compression_ratio for overlap in x_overlaps]
+        return self._stitch_tiles(rows, latent_y_overlaps, latent_x_overlaps)
+
+    def _decode_clip(self, z: torch.Tensor) -> torch.Tensor:
+        r"""Decode one temporal clip, spatially tiled when tiling is enabled."""
+        if not self.use_tiling:
+            return self.decoder(self.post_quant_conv(z))
+
+        # Tiles are laid out in pixel space and then mapped back onto the latent grid.
+        height = z.shape[-2] * self.spatial_compression_ratio
+        width = z.shape[-1] * self.spatial_compression_ratio
+        y_indices, y_lengths, y_overlaps = self._split_tiles(
+            height, self.tile_sample_min_height, self.tile_sample_min_overlap_height
+        )
+        x_indices, x_lengths, x_overlaps = self._split_tiles(
+            width, self.tile_sample_min_width, self.tile_sample_min_overlap_width
+        )
+
+        ratio = self.spatial_compression_ratio
+        rows = []
+        for i_pos, i_len in zip(y_indices, y_lengths):
+            row = []
+            for j_pos, j_len in zip(x_indices, x_lengths):
+                tile = z[
+                    ...,
+                    i_pos // ratio : i_pos // ratio + i_len // ratio,
+                    j_pos // ratio : j_pos // ratio + j_len // ratio,
+                ]
+                row.append(self.decoder(self.post_quant_conv(tile)))
+            rows.append(row)
+
+        return self._stitch_tiles(rows, y_overlaps, x_overlaps)
+
+    @apply_forward_hook
+    def _encode(self, x: torch.Tensor) -> torch.Tensor:
+        r"""
+        Encode a video in `clip_length`-frame chunks and drop the `token_drop` trailing latent frames.
+
+        MiniMax-H3 encodes a video reference through this method rather than through [`~encode`], because the
+        posterior is sampled under a fixed generator rather than through the distribution object, so it carries the
+        offload hook too.
+        """
+        clip_length = self.config.clip_length
+        num_frames = x.shape[2]
+        if num_frames % clip_length != 0:
+            pad_frames = x[:, :, -1:].repeat(1, 1, (-num_frames) % clip_length, 1, 1)
+            x = torch.cat([x, pad_frames], dim=2)
+
+        moments = torch.cat(
+            [
+                self._encode_clip(x[:, :, i * clip_length : (i + 1) * clip_length])
+                for i in range(x.shape[2] // clip_length)
+            ],
+            dim=2,
+        )
+        if self.config.token_drop > 0:
+            moments = moments[:, :, : -self.config.token_drop]
+        return moments
+
+    def _decode(self, z: torch.Tensor) -> torch.Tensor:
+        r"""
+        Decode a latent video, mirroring the chunking that `_encode` applied.
+
+        `token_drop` removed the tail of every encoded chunk, so consecutive decoded chunks overlap by
+        `frame_overlap` pixel frames and are linearly cross-faded. Latent frames are repeated at the end when the
+        length is not a whole number of chunks; the extra pixel frames are cut off again at the end.
+        """
+        tokens_chunk_size = self.tokens_chunk_size
+        token_drop = self.config.token_drop
+        temporal_ratio = self.temporal_compression_ratio
+        chunk_num_frames = tokens_chunk_size * temporal_ratio
+
+        num_tokens = z.shape[2] + token_drop
+        pad_tokens = (-num_tokens) % tokens_chunk_size
+        num_chunks = (num_tokens + pad_tokens) // tokens_chunk_size - int(token_drop > 0)
+        if pad_tokens > 0:
+            z = torch.cat([z, z[:, :, -1:].repeat(1, 1, pad_tokens, 1, 1)], dim=2)
+
+        decoded_chunks = []
+        overlap = None
+        for i in range(num_chunks):
+            start = i * tokens_chunk_size
+            clip = self._decode_clip(z[:, :, start : start + tokens_chunk_size + self.token_overlap])
+            for j in range(int(token_drop > 0) + 1):
+                frame_start = j * chunk_num_frames
+                chunk = clip[:, :, frame_start : frame_start + chunk_num_frames]
+                chunk = chunk[:, :, self.frame_pre_padding :]
+                if j == 0:
+                    if overlap is not None:
+                        chunk = self._blend(overlap, chunk, self.frame_overlap, dim=-3)
+                    decoded_chunks.append(chunk)
+                else:
+                    overlap = chunk
+        if overlap is not None:
+            decoded_chunks.append(overlap)
+
+        dec = torch.cat(decoded_chunks, dim=2)
+
+        # `pad_tokens` repeated latent frames produced trailing pixel frames that were never requested. A chunk's
+        # last latent frame only covers `clip_length % temporal_ratio` pixel frames, the others cover `temporal_ratio`.
+        if pad_tokens > 0:
+            intra_tail = self.config.clip_length % temporal_ratio
+            num_tokens_before_pad = z.shape[2] - pad_tokens
+            pad_frames = sum(
+                intra_tail if intra_tail and (num_tokens_before_pad + k) % tokens_chunk_size == 0 else temporal_ratio
+                for k in range(pad_tokens)
+            )
+            dec = dec[:, :, :-pad_frames]
+        return dec
+
+    @apply_forward_hook
+    def encode(self, x: torch.Tensor, return_dict: bool = True) -> AutoencoderKLOutput | tuple[torch.Tensor]:
+        r"""
+        Encode a batch of videos into latents.
+
+        Args:
+            x (`torch.Tensor`):
+                Input batch of videos, shape `(batch_size, in_channels, num_frames, height, width)`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoders.autoencoder_kl.AutoencoderKLOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            The latent distribution of the encoded videos. Note that MiniMax-H3 normalizes the sampled latents with
+            `latents_mean` / `latents_std` afterwards.
+        """
+        if self.use_slicing and x.shape[0] > 1:
+            moments = torch.cat([self._encode(x_slice) for x_slice in x.split(1)])
+        else:
+            moments = self._encode(x)
+        posterior = DiagonalGaussianDistribution(moments)
+        if not return_dict:
+            return (posterior,)
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    @apply_forward_hook
+    def decode(self, z: torch.Tensor, return_dict: bool = True) -> DecoderOutput | tuple[torch.Tensor]:
+        r"""
+        Decode a batch of latent videos.
+
+        Args:
+            z (`torch.Tensor`):
+                Input batch of latent videos, shape `(batch_size, latent_channels, num_latent_frames, height, width)`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoders.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.autoencoders.vae.DecoderOutput`] or `tuple`:
+                The decoded videos, shape `(batch_size, out_channels, num_frames, height, width)`.
+        """
+        if self.use_slicing and z.shape[0] > 1:
+            decoded = torch.cat([self._decode(z_slice) for z_slice in z.split(1)])
+        else:
+            decoded = self._decode(z)
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        generator: torch.Generator | None = None,
+        return_dict: bool = True,
+    ) -> DecoderOutput | tuple[torch.Tensor]:
+        r"""
+        Encode then decode a batch of videos.
+
+        Args:
+            sample (`torch.Tensor`):
+                Input batch of videos, shape `(batch_size, in_channels, num_frames, height, width)`.
+            sample_posterior (`bool`, *optional*, defaults to `False`):
+                Whether to sample the posterior instead of taking its mode.
+            generator (`torch.Generator`, *optional*):
+                Generator used when `sample_posterior=True`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoders.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.autoencoders.vae.DecoderOutput`] or `tuple`:
+                The round-tripped videos, shape `(batch_size, out_channels, num_frames, height, width)`.
+        """
+        posterior = self.encode(sample).latent_dist
+        z = posterior.sample(generator=generator) if sample_posterior else posterior.mode()
+        return self.decode(z, return_dict=return_dict)
diff --git a/invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3_audio.py b/invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3_audio.py
new file mode 100644
index 00000000000..8e0ea9a3705
--- /dev/null
+++ b/invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3_audio.py
@@ -0,0 +1,678 @@
+# Copyright 2025 The MiniMax authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""MiniMax-H3 audio autoencoder.
+
+Waveform in / waveform out — there is no mel front-end and no separate vocoder:
+
+* the **encoder** is a DAC-lineage strided convolutional stack (Snake activations, weight-normed
+  `Conv1d`) that downsamples by `prod(encoder_rates) = 800`, i.e. 40 latents/s at 32 kHz;
+* a **causal-attention projection** (`pre_block`) rewires the 2048-wide encoder trunk to the
+  32-channel latent width, followed by the `mean_proj` / `logs_proj` posterior heads;
+* the **decoder** is BigVGAN (anti-aliased SnakeBeta activations, transposed-conv upsamplers, AMP
+  residual blocks) preceded by `dec_in_proj`, upsampling by `prod(decoder_rates) = 800`.
+
+The autoencoder is **mono**. MiniMax-H3 carries stereo as two *batch* items — the pipeline decodes
+`[2, 32, T]` into `[2, 1, samples]` and interleaves at the output boundary — so no stereo handling
+belongs here.
+
+Latents are normalized with per-channel `latents_mean` / `latents_std` (32 floats each) rather than a
+scalar `scaling_factor`; both live in the config and are applied by the pipeline.
+
+Module and parameter names are identical to the original checkpoint, so conversion is a passthrough.
+That includes `torch.nn.utils.weight_norm` (the `weight_g` / `weight_v` spelling, as used by the
+other diffusers audio autoencoders) and the registered Kaiser-window resampling `filter` buffers of
+the anti-aliased activations.
+"""
+
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention import AttentionMixin, AttentionModuleMixin
+from diffusers.models.attention_dispatch import dispatch_attention_fn
+from diffusers.models.autoencoders.vae import DecoderOutput
+from diffusers.models.modeling_utils import ModelMixin, get_parameter_dtype
+from diffusers.utils import BaseOutput
+from diffusers.utils.accelerate_utils import apply_forward_hook
+from diffusers.utils.torch_utils import randn_tensor
+from torch.nn.utils import weight_norm
+
+
+class MiniMaxH3AudioDiagonalGaussianDistribution:
+    r"""Posterior of the MiniMax-H3 audio autoencoder, parameterized as `(mean, log_std)`.
+
+    The checkpoint keeps two separate `Conv1d` heads (`mean_proj`, `logs_proj`) instead of one fused
+    moments projection, and the second head predicts the **log standard deviation**, not the log
+    variance. The two tensors are therefore stored as produced, and `mode()` is bit-for-bit
+    `mean_proj`'s output.
+
+    Args:
+        mean (`torch.Tensor`): Posterior mean, `[batch_size, latent_channels, num_frames]`.
+        logs (`torch.Tensor`): Posterior log standard deviation, same shape as `mean`.
+    """
+
+    def __init__(self, mean: torch.Tensor, logs: torch.Tensor):
+        self.mean = mean
+        self.logs = logs
+        self.std = torch.exp(logs)
+
+    def mode(self) -> torch.Tensor:
+        return self.mean
+
+    def sample(self, generator: torch.Generator | None = None) -> torch.Tensor:
+        noise = randn_tensor(self.mean.shape, generator=generator, device=self.mean.device, dtype=self.mean.dtype)
+        return self.mean + self.std * noise
+
+
+@dataclass
+class MiniMaxH3AudioEncoderOutput(BaseOutput):
+    r"""
+    Output of [`AutoencoderKLMiniMaxH3Audio.encode`].
+
+    Args:
+        latent_dist (`MiniMaxH3AudioDiagonalGaussianDistribution`):
+            Posterior over the audio latents. MiniMax-H3 always consumes `latent_dist.mode()`.
+    """
+
+    latent_dist: MiniMaxH3AudioDiagonalGaussianDistribution
+
+
+def _wn_conv1d(*args, **kwargs) -> nn.Module:
+    return weight_norm(nn.Conv1d(*args, **kwargs))
+
+
+def kaiser_sinc_filter1d(cutoff: float, half_width: float, kernel_size: int) -> torch.Tensor:
+    r"""Kaiser-windowed sinc low-pass filter of shape `[1, 1, kernel_size]`.
+
+    Kept arithmetically identical to the `alias-free-torch` implementation the checkpoint was trained
+    with, because the resulting tensor is stored as a persistent buffer.
+    """
+    half_size = kernel_size // 2
+
+    attenuation = 2.285 * (half_size - 1) * math.pi * (4 * half_width) + 7.95
+    if attenuation > 50.0:
+        beta = 0.1102 * (attenuation - 8.7)
+    elif attenuation >= 21.0:
+        beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0)
+    else:
+        beta = 0.0
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    if kernel_size % 2 == 0:
+        time = torch.arange(-half_size, half_size) + 0.5
+    else:
+        time = torch.arange(kernel_size) - half_size
+
+    filter_ = 2 * cutoff * window * torch.sinc(2 * cutoff * time)
+    # Normalize to sum 1 so a constant input does not leak through the resampler.
+    filter_ /= filter_.sum()
+    return filter_.view(1, 1, kernel_size)
+
+
+class MiniMaxH3AudioSnake1d(nn.Module):
+    r"""`x + (alpha + 1e-9)^-1 * sin(alpha * x)^2` over `[batch_size, channels, length]`, with a
+    per-channel learnable `alpha` of shape `[1, channels, 1]`. Used throughout the DAC encoder."""
+
+    def __init__(self, channels: int):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.ones(1, channels, 1))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return hidden_states + (self.alpha + 1e-9).reciprocal() * torch.sin(self.alpha * hidden_states).pow(2)
+
+
+class MiniMaxH3AudioSnakeBeta(nn.Module):
+    r"""`x + (exp(beta) + 1e-9)^-1 * sin(exp(alpha) * x)^2` over `[batch_size, channels, length]`.
+
+    The BigVGAN decoder's activation: separate frequency (`alpha`) and magnitude (`beta`) parameters,
+    both stored in log space as `[channels]` vectors.
+    """
+
+    def __init__(self, channels: int):
+        super().__init__()
+        self.alpha = nn.Parameter(torch.zeros(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        alpha = torch.exp(self.alpha.unsqueeze(0).unsqueeze(-1))
+        beta = torch.exp(self.beta.unsqueeze(0).unsqueeze(-1))
+        return hidden_states + (beta + 1e-9).reciprocal() * torch.sin(alpha * hidden_states).pow(2)
+
+
+class MiniMaxH3AudioLowPassFilter1d(nn.Module):
+    r"""Depthwise Kaiser-sinc low-pass filter with a stride, i.e. the anti-aliased downsampler."""
+
+    def __init__(self, cutoff: float, half_width: float, stride: int, kernel_size: int):
+        super().__init__()
+        even = kernel_size % 2 == 0
+        self.pad_left = kernel_size // 2 - int(even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.register_buffer("filter", kaiser_sinc_filter1d(cutoff, half_width, kernel_size))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_channels = hidden_states.shape[1]
+        hidden_states = F.pad(hidden_states, (self.pad_left, self.pad_right), mode="replicate")
+        return F.conv1d(
+            hidden_states, self.filter.expand(num_channels, -1, -1), stride=self.stride, groups=num_channels
+        )
+
+
+class MiniMaxH3AudioUpSample1d(nn.Module):
+    r"""Anti-aliased `ratio`x upsampler (transposed depthwise Kaiser-sinc convolution)."""
+
+    def __init__(self, ratio: int, kernel_size: int):
+        super().__init__()
+        self.ratio = ratio
+        self.stride = ratio
+        self.pad = kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (kernel_size - self.stride) // 2
+        self.pad_right = self.pad * self.stride + (kernel_size - self.stride + 1) // 2
+        self.register_buffer(
+            "filter",
+            kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=kernel_size),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        num_channels = hidden_states.shape[1]
+        hidden_states = F.pad(hidden_states, (self.pad, self.pad), mode="replicate")
+        hidden_states = self.ratio * F.conv_transpose1d(
+            hidden_states, self.filter.expand(num_channels, -1, -1), stride=self.stride, groups=num_channels
+        )
+        return hidden_states[..., self.pad_left : -self.pad_right]
+
+
+class MiniMaxH3AudioDownSample1d(nn.Module):
+    r"""Anti-aliased `ratio`x downsampler."""
+
+    def __init__(self, ratio: int, kernel_size: int):
+        super().__init__()
+        self.lowpass = MiniMaxH3AudioLowPassFilter1d(
+            cutoff=0.5 / ratio, half_width=0.6 / ratio, stride=ratio, kernel_size=kernel_size
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.lowpass(hidden_states)
+
+
+class MiniMaxH3AudioActivation1d(nn.Module):
+    r"""Upsample -> activation -> downsample: the alias-free activation wrapper used by BigVGAN."""
+
+    def __init__(self, activation: nn.Module, ratio: int = 2, kernel_size: int = 12):
+        super().__init__()
+        self.act = activation
+        self.upsample = MiniMaxH3AudioUpSample1d(ratio, kernel_size)
+        self.downsample = MiniMaxH3AudioDownSample1d(ratio, kernel_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.upsample(hidden_states)
+        hidden_states = self.act(hidden_states)
+        return self.downsample(hidden_states)
+
+
+class MiniMaxH3AudioResidualUnit(nn.Module):
+    r"""DAC residual unit: `Snake -> dilated Conv1d(k=7) -> Snake -> Conv1d(k=1)`, plus a shortcut
+    that is center-cropped when the dilated convolution shrinks the time axis."""
+
+    def __init__(self, dim: int, dilation: int):
+        super().__init__()
+        self.block = nn.Sequential(
+            MiniMaxH3AudioSnake1d(dim),
+            _wn_conv1d(dim, dim, kernel_size=7, dilation=dilation, padding=((7 - 1) * dilation) // 2),
+            MiniMaxH3AudioSnake1d(dim),
+            _wn_conv1d(dim, dim, kernel_size=1),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = self.block(hidden_states)
+        pad = (hidden_states.shape[-1] - residual.shape[-1]) // 2
+        if pad > 0:
+            hidden_states = hidden_states[..., pad:-pad]
+        return hidden_states + residual
+
+
+class MiniMaxH3AudioEncoderBlock(nn.Module):
+    r"""Three residual units at dilations 1/3/9, then a strided channel-doubling convolution."""
+
+    def __init__(self, dim: int, stride: int):
+        super().__init__()
+        self.block = nn.Sequential(
+            MiniMaxH3AudioResidualUnit(dim // 2, dilation=1),
+            MiniMaxH3AudioResidualUnit(dim // 2, dilation=3),
+            MiniMaxH3AudioResidualUnit(dim // 2, dilation=9),
+            MiniMaxH3AudioSnake1d(dim // 2),
+            _wn_conv1d(
+                dim // 2,
+                dim,
+                kernel_size=2 * stride,
+                stride=stride,
+                padding=math.ceil(stride / 2),
+            ),
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.block(hidden_states)
+
+
+class MiniMaxH3AudioEncoder(nn.Module):
+    r"""DAC waveform encoder: `[batch_size, 1, samples] -> [batch_size, latent_dim, samples / 800]`."""
+
+    def __init__(self, d_model: int, strides: tuple[int, ...], d_latent: int):
+        super().__init__()
+        block: list[nn.Module] = [_wn_conv1d(1, d_model, kernel_size=7, padding=3)]
+        for stride in strides:
+            d_model *= 2
+            block.append(MiniMaxH3AudioEncoderBlock(d_model, stride=stride))
+        block += [
+            MiniMaxH3AudioSnake1d(d_model),
+            _wn_conv1d(d_model, d_latent, kernel_size=3, padding=1),
+        ]
+        self.block = nn.Sequential(*block)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.block(hidden_states)
+
+
+class MiniMaxH3AudioGeGluMlp(nn.Module):
+    r"""Pre-norm GeGLU MLP used inside the attention projection block."""
+
+    def __init__(self, in_features: int, hidden_features: int):
+        super().__init__()
+        self.norm = nn.LayerNorm(in_features)
+        self.act = nn.GELU(approximate="tanh")
+        self.w0 = nn.Linear(in_features, hidden_features)
+        self.w1 = nn.Linear(in_features, hidden_features)
+        self.w2 = nn.Linear(hidden_features, in_features)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.act(self.w0(hidden_states)) * self.w1(hidden_states)
+        return self.w2(hidden_states)
+
+
+class MiniMaxH3AudioAttnProcessor:
+    r"""Processor of [`MiniMaxH3AudioCausalAttention`].
+
+    The causal mask is expressed as `is_causal=True` rather than as a materialized mask. Every
+    attention backend honours that flag, with two exceptions: `_native_npu`, whose kernel takes no
+    causal argument and would compute *bidirectional* attention, and context parallelism, which
+    raises for causal attention.
+    """
+
+    _attention_backend = None
+    _parallel_config = None
+
+    def __call__(self, attn: "MiniMaxH3AudioCausalAttention", hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, seq_len, _ = hidden_states.shape
+        qkv = F.linear(
+            input=hidden_states,
+            weight=attn.qkv.weight,
+            bias=torch.cat((attn.q_bias, attn.zero_k_bias, attn.v_bias)),
+        )
+        query, key, value = (
+            qkv.reshape(batch_size, seq_len, 3, attn.num_heads, attn.head_dim).permute(2, 0, 1, 3, 4).unbind(0)
+        )
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=None,
+            is_causal=True,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+        # The heads are mean-pooled away instead of being concatenated, and the head dimension that
+        # remains is adaptively average-pooled down to `out_dim`.
+        hidden_states = torch.mean(hidden_states, dim=2)
+        hidden_states = F.adaptive_avg_pool1d(hidden_states, attn.out_dim)
+        return attn.proj(hidden_states)
+
+
+class MiniMaxH3AudioCausalAttention(nn.Module, AttentionModuleMixin):
+    r"""Causal self-attention that narrows the feature width from `in_dim` to `out_dim`.
+
+    QKV is a single bias-less `nn.Linear`; query and value biases are separate parameters and the key
+    bias is a frozen zero buffer (`zero_k_bias`), exactly as stored in the checkpoint. Heads are
+    `in_dim // num_heads` wide; instead of being concatenated they are **mean-pooled away**, and the
+    remaining head dimension is adaptively average-pooled down to `out_dim`.
+    """
+
+    _default_processor_cls = MiniMaxH3AudioAttnProcessor
+    _available_processors = [MiniMaxH3AudioAttnProcessor]
+    # The checkpoint stores one fused `qkv` projection, so there is nothing to fuse.
+    _supports_qkv_fusion = False
+
+    def __init__(self, in_dim: int, out_dim: int, num_heads: int):
+        super().__init__()
+        self.out_dim = out_dim
+        self.num_heads = num_heads
+        self.head_dim = in_dim // num_heads
+        self.qkv = nn.Linear(in_dim, in_dim * 3, bias=False)
+        self.q_bias = nn.Parameter(torch.zeros(in_dim))
+        self.v_bias = nn.Parameter(torch.zeros(in_dim))
+        self.register_buffer("zero_k_bias", torch.zeros(in_dim))
+        self.proj = nn.Linear(out_dim, out_dim)
+
+        self.set_processor(MiniMaxH3AudioAttnProcessor())
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.processor(self, hidden_states)
+
+
+class MiniMaxH3AudioAttnProjection(nn.Module):
+    r"""`pre_block`: residual causal-attention + GeGLU block that rewires `latent_dim` -> `latent_channels`."""
+
+    def __init__(self, in_dim: int, out_dim: int, num_heads: int, mlp_ratio: int = 2):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(in_dim)
+        self.attn = MiniMaxH3AudioCausalAttention(in_dim, out_dim, num_heads)
+        self.proj = nn.Linear(in_dim, out_dim)
+        self.norm3 = nn.LayerNorm(in_dim)
+        self.norm2 = nn.LayerNorm(out_dim)
+        self.mlp = MiniMaxH3AudioGeGluMlp(in_features=out_dim, hidden_features=out_dim * mlp_ratio)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.proj(self.norm3(hidden_states)) + self.attn(self.norm1(hidden_states))
+        return hidden_states + self.mlp(self.norm2(hidden_states))
+
+
+class MiniMaxH3AudioAMPBlock(nn.Module):
+    r"""BigVGAN anti-aliased multi-periodicity block (`AMPBlock1`).
+
+    Each dilation contributes a `(dilated conv, dilation-1 conv)` pair, and every convolution is
+    preceded by its own alias-free SnakeBeta activation.
+    """
+
+    def __init__(self, channels: int, kernel_size: int, dilation: tuple[int, ...]):
+        super().__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                _wn_conv1d(channels, channels, kernel_size, dilation=d, padding=(kernel_size * d - d) // 2)
+                for d in dilation
+            ]
+        )
+        self.convs2 = nn.ModuleList(
+            [_wn_conv1d(channels, channels, kernel_size, dilation=1, padding=(kernel_size - 1) // 2) for _ in dilation]
+        )
+        self.activations = nn.ModuleList(
+            [
+                MiniMaxH3AudioActivation1d(activation=MiniMaxH3AudioSnakeBeta(channels))
+                for _ in range(2 * len(dilation))
+            ]
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        acts1, acts2 = self.activations[::2], self.activations[1::2]
+        for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, acts1, acts2):
+            residual = conv1(act1(hidden_states))
+            residual = conv2(act2(residual))
+            hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class MiniMaxH3AudioBigVGANDecoder(nn.Module):
+    r"""BigVGAN decoder: `[batch_size, latent_dim, num_frames] -> [batch_size, 1, num_frames * 800]`."""
+
+    def __init__(
+        self,
+        in_channels: int,
+        upsample_initial_channel: int,
+        upsample_rates: tuple[int, ...],
+        upsample_kernel_sizes: tuple[int, ...],
+        resblock_kernel_sizes: tuple[int, ...],
+        resblock_dilation_sizes: tuple[tuple[int, ...], ...],
+    ):
+        super().__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+
+        self.conv_pre = _wn_conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3)
+
+        # Each upsampler is wrapped in a one-element `ModuleList` in the original checkpoint
+        # (`ups.<i>.0`); the extra nesting is kept so the state dict stays a passthrough.
+        self.ups = nn.ModuleList()
+        for i, (rate, kernel) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                nn.ModuleList(
+                    [
+                        weight_norm(
+                            nn.ConvTranspose1d(
+                                upsample_initial_channel // (2**i),
+                                upsample_initial_channel // (2 ** (i + 1)),
+                                kernel,
+                                rate,
+                                padding=(kernel - rate) // 2,
+                            )
