Add SANA-WM camera-controlled image-to-video pipeline by lawrence-cj · Pull Request #13881 · huggingface/diffusers

lawrence-cj · 2026-06-07T17:55:10Z

What does this PR do?

Hi @sayakpaul @dg845 , Long time no see. Hoping your are doing great. ♥️

Adds SANA-WM, the camera-controlled image-to-video world model from NVIDIA + MIT HAN Lab, as a first-class diffusers pipeline and transformer. Given a first-frame image, a text prompt, and a camera trajectory (explicit c2w poses or a WASD/IJKL action-DSL string), the pipeline generates a video whose motion follows the requested camera path. Trained natively for minute-scale generation at 704×1280.

The pipeline runs in two stages:

Stage 1 — SanaWMTransformer3DModel. A 1.6B-parameter bidirectional DiT with GDN-Triton linear attention and a UCPE camera-control branch; samples with an LTX-style flow-matching Euler scheduler at per-token timesteps. The first latent frame is the conditioning anchor.
Stage 2 — SanaWMLTX2Refiner (optional). A chunk-causal AR refiner that wraps diffusers' LTX2VideoTransformer3DModel + LTX2TextConnectors + Gemma-3 text encoder. Processes 3 latent frames at a time with a sliding window of [source_sink + recent_history + active_block] K/V, so per-block compute is bounded and total refinement cost is linear in video length.

Both stages decode through AutoencoderKLLTX2Video.

Layout

src/diffusers/
├── models/transformers/
│   ├── transformer_sana_wm.py          # SanaWMTransformer3DModel + blocks + helpers
│   └── transformer_sana_wm_kernels.py  # fused Triton kernels + camera math
└── pipelines/sana_wm/
    ├── __init__.py
    ├── pipeline_sana_wm.py             # SanaWMPipeline
    ├── pipeline_output.py              # SanaWMPipelineOutput
    ├── refiner.py                      # SanaWMLTX2Refiner + RefinerChunkRunner
    └── cam_utils.py                    # action DSL, intrinsics, resize+crop, Plücker/raymap

scripts/sana_wm/convert_sana_wm_to_diffusers.py
docs/source/en/api/{pipelines/sana_wm.md, models/sana_wm_transformer3d.md}

Usage

import torch
from PIL import Image
from diffusers import SanaWMPipeline
from diffusers.utils import export_to_video

pipe = SanaWMPipeline.from_pretrained(
    "Efficient-Large-Model/SANA-WM_bidirectional-diffusers",
    torch_dtype=torch.bfloat16,
)
pipe.vae.to(torch.float32)
pipe.enable_model_cpu_offload()

out = pipe(
    image=Image.open("input.png").convert("RGB"),
    prompt="A car driving across a vast desert plain at golden hour.",
    action="w-80,jw-40,w-40",                    # WASD-style action DSL
    intrinsics=[800.0, 800.0, 845.0, 464.0],      # fx, fy, cx, cy in original-image pixels
    num_frames=161,
    num_inference_steps=60,
)
export_to_video(list(out.frames), "sana_wm.mp4", fps=16)

Demo

5-second sample (30 stage-1 steps + 3-step distilled AR refiner, official asset/sana_wm/demo_0 inputs, 704×1280 @ 16 fps) :

sana_wm_5s.mp4

Smoke tests

End-to-end on 1× H100 80GB with `enable_model_cpu_offload` and the official `asset/sana_wm/demo_0.{png,txt,_pose.npy,_intrinsics.npy}`:

Duration	Frames	Stage-1 (30 steps)	Refiner (AR, 3 blocks)	Output
5s	80	1:11	5:24 / step	525 KB
10s	160	1:11	28:55 (7 blocks)	1.4 MB
20s	320	1:57	≈ 4 min / block (14)	3.2 MB
50s	800	5:33	30:46 (34 blocks)	6.3 MB

Checkpoint conversion

scripts/sana_wm/convert_sana_wm_to_diffusers.py --src Efficient-Large-Model/SANA-WM_bidirectional --dst /local/path converts the public release into a `from_pretrained`-loadable directory (VAE, Gemma-2 tokenizer + text_encoder, transformer, scheduler, refiner subfolders, top-level `model_index.json`).

