diff --git a/docs-site/content/cookbook/pan2/pan2.mdx b/docs-site/content/cookbook/pan2/pan2.mdx
deleted file mode 100644
index 07836529a..000000000
--- a/docs-site/content/cookbook/pan2/pan2.mdx
+++ /dev/null
@@ -1,7 +0,0 @@
----
-title: PAN 2
-description: Configure text-to-video and first-frame image-to-video generation with PAN 2.
-mode: frame
----
-
-
diff --git a/docs-site/lib/cookbook-pages.ts b/docs-site/lib/cookbook-pages.ts
index c2b2fe1a1..3dac83a84 100644
--- a/docs-site/lib/cookbook-pages.ts
+++ b/docs-site/lib/cookbook-pages.ts
@@ -66,15 +66,6 @@ export const recipePages = [
description: "Turn a reference image into video with Wan2.1.",
tags: ["Image to video", "14B", "480p"],
},
- {
- id: "pan2",
- family: "PAN 2",
- title: "PAN 2",
- mark: "PAN",
- path: "/cookbook/pan2/pan2",
- description: "Text-to-video and first-frame image-to-video from the 29B PAN 2 model.",
- tags: ["Text or image to video", "29B"],
- },
{
id: "ltx2-distilled",
family: "LTX",
@@ -119,7 +110,6 @@ const familyInfo: Record = {
"FastVideo/FastWan2.1-T2V-1.3B-Diffusers": "fastwan21",
"Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": "wan21-i2v",
"FastVideo/LTX2-Distilled-Diffusers": "ltx2-distilled",
- "IFM/PAN2": "pan2",
};
const TASK_GROUPS: Array<[RegExp, string]> = [
[/world/, "Text to world"],
@@ -175,7 +164,6 @@ export const modelDirectory = [...new Set(sourceRecipes.map((recipe) => recipe.m
kandinsky: "Kandinsky Lab",
"black-forest-labs": "Black Forest Labs",
qwen: "Qwen",
- ifm: "IFM",
stabilityai: "Stability AI",
nvidia: "NVIDIA",
};
diff --git a/docs-site/navigation.json b/docs-site/navigation.json
index e70b0fa5c..db8f15868 100644
--- a/docs-site/navigation.json
+++ b/docs-site/navigation.json
@@ -127,10 +127,6 @@
],
"display": "group"
},
- {
- "label": "PAN 2 text/image to video",
- "href": "/inference/pan2"
- },
{
"label": "Examples",
"href": "/inference/examples/examples_inference_index"
@@ -253,25 +249,6 @@
}
]
},
- {
- "label": "PAN 2",
- "display": "group",
- "collapsed": true,
- "items": [
- {
- "label": "PAN 2",
- "href": "/cookbook/pan2/pan2"
- },
- {
- "label": "Family guide",
- "href": "/cookbook/pan2"
- },
- {
- "label": "Model reference",
- "href": "/inference/pan2"
- }
- ]
- },
{
"label": "FLUX",
"href": "/cookbook/flux"
diff --git a/docs-site/tests/cookbook.spec.ts b/docs-site/tests/cookbook.spec.ts
index c7d7a95d6..0fb940950 100644
--- a/docs-site/tests/cookbook.spec.ts
+++ b/docs-site/tests/cookbook.spec.ts
@@ -223,7 +223,7 @@ test("inherited values, explicit false, zero and null retain their meanings", as
test("catalog discovery opens dedicated model recipes and filters by family and task", async ({ page }) => {
await page.goto("/FastVideo/cookbook");
const cards = page.locator(".recipe-card");
- await expect(cards).toHaveCount(38);
+ await expect(cards).toHaveCount(37);
await expect(cards.first()).toContainText("FastH3 OmniRef");
await page.getByLabel("Model family", { exact: true }).selectOption("Wan");
await page.getByLabel("Search recipes").fill("Wan2.1 I2V");
@@ -277,7 +277,7 @@ test("output tabs support keyboard navigation and precision chips use the backen
await page.getByLabel("Search recipes").fill("does not exist");
await expect(page.getByRole("heading", { name: "No matching recipes" })).toBeVisible();
await page.getByRole("button", { name: "Clear filters" }).click();
- await expect(page.locator(".recipe-card")).toHaveCount(38);
+ await expect(page.locator(".recipe-card")).toHaveCount(37);
});
test("cookbook cards and controls remain readable in dark mode and reflow at 320px", async ({ page }) => {
@@ -443,26 +443,6 @@ test("source-backed serving pages expose their actual server settings", async ({
await expect(page.getByRole("button", { name: "Copy config + command", exact: true })).toBeEnabled();
});
-test("PAN 2 has a family card with the IFM logo and an editable serving deployment", async ({ page }) => {
- await page.goto("/FastVideo/cookbook");
- await page.getByLabel("Search recipes").fill("PAN 2");
- const pan2 = page.locator(".recipe-card");
- await expect(pan2).toHaveCount(1);
- await expect(pan2.locator("img")).toHaveAttribute("src", /\/ifm\.webp$/);
- await expect(pan2.locator(".recipe-card-kicker")).toHaveText("IFM");
- await pan2.click();
- await expect(page).toHaveURL(/cookbook\/pan2\/pan2$/);
- await expect(page.locator(".recipe-matrix")).toBeVisible({ timeout: 30000 });
- await expect(page.getByRole("heading", { level: 1 })).toHaveText("PAN 2");
- await settings(page, "Serving");
- await page.getByLabel("Port", { exact: true }).fill("9010");
- await output(page, "config.yaml");
- await expect(page.getByLabel("Generated YAML", { exact: true })).toContainText(/"port": 9010/);
- await expect(page.getByLabel("Generated YAML", { exact: true })).toContainText(/"model_path": "IFM\/PAN2"/);
- await expect(page.getByLabel("Generated YAML", { exact: true })).toContainText(/"dit_layerwise": false/);
- await expect(page.getByRole("button", { name: "Download YAML", exact: false })).toBeEnabled();
-});
-
test("source model hardware preferences survive hydration and reload", async ({ page }) => {
const errors: string[] = [];
page.on("pageerror", (error) => errors.push(error.message));
diff --git a/docs/assets/cookbook-recipes.json b/docs/assets/cookbook-recipes.json
index 1a8fdbdc6..e19feae8a 100644
--- a/docs/assets/cookbook-recipes.json
+++ b/docs/assets/cookbook-recipes.json
@@ -1,5 +1,5 @@
{
- "version": 20,
+ "version": 19,
"recipes": [
{
"id": "fastwan21-t2v",
@@ -381,33 +381,6 @@
"expected_artifact": "Upscaled MP4 under outputs_video/kandinsky6_sr/",
"related": ["kandinsky6-vsr", "kandinsky6-ti2va-piflow"]
},
- {
- "id": "pan2-ti2v",
- "family": "pan2",
- "stage": "inference",
- "task": "Text or image to video",
- "label": "PAN 2",
- "summary": "Generate a 13-second, 704 × 1248 video at 24 fps from text, or set IMAGE_PATH in the maintained example to condition on a first frame.",
- "model": "IFM/PAN2",
- "source": "examples/inference/basic/basic_pan2.py",
- "serving": {
- "source": "examples/serving/openai_pan2.yaml",
- "install": "UV_TORCH_BACKEND=cu130 uv pip install -e .",
- "task": "Text to video",
- "limitations": [
- "This server walkthrough sends a text prompt only. The server API also accepts an image reference (image_reference or input_reference) and then runs image-to-video from that first frame, but the checked-in clients do not upload one."
- ]
- },
- "command": "python examples/inference/basic/basic_pan2.py",
- "gpu_types": ["NVIDIA"],
- "hardware": {"platform": "cuda", "gpu_count": 1, "evidence": "source-configured"},
- "evidence": "Source-backed",
- "expected_artifact": "MP4 video under video_samples_pan2/",
- "limitations": [
- "The full IFM/PAN2 checkpoint is not public yet; numerical parity is checked with a public random-weight checkpoint.",
- "The example keeps the 29B DiT resident in bf16 and offloads only the text encoder. GPU memory requirements have not been established for this recipe; set PAN2_NUM_GPUS to shard video tokens with sequence parallelism, which does not shard the DiT weights."