+                        )
+                    ]
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(self.num_upsamples):
+            channels = upsample_initial_channel // (2 ** (i + 1))
+            for kernel, dilation in zip(resblock_kernel_sizes, resblock_dilation_sizes):
+                self.resblocks.append(MiniMaxH3AudioAMPBlock(channels, kernel, tuple(dilation)))
+
+        self.activation_post = MiniMaxH3AudioActivation1d(activation=MiniMaxH3AudioSnakeBeta(channels))
+        self.conv_post = _wn_conv1d(channels, 1, 7, 1, padding=3, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv_pre(hidden_states)
+
+        for i in range(self.num_upsamples):
+            hidden_states = self.ups[i][0](hidden_states)
+            residual = None
+            for j in range(self.num_kernels):
+                block = self.resblocks[i * self.num_kernels + j](hidden_states)
+                residual = block if residual is None else residual + block
+            hidden_states = residual / self.num_kernels
+
+        hidden_states = self.activation_post(hidden_states)
+        hidden_states = self.conv_post(hidden_states)
+        return torch.clamp(hidden_states, min=-1.0, max=1.0)
+
+
+class AutoencoderKLMiniMaxH3Audio(ModelMixin, ConfigMixin, AttentionMixin):
+    r"""
+    The audio autoencoder used by [MiniMax-H3](https://huggingface.co/MiniMaxAI): a DAC-lineage
+    convolutional encoder and a BigVGAN decoder, operating directly on mono 32 kHz waveforms.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
+    implements for all models (such as downloading or saving).
+
+    Args:
+        encoder_dim (`int`, defaults to `64`):
+            Channel width of the encoder's first convolution; doubles at every downsampling stage.
+        encoder_rates (`tuple[int]`, defaults to `(2, 4, 4, 5, 5)`):
+            Encoder strides. Their product (`800`) is the hop length, i.e. 40 latents/s at 32 kHz.
+        latent_dim (`int`, defaults to `2048`):
+            Width of the encoder trunk and of the decoder input, before/after the latent projections.
+        latent_channels (`int`, defaults to `32`):
+            Width of the diffusion latent, i.e. the `mean_proj` / `logs_proj` output channels.
+        num_attention_heads (`int`, defaults to `8`):
+            Number of heads in the causal-attention projection `pre_block`.
+        decoder_dim (`int`, defaults to `1024`):
+            BigVGAN initial channel count; halved at every upsampling stage.
+        decoder_rates (`tuple[int]`, defaults to `(5, 5, 2, 2, 2, 2, 2)`):
+            BigVGAN upsampling rates. Their product must equal `prod(encoder_rates)`.
+        decoder_kernel_sizes (`tuple[int]`, defaults to `(9, 9, 4, 4, 4, 4, 4)`):
+            Transposed-convolution kernel size per upsampling stage.
+        resblock_kernel_sizes (`tuple[int]`, defaults to `(3, 7, 11)`):
+            Kernel sizes of the parallel AMP residual blocks at each upsampling stage.
+        resblock_dilation_sizes (`tuple[tuple[int]]`, defaults to `((1, 3, 5), (1, 3, 5), (1, 3, 5))`):
+            Per-AMP-block dilations.
+        sampling_rate (`int`, defaults to `32000`):
+            Waveform sampling rate.
+        latents_mean (`list[float]`, *optional*):
+            Per-channel latent mean the pipeline uses to normalize / denormalize latents.
+        latents_std (`list[float]`, *optional*):
+            Per-channel latent standard deviation the pipeline uses to normalize / denormalize latents.
+    """
+
+    _supports_gradient_checkpointing = False
+    # The released checkpoint is float32 and the DAC/BigVGAN stack (weight-normalized convolutions, Snake
+    # activations) degrades audibly under bfloat16 (roughly 20 dB quieter decodes), so a pipeline-level
+    # `torch_dtype=torch.bfloat16` must not downcast the weights.
+    _keep_in_fp32_modules = ["encoder", "decoder", "pre_block", "dec_in_proj", "mean_proj", "logs_proj"]
+
+    @register_to_config
+    def __init__(
+        self,
+        encoder_dim: int = 64,
+        encoder_rates: tuple[int, ...] = (2, 4, 4, 5, 5),
+        latent_dim: int = 2048,
+        latent_channels: int = 32,
+        num_attention_heads: int = 8,
+        decoder_dim: int = 1024,
+        decoder_rates: tuple[int, ...] = (5, 5, 2, 2, 2, 2, 2),
+        decoder_kernel_sizes: tuple[int, ...] = (9, 9, 4, 4, 4, 4, 4),
+        resblock_kernel_sizes: tuple[int, ...] = (3, 7, 11),
+        resblock_dilation_sizes: tuple[tuple[int, ...], ...] = ((1, 3, 5), (1, 3, 5), (1, 3, 5)),
+        sampling_rate: int = 32000,
+        latents_mean: list[float] | None = None,
+        latents_std: list[float] | None = None,
+    ):
+        super().__init__()
+
+        encoder_rates = tuple(int(rate) for rate in encoder_rates)
+        decoder_rates = tuple(int(rate) for rate in decoder_rates)
+        self.hop_length = math.prod(encoder_rates)
+        if math.prod(decoder_rates) != self.hop_length:
+            raise ValueError(
+                f"`decoder_rates` must upsample by the encoder hop length {self.hop_length}, got "
+                f"{math.prod(decoder_rates)}."
+            )
+        if latent_dim % latent_channels != 0:
+            raise ValueError(
+                f"`latent_dim` ({latent_dim}) must be a multiple of `latent_channels` ({latent_channels})."
+            )
+
+        self.encoder = MiniMaxH3AudioEncoder(d_model=encoder_dim, strides=encoder_rates, d_latent=latent_dim)
+        self.pre_block = MiniMaxH3AudioAttnProjection(latent_dim, latent_channels, num_heads=num_attention_heads)
+        self.mean_proj = nn.Conv1d(latent_channels, latent_channels, 1)
+        self.logs_proj = nn.Conv1d(latent_channels, latent_channels, 1)
+
+        self.dec_in_proj = nn.Conv1d(latent_channels, latent_dim, 1)
+        self.decoder = MiniMaxH3AudioBigVGANDecoder(
+            in_channels=latent_dim,
+            upsample_initial_channel=decoder_dim,
+            upsample_rates=decoder_rates,
+            upsample_kernel_sizes=tuple(int(kernel) for kernel in decoder_kernel_sizes),
+            resblock_kernel_sizes=tuple(int(kernel) for kernel in resblock_kernel_sizes),
+            resblock_dilation_sizes=tuple(tuple(int(d) for d in dilation) for dilation in resblock_dilation_sizes),
+        )
+
+    @apply_forward_hook
+    def encode(
+        self, sample: torch.Tensor, return_dict: bool = True
+    ) -> MiniMaxH3AudioEncoderOutput | tuple[MiniMaxH3AudioDiagonalGaussianDistribution]:
+        r"""
+        Encode a waveform into the audio latent posterior.
+
+        The waveform is right-padded to a multiple of `hop_length` (800 samples) first. MiniMax-H3
+        always consumes the posterior **mean** (`latent_dist.mode()`) — the `logs_proj` head is never
+        evaluated by the reference pipeline.
+
+        Args:
+            sample (`torch.Tensor`):
+                Mono waveform of shape `[batch_size, 1, samples]`. MiniMax-H3 passes the two stereo
+                channels of a reference clip as `batch_size = 2`.
+            return_dict (`bool`, defaults to `True`):
+                Whether to return a [`MiniMaxH3AudioEncoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`MiniMaxH3AudioEncoderOutput`] or `tuple`:
+                The latent posterior over `[batch_size, latent_channels, samples / 800]`.
+        """
+        if sample.ndim != 3 or sample.shape[1] != 1:
+            raise ValueError(f"`sample` must have shape [batch_size, 1, samples], got {tuple(sample.shape)}.")
+
+        right_pad = math.ceil(sample.shape[-1] / self.hop_length) * self.hop_length - sample.shape[-1]
+        if right_pad > 0:
+            sample = F.pad(sample, (0, right_pad))
+
+        encoder_dtype = get_parameter_dtype(self.encoder)
+        hidden_states = self.encoder(sample.to(encoder_dtype))
+        hidden_states = self.pre_block(hidden_states.transpose(1, 2)).transpose(1, 2)
+        mean, logs = self.mean_proj(hidden_states), self.logs_proj(hidden_states)
+        if encoder_dtype != torch.float32:
+            mean, logs = mean.float(), logs.float()
+
+        posterior = MiniMaxH3AudioDiagonalGaussianDistribution(mean, logs)
+        if not return_dict:
+            return (posterior,)
+        return MiniMaxH3AudioEncoderOutput(latent_dist=posterior)
+
+    @apply_forward_hook
+    def decode(self, latents: torch.Tensor, return_dict: bool = True) -> DecoderOutput | tuple[torch.Tensor]:
+        r"""
+        Decode audio latents into a waveform.
+
+        Args:
+            latents (`torch.Tensor`):
+                Denormalized latents of shape `[batch_size, latent_channels, num_frames]`. MiniMax-H3
+                passes the two stereo channels as `batch_size = 2`.
+            return_dict (`bool`, defaults to `True`):
+                Whether to return a [`~models.autoencoders.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.autoencoders.vae.DecoderOutput`] or `tuple`:
+                Waveform of shape `[batch_size, 1, num_frames * 800]`, clamped to `[-1, 1]`.
+        """
+        if latents.ndim != 3:
+            raise ValueError(
+                f"`latents` must have shape [batch_size, latent_channels, num_frames], got {tuple(latents.shape)}."
+            )
+
+        decoder_dtype = get_parameter_dtype(self.decoder)
+        decoded = self.decoder(self.dec_in_proj(latents.to(decoder_dtype)))
+        if decoder_dtype != torch.float32:
+            decoded = decoded.float()
+
+        if not return_dict:
+            return (decoded,)
+        return DecoderOutput(sample=decoded)
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: torch.Generator | None = None,
+    ) -> DecoderOutput | tuple[torch.Tensor]:
+        r"""
+        Encode then decode a waveform.
+
+        Args:
+            sample (`torch.Tensor`):
+                Mono waveform of shape `[batch_size, 1, samples]`.
+            sample_posterior (`bool`, defaults to `False`):
+                Whether to sample the posterior instead of taking its mode. MiniMax-H3 uses the mode.
+            return_dict (`bool`, defaults to `True`):
+                Whether to return a [`~models.autoencoders.vae.DecoderOutput`] instead of a plain tuple.
+            generator (`torch.Generator`, *optional*):
+                Generator used when `sample_posterior=True`.
+
+        Returns:
+            [`~models.autoencoders.vae.DecoderOutput`] or `tuple`:
+                The round-tripped waveform of shape `[batch_size, 1, num_frames * 800]`, clamped to `[-1, 1]`.
+        """
+        posterior = self.encode(sample).latent_dist
+        latents = posterior.sample(generator=generator) if sample_posterior else posterior.mode()
+        return self.decode(latents, return_dict=return_dict)
diff --git a/invokeai/backend/minimax_h3/scheduling_minimax_h3.py b/invokeai/backend/minimax_h3/scheduling_minimax_h3.py
new file mode 100644
index 00000000000..10653f1cb17
--- /dev/null
+++ b/invokeai/backend/minimax_h3/scheduling_minimax_h3.py
@@ -0,0 +1,287 @@
+# Copyright 2025 The MiniMax authors and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Rectified-flow Euler scheduler for MiniMax-H3.
+
+Three things make this incompatible with a [`FlowMatchEulerDiscreteScheduler`] config, which is why
+it is a separate class:
+
+1. **The velocity sign is reversed.** MiniMax-H3's transformer predicts a *data-ward* velocity, so
+   `x0 = x_t + sigma * v` instead of diffusers' `x0 = x_t - sigma * v`.
+2. **Timesteps are `t = 1 - sigma` in `[0, 1]`**, with `t = 1` meaning *clean*. Flow-match
+   schedulers expose `timesteps = sigma * num_train_timesteps`, i.e. the opposite direction on a
+   1000x scale. The transformer's AdaLN consumes the H3 convention directly.
+3. **The sigma grid starts from `linspace(1, 0, num_inference_steps)`** — the terminal zero is part
+   of the requested step count, and duplicates created by the shift are collapsed with
+   `unique_consecutive`. `FlowMatchEulerDiscreteScheduler` instead builds
+   `linspace(1, 1/num_train_timesteps, ...)` and appends a terminal sigma afterwards, so
+   `len(sigmas)` and every interior value differ.
+
+Everything else is ordinary rectified flow: the exponential shift `sigma' = s*sigma / (1 + (s-1)*sigma)`,
+and an Euler update written as the `x_t` / `x0` blend `x_next = r*x_t + (1 - r)*x0` with
+`r = sigma_next / sigma`, evaluated in float32. Despite the reference class being named
+"euler ancestral", `eta` is 0 — no noise is ever re-injected.
+
+MiniMax-H3 runs **two schedules per request**, one per modality (`shift=12.0` for video,
+`shift=3.0` for audio). The modality is not a property of the scheduler: a pipeline holds two
+instances, e.g. `scheduler` and `audio_scheduler`.
+"""
+
+from dataclasses import dataclass
+
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class MiniMaxH3SchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        prev_sample (`torch.FloatTensor`):
+            Computed sample `x_{t+1}` for the next step of the denoising loop.
+    """
+
+    prev_sample: torch.FloatTensor
+
+
+class MiniMaxH3Scheduler(SchedulerMixin, ConfigMixin):
+    r"""
+    Rectified-flow Euler scheduler (`eta = 0`) with an exponential sigma shift, as used by MiniMax-H3.
+
+    Args:
+        shift (`float`, defaults to `12.0`):
+            Exponential shift applied to the sigma grid, `sigma' = s*sigma / (1 + (s-1)*sigma)`. The
+            released checkpoints use `12.0` for video latents and `3.0` for audio latents.
+    """
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(self, shift: float = 12.0):
+        if shift <= 0:
+            raise ValueError(f"`shift` must be positive, got {shift}.")
+
+        self.num_inference_steps: int | None = None
+        self.sigmas: torch.Tensor | None = None
+        self.timesteps: torch.Tensor | None = None
+        self._shift = float(shift)
+        self._step_index: int | None = None
+        self._begin_index: int | None = None
+
+    @property
+    def shift(self) -> float:
+        """The exponential shift currently applied to the sigma grid."""
+        return self._shift
+
+    @property
+    def step_index(self) -> int | None:
+        """Index of the step the scheduler is about to take. Increases by one after each `step`."""
+        return self._step_index
+
+    @property
+    def begin_index(self) -> int | None:
+        """Index of the first step, set from a pipeline through [`~MiniMaxH3Scheduler.set_begin_index`]."""
+        return self._begin_index
+
+    def set_begin_index(self, begin_index: int = 0) -> None:
+        """
+        Sets the begin index for the scheduler.
+
+        Args:
+            begin_index (`int`, defaults to `0`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def set_shift(self, shift: float) -> None:
+        """
+        Overrides the configured sigma shift; call before [`~MiniMaxH3Scheduler.set_timesteps`].
+
+        MiniMax-H3 exposes this per request as `flow_shift` (video) / `audio_flow_shift` (audio).
+
+        Args:
+            shift (`float`):
+                The exponential shift to use for the next schedule.
+        """
+        if shift <= 0:
+            raise ValueError(f"`shift` must be positive, got {shift}.")
+        self._shift = float(shift)
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int | None = None,
+        device: str | torch.device | None = None,
+        sigmas: list[float] | torch.Tensor | None = None,
+    ) -> None:
+        r"""
+        Build the sigma / timestep schedule.
+
+        The grid is `linspace(1, 0, num_inference_steps)` pushed through the exponential shift, with
+        consecutive duplicates collapsed. The terminal `0` is already part of that grid — the shift maps
+        `0` to exactly `0` — so the schedule holds `num_inference_steps` sigmas and drives
+        `num_inference_steps - 1` model evaluations, exposed as `self.timesteps = 1 - sigmas[:-1]`.
+
+        Args:
+            num_inference_steps (`int`, *optional*):
+                Number of sigma grid points, terminal `0` included. Ignored when `sigmas` is given.
+            device (`str` or `torch.device`, *optional*):
+                Device the schedule tensors are moved to. The grid itself is always built on CPU in
+                float32 so the schedule does not depend on the accelerator.
+            sigmas (`list[float]` or `torch.Tensor`, *optional*):
+                A fully-formed sigma schedule, used verbatim (no shifting, no deduplication). It must be
+                strictly decreasing and terminate at `0.0`.
+        """
+        if sigmas is None:
+            if num_inference_steps is None or num_inference_steps < 2:
+                raise ValueError(
+                    "`set_timesteps` requires either an explicit `sigmas` schedule or "
+                    f"`num_inference_steps` >= 2, got {num_inference_steps}."
+                )
+
+            # The rectified-flow sigma range is fixed at [1.0, 0.0].
+            base = torch.linspace(1.0, 0.0, int(num_inference_steps), dtype=torch.float32)
+            sigmas = self._shift * base / (1 + (self._shift - 1) * base)
+            # The shift compresses the grid near sigma = 1; collapse any float32 collisions it creates.
+            sigmas = torch.unique_consecutive(sigmas)
+        else:
+            sigmas = torch.as_tensor(sigmas, dtype=torch.float32).flatten().cpu()
+            if sigmas.numel() < 2 or not bool((sigmas[1:] < sigmas[:-1]).all()) or sigmas[-1].item() != 0.0:
+                raise ValueError("`sigmas` must hold at least two strictly decreasing values ending at 0.0.")
+
+        self.sigmas = sigmas.to(device=device)
+        # t = 1 - sigma, and t = 1 is clean. The terminal sigma has no model evaluation.
+        self.timesteps = (1.0 - sigmas[:-1]).to(device=device)
+        self.num_inference_steps = int(self.timesteps.numel())
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(self, timestep: float | torch.Tensor) -> int:
+        """
+        Map a timestep value to its index in the schedule.
+
+        Args:
+            timestep (`float` or `torch.Tensor`):
+                A value taken from `self.timesteps`. The schedule is strictly increasing in `t`, so the
+                match is unique.
+
+        Returns:
+            `int`: The index of `timestep`.
+        """
+        if isinstance(timestep, torch.Tensor):
+            timestep = timestep.to(self.timesteps.device)
+        indices = (self.timesteps == timestep).nonzero()
+        if len(indices) == 0:
+            raise ValueError(
+                "Passed `timestep` is not in `self.timesteps`. Make sure to use values from `scheduler.timesteps`."
+            )
+        return indices[0].item()
+
+    def scale_noise(
+        self,
+        sample: torch.FloatTensor,
+        timestep: float | torch.FloatTensor,
+        noise: torch.FloatTensor,
+    ) -> torch.FloatTensor:
+        r"""
+        Rectified-flow forward process, in MiniMax-H3's `t` convention: `x_t = t*x_0 + (1 - t)*noise`.
+
+        MiniMax-H3 uses this to noise its conditioning anchors, where `t` is the `noise_aug` level
+        rather than a schedule entry, so `timestep` is taken at face value and is *not* looked up in
+        `self.timesteps`.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The clean sample `x_0`.
+            timestep (`float` or `torch.FloatTensor`):
+                The target time in `[0, 1]`; `1` returns `sample` unchanged.
+            noise (`torch.FloatTensor`):
+                The noise to mix in.
+
+        Returns:
+            `torch.FloatTensor`: The noised sample.
+        """
+        if not isinstance(timestep, torch.Tensor):
+            timestep = torch.tensor(timestep, dtype=sample.dtype, device=sample.device)
+        timestep = timestep.to(device=sample.device, dtype=sample.dtype)
+        while timestep.ndim < sample.ndim:
+            timestep = timestep.unsqueeze(-1)
+        return timestep * sample + (1.0 - timestep) * noise
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: float | torch.FloatTensor,
+        sample: torch.FloatTensor,
+        return_dict: bool = True,
+    ) -> MiniMaxH3SchedulerOutput | tuple:
+        r"""
+        Take one Euler (`eta = 0`) step.
+
+        The model output is a data-ward velocity, so the denoised estimate is
+        `x0 = x_t + (1 - t) * v` — note the `+`, the opposite of the usual flow-match convention.
+        The update is then the blend `x_next = r*x_t + (1 - r)*x0` with `r = sigma_next / sigma`,
+        evaluated in float32 for half-precision samples.
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                The transformer's velocity prediction at `timestep`.
+            timestep (`float` or `torch.FloatTensor`):
+                The current timestep, one of `self.timesteps` (so `timestep == 1 - sigma`).
+            sample (`torch.FloatTensor`):
+                The current sample `x_t`.
+            return_dict (`bool`, defaults to `True`):
+                Whether to return a [`MiniMaxH3SchedulerOutput`] instead of a plain tuple.
+
+        Returns:
+            [`MiniMaxH3SchedulerOutput`] or `tuple`: the sample for the next step.
+        """
+        if isinstance(timestep, int) or (isinstance(timestep, torch.Tensor) and not timestep.is_floating_point()):
+            raise ValueError(
+                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                " `MiniMaxH3Scheduler.step()` is not supported. Make sure to pass one of the"
+                " `scheduler.timesteps` values."
+            )
+
+        if self._step_index is None:
+            self._step_index = self.index_for_timestep(timestep) if self._begin_index is None else self._begin_index
+
+        # x0 from the data-ward velocity. The sigma used here is recovered from the *timestep* the
+        # transformer was conditioned on, whereas the Euler ratio below uses the sigma grid: for
+        # sigma < 0.5 the float32 round trip `1 - (1 - sigma)` is not exact, and the reference keeps
+        # the two sources apart.
+        if not isinstance(timestep, torch.Tensor):
+            timestep = torch.tensor(timestep, dtype=sample.dtype)
+        sigma_from_timestep = 1 - timestep.to(device=sample.device, dtype=sample.dtype)
+        while sigma_from_timestep.ndim < sample.ndim:
+            sigma_from_timestep = sigma_from_timestep.unsqueeze(-1)
+        denoised = sample + sigma_from_timestep * model_output
+
+        # Euler with eta = 0, written as an x_t / x0 blend and evaluated in float32.
+        compute_dtype = torch.float32 if sample.dtype in (torch.float16, torch.bfloat16) else sample.dtype
+        sigma = self.sigmas[self._step_index].to(device=sample.device, dtype=compute_dtype)
+        sigma_next = self.sigmas[self._step_index + 1].to(device=sample.device, dtype=compute_dtype)
+        ratio = sigma_next / sigma
+        prev_sample = ratio * sample.to(dtype=compute_dtype) + (1.0 - ratio) * denoised.to(dtype=compute_dtype)
+        prev_sample = prev_sample.to(dtype=sample.dtype)
+
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+        return MiniMaxH3SchedulerOutput(prev_sample=prev_sample)
diff --git a/invokeai/backend/minimax_h3/transformer_minimax_h3.py b/invokeai/backend/minimax_h3/transformer_minimax_h3.py
new file mode 100644
index 00000000000..2181533a33f
--- /dev/null
+++ b/invokeai/backend/minimax_h3/transformer_minimax_h3.py
@@ -0,0 +1,642 @@
+# Copyright 2025 The MiniMax Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.attention import AttentionMixin, AttentionModuleMixin, FeedForward
+from diffusers.models.attention_dispatch import dispatch_attention_fn
+from diffusers.models.cache_utils import CacheMixin
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput, apply_lora_scale, logging
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# MiniMax-H3 tags every row of the packed sequence with the modality it belongs to and keeps one set of AdaLN
+# modulation parameters per (timestep, modality) pair: 0 = video, 1 = text, 2 = audio.
+MINIMAX_H3_MODALITY_NUM = 3
+
+
+@dataclass
+class MiniMaxH3TransformerOutput(BaseOutput):
+    r"""
+    The output of [`MiniMaxH3Transformer3DModel`].
+
+    Args:
+        sample (`torch.Tensor` of shape `(batch_size, num_video_tokens, in_channels * prod(patch_size))`):
+            The video velocity prediction for the rows addressed by `video_indices`, in the same order. Conditioning
+            rows are returned unmasked — masking them out before the scheduler step is the caller's job.
+        audio_sample (`torch.Tensor` of shape `(batch_size, num_audio_tokens, audio_in_channels)`):
+            The audio velocity prediction for the rows addressed by `audio_indices`, in the same order.
+    """
+
+    sample: torch.Tensor
+    audio_sample: torch.Tensor
+
+
+def _apply_rotary_emb(hidden_states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+    r"""
+    Rotate the leading `rotary_dim` channels of every head and pass the remaining channels through unchanged.
+    `hidden_states` is `(batch_size, seq_len, num_heads, head_dim)` and `cos`/`sin` are `(seq_len, rotary_dim)`.
+    """
+    rotary_dim = cos.shape[-1]
+    hidden_states_rotary = hidden_states[..., :rotary_dim]
+    hidden_states_pass = hidden_states[..., rotary_dim:]
+
+    cos = cos.to(hidden_states.dtype)[None, :, None, :]
+    sin = sin.to(hidden_states.dtype)[None, :, None, :]
+    x1, x2 = hidden_states_rotary.chunk(2, dim=-1)
+    hidden_states_rotated = torch.cat((-x2, x1), dim=-1)
+    hidden_states_rotary = hidden_states_rotary * cos + hidden_states_rotated * sin
+    return torch.cat((hidden_states_rotary, hidden_states_pass), dim=-1).contiguous()
+
+
+class MiniMaxH3RotaryPosEmbed(nn.Module):
+    r"""
+    3-axis rotary embedding over the `(t, h, w)` coordinates of the packed sequence.
+
+    A single `inv_freq` buffer of `rope_freq_dim` frequencies is shared by the three axes. Each axis contributes
+    `rope_freq_dim` angles, the three blocks are concatenated to `3 * rope_freq_dim` and then concatenated with
+    themselves so that the `rotate_half` convention rotates `2 * 3 * rope_freq_dim` of the `head_dim` channels.
+    """
+
+    def __init__(self, rope_freq_dim: int = 16, rope_theta: float = 10000.0):
+        super().__init__()
+        self.rope_freq_dim = rope_freq_dim
+        inv_freq = 1.0 / (
+            rope_theta ** (torch.arange(0, 2 * rope_freq_dim, 2, dtype=torch.float32) / (2 * rope_freq_dim))
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        # position_ids: (seq_len, 3) -> cos/sin: (seq_len, 2 * 3 * rope_freq_dim)
+        position_ids = position_ids.to(torch.float32)
+        freqs = position_ids.unsqueeze(-1) * self.inv_freq.view(1, 1, -1)  # (seq_len, 3, rope_freq_dim)
+        freqs_t, freqs_h, freqs_w = freqs.unbind(dim=1)