…line Adds the public SANA-WM bidirectional camera-controlled image-to-video model as a first-class diffusers pipeline + transformer. Layout mirrors ``sana_video``: the model lives under ``src/diffusers/models/transformers/`` as a near-single-file (kernels split off so the ``@triton.jit`` decorators don't drown the model body); the pipeline lives under ``src/diffusers/pipelines/sana_wm/``. Files added: src/diffusers/models/transformers/ ├── transformer_sana_wm.py # SanaWMTransformer3DModel + blocks + helpers └── transformer_sana_wm_kernels.py # fused Triton kernels + camera math src/diffusers/pipelines/sana_wm/ ├── __init__.py ├── pipeline_sana_wm.py ├── pipeline_output.py ├── refiner.py └── cam_utils.py Pipeline architecture: * Stage 1: 1600M ``SanaWMTransformer3DModel`` DiT with bidirectional GDN-Triton linear attention + UCPE camera-control branch, LTX-style flow-matching Euler scheduler with per-token timesteps. * Stage 2: LTX-2 sink-bidirectional Euler refiner (3 distilled sigma steps, reuses diffusers' ``LTX2VideoTransformer3DModel`` + ``LTX2TextConnectors`` + Gemma-3 text encoder). * Decode through the LTX-2 VAE (``AutoencoderKLLTX2Video``). One-line usage: pipe = SanaWMPipeline.from_pretrained( "Efficient-Large-Model/SANA-WM_bidirectional-diffusers", torch_dtype=torch.bfloat16, ).to("cuda") out = pipe(image=img, prompt="...", action="w-80,jw-40,w-40", intrinsics=[fx, fy, cx, cy]) End-to-end smoke test (stage-1 + refiner + VAE decode) passes on H100. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

…xport transformer_sana_wm.py: * License header switched to the "HuggingFace Team and SANA-WM Authors" style used by merged sana_video. * Imports rewritten in stdlib -> third-party -> diffusers order; use diffusers `from ...utils import logging` instead of stdlib `logging`. * Fix 9 `Optional[X]` annotations written as `X or None` (Python's `or` short-circuits and silently returns `X`). * Fix two `assert (cond, msg)` tuple-asserts in PatchEmbedMS3D.forward that always pass (SyntaxWarning at import time). * Remove duplicate `__all__` declarations (the second silently overwrote the first). * Remove dead `reset_bn` (imports a nonexistent `packages.apps.utils`, would crash on call). * Remove the duplicate `logger = logging.getLogger(__name__)` further down in the file. transformer_sana_wm_kernels.py: * License header normalized; collapse three duplicate triton/torch import blocks into one. pipeline_sana_wm.py: * License header normalized. * `_decode_latents` now returns `(T, H, W, 3)` float in [0, 1], matching the diffusers convention used by `VideoProcessor`. Returning uint8 silently broke `export_to_video`: it does `frame * 255` assuming float input, so uint8 overflows to `(-x) mod 256` and inverts colors. * `__call__` converts to PIL/uint8 only when `output_type="pil"`. * Intrinsics argument now accepts (4,), (F, 4), (3, 3), and (F, 3, 3) forms (auto-extracts fx, fy, cx, cy from a 3x3 K) and auto-trims to `num_frames` when a longer-than-needed trajectory is passed. * Inline `retrieve_timesteps` with the standard `# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps` marker, matching merged sana_video. * Docstrings + EXAMPLE_DOC_STRING updated to reflect the new return type. pipeline_output.py: * Update `frames` field docstring to describe the new float [0, 1] return. refiner.py, cam_utils.py, scripts/sana_wm/convert_sana_wm_to_diffusers.py: * License headers normalized. Docs: * New `docs/source/en/api/pipelines/sana_wm.md` and `docs/source/en/api/models/sana_wm_transformer3d.md`, modeled on sana_video.md / sana_video_transformer3d.md, wired into `docs/source/en/_toctree.yml` under Models and Pipelines. 5s end-to-end smoke test (81 frames @ 16fps, 30 stage-1 steps + 3-step LTX-2 refiner) passes on 1x H100 80GB with `enable_model_cpu_offload`. Round-trip diff vs raw float frames is 2.06/255 mean (h264 lossy noise), confirming the export_to_video fix.