- ]
- },
{
"id": "flux2-klein-t2i",
"family": "flux",
diff --git a/docs/assets/logos/SOURCES.md b/docs/assets/logos/SOURCES.md
index c78da9133..619e476c5 100644
--- a/docs/assets/logos/SOURCES.md
+++ b/docs/assets/logos/SOURCES.md
@@ -13,7 +13,6 @@ plain typographic tile instead — that tile is a UI placeholder, not a logo.
| `nvidia.webp` | [Official NVIDIA Hugging Face organization avatar](https://huggingface.co/nvidia) | Cosmos and GEN3C cards, Cosmos page header |
| `kandinsky.webp` | [Official Kandinsky Lab Hugging Face organization avatar](https://huggingface.co/kandinskylab) | Kandinsky 5 and Kandinsky 6 cards and page headers |
| `black-forest-labs.webp` | [Official Black Forest Labs Hugging Face organization avatar](https://huggingface.co/black-forest-labs) | FLUX family card and page header |
-| `ifm.webp` | [Official Institute of Foundation Models (IFM) Hugging Face organization avatar](https://huggingface.co/IFM) | PAN 2 family card and page header |
| `qwen.webp` | [Official Qwen Hugging Face organization avatar](https://huggingface.co/Qwen) | Qwen-Image-2.1 family card and page header |
| `minimax.webp` | [Official MiniMax Hugging Face organization avatar](https://huggingface.co/MiniMaxAI) | MiniMax H3 family card and page header |
| `stabilityai.webp` | [Official Stability AI Hugging Face organization avatar](https://huggingface.co/stabilityai) | Stable Diffusion and Stable Audio cards and page headers |
diff --git a/docs/assets/logos/ifm.webp b/docs/assets/logos/ifm.webp
deleted file mode 100644
index fe4602a9f..000000000
Binary files a/docs/assets/logos/ifm.webp and /dev/null differ
diff --git a/docs/cookbook/index.md b/docs/cookbook/index.md
index f1cb374b1..10a75432c 100644
--- a/docs/cookbook/index.md
+++ b/docs/cookbook/index.md
@@ -6,7 +6,6 @@ The interactive catalog groups models by family and generates YAML for supported
- [FastH3](minimax-h3.md)
- [Wan and FastMetal](wan.md)
- [LTX](ltx.md)
-- [PAN 2](pan2.md)
- [FLUX](flux.md)
- [Qwen-Image-2.1](qwen-image21.md)
- [Stable Diffusion](stable-diffusion.md)
diff --git a/docs/cookbook/pan2.md b/docs/cookbook/pan2.md
deleted file mode 100644
index c19f53e58..000000000
--- a/docs/cookbook/pan2.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# PAN 2 recipes
-
-
-
-Generate a video from text, or from a first frame, with `IFM/PAN2`. One pipeline
-serves both modes: leave out the first frame for text-to-video and supply one
-for image-to-video. The defaults are 704×1248, 313 frames (13 s at 24 fps),
-50 steps and guidance 3.0. See [PAN 2 Text/Image to Video](../inference/pan2.md)
-for the model, sampler and parallelism details.
-
-## Run on a GPU
-
-Follow the [CUDA installation guide](../getting_started/installation/gpu.md), then run
-from the repository root. The full `IFM/PAN2` checkpoint is not public yet; pass a
-local directory or private repo you can access with `PAN2_MODEL_PATH`.
-
-```bash
-python examples/inference/basic/basic_pan2.py
-```
-
-Set `IMAGE_PATH` in the script to a first frame for image-to-video. With more
-GPUs, `PAN2_NUM_GPUS=4` shards the video tokens with sequence parallelism; the
-count must divide PAN 2's 32 attention heads. Each rank still holds the full
-29B DiT, so extra GPUs speed up generation but do not reduce per-device weight
-memory. See [CPU offloading](../inference/offloading.md) for devices that cannot
-hold the DiT.
-
-## Serve with the OpenAI-compatible API
-
-`examples/serving/openai_pan2.yaml` serves PAN 2 through the shared
-`/v1/videos` routes. It mirrors the Python example: one GPU, the text encoder
-offloaded between requests, and the pipeline's own sampling defaults.
-
-```bash
-fastvideo serve --config examples/serving/openai_pan2.yaml --server.host 127.0.0.1
-```
-
-Text-to-video uses the same request as any other served video model, with the
-alias `pan2`:
-
-```bash
-curl --fail-with-body http://127.0.0.1:8000/v1/videos \
- -H 'Content-Type: application/json' \
- -d '{"model": "pan2", "prompt": "A red fox trots through fresh snow in a pine forest at dawn."}'
-```
-
-For image-to-video, add a first frame in `image_reference` (or the legacy
-`input_reference`). The server then runs image-to-video; no second deployment or
-workload is needed.
-
-```bash
-curl --fail-with-body http://127.0.0.1:8000/v1/videos \
- -H 'Content-Type: application/json' \
- -d '{"model": "pan2", "prompt": "The fox turns toward the camera.",
- "image_reference": {"image_url": "https://example.com/first-frame.png"}}'
-```
-
-Local image paths must sit under `server.media_roots`, and private or loopback
-URLs need `server.allow_private_media_urls`. Poll `/v1/videos/{id}` and download
-`/v1/videos/{id}/content` as in the [serving guide](openai-api.md). Generation
-time for the default 13-second clip is not measured, so raise the client's
-polling deadline for long jobs instead of treating a slow job as failed.
-
-The cookbook's generated serving clients send a text prompt only. The image
-request above is the supported way to supply a first frame over HTTP, and no
-recorded generation backs it yet.
-
-## Evidence
-
-Both recipes are **Source-backed**: the model ID, example and serving YAML are
-checked against each other, and the serving defaults are tested against the
-registered `pan2_t2v` preset. No GPU generation, memory measurement or HTTP
-client run is recorded, so hardware fit and throughput are unknown. Numerical
-parity with the reference implementation is checked on CPU with a public
-random-weight checkpoint, which verifies the port and produces noise rather than
-useful video.
diff --git a/docs/cookbook/recipes/pan2.yaml b/docs/cookbook/recipes/pan2.yaml
deleted file mode 100644
index bfd1a0540..000000000
--- a/docs/cookbook/recipes/pan2.yaml
+++ /dev/null
@@ -1,38 +0,0 @@
-title: PAN 2
-summary: Text-to-video and first-frame image-to-video from the 29B PAN 2 mixture-of-transformers model.
-
-deployments:
- cuda-rest:
- runtime: fastvideo-cuda-rest
- workload: t2v
- defaults: examples/serving/openai_pan2.yaml
- requirements:
- - >-
- Requests without a reference run text-to-video. Add image_reference or input_reference
- to condition on a first frame; the same server and pipeline handle both.
- - >-
- To use more than one GPU, set sp_size equal to num_gpus (Ulysses sequence parallelism).
- It must divide PAN 2's 32 attention heads, and each rank still holds the full DiT weights.
- options:
- - server.*
- - generator.engine.num_gpus
- - generator.engine.parallelism.*
- - "!generator.engine.parallelism.hsdp_*"
- - generator.engine.offload.text_encoder
- - generator.engine.offload.vae
- - default_request.sampling.num_frames
- - default_request.sampling.height
- - default_request.sampling.width
- - default_request.sampling.fps
- overrides:
- default_request.sampling.num_frames:
- default: 313
- description: num_frames - 1 must be a multiple of 4.
- default_request.sampling.height:
- default: 704
- minimum: 32
- multipleOf: 32
- default_request.sampling.width:
- default: 1248
- minimum: 32
- multipleOf: 32
diff --git a/docs/cookbook/serving-coverage.md b/docs/cookbook/serving-coverage.md
index 197b90e10..23280315f 100644
--- a/docs/cookbook/serving-coverage.md
+++ b/docs/cookbook/serving-coverage.md
@@ -49,7 +49,6 @@ never deleted. See `examples/serving/README.md`.
| `openai_hunyuan15_i2v_480p.yaml` | HunyuanVideo 1.5 I2V 480p | `i2v` | CUDA REST | `hunyuan15-i2v-480p` | Source-backed; preset renders 720x1280 |
| `openai_kandinsky6_ti2va.yaml` | Kandinsky 6 Pro TI2VA | `t2v` | CUDA REST | `kandinsky6-ti2va` | Source-backed |
| `openai_kandinsky6_ti2va_distilled.yaml` | Kandinsky 6 Pro-distill TI2VA | `t2v` | CUDA REST | `kandinsky6-ti2va-distilled` | Source-backed |
-| `openai_pan2.yaml` | PAN 2 | `t2v` (a reference switches to image-to-video) | CUDA REST | `pan2/cuda-rest` | Source-backed; checkpoint not public, no recorded run |
| `openai_ltx2_distilled.yaml` | LTX2 Distilled | `t2v` | CUDA REST | `ltx2-distilled/cuda-rest` | Source-backed |
| `streaming_demo.yaml` | LTX2 Distilled | `t2v` | CUDA streaming | `ltx2-distilled/cuda-streaming` | Source-backed |
| `mlx_fasth3.yaml` | FastH3 V1 converted | `t2v` | MLX REST | none (legacy catalog only) | Source runtime; T2VA only |
diff --git a/docs/design/inference_schema_parity_inventory.yaml b/docs/design/inference_schema_parity_inventory.yaml
index 7224394ce..9027bb660 100644
--- a/docs/design/inference_schema_parity_inventory.yaml
+++ b/docs/design/inference_schema_parity_inventory.yaml
@@ -283,18 +283,6 @@ surfaces:
- fastvideo.configs.pipelines.hunyuan15.Hunyuan15T2V720PConfig
- fastvideo.configs.pipelines.hyworld.HYWorldConfig
- fastvideo.configs.pipelines.hyworld.Hunyuan15T2V480PConfig
- text_system_prompt:
- sources:
- - fastvideo.configs.pipelines.pan2.PAN2PipelineConfig
- text_encoder_skip_layers:
- sources:
- - fastvideo.configs.pipelines.pan2.PAN2PipelineConfig
- text_max_sequence_length:
- sources:
- - fastvideo.configs.pipelines.pan2.PAN2PipelineConfig
- i2v_skip_first_latent_cfg:
- sources:
- - fastvideo.configs.pipelines.pan2.PAN2PipelineConfig
text_encoder_max_lengths:
sources:
- fastvideo.configs.pipelines.hunyuan15.Hunyuan15I2V480PStepDistilledConfig
diff --git a/docs/generate_examples.py b/docs/generate_examples.py
index e2c3f5a97..b962e8ae8 100644
--- a/docs/generate_examples.py
+++ b/docs/generate_examples.py
@@ -35,7 +35,6 @@ COOKBOOK_FAMILIES = {
"cosmos",
"kandinsky5",
"kandinsky6",
- "pan2",
"flux",
"qwen_image21",
"sd35",
diff --git a/docs/inference/pan2.md b/docs/inference/pan2.md
deleted file mode 100644
index 570594a6c..000000000
--- a/docs/inference/pan2.md
+++ /dev/null
@@ -1,86 +0,0 @@
-# PAN 2 Text/Image to Video
-
-PAN 2 is a 29B mixture-of-transformers video DiT with separate video and text streams, a Qwen3.5 text encoder and the
-Wan2.2 VAE. One pipeline serves text-to-video and image-to-video: pass `image_path` to condition on a first frame and
-leave it out for text only.