+        freqs = torch.cat((freqs_t, freqs_h, freqs_w), dim=-1)
+        freqs = torch.cat((freqs, freqs), dim=-1)
+        return freqs.cos(), freqs.sin()
+
+
+class MiniMaxH3AdaLayerNormModulation(nn.Module):
+    r"""
+    Projects the shared timestep embedding into the six per-(timestep, modality) modulation parameters of one
+    transformer block.
+
+    `(num_timesteps, time_embed_dim)` -> six tensors of shape `(num_timesteps * MINIMAX_H3_MODALITY_NUM,
+    hidden_size)`, in the diffusers `shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp` order. The row
+    layout of the returned tensors is `[t0_mod0, t0_mod1, t0_mod2, t1_mod0, ...]`, which is what `timestep_indices *
+    MINIMAX_H3_MODALITY_NUM + token_tags` addresses.
+
+    A single projection is shared by `norm1` and `norm2` and by the three modalities, so it cannot be folded into
+    either norm the way [`~models.normalization.AdaLayerNormZero`] does. It is therefore a block-level module of its
+    own, named after the checkpoint's `adaln_proj`, with the modulation projection under the `linear` name diffusers
+    uses inside every AdaLN module.
+    """
+
+    def __init__(self, time_embed_dim: int, hidden_size: int):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.linear = nn.Linear(time_embed_dim, 6 * hidden_size * MINIMAX_H3_MODALITY_NUM, bias=True)
+
+    def forward(self, temb: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        # The activation runs at `temb`'s own precision — float32, since `time_embedder` is a float32 module in this
+        # mixed-precision checkpoint — and only its result is cast down to the bfloat16 projection. Every block reads
+        # the same `temb`, so a rounding applied before the activation biases every block's modulation parameters
+        # identically at every sampling step, which accumulates coherently over the denoising trajectory.
+        temb = self.linear(nn.functional.silu(temb).to(self.linear.weight.dtype))
+        temb = temb.view(-1, 6 * self.hidden_size)
+        return temb.chunk(6, dim=-1)
+
+
+class MiniMaxH3AdaLayerNormOut(nn.Module):
+    r"""
+    Final norm of the packed sequence, shift/scale modulated per row.
+
+    Same module layout and checkpoint keys as [`~models.normalization.AdaLayerNormContinuous`] (`norm` plus a `linear`
+    projecting the conditioning embedding to `2 * hidden_size`), with two MiniMax-H3 specifics: the modulation table
+    holds one row per *timestep* and is addressed per row of the packed sequence rather than per batch item, and the
+    two halves of the projection are `shift` then `scale`, the order `LTX2Transformer3DModel` and
+    `WanTransformer3DModel` also use in their output layers.
+    """
+
+    def __init__(self, hidden_size: int, time_embed_dim: int, eps: float):
+        super().__init__()
+        self.norm = nn.RMSNorm(hidden_size, eps=eps)
+        self.linear = nn.Linear(time_embed_dim, 2 * hidden_size, bias=True)
+
+    def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor, timestep_indices: torch.Tensor) -> torch.Tensor:
+        # As in `MiniMaxH3AdaLayerNormModulation`: activate at `temb`'s precision, cast to the projection's dtype after.
+        shift, scale = self.linear(nn.functional.silu(temb).to(self.linear.weight.dtype)).chunk(2, dim=-1)
+        # The modulation itself stays at the block stack's precision; `forward` casts to the output heads' dtype.
+        hidden_states = self.norm(hidden_states)
+        return hidden_states * (1.0 + scale.index_select(0, timestep_indices)) + shift.index_select(
+            0, timestep_indices
+        )
+
+
+class MiniMaxH3AttnProcessor:
+    r"""
+    Full self-attention over one packed sequence. There is no cross-attention anywhere in MiniMax-H3.
+    """
+
+    _attention_backend = None
+    _parallel_config = None
+
+    def __call__(
+        self,
+        attn: "MiniMaxH3Attention",
+        hidden_states: torch.Tensor,
+        rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if attn.fused_projections:
+            query, key, value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
+        else:
+            query = attn.to_q(hidden_states)
+            key = attn.to_k(hidden_states)
+            value = attn.to_v(hidden_states)
+
+        query = query.unflatten(-1, (attn.heads, -1))
+        key = key.unflatten(-1, (attn.heads, -1))
+        value = value.unflatten(-1, (attn.heads, -1))
+
+        query = attn.norm_q(query)
+        key = attn.norm_k(key)
+
+        if rotary_emb is not None:
+            query = _apply_rotary_emb(query, *rotary_emb)
+            key = _apply_rotary_emb(key, *rotary_emb)
+
+        # Without padding rows the packed sequence is a single attention document and no mask is needed (passing an
+        # all-zero float mask here would hard-fail the flash / sage backends). When padding rows are present, the
+        # caller supplies a boolean mask that keeps them in their own attention document, mirroring the reference's
+        # `cu_seqlens = [0, used, S]` split; masked backends (SDPA & co.) are required in that case.
+        hidden_states = dispatch_attention_fn(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=0.0,
+            is_causal=False,
+            backend=self._attention_backend,
+            parallel_config=self._parallel_config,
+        )
+        hidden_states = hidden_states.flatten(2, 3).type_as(query)
+        hidden_states = attn.to_out[0](hidden_states)
+        hidden_states = attn.to_out[1](hidden_states)
+        return hidden_states
+
+
+class MiniMaxH3Attention(nn.Module, AttentionModuleMixin):
+    _default_processor_cls = MiniMaxH3AttnProcessor
+    _available_processors = [MiniMaxH3AttnProcessor]
+
+    def __init__(
+        self,
+        hidden_size: int,
+        heads: int,
+        dim_head: int,
+        qk_norm_eps: float = 1e-5,
+        processor=None,
+    ):
+        super().__init__()
+        self.heads = heads
+        self.head_dim = dim_head
+        self.inner_dim = heads * dim_head
+        self.use_bias = False
+
+        self.to_q = nn.Linear(hidden_size, self.inner_dim, bias=False)
+        self.to_k = nn.Linear(hidden_size, self.inner_dim, bias=False)
+        self.to_v = nn.Linear(hidden_size, self.inner_dim, bias=False)
+        self.norm_q = nn.RMSNorm(dim_head, eps=qk_norm_eps)
+        self.norm_k = nn.RMSNorm(dim_head, eps=qk_norm_eps)
+        self.to_out = nn.ModuleList([nn.Linear(self.inner_dim, hidden_size, bias=False), nn.Dropout(0.0)])
+
+        if processor is None:
+            processor = self._default_processor_cls()
+        self.set_processor(processor)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        rotary_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        return self.processor(self, hidden_states, rotary_emb, attention_mask)
+
+
+class MiniMaxH3TokenRefinerBlock(nn.Module):
+    r"""
+    Plain pre-norm transformer block used to refine the projected text stream. No AdaLN and no rotary embedding.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        ffn_dim: int,
+        norm_eps: float,
+        qk_norm_eps: float,
+    ):
+        super().__init__()
+        self.norm1 = nn.RMSNorm(hidden_size, eps=norm_eps)
+        self.attn = MiniMaxH3Attention(
+            hidden_size=hidden_size,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            qk_norm_eps=qk_norm_eps,
+        )
+        self.norm2 = nn.RMSNorm(hidden_size, eps=norm_eps)
+        self.ff = FeedForward(hidden_size, inner_dim=ffn_dim, activation_fn="swiglu", bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(self.norm1(hidden_states))
+        hidden_states = hidden_states + self.ff(self.norm2(hidden_states))
+        return hidden_states
+
+
+class MiniMaxH3TokenRefiner(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        ffn_dim: int,
+        num_layers: int,
+        norm_eps: float,
+        qk_norm_eps: float,
+        final_norm_eps: float,
+    ):
+        super().__init__()
+        self.refiner_blocks = nn.ModuleList(
+            [
+                MiniMaxH3TokenRefinerBlock(
+                    hidden_size=hidden_size,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    ffn_dim=ffn_dim,
+                    norm_eps=norm_eps,
+                    qk_norm_eps=qk_norm_eps,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+        self.final_norm = nn.RMSNorm(hidden_size, eps=final_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for block in self.refiner_blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(block, hidden_states)
+            else:
+                hidden_states = block(hidden_states)
+        return self.final_norm(hidden_states)
+
+
+class MiniMaxH3TransformerBlock(nn.Module):
+    r"""
+    MiniMax-H3 block: pre-norm self-attention and feed-forward, each modulated by AdaLN parameters selected per row of
+    the packed sequence from the `(timestep, modality)` table.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        ffn_dim: int,
+        time_embed_dim: int,
+        norm_eps: float,
+        qk_norm_eps: float,
+    ):
+        super().__init__()
+        self.norm1 = nn.RMSNorm(hidden_size, eps=norm_eps)
+        self.attn = MiniMaxH3Attention(
+            hidden_size=hidden_size,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            qk_norm_eps=qk_norm_eps,
+        )
+        self.norm2 = nn.RMSNorm(hidden_size, eps=norm_eps)
+        self.ff = FeedForward(hidden_size, inner_dim=ffn_dim, activation_fn="swiglu", bias=False)
+        self.adaln_proj = MiniMaxH3AdaLayerNormModulation(time_embed_dim=time_embed_dim, hidden_size=hidden_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        adaln_indices: torch.Tensor,
+        rotary_emb: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaln_proj(temb)
+
+        residual = hidden_states
+        norm_hidden_states = self.norm1(hidden_states)
+        norm_hidden_states = norm_hidden_states * (
+            1.0 + scale_msa.index_select(0, adaln_indices)
+        ) + shift_msa.index_select(0, adaln_indices)
+        attn_output = self.attn(norm_hidden_states, rotary_emb, attention_mask)
+        hidden_states = residual + gate_msa.index_select(0, adaln_indices) * attn_output
+
+        residual = hidden_states
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = norm_hidden_states * (
+            1.0 + scale_mlp.index_select(0, adaln_indices)
+        ) + shift_mlp.index_select(0, adaln_indices)
+        ff_output = self.ff(norm_hidden_states)
+        hidden_states = residual + gate_mlp.index_select(0, adaln_indices) * ff_output
+
+        return hidden_states
+
+
+class MiniMaxH3Transformer3DModel(ModelMixin, ConfigMixin, AttentionMixin, PeftAdapterMixin, CacheMixin):
+    r"""
+    A Transformer model for joint video + audio generation, introduced in MiniMax-H3.
+
+    MiniMax-H3 runs a single stack of blocks over **one packed 1-D sequence** that holds the text condition, the
+    conditioning image / video rows, the audio rows and the target video rows. Attention is full self-attention over
+    that sequence; there is no cross-attention and no per-modality block weights. Modality-specific behaviour comes
+    only from the two input patch projections, the per-row AdaLN modality tag, and the two output heads.
+
+    The caller is responsible for building the packed layout: patchifying the video latents, ordering the rows, and
+    producing the `(t, h, w)` position grid, the per-row modality tags and the per-row timestep indices. Padding rows
+    (tag `-1`) are kept in a separate attention document, matching the reference implementation, which pads to a
+    multiple of 64 for FlashAttention with `cu_seqlens = [0, used, S]`. Prefer dropping them — a padless sequence
+    needs no attention mask, keeping the unmasked attention backends available.
+
+    The batch axis is a pure replication axis: the structural arguments (`timestep`, `timestep_indices`, `token_tags`,
+    `position_ids` and the three index tensors) describe one packed layout that every batch item shares, and each item
+    is a single attention document.
+
+    Args:
+        num_attention_heads (`int`, defaults to `56`):
+            The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of channels in each attention head. Note that `num_attention_heads * attention_head_dim` is
+            *larger* than `hidden_size` in MiniMax-H3.
+        hidden_size (`int`, defaults to `5376`):
+            The number of channels of the packed sequence (the residual stream).
+        num_layers (`int`, defaults to `50`):
+            The number of transformer blocks.
+        num_refiner_layers (`int`, defaults to `2`):
+            The number of token refiner blocks applied to the projected text stream.
+        ffn_dim (`int`, defaults to `14336`):
+            The inner dimension of the SwiGLU feed-forward layers.
+        in_channels (`int`, defaults to `24`):
+            The number of channels of the video latents.
+        audio_in_channels (`int`, defaults to `32`):
+            The number of channels of the audio latents.
+        patch_size (`tuple[int, int, int]`, defaults to `(1, 2, 2)`):
+            The `(t, h, w)` patch used to pack the video latents into rows.
+        text_dim (`int`, defaults to `5120`):
+            The number of channels of the text conditioning produced by the text encoder.
+        freq_dim (`int`, defaults to `256`):
+            The dimension of the sinusoidal timestep embedding. Timesteps are consumed unscaled in `[0, 1]`.
+        time_embed_hidden_dim (`int`, defaults to `5376`):
+            The inner dimension of the timestep MLP.
+        time_embed_dim (`int`, defaults to `2688`):
+            The output dimension of the timestep MLP, i.e. the input of every AdaLN projection.
+        rope_freq_dim (`int`, defaults to `16`):
+            The number of rotary frequencies per axis. The `(t, h, w)` axes share one `inv_freq` buffer of this length
+            and `2 * 3 * rope_freq_dim` of the `attention_head_dim` channels are rotated.
+        rope_theta (`float`, defaults to `10000.0`):
+            The base of the rotary frequency schedule the `rope.inv_freq` buffer is computed from.
+        norm_eps (`float`, defaults to `1e-5`):
+            Epsilon of the pre-attention and pre-feed-forward norms.
+        qk_norm_eps (`float`, defaults to `1e-5`):
+            Epsilon of the per-head query/key norms.
+        final_norm_eps (`float`, defaults to `1e-5`):
+            Epsilon of the token refiner output norm and of `norm_out`.
+    """
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["MiniMaxH3TransformerBlock", "MiniMaxH3TokenRefinerBlock", "MiniMaxH3AdaLayerNormOut"]
+    _repeated_blocks = ["MiniMaxH3TransformerBlock", "MiniMaxH3TokenRefinerBlock"]
+    _skip_layerwise_casting_patterns = ["norm"]
+    # MiniMax-H3 ships a mixed-precision checkpoint: the two input patch projections, the timestep MLP and the two
+    # output heads are float32 while everything else (including the AdaLN projections) is bfloat16. The `rope.inv_freq`
+    # buffer is computed rather than loaded and is kept float32 for the same reason the reference ships it float32.
+    # Entries are matched as substrings of the parameter name, so `proj_in` / `proj_out` also cover the audio heads.
+    _keep_in_fp32_modules = [
+        "proj_in",
+        "audio_proj_in",
+        "time_embedder",
+        "proj_out",
+        "audio_proj_out",
+        "rope",
+    ]
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 56,
+        attention_head_dim: int = 128,
+        hidden_size: int = 5376,
+        num_layers: int = 50,
+        num_refiner_layers: int = 2,
+        ffn_dim: int = 14336,
+        in_channels: int = 24,
+        audio_in_channels: int = 32,
+        patch_size: tuple[int, int, int] = (1, 2, 2),
+        text_dim: int = 5120,
+        freq_dim: int = 256,
+        time_embed_hidden_dim: int = 5376,
+        time_embed_dim: int = 2688,
+        rope_freq_dim: int = 16,
+        rope_theta: float = 10000.0,
+        norm_eps: float = 1e-5,
+        qk_norm_eps: float = 1e-5,
+        final_norm_eps: float = 1e-5,
+    ) -> None:
+        super().__init__()
+
+        video_patch_dim = in_channels * patch_size[0] * patch_size[1] * patch_size[2]
+
+        # 1. Per-modality input projections
+        self.proj_in = nn.Linear(video_patch_dim, hidden_size, bias=True)
+        self.audio_proj_in = nn.Linear(audio_in_channels, hidden_size, bias=True)
+        self.context_embedder = nn.Linear(text_dim, hidden_size, bias=True)
+
+        # 2. Timestep embedding, shared by every AdaLN projection
+        self.time_proj = Timesteps(num_channels=freq_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.time_embedder = TimestepEmbedding(
+            in_channels=freq_dim, time_embed_dim=time_embed_hidden_dim, out_dim=time_embed_dim
+        )
+
+        # 3. Rotary embedding over the packed (t, h, w) grid
+        self.rope = MiniMaxH3RotaryPosEmbed(rope_freq_dim=rope_freq_dim, rope_theta=rope_theta)
+
+        # 4. Text stream refiner
+        self.token_refiner = MiniMaxH3TokenRefiner(
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=attention_head_dim,
+            ffn_dim=ffn_dim,
+            num_layers=num_refiner_layers,
+            norm_eps=norm_eps,
+            qk_norm_eps=qk_norm_eps,
+            final_norm_eps=final_norm_eps,
+        )
+
+        # 5. The block stack
+        self.transformer_blocks = nn.ModuleList(
+            [
+                MiniMaxH3TransformerBlock(
+                    hidden_size=hidden_size,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    ffn_dim=ffn_dim,
+                    time_embed_dim=time_embed_dim,
+                    norm_eps=norm_eps,
+                    qk_norm_eps=qk_norm_eps,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        # 6. Shared output norm and the two per-modality output heads. Both heads run over every row of the packed
+        # sequence; the rows of each modality are selected afterwards.
+        self.norm_out = MiniMaxH3AdaLayerNormOut(
+            hidden_size=hidden_size, time_embed_dim=time_embed_dim, eps=final_norm_eps
+        )
+        self.proj_out = nn.Linear(hidden_size, video_patch_dim, bias=True)
+        self.audio_proj_out = nn.Linear(hidden_size, audio_in_channels, bias=True)
+
+        self.gradient_checkpointing = False
+
+    @apply_lora_scale("attention_kwargs")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        audio_hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        timestep_indices: torch.Tensor,
+        token_tags: torch.Tensor,
+        position_ids: torch.Tensor,
+        video_indices: torch.Tensor,
+        audio_indices: torch.Tensor,
+        text_indices: torch.Tensor,
+        attention_kwargs: dict[str, Any] | None = None,
+        return_dict: bool = True,
+    ) -> MiniMaxH3TransformerOutput | tuple[torch.Tensor, torch.Tensor]:
+        r"""
+        Args:
+            hidden_states (`torch.Tensor` of shape `(batch_size, num_video_tokens, in_channels * prod(patch_size))`):
+                Patchified video latent rows — conditioning rows and target rows — ordered as they appear in the packed
+                sequence, i.e. matching `video_indices`.
+            audio_hidden_states (`torch.Tensor` of shape `(batch_size, num_audio_tokens, audio_in_channels)`):
+                Audio latent rows, ordered to match `audio_indices`.
+            encoder_hidden_states (`torch.Tensor` of shape `(batch_size, num_text_tokens, text_dim)`):
+                Text conditioning, ordered to match `text_indices`.
+            timestep (`torch.Tensor` of shape `(num_timesteps,)`):
+                The *distinct* timestep values present in the packed sequence, in `[0, 1]` and unscaled. One forward
+                serves rows at different noise levels (target video, target audio, conditioning rows).
+            timestep_indices (`torch.Tensor` of shape `(seq_len,)`):
+                For every row of the packed sequence, the index of its timestep in `timestep`.
+            token_tags (`torch.Tensor` of shape `(seq_len,)`):
+                For every row of the packed sequence, its modality: `0` video, `1` text, `2` audio, `-1` padding.
+                Padding rows form their own attention document and never reach the outputs.
+            position_ids (`torch.Tensor` of shape `(seq_len, 3)`):
+                The `(t, h, w)` rotary coordinates of every row of the packed sequence.
+            video_indices (`torch.Tensor` of shape `(num_video_tokens,)`):
+                Positions of the video rows in the packed sequence.
+            audio_indices (`torch.Tensor` of shape `(num_audio_tokens,)`):
+                Positions of the audio rows in the packed sequence.
+            text_indices (`torch.Tensor` of shape `(num_text_tokens,)`):
+                Positions of the text rows in the packed sequence.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that, if specified, may carry a `scale` entry which is applied to the LoRA layers.
+            return_dict (`bool`, defaults to `True`):
+                Whether to return a [`MiniMaxH3TransformerOutput`] instead of a plain tuple.
+
+        Returns:
+            [`MiniMaxH3TransformerOutput`] or `tuple`:
+                The video velocity of shape `(batch_size, num_video_tokens, in_channels * prod(patch_size))` and the
+                audio velocity of shape `(batch_size, num_audio_tokens, audio_in_channels)`, in the row order of
+                `video_indices` and `audio_indices`.
+        """
+        # `attention_kwargs` is consumed by the `@apply_lora_scale` decorator on this method.
+        if position_ids.ndim != 2 or position_ids.shape[-1] != 3:
+            raise ValueError(f"`position_ids` must be a `(seq_len, 3)` tensor, got {list(position_ids.shape)}.")
+        sequence_length = position_ids.shape[0]
+        if token_tags.shape != (sequence_length,) or timestep_indices.shape != (sequence_length,):
+            raise ValueError(
+                "`token_tags` and `timestep_indices` must both be `(seq_len,)` tensors matching `position_ids`, got "
+                f"{list(token_tags.shape)} and {list(timestep_indices.shape)} for seq_len={sequence_length}."
+            )
+
+        rotary_emb = self.rope(position_ids)
+
+        # 1. Project each modality and scatter the rows into the packed sequence buffer. The checkpoint is
+        # mixed-precision (the two patch projections are float32 while `context_embedder` and the block stack are
+        # bfloat16 — see `_keep_in_fp32_modules`), so every input is aligned with its projection's parameter dtype,
+        # mirroring the reference's explicit casts. The text stream sets the dtype of the packed sequence.
+        video_embeds = self.proj_in(hidden_states.to(self.proj_in.weight.dtype))
+        audio_embeds = self.audio_proj_in(audio_hidden_states.to(self.audio_proj_in.weight.dtype))
+        text_embeds = self.context_embedder(encoder_hidden_states.to(self.context_embedder.weight.dtype))
+        text_embeds = self.token_refiner(text_embeds)
+
+        hidden_states = text_embeds.new_zeros((text_embeds.shape[0], sequence_length, text_embeds.shape[-1]))
+        hidden_states = hidden_states.index_copy(1, text_indices, text_embeds)
+        hidden_states = hidden_states.index_copy(1, video_indices, video_embeds.to(text_embeds.dtype))
+        hidden_states = hidden_states.index_copy(1, audio_indices, audio_embeds.to(text_embeds.dtype))
+
+        # 2. One timestep embedding per distinct noise level. `temb` is shared by all AdaLN projections, which are
+        # bfloat16 in the checkpoint while `time_embedder` is float32, so it stays at the time embedder's precision:
+        # each AdaLN module applies its own activation to it and casts to its projection's dtype afterwards.
+        temb = self.time_proj(timestep)
+        temb = self.time_embedder(temb.to(self.time_embedder.linear_1.weight.dtype))
+
+        # 3. Row -> AdaLN table row. `clamp(min=0)` mirrors the reference, where padding rows carry the tag `-1`; the
+        # clamp keeps the `-1` from indexing backwards (padding rows never reach the outputs, which are selected by
+        # `video_indices` / `audio_indices`).
+        adaln_indices = timestep_indices * MINIMAX_H3_MODALITY_NUM + token_tags.clamp(min=0)
+
+        # 4. Padding rows (tag `-1`) must not exchange attention with live rows: the reference keeps the padding tail
+        # as a separate attention document (`cu_seqlens = [0, used, S]`). A boolean mask that pairs live rows with live
+        # rows and padding rows with padding rows reproduces that split exactly. Padless sequences keep `None` so the
+        # unmasked fast paths (flash & co.) stay available.
+        attention_mask = None
+        is_pad = token_tags < 0
+        if bool(is_pad.any()):
+            attention_mask = is_pad[None, :] == is_pad[:, None]
+
+        for block in self.transformer_blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, temb, adaln_indices, rotary_emb, attention_mask
+                )
+            else:
+                hidden_states = block(hidden_states, temb, adaln_indices, rotary_emb, attention_mask)
+
+        # 5. Both heads run over every row, then the rows of each modality are selected. The heads are listed in
+        # `_keep_in_fp32_modules`, so they stay float32 while the block stack runs in the requested `torch_dtype`;
+        # align the activation with their parameter dtype.
+        hidden_states = self.norm_out(hidden_states, temb, timestep_indices).to(self.proj_out.weight.dtype)
+        video_output = self.proj_out(hidden_states).index_select(1, video_indices)
+        audio_output = self.audio_proj_out(hidden_states).index_select(1, audio_indices)
+
+        if not return_dict:
+            return (video_output, audio_output)
+        return MiniMaxH3TransformerOutput(sample=video_output, audio_sample=audio_output)
diff --git a/invokeai/backend/model_manager/configs/factory.py b/invokeai/backend/model_manager/configs/factory.py
index 7d7ae4dc1bc..6462d53d0a2 100644
--- a/invokeai/backend/model_manager/configs/factory.py
+++ b/invokeai/backend/model_manager/configs/factory.py
@@ -85,6 +85,7 @@
     Main_Diffusers_FLUX_Config,
     Main_Diffusers_Ideogram4_Config,
     Main_Diffusers_Krea2_Config,
+    Main_Diffusers_MiniMaxH3_Config,
     Main_Diffusers_QwenImage_Config,
     Main_Diffusers_SD1_Config,
     Main_Diffusers_SD2_Config,
@@ -183,6 +184,7 @@
 # Known config file names for diffusers/transformers models
 _CONFIG_FILES = {
     "model_index.json",
+    "modular_model_index.json",
     "config.json",
 }
 