…+ KV cache hooks) The first cleanup pass only kept the legacy single-shot refiner path. That path is what the model was *not* trained on — its docstring even says "feeding the full sequence at once is out-of-distribution" — and its cost is O(T^2) attention over the full latent volume, which made longer videos unusable (~21 min per refiner step at 321 frames on an H100). Port the chunk-causal AR mode from the upstream reference so the refiner matches the training contract: * `refine_latents` now defaults to `block_size=3, kv_max_frames=11` (the canonical AR recipe). Pass `block_size=None` to fall back to the legacy single-shot path. * New `_refine_latents_ar` + `_RefinerChunkRunner` orchestrate the sliding window: pre-capture pre-RoPE sink K/V on `z_sana[:source_sink_frames]` at sigma=0, then for each `block_size`-frame chunk run a 3-step Euler with prefix `{sink_k_pre, sink_v, sink_pe, history_k, history_v}` and capture post-RoPE K/V to feed the next window. History is bounded to `kv_max_frames - source_sink_frames` so per-block compute is constant. * New `_predict_x0_active_block` runs the transformer on the active block only (Q from active, K/V from prefix+active). * New `_capture_block_kv` runs sigma=0 forward with a pre_rope/post_rope capture flag set on each `attn1`. * New `_forward_video_only_with_rope` takes a pre-built RoPE so each block can use absolute frame positions in the source video. * `_streaming_self_attention` extended with the `_kv_cache_capture`, `_tf_capture_kv`, `_tf_kv_prefix` hook contract that AR mode uses to inject and capture K/V on each block. * New helpers: `_build_rotary_emb_for_absolute_positions`, `_set_kv_prefix_on_blocks`, `_clear_kv_prefix_on_blocks`, `_set_capture_flag_on_blocks`, `_collect_captured_kv_from_blocks`. * `_encode_prompt` now also moves the Gemma-3 text encoder back to CPU after producing the embeds — otherwise it stays resident through the entire AR loop and gates how much GPU memory the refiner transformer has left. Module-level docstring updated to document both modes; existing single-shot path preserved verbatim.

…eemption) The AR refiner is expensive (~3-5 min per block) and the refinement loop ran end-to-end has no in-progress state to recover, so a SLURM preemption mid-refinement loses all progress. With the canonical ``block_size=3, kv_max_frames=11`` setup, refining a 50s video is 34 blocks of work that has to make it through without preemption on a backfill queue. Add per-block atomic checkpointing: * ``SanaWMLTX2Refiner.refine_latents(checkpoint_dir=Path)`` and ``_refine_latents_ar`` accept a directory. After each completed AR block, the AR loop writes ``checkpoint_dir/state.pt`` atomically (tmp + os.replace). * The payload is ``{block_idx_done, n_blocks, sink_size, block_size, output_shape, output, runner_state}``. ``runner_state`` is a CPU snapshot of the runner's ``_sink_kv_pre``, ``_history_kv_post``, ``_history_frames`` and ``torch.Generator`` state. * On entry, if ``state.pt`` exists with a compatible shape signature, the AR loop loads the persisted output tensor + runner state and resumes from ``block_idx_done + 1`` instead of recomputing from scratch. * ``SanaWMPipeline.__call__(refiner_checkpoint_dir=...)`` plumbs the directory through to the refiner. Checkpoint size: ~output_volume + sink_KV (~360MB for 50 layers) + rolling history KV (~3-4GB at full capacity) — saved once per block, total per-block save overhead ~10s on lustre.