-
-## Model
-
-| Hub repo | Pipeline | Workloads | Example |
-|---|---|---|---|
-| `IFM/PAN2` | `PAN2ModularPipeline` | T2V, I2V | [`basic_pan2.py`](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_pan2.py) |
-
-PAN2 checkpoints use the Modular Diffusers layout (`modular_model_index.json`). Any repo id or local directory whose
-name contains `pan2` (or whose manifest declares `PAN2ModularPipeline`) resolves to this pipeline. The defaults come
-from the `pan2_t2v` and `pan2_i2v` presets:
-
-| Setting | Default |
-|---|---|
-| Resolution | 704x1248 (height and width must be multiples of 32) |
-| Frames | 313, 13 s at 24 fps (`num_frames - 1` must be a multiple of 4) |
-| Steps | 50, uniform sigmas from 1 to 0 with the checkpoint's flow shift (9 for `IFM/PAN2-TEST`, the reference sampler's value) |
-| Guidance | 3.0, classifier-free guidance. Image-to-video keeps the first latent frame out of guidance (`i2v_skip_first_latent_cfg`, on by default, as in the reference sampler) |
-| Negative prompt | None unless the request carries one. The reference sampler always sends the release's structured negative caption (`negative_prompt.json` of the PAN2 repository) with its three `time_range_sec` values set to the clip length, `0 to 20` for 481 frames at 24 fps |
-
-PAN 2 is trained on the structured captions its prompt rewriter produces, serialized as one-line JSON. Short raw
-prompts work, with weaker results.
-
-## Usage
-
-```bash
-python examples/inference/basic/basic_pan2.py
-```
-
-```python
-from fastvideo import VideoGenerator
-
-generator = VideoGenerator.from_pretrained(
- "IFM/PAN2",
- num_gpus=1,
- dit_cpu_offload=False,
- dit_layerwise_offload=False,
- text_encoder_cpu_offload=True,
-)
-generator.generate_video(
- "A red fox trots through fresh snow in a pine forest at dawn.",
- image_path=None, # or the path of a first frame for image-to-video
- output_path="video_samples_pan2",
-)
-```
-
-For image-to-video, the first frame is scaled to cover the requested resolution and center cropped, then encoded by the
-VAE into the first latent frame of the conditioning input.
-
-## Parallelism
-
-With `num_gpus > 1`, FastVideo runs Ulysses sequence parallelism: the video tokens are sharded across ranks and the
-text tokens are replicated. The number of GPUs must divide the 32 attention heads. Sequence-parallel padding of the
-video tokens is trimmed before attention, so no attention mask is needed.
-
-Padding is always excluded, without an opt-in setting. At 704x1248 with 313 frames, the 67,782 video tokens need
-two padding tokens at SP=4. They are removed before joining the text tokens in every attention block. This provides
-the same exclusion as vLLM-Omni's unconditional padding mask in
-[revision `e0c4c731`](https://github.com/vllm-project/vllm-omni/commit/e0c4c73158a5e4f44eda8eaa8879f7fd9cedbd74).
-The trimmed sequence can use either FlashAttention or Torch SDPA.
-
-```bash
-PAN2_NUM_GPUS=4 python examples/inference/basic/basic_pan2.py
-```
-
-## Memory and offloading
-
-The example keeps the DiT on the GPUs and offloads the text encoder between requests. Sequence parallelism shards
-video tokens; each rank still holds the DiT weights. Increasing `num_gpus` alone does not shard those weights.
-For devices that cannot hold the DiT, see [CPU offloading](offloading.md) for `dit_cpu_offload` and
-`dit_layerwise_offload`. Hardware recipes and timings reported by vLLM-Omni use its own offload implementation;
-they have not been validated for FastVideo.
-
-The full `IFM/PAN2` checkpoint is not public yet. Local parity tests use the public random-weight
-`wuqing157/tiny-pan2-modular-pipe` checkpoint; it verifies the integration and produces noise rather than useful video.
-
-## Differences from the vLLM-Omni integration
-
-- The optional prompt and video guardrails (`pan2-guardrail`) are not included.
-- The Cache-DiT `quality` policy and CFG-parallel execution are vLLM-Omni serving features and are not ported.
-- The text encoder is a FastVideo-native Qwen3.5 port. Its Gated DeltaNet layers use a PyTorch chunked delta rule
- instead of the `fla` kernels.
diff --git a/docs/inference/support_matrix.md b/docs/inference/support_matrix.md
index ee4f51ecf..722e5cf28 100644
--- a/docs/inference/support_matrix.md
+++ b/docs/inference/support_matrix.md
@@ -68,7 +68,6 @@ column links a runnable script in `examples/inference/basic/` where one exists.
| matrixgame | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers`
`FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers`
`FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers`
`FastVideo/Matrix-Game-2.0-Base-Diffusers`
`FastVideo/Matrix-Game-2.0-GTA-Diffusers`
`FastVideo/Matrix-Game-2.0-TempleRun-Diffusers`
`mignonjia/mg_longtuning_distilled_zelda`
`mignonjia/mg_sf_distilled_zelda_1k_steps`
`mignonjia/mg_sf_distilled_zelda`
`mignonjia/mg_causal_zelda`
`mignonjia/mg_bidirectional_zelda` | I2V | [basic_matrixgame2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame2.py) |
| matrixgame | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | I2V | [basic_matrixgame3.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_matrixgame3.py) |
| minimax_h3 | `MiniMaxAI/MiniMax-H3` | T2V, I2V | [T2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_t2v.py)
[FL2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_fl2va.py)
[Ref2VA](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_minimax_h3_ref2va.py) |
-| pan2 | `IFM/PAN2` | T2V, I2V | [basic_pan2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_pan2.py) |
| sd35 | `stabilityai/stable-diffusion-3.5-medium` | T2I | [basic_sd35_t2i.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_sd35_t2i.py) |
| qwen_image21 | `Qwen/Qwen-Image-2.1` | T2I, I2I | [basic_qwen_image21.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_qwen_image21.py) |
| stable_audio | `FastVideo/stable-audio-open-1.0-Diffusers` | T2V | [basic_stable_audio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio.py) |
diff --git a/examples/inference/basic/basic_pan2.py b/examples/inference/basic/basic_pan2.py
deleted file mode 100644
index 10600ca96..000000000
--- a/examples/inference/basic/basic_pan2.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import os
-
-from fastvideo import VideoGenerator
-
-# PAN2 is trained on the structured captions its prompt rewriter produces (serialized as one-line JSON). Plain
-# prompts like this one work, with weaker results.
-PROMPT = ("A red fox trots through fresh snow in a pine forest at dawn, its breath visible in the cold air. "
- "The camera tracks it from the side at ground level as low golden light filters through the trees.")
-
-OUTPUT_PATH = "video_samples_pan2"
-MODEL_PATH = os.environ.get("PAN2_MODEL_PATH", "IFM/PAN2")
-
-# Set to a first frame (e.g. "assets/images/girl.png") for image-to-video; the same pipeline runs both modes.
-IMAGE_PATH = None
-
-
-def main():
- # The 29B DiT runs in bf16; with more GPUs, num_gpus enables Ulysses sequence parallelism (the 32 attention
- # heads must be divisible by it).