@@ -195,7 +197,15 @@
 # Classes introduced by the versions pinned by this checkout may not exist in the interpreter used by
 # lightweight config tests. Keep explicit markers only for those newly supported classes; established
 # classes are resolved from the installed Diffusers/Transformers exports below.
-_PINNED_MODEL_CLASS_MARKERS = {"Krea2Pipeline"}
+_PINNED_MODEL_CLASS_MARKERS = {
+    "Krea2Pipeline",
+    # MiniMax H3 classes exist only in an unreleased diffusers branch (vendored under
+    # invokeai/backend/minimax_h3); the installed diffusers cannot resolve them.
+    "MiniMaxH3ModularPipeline",
+    "MiniMaxH3Transformer3DModel",
+    "AutoencoderKLMiniMaxH3",
+    "AutoencoderKLMiniMaxH3Audio",
+}
 
 
 def _is_known_model_marker(config_name: str, config: Any) -> bool:
@@ -221,15 +231,16 @@ def has_model_export(module: Any, name: Any, expected_bases: tuple[type, ...]) -
         except Exception:
             return False
 
-    if config_name == "model_index.json":
+    if config_name in ("model_index.json", "modular_model_index.json"):
         class_name = config.get("_class_name")
-        return class_name in _PINNED_MODEL_CLASS_MARKERS or has_model_export(
+        # Non-str _class_name (e.g. a list) must read as "not a marker", not TypeError on the set lookup.
+        return (isinstance(class_name, str) and class_name in _PINNED_MODEL_CLASS_MARKERS) or has_model_export(
             diffusers, class_name, (DiffusionPipeline,)
         )
 