* CPU unit tests for cam_utils helpers (action DSL → c2w, intrinsics rescale-for-crop, resize+center-crop, snap_num_frames 8k+1 rounding). * Public-surface registration tests (top-level diffusers symbols, SanaWMPipelineOutput dataclass shape, refiner signature has AR defaults + checkpoint_dir, pipeline __call__ accepts c2w/action/intrinsics/ refiner_checkpoint_dir). * @slow @require_torch_accelerator integration stub for an end-to-end I2V against the public checkpoint, currently @unittest.skip — wires up the nightly GPU path without exploding regular CI. SanaWMTransformer3DModel has hardcoded depth/hidden_size/num_heads inside its inner SanaMSVideoCamCtrl (not exposed through register_to_config), so the usual PipelineTesterMixin small-config fast tests aren't applicable without a transformer refactor (followup PR).

dg845 · 2026-06-16T04:46:10Z

As a preliminary comment, would it be possible to use PyTorch ops instead of custom Triton kernels (or add pure PyTorch fallback paths) for now? We will work on supporting the custom kernels through kernels. CC @sayakpaul

lawrence-cj · 2026-06-16T07:41:17Z

As a preliminary comment, would it be possible to use PyTorch ops instead of custom Triton kernels (or add pure PyTorch fallback paths) for now? We will work on supporting the custom kernels through kernels. CC @sayakpaul

Yes, love to do that.

…ttention `transformer_sana_wm_kernels.py` previously did a hard `import triton` at the top of the file. That blocked importing the SANA-WM transformer on any environment without Triton (CPU-only, ROCm without Triton, older Triton, etc.), even though the model has pure-PyTorch attention classes for every `*Triton` variant. Make Triton optional and have the dispatcher transparently fall back: * Wrap `import triton` / `import triton.language as tl` in try/except. When unavailable, install a shim where `@triton.jit` is a no-op so the kernel function definitions still load (they just aren't compiled by Triton). Module-level `triton.X` / `tl.X` lookups return a self-shimming sentinel so signature parsing doesn't blow up either. * Add `is_triton_available()` + `_require_triton(entry_point)`. The four Triton-backed entry points called by the model (`fused_qk_inv_rms`, `fused_bigdn_func`, `cam_prep_func`, `cam_scan_bidi_chunkwise`) now raise a clear RuntimeError on a Triton-less host with a hint to use the pure-PyTorch attention variants — but the dispatcher does this automatically (see below) so users shouldn't ever see it. * Delete the leftover duplicate `import torch / triton / triton.language` block at line 262 (left over from the upstream port). * Register `BidirectionalGDNUCPESinglePathLiteLA` in `ATTENTION_BLOCKS` so the fallback chain can find it. * New `_resolve_attention_block(name, role)` walks the requested class's MRO at dispatch time. If Triton isn't usable AND the requested class name ends in `Triton`, route to the closest registered non-`Triton` ancestor (BidirectionalGDNUCPESinglePathLiteLABothTriton -> BidirectionalGDNUCPESinglePathLiteLA, etc.) and log a one-shot warning. * Rewire both `SanaVideoMSCamCtrlBlock` dispatch sites to use `_resolve_attention_block` for the GDN+UCPE camera branch and the main attention branch (the `BidirectionalSoftmaxUCPESinglePathLiteLA` branch doesn't use Triton at all so it stays hard-coded). Tests: * `test_kernels_module_imports_with_triton_hidden` — reloads the kernels module with `sys.modules['triton'] = None` and verifies the module imports, `is_triton_available()` is False, and the pure-PyTorch helpers remain callable. * `test_resolve_attention_block_cpu_fallback` — on a CPU-only host, the three `*Triton` attn types resolve to the correct non-Triton ancestor. * `test_triton_entry_point_raises_clean_error_without_triton` — verifies the `_require_triton` guard yields a RuntimeError that mentions Triton.

lawrence-cj · 2026-06-16T08:07:23Z

Done in c0712d3f8 — Triton is now optional, with an automatic pure-PyTorch fallback at dispatch time. Mapping when Triton isn't usable:

Requested	Falls back to
`BidirectionalGDNTriton`	`BidirectionalGDN`
`BidirectionalGDNUCPESinglePathLiteLATriton`	`BidirectionalGDNUCPESinglePathLiteLA`
`BidirectionalGDNUCPESinglePathLiteLABothTriton`	`BidirectionalGDNUCPESinglePathLiteLA`

Triton remains the default on CUDA + Triton ≥ 3. CPU tests added under tests/pipelines/sana_wm/.

lawrence-cj · 2026-06-18T03:26:35Z

@dg845 @yiyixuxu Gentle ping here.