- generator = VideoGenerator.from_pretrained(
- MODEL_PATH,
- num_gpus=int(os.environ.get("PAN2_NUM_GPUS", "1")),
- use_fsdp_inference=False,
- dit_cpu_offload=False,
- dit_layerwise_offload=False,
- vae_cpu_offload=False,
- text_encoder_cpu_offload=True,
- pin_cpu_memory=True,
- )
-
- generator.generate_video(
- PROMPT,
- image_path=IMAGE_PATH,
- output_path=OUTPUT_PATH,
- save_video=True,
- )
-
-
-if __name__ == "__main__":
- main()
diff --git a/examples/serving/README.md b/examples/serving/README.md
index 7cbd01ec3..b95910345 100644
--- a/examples/serving/README.md
+++ b/examples/serving/README.md
@@ -90,7 +90,6 @@ uncovered registry families.
| `openai_hunyuan15_i2v_480p.yaml` | HunyuanVideo 1.5 I2V 480p | image-to-video | 1 |
| `openai_kandinsky6_ti2va.yaml` | Kandinsky 6 Pro TI2VA | text/image-to-video with audio | 1 |
| `openai_kandinsky6_ti2va_distilled.yaml` | Kandinsky 6 Pro-distill TI2VA | text/image-to-video with audio | 1 |
-| `openai_pan2.yaml` | PAN 2 | text/image-to-video | 1 |
| `openai_ltx2_distilled.yaml` | LTX2 Distilled | text-to-video with audio | 4 |
| `openai_flux1_dev.yaml` | FLUX.1-dev | text-to-image | 1 |
| `openai_flux2_dev.yaml` | FLUX.2-dev | text-to-image | 1 |
diff --git a/examples/serving/openai_pan2.yaml b/examples/serving/openai_pan2.yaml
deleted file mode 100644
index 17bc68793..000000000
--- a/examples/serving/openai_pan2.yaml
+++ /dev/null
@@ -1,36 +0,0 @@
-# OpenAI-compatible server for PAN 2 text-to-video and first-frame image-to-video.
-# fastvideo serve --config examples/serving/openai_pan2.yaml
-# Omit image_reference for text-to-video; send it (or input_reference) for image-to-video.
-generator:
- model_path: IFM/PAN2
- engine:
- num_gpus: 1
- use_fsdp_inference: false
- parallelism:
- tp_size: 1
- sp_size: 1
- offload:
- dit: false
- dit_layerwise: false
- vae: false
- text_encoder: true
- pin_cpu_memory: true
- pipeline:
- workload_type: t2v
-
-server:
- host: 0.0.0.0
- port: 8000
- output_dir: outputs/openai_pan2
- served_model_name: pan2
-
-default_request:
- sampling:
- height: 704
- width: 1248
- num_frames: 313
- fps: 24
- num_inference_steps: 50
- guidance_scale: 3.0
- output:
- return_frames: false
diff --git a/fastvideo/configs/models/dits/__init__.py b/fastvideo/configs/models/dits/__init__.py
index 4490c2cc5..ab4a4e0f7 100644
--- a/fastvideo/configs/models/dits/__init__.py
+++ b/fastvideo/configs/models/dits/__init__.py
@@ -8,7 +8,6 @@ from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
from fastvideo.configs.models.dits.magi_human import MagiHumanVideoConfig
from fastvideo.configs.models.dits.minimax_h3 import MiniMaxH3Config
-from fastvideo.configs.models.dits.pan2 import PAN2VideoConfig
from fastvideo.configs.models.dits.mmaudio import (
MMAUDIO_44K_TRAINING_VARIANTS,
MMAUDIO_TRAINING_VARIANTS,
@@ -33,7 +32,7 @@ __all__ = [
"FluxDiTConfig", "Flux2Config", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig", "Kandinsky6ArchConfig",
"Kandinsky6VideoAudioConfig", "Kandinsky6SRArchConfig", "Kandinsky6SRConfig", "MagiHumanVideoConfig",
"StableAudioConfig", "HeliosConfig", "LingBotWorld2CausalFastVideoConfig", "LingBotWorldFastVideoConfig",
- "LingBotVideoConfig", "MiniMaxH3Config", "PAN2VideoConfig", "MMAudioArchConfig", "MMAudioTransformerConfig",
+ "LingBotVideoConfig", "MiniMaxH3Config", "MMAudioArchConfig", "MMAudioTransformerConfig",
"MMAUDIO_VARIANT_ARCHITECTURES", "MMAUDIO_TRAINING_VARIANTS", "MMAUDIO_44K_TRAINING_VARIANTS",
"get_mmaudio_transformer_config", "QwenImage21ArchConfig", "QwenImage21Config"
]
diff --git a/fastvideo/configs/models/dits/pan2.py b/fastvideo/configs/models/dits/pan2.py
deleted file mode 100644
index 8ec5b6d03..000000000
--- a/fastvideo/configs/models/dits/pan2.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-from fastvideo.platforms import AttentionBackendEnum
-
-
-def is_transformer_block(n: str, m) -> bool:
- return "transformer_blocks" in n and str.isdigit(n.split(".")[-1])
-
-
-def is_refiner_block(n: str, m) -> bool:
- return "context_refiner" in n and str.isdigit(n.split(".")[-1])
-
-
-@dataclass
-class PAN2ArchConfig(DiTArchConfig):
- _fsdp_shard_conditions: list = field(default_factory=lambda: [is_transformer_block, is_refiner_block])
- _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
- AttentionBackendEnum.FLASH_ATTN,
- AttentionBackendEnum.TORCH_SDPA,
- )
-
- # Diffusers PAN2Transformer3DModel keys -> native keys. The per-stream Q/K/V projections are fused on load.
- param_names_mapping: dict = field(
- default_factory=lambda: {
- r"^x_embedder\.(.*)$": r"x_embedder.proj.\1",
- r"^time_embedder\.linear_1\.(.*)$": r"time_embedder.mlp.fc_in.\1",
- r"^time_embedder\.linear_2\.(.*)$": r"time_embedder.mlp.fc_out.\1",
- r"^context_embedder\.0\.(.*)$": r"context_norm.\1",
- r"^context_embedder\.1\.(.*)$": r"context_proj.\1",
- r"^context_refiner\.(\d+)\.attn\.to_q\.(.*)$": (r"context_refiner.\1.attn.to_qkv.\2", 0, 3),
- r"^context_refiner\.(\d+)\.attn\.to_k\.(.*)$": (r"context_refiner.\1.attn.to_qkv.\2", 1, 3),
- r"^context_refiner\.(\d+)\.attn\.to_v\.(.*)$": (r"context_refiner.\1.attn.to_qkv.\2", 2, 3),
- r"^context_refiner\.(\d+)\.attn\.to_out\.0\.(.*)$": r"context_refiner.\1.attn.to_out.\2",
- r"^context_refiner\.(\d+)\.ff\.net\.0\.proj\.(.*)$": r"context_refiner.\1.ff.fc_in.\2",
- r"^context_refiner\.(\d+)\.ff\.net\.2\.(.*)$": r"context_refiner.\1.ff.fc_out.\2",
- r"^transformer_blocks\.(\d+)\.attn\.to_q\.(.*)$": (r"transformer_blocks.\1.attn.to_qkv.\2", 0, 3),
- r"^transformer_blocks\.(\d+)\.attn\.to_k\.(.*)$": (r"transformer_blocks.\1.attn.to_qkv.\2", 1, 3),
- r"^transformer_blocks\.(\d+)\.attn\.to_v\.(.*)$": (r"transformer_blocks.\1.attn.to_qkv.\2", 2, 3),
- r"^transformer_blocks\.(\d+)\.attn\.add_q_proj\.(.*)$":
- (r"transformer_blocks.\1.attn.add_qkv_proj.\2", 0, 3),
- r"^transformer_blocks\.(\d+)\.attn\.add_k_proj\.(.*)$":
- (r"transformer_blocks.\1.attn.add_qkv_proj.\2", 1, 3),
- r"^transformer_blocks\.(\d+)\.attn\.add_v_proj\.(.*)$":
- (r"transformer_blocks.\1.attn.add_qkv_proj.\2", 2, 3),
- r"^transformer_blocks\.(\d+)\.attn\.to_out\.0\.(.*)$": r"transformer_blocks.\1.attn.to_out.\2",
- r"^transformer_blocks\.(\d+)\.(ff|ff_context)\.net\.0\.proj\.(.*)$": r"transformer_blocks.\1.\2.fc_in.\3",
- r"^transformer_blocks\.(\d+)\.(ff|ff_context)\.net\.2\.(.*)$": r"transformer_blocks.\1.\2.fc_out.\3",
- })
-
- reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
- patch_size: tuple[int, ...] = (1, 2, 2)
- in_channels: int = 97
- out_channels: int = 48
- num_attention_heads: int = 32
- attention_head_dim: int = 128
- num_layers: int = 44
- num_refiner_layers: int = 4
- mlp_ratio: float = 8.0
- context_mlp_ratio: float = 4.0
- refiner_mlp_ratio: float = 4.0
- text_embed_dim: int = 4096
- rope_axes_dim: tuple[int, ...] = (16, 56, 56)
- rope_theta: float = 256.0
- # Frame positions are rescaled to the frame rate the rotary embedding was trained at (16 fps of 24 fps).
- rope_temporal_scale: float = 16 / 24
- # PAN2 runs its text stream in the DiT dtype.