     class_name = config.get("_class_name")
     if (
-        class_name in _PINNED_MODEL_CLASS_MARKERS
+        (isinstance(class_name, str) and class_name in _PINNED_MODEL_CLASS_MARKERS)
         or has_model_export(diffusers, class_name, (ModelMixin, DiffusionPipeline))
         or has_model_export(transformers, class_name, (PreTrainedModel, PretrainedConfig))
     ):
@@ -262,6 +273,7 @@ def has_model_export(module: Any, name: Any, expected_bases: tuple[type, ...]) -
         Annotated[Main_Diffusers_ErnieImage_Config, Main_Diffusers_ErnieImage_Config.get_tag()],
         Annotated[Main_Diffusers_Ideogram4_Config, Main_Diffusers_Ideogram4_Config.get_tag()],
         Annotated[Main_Diffusers_Krea2_Config, Main_Diffusers_Krea2_Config.get_tag()],
+        Annotated[Main_Diffusers_MiniMaxH3_Config, Main_Diffusers_MiniMaxH3_Config.get_tag()],
         # Main (Pipeline) - checkpoint format
         # IMPORTANT: FLUX.2 must be checked BEFORE FLUX.1 because FLUX.2 has specific validation
         # that will reject FLUX.1 models, but FLUX.1 validation may incorrectly match FLUX.2 models
diff --git a/invokeai/backend/model_manager/configs/main.py b/invokeai/backend/model_manager/configs/main.py
index 8c80f5eaeb9..6defd7a1277 100644
--- a/invokeai/backend/model_manager/configs/main.py
+++ b/invokeai/backend/model_manager/configs/main.py
@@ -28,6 +28,7 @@
     Flux2VariantType,
     FluxVariantType,
     Krea2VariantType,
+    MiniMaxH3VariantType,
     ModelFormat,
     ModelType,
     ModelVariantType,
@@ -73,6 +74,7 @@ def from_base(
         | WanVariantType
         | ZImageVariantType
         | Krea2VariantType
+        | MiniMaxH3VariantType
         | None = None,
         name: str | None = None,
         path: str | None = None,
@@ -136,6 +138,11 @@ def from_base(
                     return cls(steps=30, cfg_scale=5.0, width=1024, height=1024)
                 # Default to A14B settings (also used when variant is unknown).
                 return cls(steps=40, cfg_scale=4.0, width=1024, height=1024)
+            case BaseModelType.MiniMaxH3:
+                # H3 is guidance-distilled (no CFG; cfg_scale 1.0 means "no guidance") and was
+                # released for a fixed 768px short edge; 1344x768 is its native 16:9 canvas.
+                # Dimensions must be multiples of 32.
+                return cls(steps=50, cfg_scale=1.0, width=1344, height=768)
             case _:
                 # TODO(psyche): Do we want defaults for other base types?
                 return None
@@ -1475,6 +1482,63 @@ def _get_variant(cls, mod: ModelOnDisk) -> Krea2VariantType:
         return Krea2VariantType.Turbo
 