Three CI checks were failing on the PR: 1. `check_code_quality` (43 ruff errors): mix of unused imports / import sorting / E731 lambdas (auto-fixable) plus a handful of F821 dead-code references inherited from the upstream research codebase (`xformers.*` inside `if _xformers_available:` blocks, an undefined `BlockHook` type annotation, two `x_sa`/`mlp_out` references in a block forward whose live assignment was already overridden by subclasses). Ran `ruff check --fix --unsafe-fixes` + `ruff format`, fixed the type annotation manually, and added targeted `# noqa: F821` markers on the conditionally unreachable lines. 2. `check_torch_dependencies`: `transformer_sana_wm.py` hard-imported `einops`, `fla`, `timm`, `termcolor`. The minimum-deps CI environment doesn't have them, and diffusers' lazy loader rewrites `ModuleNotFoundError` as `RuntimeError` so `test_pipeline_imports` blew up. Wrapped each of the four optional imports in a try/except shim — `rearrange`/ `ShortConvolution`/`DropPath`/`Attention_`/`Mlp` become placeholders that raise a clear `ImportError` on construction, `colored` falls back to plain text. Class bodies that subclass these still parse at module load, so `import diffusers.models.transformers.transformer_sana_wm` succeeds anywhere. Same treatment for the kernels file's `from einops import rearrange, repeat`. 3. `build_pr_documentation`: doc-builder imported `SanaWMTransformer3DModel` from `diffusers.models.transformers` (not the diffusers top level) and that subpackage's `__init__.py` was missing the entry. Added the import.

* `doc-builder style src/diffusers docs/source --max_len 119` rewraps docstrings in the six SANA-WM files (transformer, kernels, pipeline, refiner, output, cam_utils) to the repo-wide 119-column limit. No behaviour change — purely whitespace inside docstrings. * `make fix-copies` regenerates `dummy_pt_objects.py` and `dummy_torch_and_transformers_objects.py` to add `DummyObject` stubs for the three new public classes (`SanaWMTransformer3DModel`, `SanaWMPipeline`, `SanaWMLTX2Refiner`), so `from diffusers import …` gives the standard "missing backend" message on installs without torch / transformers. Verified: `make quality` passes (ruff check, ruff format check, doc-builder style check_only, check_doc_toc). Test suite still 15 passed / 1 skipped.

HuggingFaceDocBuilderDev · 2026-06-25T06:02:01Z

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

* Split each attention class's main and camera branches into their own modules, so `forward` is a flat list of layer calls. The branch modules deliberately hold no parameters: if they owned `qkv` / `q_proj_cam` / ... PyTorch would prefix by attribute name and rename 390 of the 871 checkpoint keys. (`state_dict` duplicates a module aliased in two places rather than de-duplicating it, so aliasing isn't a way out either.) The compute is split; the ownership isn't. * `transformer_sana_wm_refiner.py` is self-contained -- nine `# Copied from` markers replace the `transformer_ltx2` import, at whole-function, whole-class and method-level granularity as each piece required. That costs +831 lines, since the copied block `__init__` drags in both attention processors. * The refiner's streaming self-attention is a real attention processor, `SanaWMLTX2RefinerAttnProcessor`, and the block calls `self.attn1(...)`. The processor swap happens in the model `__init__` after the blocks are built, because `attn1` is constructed inside the `# Copied from` body. * Inline the KV-cache mode strings, drop `plucker_proj`'s zero-init. State dicts unchanged: 871/871 and 3510/3510, ordered lists identical. Equivalence is exact -- max abs diff 0.0 -- against the real released checkpoint for the attention split, and across five refiner configurations driven through two AR blocks, with negative controls throughout. `check_copies` passes. GPU smoke unchanged at refined-latent mean 0.035024 / std 0.887467.