- cast_prompt_embeds_to_dit_dtype: bool = True
- exclude_lora_layers: list[str] = field(
- default_factory=lambda: ["x_embedder", "time_embedder", "context_norm", "context_proj", "context_refiner"])
-
- def __post_init__(self):
- super().__post_init__()
- self.patch_size = tuple(self.patch_size)
- self.rope_axes_dim = tuple(self.rope_axes_dim)
- self.hidden_size: int = self.attention_head_dim * self.num_attention_heads
- self.num_channels_latents: int = self.out_channels
-
-
-@dataclass
-class PAN2VideoConfig(DiTConfig):
- arch_config: DiTArchConfig = field(default_factory=PAN2ArchConfig)
-
- prefix: str = "PAN2"
diff --git a/fastvideo/configs/models/encoders/__init__.py b/fastvideo/configs/models/encoders/__init__.py
index 67f442a91..d2797f2db 100644
--- a/fastvideo/configs/models/encoders/__init__.py
+++ b/fastvideo/configs/models/encoders/__init__.py
@@ -16,7 +16,6 @@ from fastvideo.configs.models.encoders.mistral3 import Mistral3TextConfig
from fastvideo.configs.models.encoders.minimax_h3_qwen3_vl import (MiniMaxH3Qwen3VLArchConfig, MiniMaxH3Qwen3VLConfig)
from fastvideo.configs.models.encoders.qwen3 import Qwen3TextConfig
from fastvideo.configs.models.encoders.qwen_image21 import QwenImage21Qwen3VLArchConfig, QwenImage21Qwen3VLConfig
-from fastvideo.configs.models.encoders.qwen3_5 import Qwen3_5TextArchConfig, Qwen3_5TextConfig
from fastvideo.configs.models.encoders.lingbot_video import LingBotVideoQwen3VLTextConfig
from fastvideo.configs.models.encoders.stable_audio_conditioner import (
StableAudioConditionerArchConfig,
@@ -53,8 +52,6 @@ __all__ = [
"Qwen3TextConfig",
"QwenImage21Qwen3VLArchConfig",
"QwenImage21Qwen3VLConfig",
- "Qwen3_5TextArchConfig",
- "Qwen3_5TextConfig",
"Mistral3TextConfig",
"LingBotWorld2UMT5ArchConfig",
"LingBotWorld2UMT5Config",
diff --git a/fastvideo/configs/models/encoders/qwen3_5.py b/fastvideo/configs/models/encoders/qwen3_5.py
deleted file mode 100644
index f469657ab..000000000
--- a/fastvideo/configs/models/encoders/qwen3_5.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Qwen3.5 text encoder configuration (hybrid Gated DeltaNet + gated attention)."""
-from dataclasses import dataclass, field
-from typing import Any
-
-from fastvideo.configs.models.encoders.base import (
- TextEncoderArchConfig,
- TextEncoderConfig,
-)
-
-_LEGACY_LAYER_TYPES = {
- "mamba": "linear_attention",
- "conv": "linear_attention",
- "attention": "full_attention",
-}
-_DEFAULT_ROPE_THETA = 10000.0
-_DEFAULT_PARTIAL_ROTARY_FACTOR = 0.25
-
-
-def _is_transformer_layer(n: str, m: Any) -> bool:
- return "layers" in n and str.isdigit(n.split(".")[-1])
-
-
-def _is_embeddings(n: str, m: Any) -> bool:
- return n.endswith("embed_tokens")
-
-
-def _is_final_norm(n: str, m: Any) -> bool:
- return n == "norm"
-
-
-@dataclass
-class Qwen3_5TextArchConfig(TextEncoderArchConfig):
- """Architecture config for the Qwen3.5 text model (``Qwen3_5TextModel``).
-
- Layers alternate between Gated DeltaNet linear attention and gated full
- attention according to ``layer_types``. The defaults describe the 9B text
- tower; checkpoint ``config.json`` values override them at load time.
- """
-
- vocab_size: int = 248320
- hidden_size: int = 4096
- intermediate_size: int = 12288
- num_hidden_layers: int = 32
- num_attention_heads: int = 16
- num_key_value_heads: int = 4
- head_dim: int = 256
- hidden_act: str = "silu"
- max_position_embeddings: int = 32768
- initializer_range: float = 0.02
- rms_norm_eps: float = 1e-6
- use_cache: bool = True
- tie_word_embeddings: bool = False
- attention_bias: bool = False
- attention_dropout: float = 0.0
- linear_conv_kernel_dim: int = 4
- linear_key_head_dim: int = 128
- linear_value_head_dim: int = 128
- linear_num_key_heads: int = 16
- linear_num_value_heads: int = 32
- layer_types: list[str] | None = None
- full_attention_interval: int = 4
- # RoPE settings as written by Transformers 5 (``rope_parameters``) or by
- # older configs (top-level ``rope_theta`` / ``partial_rotary_factor`` /
- # ``rope_scaling``). Values inside ``rope_parameters`` take precedence.
- rope_parameters: dict[str, Any] | None = None
- rope_scaling: dict[str, Any] | None = None
- rope_theta: float | None = None
- partial_rotary_factor: float | None = None
- pad_token_id: int | None = None
- bos_token_id: int | None = None
- eos_token_id: int | list[int] | None = None
-
- _fsdp_shard_conditions: list = field(
- default_factory=lambda: [_is_transformer_layer, _is_embeddings, _is_final_norm])
-
- def __post_init__(self) -> None:
- super().__post_init__()
- # Validate eagerly; the resolved views are recomputed on demand because
- # ``update_model_arch`` re-runs this hook after replacing raw fields.
- self.resolved_layer_types()
- self.resolved_rope_parameters()
-
- def resolved_layer_types(self) -> list[str]:
- """Return one ``linear_attention`` / ``full_attention`` entry per layer."""
- if self.layer_types is None:
- interval = self.full_attention_interval
- layer_types = [
- "linear_attention" if (index + 1) % interval else "full_attention"
- for index in range(self.num_hidden_layers)
- ]
- else:
- layer_types = [_LEGACY_LAYER_TYPES.get(kind, kind) for kind in self.layer_types]
- if len(layer_types) < self.num_hidden_layers:
- raise ValueError(f"Qwen3.5 layer_types has {len(layer_types)} entries for "
- f"{self.num_hidden_layers} layers")
- unknown = set(layer_types) - {"linear_attention", "full_attention"}
- if unknown:
- raise ValueError(f"Unsupported Qwen3.5 layer types: {sorted(unknown)}")
- return layer_types[:self.num_hidden_layers]
-
- def resolved_rope_parameters(self) -> dict[str, Any]:
- """Return the effective text RoPE settings with Transformers' precedence."""
- rope = dict(self.rope_scaling or self.rope_parameters or {})
- rope.setdefault("rope_type", rope.get("type", "default"))
- rope.setdefault("rope_theta", self.rope_theta if self.rope_theta is not None else _DEFAULT_ROPE_THETA)
- rope.setdefault(
- "partial_rotary_factor",
- self.partial_rotary_factor if self.partial_rotary_factor is not None else _DEFAULT_PARTIAL_ROTARY_FACTOR,
- )
- return rope
-
-
-@dataclass
-class Qwen3_5TextConfig(TextEncoderConfig):
- """Top-level config for the Qwen3.5 text encoder."""
-
- arch_config: TextEncoderArchConfig = field(default_factory=Qwen3_5TextArchConfig)
- prefix: str = "qwen3_5"
diff --git a/fastvideo/configs/pipelines/__init__.py b/fastvideo/configs/pipelines/__init__.py
index a925d5123..f42e517b8 100644
--- a/fastvideo/configs/pipelines/__init__.py
+++ b/fastvideo/configs/pipelines/__init__.py
@@ -15,7 +15,6 @@ from fastvideo.configs.pipelines.matrixgame2 import MatrixGame2I2V480PConfig
from fastvideo.configs.pipelines.matrixgame3 import MatrixGame3I2V720PConfig
from fastvideo.configs.pipelines.mmaudio import MMAudioV2AConfig
from fastvideo.configs.pipelines.qwen_image21 import QwenImage21PipelineConfig
-from fastvideo.configs.pipelines.pan2 import PAN2PipelineConfig
from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
from fastvideo.registry import get_pipeline_config_cls_from_name
from fastvideo.models.wan.pipeline_config import (LucyEditDevConfig, SelfForcingWanT2V480PConfig, WanI2V480PConfig,
@@ -28,5 +27,5 @@ __all__ = [
"Kandinsky5T2VConfig", "Kandinsky5I2VConfig", "Kandinsky5DMDConfig", "Kandinsky6TI2VAConfig",
"Kandinsky6SRPipelineConfig", "LingBotWorld2CausalFastI2V480PConfig", "LingBotWorldFastI2V480PConfig",
"LingBotVideoT2VConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "MMAudioV2AConfig",
- "QwenImage21PipelineConfig", "PAN2PipelineConfig", "get_pipeline_config_cls_from_name"
+ "QwenImage21PipelineConfig", "get_pipeline_config_cls_from_name"
]
diff --git a/fastvideo/configs/pipelines/pan2.py b/fastvideo/configs/pipelines/pan2.py
deleted file mode 100644
index 8e58c33b9..000000000
--- a/fastvideo/configs/pipelines/pan2.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 pipeline configuration: text-to-video, and image-to-video from a first frame."""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits import PAN2VideoConfig
-from fastvideo.configs.models.encoders import Qwen3_5TextConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.models.wan.vae_config import WanVAEConfig
-
-PAN2_SYSTEM_PROMPT = "You are a helpful assistant. Generate the video with the following instructions."
-
-
-@dataclass
-class PAN2PipelineConfig(PipelineConfig):
- """One configuration serves text-to-video and, given a first frame, image-to-video."""
-
- dit_config: DiTConfig = field(default_factory=PAN2VideoConfig)
- # The Wan2.2 48-channel VAE; its architecture and latent statistics come from vae/config.json at load time.