 
+class Main_Diffusers_MiniMaxH3_Config(Diffusers_Config_Base, Main_Config_Base, Config_Base):
+    """Model config for MiniMax H3 (Hailuo 3.0) diffusers-format models."""
+
+    base: Literal[BaseModelType.MiniMaxH3] = Field(BaseModelType.MiniMaxH3)
+    variant: MiniMaxH3VariantType = Field()
+
+    @classmethod
+    def from_model_on_disk(cls, mod: ModelOnDisk, override_fields: dict[str, Any]) -> Self:
+        raise_if_not_dir(mod)
+
+        raise_for_override_fields(cls, override_fields)
+
+        # H3 ships as a Modular Diffusers pipeline: the root config is modular_model_index.json, not
+        # model_index.json, and the class name implies the base type. The HF repo's FL2VA/ and
+        # Ref2VA/ subtrees are the original remote-code checkpoints and declare "MiniMaxH3Pipeline"
+        # instead - deliberately not matched, since their custom Python cannot be run here.
+        raise_for_class_name(
+            mod.path / "modular_model_index.json",
+            {"MiniMaxH3ModularPipeline"},
+        )
+
+        # The jointly-denoised audio track is what distinguishes H3 from every other video family.
+        # Require the audio VAE so a partial download fails identification rather than failing
+        # mid-generation.
+        raise_for_class_name(
+            mod.path / "audio_vae" / "config.json",
+            {"AutoencoderKLMiniMaxH3Audio"},
+        )
+
+        variant = override_fields.pop("variant", None) or cls._get_variant(mod)
+
+        repo_variant = override_fields.pop("repo_variant", None) or cls._get_repo_variant_or_raise(mod)
+
+        return cls(
+            **override_fields,
+            variant=variant,
+            repo_variant=repo_variant,
+        )
+
+    @classmethod
+    def _get_variant(cls, mod: ModelOnDisk) -> MiniMaxH3VariantType:
+        """Determine the H3 variant from which task transformer is present.
+
+        H3's task checkpoints share every component except the transformer folder: ``transformer``
+        (FL2VA: text / first/last-frame to audio-video) vs ``transformer_ref`` (Ref2VA: multi-
+        reference). Only FL2VA is supported so far. A Ref2VA-only download is a real H3 model this
+        version cannot run, so identification fails rather than mislabeling it as FL2VA.
+        """
+        transformer_config = mod.path / "transformer" / "config.json"
+        if not transformer_config.exists():
+            raise NotAMatchError(
+                "no FL2VA transformer folder (`transformer/`); Ref2VA-only installs are not supported yet"
+            )
+        raise_for_class_name(transformer_config, {"MiniMaxH3Transformer3DModel"})
+        return MiniMaxH3VariantType.FL2VA
+
+
 class Main_Checkpoint_Krea2_Config(Checkpoint_Config_Base, Main_Config_Base, Config_Base):
     """Model config for Krea-2 single-file checkpoint models (safetensors, etc)."""
 
diff --git a/invokeai/backend/model_manager/load/model_loaders/krea2.py b/invokeai/backend/model_manager/load/model_loaders/krea2.py
index 98cd4a2dec3..71d5e31e79f 100644
--- a/invokeai/backend/model_manager/load/model_loaders/krea2.py
+++ b/invokeai/backend/model_manager/load/model_loaders/krea2.py
@@ -24,18 +24,13 @@
     ModelType,
     SubModelType,
 )
+from invokeai.backend.model_manager.util.qwen3_vl import normalize_qwen3vl_rope_config
 from invokeai.backend.quantization.gguf.loaders import gguf_sd_loader
 from invokeai.backend.util.devices import TorchDevice
 
-
-def _normalize_qwen3vl_rope_config(config: Any) -> Any:
-    """Mirror Qwen3-VL rope_parameters into rope_scaling for Transformers compatibility."""
-    text_config = getattr(config, "text_config", None)
-    if text_config is not None:
-        rope_params = getattr(text_config, "rope_parameters", None)
-        if getattr(text_config, "rope_scaling", None) is None and rope_params is not None:
-            text_config.rope_scaling = rope_params
-    return config
+# Kept as a module-level alias: this helper moved to model_manager.util.qwen3_vl so the MiniMax H3
+# loader can share it without importing across family loaders.
+_normalize_qwen3vl_rope_config = normalize_qwen3vl_rope_config
 
 
 def _strip_comfyui_prefix(sd: dict[str, Any]) -> dict[str, Any]:
diff --git a/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py b/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py
new file mode 100644
index 00000000000..14a1a148fa7
--- /dev/null
+++ b/invokeai/backend/model_manager/load/model_loaders/minimax_h3.py
@@ -0,0 +1,104 @@
+"""Loader registrations for MiniMax H3 (Hailuo 3.0) audio-video generation models.
+
+Currently covers the diffusers-format Modular Diffusers layout only (the layout at the root of
+the ``MiniMaxAI/MiniMax-H3`` HF repo). The model classes are vendored under
+``invokeai.backend.minimax_h3`` because they only exist in an unreleased diffusers branch, so this
+loader dispatches submodels explicitly instead of subclassing ``GenericDiffusersLoader`` (whose
+``get_hf_load_class`` resolves class names against the installed diffusers and would fail).
+
+Submodel map (subfolder == ``SubModelType`` value):
+- ``transformer``  -> vendored ``MiniMaxH3Transformer3DModel`` (33B DiT, bf16)
+- ``text_encoder`` -> ``transformers.Qwen3VLForConditionalGeneration`` (Qwen3-VL-32B, bf16; H3
+  conditions on layer-50 hidden states and never uses the LM head, but the checkpoint is the full
+  model per the repo's modular_model_index.json)
+- ``tokenizer``    -> ``AutoTokenizer`` (Qwen2TokenizerFast)
+- ``processor``    -> ``AutoProcessor`` (Qwen3VLProcessor; needed even for text-only encoding,
+  which uses its multimodal token-type ids)
+- ``vae``          -> vendored ``AutoencoderKLMiniMaxH3`` (video VAE, bf16)
+- ``audio_vae``    -> vendored ``AutoencoderKLMiniMaxH3Audio`` (kept fp32: it is ~0.6 GB and
+  half-precision artifacts in decoded audio are audible)
+
+The two ``MiniMaxH3Scheduler`` instances (video shift 12.0, audio shift 3.0) are constructed
+directly by the denoise invocation - they are stateless configs, not loaded weights.
+"""
+
+from pathlib import Path
+from typing import Optional
+
+import torch
+
+from invokeai.backend.model_manager.configs.factory import AnyModelConfig
+from invokeai.backend.model_manager.configs.main import Main_Diffusers_MiniMaxH3_Config
+from invokeai.backend.model_manager.load.load_default import ModelLoader
+from invokeai.backend.model_manager.load.model_loader_registry import ModelLoaderRegistry
+from invokeai.backend.model_manager.taxonomy import (
+    AnyModel,
+    BaseModelType,
+    ModelFormat,
+    ModelType,
+    SubModelType,
+)
+from invokeai.backend.model_manager.util.qwen3_vl import normalize_qwen3vl_rope_config
+from invokeai.backend.util.devices import TorchDevice
+
+
+@ModelLoaderRegistry.register(base=BaseModelType.MiniMaxH3, type=ModelType.Main, format=ModelFormat.Diffusers)
+class MiniMaxH3DiffusersModel(ModelLoader):
+    """Loader for MiniMax H3 diffusers-format models (FL2VA)."""
+
+    def _load_model(
+        self,
+        config: AnyModelConfig,
+        submodel_type: Optional[SubModelType] = None,
+    ) -> AnyModel:
+        if not isinstance(config, Main_Diffusers_MiniMaxH3_Config):
+            raise ValueError(f"Unexpected config type {type(config).__name__} for a MiniMax H3 loader.")
+        if submodel_type is None:
+            raise Exception("A submodel type must be provided when loading MiniMax H3 main models.")
+
+        model_path = Path(config.path)
+        submodel_path = model_path / submodel_type.value
+
+        target_device = TorchDevice.choose_torch_device()
+        dtype = TorchDevice.choose_bfloat16_safe_dtype(target_device)
+
+        match submodel_type:
+            case SubModelType.Transformer:
+                from invokeai.backend.minimax_h3 import MiniMaxH3Transformer3DModel
+
+                return MiniMaxH3Transformer3DModel.from_pretrained(
+                    submodel_path, torch_dtype=dtype, local_files_only=True
+                )
+            case SubModelType.TextEncoder:
+                from transformers import AutoConfig, Qwen3VLForConditionalGeneration
+
+                te_config = normalize_qwen3vl_rope_config(
+                    AutoConfig.from_pretrained(submodel_path, local_files_only=True)
+                )
+                return Qwen3VLForConditionalGeneration.from_pretrained(
+                    submodel_path,
+                    config=te_config,
+                    torch_dtype=dtype,
+                    low_cpu_mem_usage=True,
+                    local_files_only=True,
+                )
+            case SubModelType.Tokenizer:
+                from transformers import AutoTokenizer
+
+                return AutoTokenizer.from_pretrained(submodel_path, local_files_only=True)
+            case SubModelType.Processor:
+                from transformers import AutoProcessor
+
+                return AutoProcessor.from_pretrained(submodel_path, local_files_only=True)
+            case SubModelType.VAE:
+                from invokeai.backend.minimax_h3 import AutoencoderKLMiniMaxH3
+
+                return AutoencoderKLMiniMaxH3.from_pretrained(submodel_path, torch_dtype=dtype, local_files_only=True)
+            case SubModelType.AudioVAE:
+                from invokeai.backend.minimax_h3 import AutoencoderKLMiniMaxH3Audio
+
+                return AutoencoderKLMiniMaxH3Audio.from_pretrained(
+                    submodel_path, torch_dtype=torch.float32, local_files_only=True
+                )
+            case _:
+                raise ValueError(f"Unsupported submodel type {submodel_type} for MiniMax H3 models.")
diff --git a/invokeai/backend/model_manager/taxonomy.py b/invokeai/backend/model_manager/taxonomy.py
index e34345d1735..4aed841df65 100644
--- a/invokeai/backend/model_manager/taxonomy.py
+++ b/invokeai/backend/model_manager/taxonomy.py
@@ -66,6 +66,9 @@ class BaseModelType(str, Enum):
     """Indicates the model is associated with the Krea 2 model architecture, including Krea-2-Turbo."""
     Wan = "wan"
     """Indicates the model is associated with the Wan 2.2 model architecture (T2V-A14B / TI2V-5B), used for image generation at num_frames=1."""
+    MiniMaxH3 = "minimax-h3"
+    """Indicates the model is associated with the MiniMax H3 (Hailuo 3.0) omni-modal architecture, which
+    generates video with jointly-denoised stereo audio."""
     Unknown = "unknown"
     """Indicates the model's base architecture is unknown."""
 
@@ -113,11 +116,13 @@ class SubModelType(str, Enum):
     Tokenizer = "tokenizer"
     Tokenizer2 = "tokenizer_2"
     Tokenizer3 = "tokenizer_3"
+    Processor = "processor"
     PromptEnhancer = "pe"
     PromptEnhancerTokenizer = "pe_tokenizer"
     VAE = "vae"
     VAEDecoder = "vae_decoder"
     VAEEncoder = "vae_encoder"
+    AudioVAE = "audio_vae"
     Scheduler = "scheduler"
     SafetyChecker = "safety_checker"
 
@@ -248,6 +253,14 @@ class Qwen3VariantType(str, Enum):
     """Qwen3 0.6B text encoder (hidden_size=1024). Used by Anima."""
 
 
+class MiniMaxH3VariantType(str, Enum):
+    """MiniMax H3 model variants (task-specific transformer checkpoints sharing every other component)."""
+
+    FL2VA = "fl2va"
+    """First/last-frame + text to audio-video: text-to-video and first/last-frame image-to-video
+    (HF repo subfolder ``transformer``)."""
+
+
 class PiDDecoderVariantType(str, Enum):
     """PiD (Pixel Diffusion Decoder) resolution presets distributed by NVIDIA.
 