… tests `SanaWMTemporalShortConvolution` allocates its weight with `torch.zeros` and nothing initialises it any more -- the hand-written init came out earlier in this PR because `from_pretrained` overwrites it. That is true for real checkpoints, but a from-scratch model gets a zero `conv_k`, so the GDN key is identically zero and the whole branch returns zeros. Every test in this file builds a random model, so they were all passing while exercising none of the GDN maths. Two tests: one pins the zero-init as deliberate rather than accidental, the other fills the short convolutions with noise and asserts the output moves. Checked that the second one bites -- forcing the short convolution to return zeros makes it fail.

yiyixuxu

thanks

yiyixuxu · 2026-09-14T04:11:12Z

cc @sayakpaul
can you take a look on tests when you have a chance? feel free to review as much as you like for the rest of PR as well

lawrence-cj · 2026-09-14T04:36:08Z

Thanks all. @yiyixuxu @dg845 @sayakpaul

sayakpaul · 2026-09-30T04:51:49Z

@lawrence-cj LMK if any of my feedback is unclear.

lawrence-cj · 2026-10-10T10:58:33Z

@sayakpaul thanks, all addressed in 74028dd. The new test mixins also caught real bugs (RoPE complex buffer under .to(dtype), sequential offload, callbacks), and those are fixed. The only remaining failure is test_compile_works_with_aot, and it's an env issue on my side (cuda.h missing; other models fail the same way).

…ing, callbacks, offload fixes - Reuse Timesteps/TimestepEmbedding; drop unused cfg_embedder / y_embedding / model_max_length - Rename Mlp/PatchEmbed/CaptionEmbedder with SanaWM prefix - Support gradient checkpointing in SanaWMTransformer3DModel - Keep complex RoPE freqs out of buffers so Module.to(dtype) doesn't drop the imaginary part - Disable autocast around camera geometry (torch.cross) - Pipeline: callback_on_step_end, Literal output_type, _execution_device, maybe_free_model_hooks - Fix output docstring, bump copyright to 2026 - Port tests to new pytest mixins, add refiner transformer tests

lawrence-cj · 2026-10-10T12:08:10Z

Sample outputs from the latest commit (74028dd). These use the public Efficient-Large-Model/SANA-WM_bidirectional-diffusers and -refiner checkpoints and follow the docs example: 161 frames, 60 steps, stage 1 + refiner, enable_model_cpu_offload, single GPU.

camera trajectory (`c2w`)	camera trajectory (`c2w`)	action `w-40,a-40,w-40,d-40`

refined · stage 1	refined · stage 1	refined · stage 1

GIFs are downsampled; the mp4 links are full 704×1280. Refined latents are bit-identical to the pre-review code.

cc @sayakpaul @yiyixuxu @dg845

sayakpaul

Thanks just two minor comments.

sayakpaul · 2026-10-13T02:04:45Z

@lawrence-cj do you know why "camera trajectory (c2w)" is not showing up in the table?

sayakpaul · 2026-10-13T02:12:33Z

/diffusers-bot pytest tests/models/transformers/test_models_transformer_sana_wm.py tests/models/transformers/test_models_transformer_sana_wm_refiner.py tests/pipelines/sana_wm/test_sana_wm.py

github-actions · 2026-10-13T02:12:48Z

✅ pytest tests/models/transformers/test_models_transformer_sana_wm.py tests/models/transformers/test_models_transformer_sana_wm_refiner.py tests/pipelines/sana_wm/test_sana_wm.py passed on GPU — view logs.

lawrence-cj · 2026-10-13T02:40:16Z

@dg845 @yiyixuxu @sayakpaul thanks for merging! The first GIF was over GitHub's 5 MiB image-proxy limit. I replaced it with a smaller one and the table should render fully now.

lawrence-cj · 2026-10-13T02:43:30Z

@lawrence-cj do you know why "camera trajectory (c2w)" is not showing up in the table?

It's showing up now :) hh

HaoyiZhu and others added 4 commits June 1, 2026 01:28

github-actions Bot added size/L PR with diff > 200 LOC documentation Improvements or additions to documentation models pipelines and removed size/L PR with diff > 200 LOC labels Jun 7, 2026

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lawrence-cj added 2 commits October 12, 2026 04:59

[SANA-WM] Drop cam_utils and registration tests

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[SANA-WM] Add return type annotations to forward methods

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sayakpaul merged commit 1628cb1 into huggingface:main Oct 13, 2026
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github-project-automation Bot moved this from In Progress to Done in Diffusers Roadmap Oct 13, 2026

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lawrence-cj commented Jun 7, 2026 •

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