- vae_config: VAEConfig = field(default_factory=WanVAEConfig)
- text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (Qwen3_5TextConfig(), ))
- text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))
-
- dit_precision: str = "bf16"
- vae_precision: str = "fp32"
- # Unset: the checkpoint's scheduler config decides, which is shift 9 for IFM/PAN2-TEST, the reference sampler's
- # fixed value. A fixed default here would override the released shift with whatever a test checkpoint declares.
- flow_shift: float | None = None
- # Image-to-video: the first latent frame (the conditioning image) takes the positive prediction instead of the
- # guided one, as the reference sampler does by default (``--i2v_skip_first_latent_cfg``).
- i2v_skip_first_latent_cfg: bool = True
- # PAN2 samples on fp32 latents; keep the Euler update out of autocast.
- scheduler_step_in_fp32: bool = True
-
- # Prompt encoding: PAN2 conditions on the tokens after the chat template's user header, taken from the text
- # encoder's hidden states with its last two layers skipped (``hidden_states[-3]``).
- text_system_prompt: str = PAN2_SYSTEM_PROMPT
- text_encoder_skip_layers: int = 2
- text_max_sequence_length: int = 1536
-
- def __post_init__(self) -> None:
- # The encoder is needed for the image-to-video first frame.
- self.vae_config.load_encoder = True
- self.vae_config.load_decoder = True
- self.text_encoder_configs[0].arch_config.output_hidden_states = True
diff --git a/fastvideo/models/dits/pan2.py b/fastvideo/models/dits/pan2.py
deleted file mode 100644
index 40c809d88..000000000
--- a/fastvideo/models/dits/pan2.py
+++ /dev/null
@@ -1,398 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 video transformer.
-
-A mixture-of-transformers DiT: the video and text streams keep separate weights and meet only in one joint
-attention per block, over ``[video; text]``. The text stream is modulated by a learned, timestep-free table.
-Under sequence parallelism the video tokens are sharded and the text tokens are replicated on every rank.
-"""
-
-from typing import Any
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from fastvideo.attention import DistributedAttention, LocalAttention
-from fastvideo.configs.models.dits.pan2 import PAN2VideoConfig
-from fastvideo.distributed.communication_op import (sequence_model_parallel_all_gather_with_unpad,
- sequence_model_parallel_shard)
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.layernorm import RMSNorm
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import get_1d_rotary_pos_embed
-from fastvideo.layers.visual_embedding import PatchEmbed, TimestepEmbedder
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.platforms import AttentionBackendEnum
-
-
-class PAN2SwiGLU(nn.Module):
- """SwiGLU MLP of the text refiner; the fused input projection stores ``[value; gate]``."""
-
- def __init__(self, dim: int, inner_dim: int, prefix: str = ""):
- super().__init__()
- self.fc_in = ReplicatedLinear(dim, 2 * inner_dim, bias=False, prefix=f"{prefix}.fc_in")
- self.fc_out = ReplicatedLinear(inner_dim, dim, bias=False, prefix=f"{prefix}.fc_out")
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x, _ = self.fc_in(x)
- value, gate = x.chunk(2, dim=-1)
- x, _ = self.fc_out(value * F.silu(gate))
- return x
-
-
-class PAN2RefinerAttention(nn.Module):
- """Text-only self-attention of the refiner; the text is replicated on every sequence-parallel rank."""
-
- def __init__(self,
- dim: int,
- num_heads: int,
- head_dim: int,
- eps: float,
- supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
- prefix: str = ""):
- super().__init__()
- self.num_heads = num_heads
- self.to_qkv = ReplicatedLinear(dim, 3 * num_heads * head_dim, bias=False, prefix=f"{prefix}.to_qkv")
- self.norm_q = RMSNorm(head_dim, eps=eps)
- self.norm_k = RMSNorm(head_dim, eps=eps)
- self.to_out = ReplicatedLinear(num_heads * head_dim, dim, bias=False, prefix=f"{prefix}.to_out")
- self.attn = LocalAttention(num_heads=num_heads,
- head_size=head_dim,
- causal=False,
- supported_attention_backends=supported_attention_backends)
-
- def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
- qkv, _ = self.to_qkv(hidden_states)
- query, key, value = qkv.unflatten(-1, (3, self.num_heads, -1)).unbind(2)
- query = self.norm_q(query).to(value.dtype)
- key = self.norm_k(key).to(value.dtype)
- hidden_states = self.attn(query, key, value)
- hidden_states, _ = self.to_out(hidden_states.flatten(2))
- return hidden_states
-
-
-class PAN2TokenRefinerBlock(nn.Module):
-
- def __init__(self,
- dim: int,
- num_heads: int,
- head_dim: int,
- mlp_ratio: float,
- eps: float,
- supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
- prefix: str = ""):
- super().__init__()
- self.norm1 = RMSNorm(dim, eps=eps)
- self.attn = PAN2RefinerAttention(dim,
- num_heads,
- head_dim,
- eps,
- supported_attention_backends=supported_attention_backends,
- prefix=f"{prefix}.attn")
- self.norm2 = RMSNorm(dim, eps=eps)
- self.ff = PAN2SwiGLU(dim, int(dim * mlp_ratio), prefix=f"{prefix}.ff")
-
- 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 PAN2AdaLayerNormZero(nn.Module):
- """Timestep modulation of the video stream: modulated attention input plus the gate/shift/scale for the rest."""
-
- def __init__(self, dim: int, eps: float = 1e-6, prefix: str = ""):
- super().__init__()
- self.linear = ReplicatedLinear(dim, 6 * dim, bias=True, prefix=f"{prefix}.linear")
- self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
-
- def forward(self, x: torch.Tensor,
- temb: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
- emb, _ = self.linear(F.silu(temb))
- shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=-1)
- x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
- return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
-
-
-class PAN2AdaLayerNormOut(nn.Module):
-
- def __init__(self, dim: int, eps: float = 1e-6, prefix: str = ""):
- super().__init__()
- self.linear = ReplicatedLinear(dim, 2 * dim, bias=True, prefix=f"{prefix}.linear")
- self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
-
- def forward(self, x: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
- emb, _ = self.linear(F.silu(temb).to(x.dtype))
- scale, shift = emb.chunk(2, dim=-1)
- return self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
-
-
-class PAN2Attention(nn.Module):
- """Joint attention over ``[video; text]`` with per-stream projections and Q/K RMSNorm.
-
- RoPE applies to the video tokens only; ``DistributedAttention`` applies it after its all-to-all, where each rank
- holds the full (unpadded) video sequence for its heads.
- """
-
- def __init__(self,
- dim: int,
- num_heads: int,
- head_dim: int,
- eps: float,
- supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
- prefix: str = ""):
- super().__init__()
- self.num_heads = num_heads
- inner_dim = num_heads * head_dim
-
- self.to_qkv = ReplicatedLinear(dim, 3 * inner_dim, bias=True, prefix=f"{prefix}.to_qkv")
- self.norm_q = RMSNorm(head_dim, eps=eps)
- self.norm_k = RMSNorm(head_dim, eps=eps)
- self.to_out = ReplicatedLinear(inner_dim, dim, bias=True, prefix=f"{prefix}.to_out")
-
- self.add_qkv_proj = ReplicatedLinear(dim, 3 * inner_dim, bias=True, prefix=f"{prefix}.add_qkv_proj")
- self.norm_added_q = RMSNorm(head_dim, eps=eps)
- self.norm_added_k = RMSNorm(head_dim, eps=eps)
- self.to_add_out = ReplicatedLinear(inner_dim, dim, bias=True, prefix=f"{prefix}.to_add_out")
-
- self.attn = DistributedAttention(num_heads=num_heads,
- head_size=head_dim,
- causal=False,
- supported_attention_backends=supported_attention_backends,
- prefix=f"{prefix}.attn")
-
- def _project(self, projection: ReplicatedLinear,
- hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
- qkv, _ = projection(hidden_states)
- query, key, value = qkv.unflatten(-1, (3, self.num_heads, -1)).unbind(2)
- return query, key, value
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- freqs_cis: tuple[torch.Tensor, torch.Tensor],
- original_seq_len: int,
- ) -> tuple[torch.Tensor, torch.Tensor]:
- query, key, value = self._project(self.to_qkv, hidden_states)
- query = self.norm_q(query).to(value.dtype)
- key = self.norm_k(key).to(value.dtype)
-
- encoder_query, encoder_key, encoder_value = self._project(self.add_qkv_proj, encoder_hidden_states)
- encoder_query = self.norm_added_q(encoder_query).to(encoder_value.dtype)
- encoder_key = self.norm_added_k(encoder_key).to(encoder_value.dtype)
-
- hidden_states, encoder_hidden_states = self.attn(query,
- key,
- value,
- original_seq_len,
- replicated_q=encoder_query,
- replicated_k=encoder_key,
- replicated_v=encoder_value,
- freqs_cis=freqs_cis)
- hidden_states, _ = self.to_out(hidden_states.flatten(2))
- encoder_hidden_states, _ = self.to_add_out(encoder_hidden_states.flatten(2))
- return hidden_states, encoder_hidden_states
-
-
-class PAN2TransformerBlock(nn.Module):
-
- def __init__(self,
- dim: int,
- num_heads: int,
- head_dim: int,
- mlp_ratio: float,
- context_mlp_ratio: float,
- eps: float = 1e-6,
- supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
- prefix: str = ""):
- super().__init__()
- self.norm1 = PAN2AdaLayerNormZero(dim, eps=eps, prefix=f"{prefix}.norm1")
- self.norm1_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
- self.scale_shift_table_context = nn.Parameter(torch.zeros(6 * dim))
-
- self.attn = PAN2Attention(dim,
- num_heads,
- head_dim,
- eps,
- supported_attention_backends=supported_attention_backends,
- prefix=f"{prefix}.attn")
-
- self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
- self.ff = MLP(dim, int(dim * mlp_ratio), act_type="gelu_pytorch_tanh", prefix=f"{prefix}.ff")
- self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
- self.ff_context = MLP(dim,
- int(dim * context_mlp_ratio),
- act_type="gelu_pytorch_tanh",
- prefix=f"{prefix}.ff_context")
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor,
- temb: torch.Tensor,
- freqs_cis: tuple[torch.Tensor, torch.Tensor],
- original_seq_len: int,
- ) -> tuple[torch.Tensor, torch.Tensor]:
- norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
- (
- context_shift_msa,
- context_scale_msa,
- context_gate_msa,
- context_shift_mlp,
- context_scale_mlp,
- context_gate_mlp,
- ) = self.scale_shift_table_context.to(temb.dtype).chunk(6, dim=-1)
- norm_encoder_hidden_states = (self.norm1_context(encoder_hidden_states) *
- (1 + context_scale_msa) + context_shift_msa)
-
- attn_output, context_attn_output = self.attn(norm_hidden_states, norm_encoder_hidden_states, freqs_cis,
- original_seq_len)
- hidden_states = hidden_states + attn_output * gate_msa.unsqueeze(1)
- encoder_hidden_states = encoder_hidden_states + context_attn_output * context_gate_msa
-
- norm_hidden_states = self.norm2(hidden_states) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
- norm_encoder_hidden_states = (self.norm2_context(encoder_hidden_states) *
- (1 + context_scale_mlp) + context_shift_mlp)
-
- hidden_states = hidden_states + self.ff(norm_hidden_states) * gate_mlp.unsqueeze(1)
- encoder_hidden_states = encoder_hidden_states + self.ff_context(norm_encoder_hidden_states) * context_gate_mlp
- return hidden_states, encoder_hidden_states
-
-
-class PAN2Transformer3DModel(BaseDiT):
- """PAN2 DiT. The input stacks the noisy latents, the conditioning latents and a one-channel conditioning mask
- along channels, so one model serves text-to-video and first-frame image-to-video."""