@@ -342,6 +355,7 @@ class FluxLoRAFormat(str, Enum):
     WanLoRAVariantType,
     Qwen3VariantType,
     Krea2VariantType,
+    MiniMaxH3VariantType,
     PiDDecoderVariantType,
 ]
 variant_type_adapter = TypeAdapter[
@@ -355,6 +369,7 @@ class FluxLoRAFormat(str, Enum):
     | WanLoRAVariantType
     | Qwen3VariantType
     | Krea2VariantType
+    | MiniMaxH3VariantType
     | PiDDecoderVariantType
 ](
     ModelVariantType
@@ -367,5 +382,6 @@ class FluxLoRAFormat(str, Enum):
     | WanLoRAVariantType
     | Qwen3VariantType
     | Krea2VariantType
+    | MiniMaxH3VariantType
     | PiDDecoderVariantType
 )
diff --git a/invokeai/backend/model_manager/util/qwen3_vl.py b/invokeai/backend/model_manager/util/qwen3_vl.py
new file mode 100644
index 00000000000..a524fbc4f93
--- /dev/null
+++ b/invokeai/backend/model_manager/util/qwen3_vl.py
@@ -0,0 +1,17 @@
+"""Shared helpers for loading Qwen3-VL encoder checkpoints (used by Krea-2 and MiniMax H3)."""
+
+from typing import Any
+
+
+def normalize_qwen3vl_rope_config(config: Any) -> Any:
+    """Mirror Qwen3-VL rope_parameters into rope_scaling for Transformers compatibility.
+
+    Some Qwen3-VL checkpoints store rope settings under ``rope_parameters``, but the installed
+    transformers' Qwen3VL rotary embedding reads ``rope_scaling`` (None there) and crashes.
+    """
+    text_config = getattr(config, "text_config", None)
+    if text_config is not None:
+        rope_params = getattr(text_config, "rope_parameters", None)
+        if getattr(text_config, "rope_scaling", None) is None and rope_params is not None:
+            text_config.rope_scaling = rope_params
+    return config
diff --git a/invokeai/frontend/web/openapi.json b/invokeai/frontend/web/openapi.json
index e19d0163e31..2d5b5ee4261 100644
--- a/invokeai/frontend/web/openapi.json
+++ b/invokeai/frontend/web/openapi.json
@@ -856,6 +856,9 @@
                     {
                       "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                     },
@@ -1251,6 +1254,9 @@
                     {
                       "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                     },
@@ -1646,6 +1652,9 @@
                     {
                       "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                     },
@@ -2086,6 +2095,9 @@
                     {
                       "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                     },
@@ -2550,6 +2562,9 @@
                     {
                       "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                     },
@@ -3844,6 +3859,9 @@
                     {
                       "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                     },
+                    {
+                      "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                    },
                     {
                       "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                     },
@@ -14237,6 +14255,9 @@
           {
             "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
           },
+          {
+            "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+          },
           {
             "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
           },
@@ -14834,6 +14855,7 @@
           "anima",
           "krea-2",
           "wan",
+          "minimax-h3",
           "unknown"
         ],
         "title": "BaseModelType",
@@ -58546,6 +58568,167 @@
         "title": "Main_Diffusers_Krea2_Config",
         "description": "Model config for Krea-2 diffusers models (Krea-2-Turbo)."
       },
+      "Main_Diffusers_MiniMaxH3_Config": {
+        "properties": {
+          "key": {
+            "type": "string",
+            "title": "Key",
+            "description": "A unique key for this model."
+          },
+          "hash": {
+            "type": "string",
+            "title": "Hash",
+            "description": "The hash of the model file(s)."
+          },
+          "path": {
+            "type": "string",
+            "title": "Path",
+            "description": "Path to the model on the filesystem. Relative paths are relative to the Invoke root directory."
+          },
+          "file_size": {
+            "type": "integer",
+            "title": "File Size",
+            "description": "The size of the model in bytes."
+          },
+          "name": {
+            "type": "string",
+            "title": "Name",
+            "description": "Name of the model."
+          },
+          "description": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Description",
+            "description": "Model description"
+          },
+          "source": {
+            "type": "string",
+            "title": "Source",
+            "description": "The original source of the model (path, URL or repo_id)."
+          },
+          "source_type": {
+            "$ref": "#/components/schemas/ModelSourceType",
+            "description": "The type of source"
+          },
+          "source_api_response": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Source Api Response",
+            "description": "The original API response from the source, as stringified JSON."
+          },
+          "source_url": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Source Url",
+            "description": "Optional URL for the model (e.g. download page or model page)."
+          },
+          "cover_image": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Cover Image",
+            "description": "Url for image to preview model"
+          },
+          "type": {
+            "type": "string",
+            "const": "main",
+            "title": "Type",
+            "default": "main"
+          },
+          "trigger_phrases": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "string"
+                },
+                "type": "array",
+                "uniqueItems": true
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Trigger Phrases",
+            "description": "Set of trigger phrases for this model"
+          },
+          "default_settings": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/MainModelDefaultSettings"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "description": "Default settings for this model"
+          },
+          "format": {
+            "type": "string",
+            "const": "diffusers",
+            "title": "Format",
+            "default": "diffusers"
+          },
+          "repo_variant": {
+            "$ref": "#/components/schemas/ModelRepoVariant",
+            "default": ""
+          },
+          "base": {
+            "type": "string",
+            "const": "minimax-h3",
+            "title": "Base",
+            "default": "minimax-h3"
+          },
+          "variant": {
+            "$ref": "#/components/schemas/MiniMaxH3VariantType"
+          }
+        },
+        "type": "object",
+        "required": [
+          "key",
+          "hash",
+          "path",
+          "file_size",
+          "name",
+          "description",
+          "source",
+          "source_type",
+          "source_api_response",
+          "source_url",
+          "cover_image",
+          "type",
+          "trigger_phrases",
+          "default_settings",
+          "format",
+          "repo_variant",
+          "base",
+          "variant"
+        ],
+        "title": "Main_Diffusers_MiniMaxH3_Config",
+        "description": "Model config for MiniMax H3 (Hailuo 3.0) diffusers-format models."
+      },
       "Main_Diffusers_QwenImage_Config": {
         "properties": {
           "key": {
@@ -64425,6 +64608,12 @@
           "$ref": "#/components/schemas/VAEOutput"
         }
       },
+      "MiniMaxH3VariantType": {
+        "type": "string",
+        "enum": ["fl2va"],
+        "title": "MiniMaxH3VariantType",
+        "description": "MiniMax H3 model variants (task-specific transformer checkpoints sharing every other component)."
+      },
       "Mistral3EncoderField": {
         "description": "Field for Mistral3 text encoder used by ERNIE-Image models.",
         "properties": {
@@ -64744,6 +64933,9 @@
               {
                 "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
               },
+              {
+                "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+              },
               {
                 "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
               },
@@ -65385,6 +65577,9 @@
                   {
                     "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                   },
+                  {
+                    "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                  },
                   {
                     "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                   },
@@ -65911,6 +66106,9 @@
               {
                 "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
               },
+              {
+                "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+              },
               {
                 "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
               },
@@ -66293,6 +66491,9 @@
               {
                 "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
               },
+              {
+                "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+              },
               {
                 "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
               },
@@ -66930,6 +67131,9 @@
               {
                 "$ref": "#/components/schemas/Krea2VariantType"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3VariantType"
+              },
               {
                 "$ref": "#/components/schemas/PiDDecoderVariantType"
               },
@@ -67141,6 +67345,9 @@
                 {
                   "$ref": "#/components/schemas/Main_Diffusers_Krea2_Config"
                 },
+                {
+                  "$ref": "#/components/schemas/Main_Diffusers_MiniMaxH3_Config"
+                },
                 {
                   "$ref": "#/components/schemas/Main_Checkpoint_SD1_Config"
                 },
@@ -78648,6 +78855,9 @@
               {
                 "$ref": "#/components/schemas/Krea2VariantType"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3VariantType"
+              },
               {
                 "$ref": "#/components/schemas/PiDDecoderVariantType"
               },
@@ -78820,6 +79030,9 @@
               {
                 "$ref": "#/components/schemas/Krea2VariantType"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3VariantType"
+              },
               {
                 "$ref": "#/components/schemas/PiDDecoderVariantType"
               },
@@ -79715,11 +79928,13 @@
           "tokenizer",
           "tokenizer_2",
           "tokenizer_3",
+          "processor",
           "pe",
           "pe_tokenizer",
           "vae",
           "vae_decoder",
           "vae_encoder",
+          "audio_vae",
           "scheduler",
           "safety_checker"
         ],
@@ -79767,6 +79982,9 @@
               {
                 "$ref": "#/components/schemas/Krea2VariantType"
               },
+              {
+                "$ref": "#/components/schemas/MiniMaxH3VariantType"
+              },
               {
                 "$ref": "#/components/schemas/PiDDecoderVariantType"
               },
diff --git a/invokeai/frontend/web/src/features/modelManagerV2/models.ts b/invokeai/frontend/web/src/features/modelManagerV2/models.ts
index 6548aff79a4..91d72d289af 100644
--- a/invokeai/frontend/web/src/features/modelManagerV2/models.ts
+++ b/invokeai/frontend/web/src/features/modelManagerV2/models.ts
@@ -193,6 +193,7 @@ export const MODEL_BASE_TO_COLOR: Record<BaseModelType, string> = {
   external: 'orange',
   anima: 'invokePurple',
   wan: 'cyan',
+  'minimax-h3': 'yellow',
   unknown: 'red',
 };
 
@@ -249,6 +250,7 @@ export const MODEL_BASE_TO_LONG_NAME: Record<BaseModelType, string> = {
   external: 'External',
   anima: 'Anima',
   wan: 'Wan 2.2',
+  'minimax-h3': 'MiniMax H3',
   unknown: 'Unknown',
 };
 
@@ -273,6 +275,7 @@ export const MODEL_BASE_TO_SHORT_NAME: Record<BaseModelType, string> = {
   external: 'External',
   anima: 'Anima',
   wan: 'Wan',
+  'minimax-h3': 'MiniMax H3',
   unknown: 'Unknown',
 };
 