-
- _fsdp_shard_conditions = PAN2VideoConfig()._fsdp_shard_conditions
- _compile_conditions = PAN2VideoConfig()._compile_conditions
- _supported_attention_backends = PAN2VideoConfig()._supported_attention_backends
- param_names_mapping = PAN2VideoConfig().param_names_mapping
- reverse_param_names_mapping = PAN2VideoConfig().reverse_param_names_mapping
- lora_param_names_mapping = PAN2VideoConfig().lora_param_names_mapping
-
- def __init__(self, config: PAN2VideoConfig, hf_config: dict[str, Any], **kwargs) -> None:
- super().__init__(config=config, hf_config=hf_config)
- inner_dim = config.num_attention_heads * config.attention_head_dim
- self.hidden_size = inner_dim
- self.num_attention_heads = config.num_attention_heads
- self.num_channels_latents = config.num_channels_latents
- self.out_channels = config.out_channels
- self.patch_size = tuple(config.patch_size)
- self.rope_axes_dim = tuple(config.rope_axes_dim)
- if sum(self.rope_axes_dim) != config.attention_head_dim:
- raise ValueError(f"rope_axes_dim {self.rope_axes_dim} must sum to the head dim {config.attention_head_dim}")
- if config.num_attention_heads % get_sp_world_size() != 0:
- raise ValueError(f"The number of attention heads ({config.num_attention_heads}) must be divisible by the "
- f"sequence parallel size ({get_sp_world_size()})")
-
- self.x_embedder = PatchEmbed(self.patch_size,
- config.in_channels,
- inner_dim,
- prefix=f"{config.prefix}.x_embedder")
- self.time_embedder = TimestepEmbedder(inner_dim, act_layer="silu", frequency_embedding_size=256)
-
- self.context_norm = RMSNorm(config.text_embed_dim, eps=1e-5)
- self.context_proj = ReplicatedLinear(config.text_embed_dim,
- inner_dim,
- bias=True,
- prefix=f"{config.prefix}.context_proj")
- # Layerwise offload hooks the first nn.ModuleList child, which must be the main blocks.
- self.transformer_blocks = nn.ModuleList([
- PAN2TransformerBlock(inner_dim,
- config.num_attention_heads,
- config.attention_head_dim,
- config.mlp_ratio,
- config.context_mlp_ratio,
- supported_attention_backends=self._supported_attention_backends,
- prefix=f"{config.prefix}.transformer_blocks.{i}") for i in range(config.num_layers)
- ])
-
- self.context_refiner = nn.ModuleList([
- PAN2TokenRefinerBlock(inner_dim,
- config.num_attention_heads,
- config.attention_head_dim,
- config.refiner_mlp_ratio,
- eps=1e-5,
- supported_attention_backends=self._supported_attention_backends,
- prefix=f"{config.prefix}.context_refiner.{i}")
- for i in range(config.num_refiner_layers)
- ])
- self.context_type_embedding = nn.Parameter(torch.zeros(inner_dim))
-
- self.norm_out = PAN2AdaLayerNormOut(inner_dim, eps=1e-6, prefix=f"{config.prefix}.norm_out")
- self.proj_out = ReplicatedLinear(inner_dim,
- self.patch_size[0] * self.patch_size[1] * self.patch_size[2] *
- self.out_channels,
- bias=True,
- prefix=f"{config.prefix}.proj_out")
-
- self.gradient_checkpointing = False
- self.__post_init__()
-
- def _rotary_pos_embed(self, num_frames: int, height: int, width: int,
- device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
- """Half-dim cos/sin of the 3-axis ``(t, h, w)`` RoPE over the full (unsharded) video token grid."""
- axes = [torch.arange(size, device=device, dtype=torch.float32) for size in (num_frames, height, width)]
- grid = torch.stack(torch.meshgrid(*axes, indexing="ij"), dim=0)
- grid[0] = grid[0] * self.config.rope_temporal_scale
- cos_sin = [
- get_1d_rotary_pos_embed(dim, grid[i].reshape(-1), theta=self.config.rope_theta, use_real=False)
- for i, dim in enumerate(self.rope_axes_dim)
- ]
- freqs_cos = torch.cat([cos for cos, _ in cos_sin], dim=1).float()
- freqs_sin = torch.cat([sin for _, sin in cos_sin], dim=1).float()
- return freqs_cos, freqs_sin
-
- def forward(self,
- hidden_states: torch.Tensor,
- encoder_hidden_states: torch.Tensor | list[torch.Tensor],
- timestep: torch.LongTensor,
- encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
- guidance=None,
- **kwargs) -> torch.Tensor:
- if not isinstance(encoder_hidden_states, torch.Tensor):
- encoder_hidden_states = encoder_hidden_states[0]
-
- batch_size, _, num_frames, height, width = hidden_states.shape
- p_t, p_h, p_w = self.patch_size
- post_patch_num_frames = num_frames // p_t
- post_patch_height = height // p_h
- post_patch_width = width // p_w
-
- freqs_cis = self._rotary_pos_embed(post_patch_num_frames, post_patch_height, post_patch_width,
- hidden_states.device)
-
- hidden_states = self.x_embedder(hidden_states)
- hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
-
- if timestep.dim() == 0:
- timestep = timestep.expand(batch_size)
- temb = self.time_embedder(timestep).to(hidden_states.dtype)
-
- encoder_hidden_states, _ = self.context_proj(self.context_norm(encoder_hidden_states))
- for refiner_block in self.context_refiner:
- encoder_hidden_states = refiner_block(encoder_hidden_states)
- encoder_hidden_states = encoder_hidden_states + self.context_type_embedding
-
- for block in self.transformer_blocks:
- if torch.is_grad_enabled() and self.gradient_checkpointing:
- hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
- block, hidden_states, encoder_hidden_states, temb, freqs_cis, original_seq_len)
- else:
- hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, temb, freqs_cis,
- original_seq_len)
-
- hidden_states = self.norm_out(hidden_states, temb)
- hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
- hidden_states, _ = self.proj_out(hidden_states)
-
- # proj_out emits channels before the patch offsets: (C, p_t, p_h, p_w).
- hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames, post_patch_height, post_patch_width,
- -1, p_t, p_h, p_w)
- hidden_states = hidden_states.permute(0, 4, 1, 5, 2, 6, 3, 7)
- return hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-
-
-EntryClass = PAN2Transformer3DModel
diff --git a/fastvideo/models/encoders/qwen3_5.py b/fastvideo/models/encoders/qwen3_5.py
deleted file mode 100644
index c036620fd..000000000
--- a/fastvideo/models/encoders/qwen3_5.py
+++ /dev/null
@@ -1,574 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Qwen3.5 text encoder: hybrid Gated DeltaNet linear attention + gated full attention.