@@ -295,6 +298,7 @@ export const MODEL_VARIANT_TO_LONG_NAME: Record<AnyModelVariant, string> = {
   gigantic: 'CLIP G',
   generate: 'Qwen Image',
   edit: 'Qwen Image Edit',
+  fl2va: 'MiniMax H3 FL2VA',
   t2v_a14b: 'Wan 2.2 T2V A14B',
   i2v_a14b: 'Wan 2.2 I2V A14B',
   ti2v_5b: 'Wan 2.2 TI2V 5B',
diff --git a/invokeai/frontend/web/src/features/nodes/types/common.ts b/invokeai/frontend/web/src/features/nodes/types/common.ts
index 75b6e361d09..41bcb1e8cea 100644
--- a/invokeai/frontend/web/src/features/nodes/types/common.ts
+++ b/invokeai/frontend/web/src/features/nodes/types/common.ts
@@ -120,6 +120,7 @@ export const zBaseModelType = z.enum([
   'external',
   'anima',
   'wan',
+  'minimax-h3',
   'unknown',
 ]);
 export type BaseModelType = z.infer<typeof zBaseModelType>;
@@ -138,6 +139,7 @@ export const zMainModelBase = z.enum([
   'ideogram-4',
   'anima',
   'wan',
+  'minimax-h3',
 ]);
 type MainModelBase = z.infer<typeof zMainModelBase>;
 export const isMainModelBase = (base: unknown): base is MainModelBase => zMainModelBase.safeParse(base).success;
@@ -180,11 +182,13 @@ export const zSubModelType = z.enum([
   'tokenizer',
   'tokenizer_2',
   'tokenizer_3',
+  'processor',
   'pe',
   'pe_tokenizer',
   'vae',
   'vae_decoder',
   'vae_encoder',
+  'audio_vae',
   'scheduler',
   'safety_checker',
 ]);
@@ -201,6 +205,7 @@ const zWanVariantType = z.enum(['t2v_a14b', 'i2v_a14b', 'ti2v_5b']);
  *  targets. A14B = inner_dim 5120 (both T2V and I2V), 5B = inner_dim 3072. */
 const zWanLoRAVariantType = z.enum(['a14b', '5b']);
 export const zQwen3VariantType = z.enum(['qwen3_4b', 'qwen3_8b', 'qwen3_06b']);
+const zMiniMaxH3VariantType = z.enum(['fl2va']);
 const zPiDDecoderVariantType = z.enum(['res2k_sr4x', 'res2kto4k_sr4x']);
 export const zAnyModelVariant = z.union([
   zModelVariantType,
@@ -213,6 +218,7 @@ export const zAnyModelVariant = z.union([
   zWanVariantType,
   zWanLoRAVariantType,
   zQwen3VariantType,
+  zMiniMaxH3VariantType,
   zPiDDecoderVariantType,
 ]);
 export type AnyModelVariant = z.infer<typeof zAnyModelVariant>;
diff --git a/invokeai/frontend/web/src/services/api/schema.ts b/invokeai/frontend/web/src/services/api/schema.ts
index 596472a0d43..66f90f8950b 100644
--- a/invokeai/frontend/web/src/services/api/schema.ts
+++ b/invokeai/frontend/web/src/services/api/schema.ts
@@ -4200,7 +4200,7 @@ export type components = {
              */
             type: "anima_text_encoder";
         };
-        AnyModelConfig: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+        AnyModelConfig: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
         /**
          * AppVersion
          * @description App Version Response
@@ -4352,7 +4352,7 @@ export type components = {
          *     fallback/null value `BaseModelType.Any` for these models, instead of making the model base optional.
          * @enum {string}
          */
-        BaseModelType: "any" | "sd-1" | "sd-2" | "sd-3" | "sdxl" | "sdxl-refiner" | "flux" | "flux2" | "cogview4" | "z-image" | "ernie-image" | "ideogram-4" | "external" | "qwen-image" | "anima" | "krea-2" | "wan" | "unknown";
+        BaseModelType: "any" | "sd-1" | "sd-2" | "sd-3" | "sdxl" | "sdxl-refiner" | "flux" | "flux2" | "cogview4" | "z-image" | "ernie-image" | "ideogram-4" | "external" | "qwen-image" | "anima" | "krea-2" | "wan" | "minimax-h3" | "unknown";
         /** Batch */
         Batch: {
             /**
@@ -24321,6 +24321,92 @@ export type components = {
             base: "krea-2";
             variant: components["schemas"]["Krea2VariantType"];
         };
+        /**
+         * Main_Diffusers_MiniMaxH3_Config
+         * @description Model config for MiniMax H3 (Hailuo 3.0) diffusers-format models.
+         */
+        Main_Diffusers_MiniMaxH3_Config: {
+            /**
+             * Key
+             * @description A unique key for this model.
+             */
+            key: string;
+            /**
+             * Hash
+             * @description The hash of the model file(s).
+             */
+            hash: string;
+            /**
+             * Path
+             * @description Path to the model on the filesystem. Relative paths are relative to the Invoke root directory.
+             */
+            path: string;
+            /**
+             * File Size
+             * @description The size of the model in bytes.
+             */
+            file_size: number;
+            /**
+             * Name
+             * @description Name of the model.
+             */
+            name: string;
+            /**
+             * Description
+             * @description Model description
+             */
+            description: string | null;
+            /**
+             * Source
+             * @description The original source of the model (path, URL or repo_id).
+             */
+            source: string;
+            /** @description The type of source */
+            source_type: components["schemas"]["ModelSourceType"];
+            /**
+             * Source Api Response
+             * @description The original API response from the source, as stringified JSON.
+             */
+            source_api_response: string | null;
+            /**
+             * Source Url
+             * @description Optional URL for the model (e.g. download page or model page).
+             */
+            source_url: string | null;
+            /**
+             * Cover Image
+             * @description Url for image to preview model
+             */
+            cover_image: string | null;
+            /**
+             * Type
+             * @default main
+             * @constant
+             */
+            type: "main";
+            /**
+             * Trigger Phrases
+             * @description Set of trigger phrases for this model
+             */
+            trigger_phrases: string[] | null;
+            /** @description Default settings for this model */
+            default_settings: components["schemas"]["MainModelDefaultSettings"] | null;
+            /**
+             * Format
+             * @default diffusers
+             * @constant
+             */
+            format: "diffusers";
+            /** @default  */
+            repo_variant: components["schemas"]["ModelRepoVariant"];
+            /**
+             * Base
+             * @default minimax-h3
+             * @constant
+             */
+            base: "minimax-h3";
+            variant: components["schemas"]["MiniMaxH3VariantType"];
+        };
         /**
          * Main_Diffusers_QwenImage_Config
          * @description Model config for Qwen Image diffusers models (both txt2img and edit).
@@ -27261,6 +27347,12 @@ export type components = {
              */
             type: "metadata_to_vae";
         };
+        /**
+         * MiniMaxH3VariantType
+         * @description MiniMax H3 model variants (task-specific transformer checkpoints sharing every other component).
+         * @enum {string}
+         */
+        MiniMaxH3VariantType: "fl2va";
         /**
          * Mistral3EncoderField
          * @description Field for Mistral3 text encoder used by ERNIE-Image models.
@@ -27413,7 +27505,7 @@ export type components = {
              * Config
              * @description The installed model's config
              */
-            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
         };
         /**
          * ModelInstallDownloadProgressEvent
@@ -27579,7 +27671,7 @@ export type components = {
              * Config Out
              * @description After successful installation, this will hold the configuration object.
              */
-            config_out?: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]) | null;
+            config_out?: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"]) | null;
             /**
              * Inplace
              * @description Leave model in its current location; otherwise install under models directory
@@ -27665,7 +27757,7 @@ export type components = {
              * Config
              * @description The model's config
              */
-            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
             /**
              * @description The submodel type, if any
              * @default null
@@ -27692,7 +27784,7 @@ export type components = {
              * Config
              * @description The model's config
              */
-            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+            config: components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
             /**
              * @description The submodel type, if any
              * @default null
@@ -27824,7 +27916,7 @@ export type components = {
              * Variant
              * @description The variant of the model.
              */
-            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
+            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
             /** @description The prediction type of the model. */
             prediction_type?: components["schemas"]["SchedulerPredictionType"] | null;
             /**
@@ -27919,7 +28011,7 @@ export type components = {
          */
         ModelsList: {
             /** Models */
-            models: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"])[];
+            models: (components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"])[];
         };
         /**
          * Multiply Integers
@@ -33862,7 +33954,7 @@ export type components = {
             type: components["schemas"]["ModelType"];
             format?: components["schemas"]["ModelFormat"] | null;
             /** Variant */
-            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
+            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
             /**
              * Is Installed
              * @default false
@@ -33907,7 +33999,7 @@ export type components = {
             type: components["schemas"]["ModelType"];
             format?: components["schemas"]["ModelFormat"] | null;
             /** Variant */
-            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
+            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
             /**
              * Is Installed
              * @default false
@@ -34431,14 +34523,14 @@ export type components = {
          * @description Submodel type.
          * @enum {string}
          */
-        SubModelType: "unet" | "transformer" | "transformer_2" | "text_encoder" | "text_encoder_2" | "text_encoder_3" | "tokenizer" | "tokenizer_2" | "tokenizer_3" | "pe" | "pe_tokenizer" | "vae" | "vae_decoder" | "vae_encoder" | "scheduler" | "safety_checker";
+        SubModelType: "unet" | "transformer" | "transformer_2" | "text_encoder" | "text_encoder_2" | "text_encoder_3" | "tokenizer" | "tokenizer_2" | "tokenizer_3" | "processor" | "pe" | "pe_tokenizer" | "vae" | "vae_decoder" | "vae_encoder" | "audio_vae" | "scheduler" | "safety_checker";
         /** SubmodelDefinition */
         SubmodelDefinition: {
             /** Path Or Prefix */
             path_or_prefix: string;
             model_type: components["schemas"]["ModelType"];
             /** Variant */
-            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
+            variant?: components["schemas"]["ModelVariantType"] | components["schemas"]["ClipVariantType"] | components["schemas"]["FluxVariantType"] | components["schemas"]["Flux2VariantType"] | components["schemas"]["ZImageVariantType"] | components["schemas"]["QwenImageVariantType"] | components["schemas"]["WanVariantType"] | components["schemas"]["WanLoRAVariantType"] | components["schemas"]["Qwen3VariantType"] | components["schemas"]["Krea2VariantType"] | components["schemas"]["MiniMaxH3VariantType"] | components["schemas"]["PiDDecoderVariantType"] | null;
         };
         /**
          * Subtract Integers
@@ -41349,7 +41441,7 @@ export interface operations {
                     [name: string]: unknown;
                 };
                 content: {
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Validation Error */
@@ -41381,7 +41473,7 @@ export interface operations {
                     [name: string]: unknown;
                 };
                 content: {
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Validation Error */
@@ -41433,7 +41525,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
@@ -41540,7 +41632,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
@@ -41613,7 +41705,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
@@ -42348,7 +42440,7 @@ export interface operations {
                      *       "upcast_attention": false
                      *     }
                      */
-                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
+                    "application/json": components["schemas"]["Main_Diffusers_SD1_Config"] | components["schemas"]["Main_Diffusers_SD2_Config"] | components["schemas"]["Main_Diffusers_SDXL_Config"] | components["schemas"]["Main_Diffusers_SDXLRefiner_Config"] | components["schemas"]["Main_Diffusers_SD3_Config"] | components["schemas"]["Main_Diffusers_FLUX_Config"] | components["schemas"]["Main_Diffusers_Flux2_Config"] | components["schemas"]["Main_Diffusers_CogView4_Config"] | components["schemas"]["Main_Diffusers_QwenImage_Config"] | components["schemas"]["Main_Diffusers_Wan_Config"] | components["schemas"]["Main_Diffusers_ZImage_Config"] | components["schemas"]["Main_Diffusers_ErnieImage_Config"] | components["schemas"]["Main_Diffusers_Ideogram4_Config"] | components["schemas"]["Main_Diffusers_Krea2_Config"] | components["schemas"]["Main_Diffusers_MiniMaxH3_Config"] | components["schemas"]["Main_Checkpoint_SD1_Config"] | components["schemas"]["Main_Checkpoint_SD2_Config"] | components["schemas"]["Main_Checkpoint_SDXL_Config"] | components["schemas"]["Main_Checkpoint_SDXLRefiner_Config"] | components["schemas"]["Main_Checkpoint_Flux2_Config"] | components["schemas"]["Main_Checkpoint_FLUX_Config"] | components["schemas"]["Main_Checkpoint_QwenImage_Config"] | components["schemas"]["Main_Checkpoint_ZImage_Config"] | components["schemas"]["Main_Checkpoint_Krea2_Config"] | components["schemas"]["Main_Checkpoint_Anima_Config"] | components["schemas"]["Main_BnBNF4_FLUX_Config"] | components["schemas"]["Main_GGUF_Flux2_Config"] | components["schemas"]["Main_GGUF_FLUX_Config"] | components["schemas"]["Main_GGUF_QwenImage_Config"] | components["schemas"]["Main_GGUF_Wan_Config"] | components["schemas"]["Main_GGUF_ZImage_Config"] | components["schemas"]["Main_GGUF_Krea2_Config"] | components["schemas"]["VAE_Checkpoint_SD1_Config"] | components["schemas"]["VAE_Checkpoint_SD2_Config"] | components["schemas"]["VAE_Checkpoint_SDXL_Config"] | components["schemas"]["VAE_Checkpoint_FLUX_Config"] | components["schemas"]["VAE_Checkpoint_Flux2_Config"] | components["schemas"]["VAE_Checkpoint_Wan_Config"] | components["schemas"]["VAE_Checkpoint_QwenImage_Config"] | components["schemas"]["VAE_Checkpoint_Anima_Config"] | components["schemas"]["VAE_Diffusers_SD1_Config"] | components["schemas"]["VAE_Diffusers_SDXL_Config"] | components["schemas"]["VAE_Diffusers_Flux2_Config"] | components["schemas"]["VAE_Diffusers_Wan_Config"] | components["schemas"]["PiDDecoder_Checkpoint_FLUX_Config"] | components["schemas"]["PiDDecoder_Checkpoint_Flux2_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SD3_Config"] | components["schemas"]["PiDDecoder_Checkpoint_SDXL_Config"] | components["schemas"]["PiDDecoder_Checkpoint_QwenImage_Config"] | components["schemas"]["ControlNet_Checkpoint_SD1_Config"] | components["schemas"]["ControlNet_Checkpoint_SD2_Config"] | components["schemas"]["ControlNet_Checkpoint_SDXL_Config"] | components["schemas"]["ControlNet_Checkpoint_FLUX_Config"] | components["schemas"]["ControlNet_Checkpoint_ZImage_Config"] | components["schemas"]["ControlNet_Checkpoint_Anima_Config"] | components["schemas"]["ControlNet_Diffusers_SD1_Config"] | components["schemas"]["ControlNet_Diffusers_SD2_Config"] | components["schemas"]["ControlNet_Diffusers_SDXL_Config"] | components["schemas"]["ControlNet_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_SD1_Config"] | components["schemas"]["LoRA_LyCORIS_SD2_Config"] | components["schemas"]["LoRA_LyCORIS_SDXL_Config"] | components["schemas"]["LoRA_LyCORIS_Flux2_Config"] | components["schemas"]["LoRA_LyCORIS_FLUX_Config"] | components["schemas"]["LoRA_LyCORIS_ZImage_Config"] | components["schemas"]["LoRA_LyCORIS_Krea2_Config"] | components["schemas"]["LoRA_LyCORIS_QwenImage_Config"] | components["schemas"]["LoRA_LyCORIS_Wan_Config"] | components["schemas"]["LoRA_LyCORIS_Anima_Config"] | components["schemas"]["LoRA_OMI_SDXL_Config"] | components["schemas"]["LoRA_OMI_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_SD1_Config"] | components["schemas"]["LoRA_Diffusers_SD2_Config"] | components["schemas"]["LoRA_Diffusers_SDXL_Config"] | components["schemas"]["LoRA_Diffusers_Flux2_Config"] | components["schemas"]["LoRA_Diffusers_FLUX_Config"] | components["schemas"]["LoRA_Diffusers_ZImage_Config"] | components["schemas"]["ControlLoRA_LyCORIS_FLUX_Config"] | components["schemas"]["T5Encoder_T5Encoder_Config"] | components["schemas"]["T5Encoder_BnBLLMint8_Config"] | components["schemas"]["T5Encoder_GGUF_Config"] | components["schemas"]["Qwen3VLEncoder_Checkpoint_Config"] | components["schemas"]["Qwen3VLEncoder_Qwen3VLEncoder_Config"] | components["schemas"]["Qwen3Encoder_Qwen3Encoder_Config"] | components["schemas"]["Qwen3Encoder_Checkpoint_Config"] | components["schemas"]["Qwen3Encoder_GGUF_Config"] | components["schemas"]["Gemma2Encoder_Gemma2Encoder_Config"] | components["schemas"]["Gemma2Encoder_GGUF_Config"] | components["schemas"]["QwenVLEncoder_Diffusers_Config"] | components["schemas"]["QwenVLEncoder_Checkpoint_Config"] | components["schemas"]["WanT5Encoder_WanT5Encoder_Config"] | components["schemas"]["TI_File_SD1_Config"] | components["schemas"]["TI_File_SD2_Config"] | components["schemas"]["TI_File_SDXL_Config"] | components["schemas"]["TI_Folder_SD1_Config"] | components["schemas"]["TI_Folder_SD2_Config"] | components["schemas"]["TI_Folder_SDXL_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD1_Config"] | components["schemas"]["IPAdapter_InvokeAI_SD2_Config"] | components["schemas"]["IPAdapter_InvokeAI_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD1_Config"] | components["schemas"]["IPAdapter_Checkpoint_SD2_Config"] | components["schemas"]["IPAdapter_Checkpoint_SDXL_Config"] | components["schemas"]["IPAdapter_Checkpoint_FLUX_Config"] | components["schemas"]["T2IAdapter_Diffusers_SD1_Config"] | components["schemas"]["T2IAdapter_Diffusers_SDXL_Config"] | components["schemas"]["Spandrel_Checkpoint_Config"] | components["schemas"]["CLIPEmbed_Diffusers_G_Config"] | components["schemas"]["CLIPEmbed_Diffusers_L_Config"] | components["schemas"]["CLIPVision_Diffusers_Config"] | components["schemas"]["SigLIP_Diffusers_Config"] | components["schemas"]["FLUXRedux_Checkpoint_Config"] | components["schemas"]["LlavaOnevision_Diffusers_Config"] | components["schemas"]["TextLLM_Diffusers_Config"] | components["schemas"]["ExternalApiModelConfig"] | components["schemas"]["Unknown_Config"];
                 };
             };
             /** @description Bad request */
diff --git a/pyproject.toml b/pyproject.toml
index 20087cc0955..04828e8b731 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -301,6 +301,14 @@ ignore = [
 ]
 select = ["B", "C", "E", "F", "W", "I", "TID"]
 
+[tool.ruff.lint.per-file-ignores]
+# Vendored from the diffusers MiniMax-H3 branch (see invokeai/backend/minimax_h3/__init__.py);
+# kept as close to upstream as possible, so upstream's zip() style is tolerated.
+"invokeai/backend/minimax_h3/transformer_minimax_h3.py" = ["B905"]
+"invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3.py" = ["B905"]
+"invokeai/backend/minimax_h3/autoencoder_kl_minimax_h3_audio.py" = ["B905"]
+"invokeai/backend/minimax_h3/scheduling_minimax_h3.py" = ["B905"]
+
 [tool.ruff.lint.flake8-tidy-imports]
 # Disallow all relative imports.
 ban-relative-imports = "all"
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json
new file mode 100644
index 00000000000..b2fd9454b76
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/__test_metadata__.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2b02183348847b4b2def482b47bae45ebe047748646b1dcce38073ba5c18acd
+size 168
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_scheduler/scheduler_config.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_scheduler/scheduler_config.json
new file mode 100644
index 00000000000..bcd5e01bf83
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_scheduler/scheduler_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:804780f7133477067bd6bbfbc02dc8b3cf9feeb400f97c08f5b1d5f6cbab3840
+size 96
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_vae/config.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_vae/config.json
new file mode 100644
index 00000000000..83b0d085b96
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_vae/config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a3c645ff892b376c6f5f4c8685964cd75474731af594ff058492a0000caabb6
+size 2271
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_vae/model.safetensors b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_vae/model.safetensors
new file mode 100644
index 00000000000..237b961654c
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/audio_vae/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb2e2a2f7686fd2e45fa37dd632d66cdf9f4274d888b842fb6fa7ee01776a819
+size 95
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/modular_model_index.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/modular_model_index.json
new file mode 100644
index 00000000000..20c48eddc34
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/modular_model_index.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2b6a210e482ffb78e613b553f570c44e101afce6741bd4ed91429d0559af031
+size 2935
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/processor/preprocessor_config.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/processor/preprocessor_config.json
new file mode 100644
index 00000000000..e7a1091ec59
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/processor/preprocessor_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:27225450ac9c6529872ee1924fcb0962ff5634834f817040f444118116f4e516
+size 390
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/processor/video_preprocessor_config.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/processor/video_preprocessor_config.json
new file mode 100644
index 00000000000..32579be08bc
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/processor/video_preprocessor_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13
+size 385
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/scheduler/scheduler_config.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/scheduler/scheduler_config.json
new file mode 100644
index 00000000000..b2e98c58edc
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/scheduler/scheduler_config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fa6c3aa70dc9e691e1a6df899fd1b6f75f70481a27cee6e18a303817075c304
+size 97
diff --git a/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/text_encoder/config.json b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/text_encoder/config.json
new file mode 100644
index 00000000000..aff8e7e11f4
--- /dev/null
+++ b/tests/model_identification/stripped_models/6f4fe53b-31e2-45dd-83da-2753fa1b4c08/text_encoder/config.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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