-
-The encoder runs replicated (no tensor or sequence parallelism) and
-inference-only, so the linear-attention layers use a pure-PyTorch chunked gated
-delta rule instead of the fused ``fla`` / ``causal_conv1d`` kernels. Full
-attention calls ``scaled_dot_product_attention`` directly with head-major
-``[B, H, L, D]`` inputs: that is the layout the reference text model feeds SDPA,
-and keeping it makes bf16 hidden states reproducible against that reference.
-"""
-from collections.abc import Iterable
-from typing import Any
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from fastvideo.configs.models.encoders import BaseEncoderOutput
-from fastvideo.configs.models.encoders.qwen3_5 import Qwen3_5TextArchConfig, Qwen3_5TextConfig
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.quantization import QuantizationConfig
-from fastvideo.layers.vocab_parallel_embedding import VocabParallelEmbedding
-from fastvideo.logger import init_logger
-from fastvideo.models.encoders.base import TextEncoder
-from fastvideo.models.loader.weight_utils import default_weight_loader
-
-logger = init_logger(__name__)
-
-_GATED_DELTA_CHUNK_SIZE = 64
-# Checkpoints saved from the multimodal wrapper nest the text tower.
-_CHECKPOINT_PREFIXES = ("model.language_model.", "language_model.", "model.")
-_SKIPPED_CHECKPOINT_PREFIXES = ("lm_head.", "mtp.", "visual.", "model.visual.")
-
-
-class Qwen3_5RMSNorm(nn.Module):
- """Zero-centered RMSNorm: the stored weight is an offset from one."""
-
- def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
- super().__init__()
- self.eps = eps
- self.weight = nn.Parameter(torch.zeros(hidden_size))
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- output = x.float()
- output = output * torch.rsqrt(output.pow(2).mean(-1, keepdim=True) + self.eps)
- # The scale is applied in float32 before the downcast.
- output = output * (1.0 + self.weight.float())
- return output.type_as(x)
-
-
-class Qwen3_5RMSNormGated(nn.Module):
- """Per-head RMSNorm followed by a SiLU output gate (plain, not zero-centered, weight)."""
-
- def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
- super().__init__()
- self.eps = eps
- self.weight = nn.Parameter(torch.ones(hidden_size))
-
- def forward(self, hidden_states: torch.Tensor, gate: torch.Tensor) -> torch.Tensor:
- input_dtype = hidden_states.dtype
- hidden_states = hidden_states.to(torch.float32)
- variance = hidden_states.pow(2).mean(-1, keepdim=True)
- hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
- hidden_states = self.weight * hidden_states.to(input_dtype)
- hidden_states = hidden_states * F.silu(gate.to(torch.float32))
- return hidden_states.to(input_dtype)
-
-
-def _l2norm(x: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
- return x * torch.rsqrt((x * x).sum(dim=-1, keepdim=True) + eps)
-
-
-def chunk_gated_delta_rule(
- query: torch.Tensor,
- key: torch.Tensor,
- value: torch.Tensor,
- g: torch.Tensor,
- beta: torch.Tensor,
- chunk_size: int = _GATED_DELTA_CHUNK_SIZE,
-) -> torch.Tensor:
- """Chunked gated delta rule over a full prefix with a zero initial state.
-
- Inputs are ``[batch, seq, heads, dim]`` (``g`` / ``beta`` are
- ``[batch, seq, heads]``); q/k must already be L2-normalized. The recurrence
- runs in float32 and the output is returned in the query dtype.
- """
- output_dtype = query.dtype
- query, key, value, beta, g = (x.transpose(1, 2).contiguous().to(torch.float32)
- for x in (query, key, value, beta, g))
-
- batch_size, num_heads, sequence_length, k_head_dim = key.shape
- v_head_dim = value.shape[-1]
- pad_size = (chunk_size - sequence_length % chunk_size) % chunk_size
- query = F.pad(query, (0, 0, 0, pad_size))
- key = F.pad(key, (0, 0, 0, pad_size))
- value = F.pad(value, (0, 0, 0, pad_size))
- beta = F.pad(beta, (0, pad_size))
- g = F.pad(g, (0, pad_size))
- total_sequence_length = sequence_length + pad_size
- query = query * (1 / (query.shape[-1]**0.5))
-
- v_beta = value * beta.unsqueeze(-1)
- k_beta = key * beta.unsqueeze(-1)
- query, key, value, k_beta, v_beta = (x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1])
- for x in (query, key, value, k_beta, v_beta))
- g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size)
- upper_with_diagonal = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device),
- diagonal=0)
-
- # Intra-chunk decay and the (I + strictly-lower)^-1 WY transform, solved by
- # forward substitution row by row.
- g = g.cumsum(dim=-1)
- decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril()
- attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(upper_with_diagonal, 0)
- for i in range(1, chunk_size):
- row = attn[..., i, :i].clone()
- sub = attn[..., :i, :i].clone()
- attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
- attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
- value = attn @ v_beta
- k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
-
- recurrent_state = torch.zeros(batch_size,
- num_heads,
- k_head_dim,
- v_head_dim,
- dtype=value.dtype,
- device=value.device)
- core_attn_out = torch.zeros_like(value)
- for i in range(total_sequence_length // chunk_size):
- q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i]
- # decay_mask is already zero above the diagonal, which keeps this causal.
- attn = q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]
- v_prime = k_cumdecay[:, :, i] @ recurrent_state
- v_new = v_i - v_prime
- attn_inter = (q_i * g[:, :, i, :, None].exp()) @ recurrent_state
- core_attn_out[:, :, i] = attn_inter + attn @ v_new
- recurrent_state = (recurrent_state * g[:, :, i, -1, None, None].exp() +
- (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new)
-
- core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1])
- core_attn_out = core_attn_out[:, :, :sequence_length]
- return core_attn_out.transpose(1, 2).contiguous().to(output_dtype)
-
-
-class Qwen3_5GatedDeltaNet(nn.Module):
- """Gated DeltaNet token mixer used by the ``linear_attention`` layers."""
-
- def __init__(
- self,
- config: Qwen3_5TextArchConfig,
- quant_config: QuantizationConfig | None = None,
- prefix: str = "",
- ) -> None:
- super().__init__()
- self.hidden_size = config.hidden_size
- self.num_v_heads = config.linear_num_value_heads
- self.num_k_heads = config.linear_num_key_heads
- if self.num_v_heads % self.num_k_heads:
- raise ValueError(f"Qwen3.5 linear value heads ({self.num_v_heads}) must be a multiple of key heads "
- f"({self.num_k_heads})")
- self.head_k_dim = config.linear_key_head_dim
- self.head_v_dim = config.linear_value_head_dim
- self.key_dim = self.head_k_dim * self.num_k_heads
- self.value_dim = self.head_v_dim * self.num_v_heads
- self.conv_kernel_size = config.linear_conv_kernel_dim
- if config.hidden_act != "silu":
- raise ValueError(f"Unsupported Qwen3.5 activation: {config.hidden_act}")
-
- self.conv_dim = self.key_dim * 2 + self.value_dim
- self.conv1d = nn.Conv1d(
- in_channels=self.conv_dim,
- out_channels=self.conv_dim,
- bias=False,
- kernel_size=self.conv_kernel_size,
- groups=self.conv_dim,
- padding=self.conv_kernel_size - 1,
- )
- self.dt_bias = nn.Parameter(torch.ones(self.num_v_heads))
- self.A_log = nn.Parameter(torch.zeros(self.num_v_heads))
- self.norm = Qwen3_5RMSNormGated(self.head_v_dim, eps=config.rms_norm_eps)
- self.out_proj = ReplicatedLinear(self.value_dim,
- self.hidden_size,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.out_proj")
- self.in_proj_qkv = ReplicatedLinear(self.hidden_size,
- self.conv_dim,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.in_proj_qkv")
- self.in_proj_z = ReplicatedLinear(self.hidden_size,
- self.value_dim,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.in_proj_z")
- self.in_proj_b = ReplicatedLinear(self.hidden_size,
- self.num_v_heads,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.in_proj_b")
- self.in_proj_a = ReplicatedLinear(self.hidden_size,
- self.num_v_heads,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.in_proj_a")
-
- def forward(self, hidden_states: torch.Tensor, padding_mask: torch.Tensor | None = None) -> torch.Tensor:
- if padding_mask is not None:
- # Zero padded tokens so they cannot leak into the recurrent state.
- hidden_states = (hidden_states * padding_mask[:, :, None]).to(hidden_states.dtype)
- batch_size, seq_len, _ = hidden_states.shape
-
- mixed_qkv, _ = self.in_proj_qkv(hidden_states)
- z, _ = self.in_proj_z(hidden_states)
- b, _ = self.in_proj_b(hidden_states)
- a, _ = self.in_proj_a(hidden_states)
-
- # Causal depthwise conv: left padding of kernel-1, keep the first seq_len outputs.
- mixed_qkv = mixed_qkv.transpose(1, 2)
- mixed_qkv = F.silu(self.conv1d(mixed_qkv.to(self.conv1d.weight.dtype))[:, :, :seq_len]).to(mixed_qkv.dtype)
- mixed_qkv = mixed_qkv.transpose(1, 2)
- query, key, value = torch.split(mixed_qkv, [self.key_dim, self.key_dim, self.value_dim], dim=-1)
- query = query.reshape(batch_size, seq_len, -1, self.head_k_dim)
- key = key.reshape(batch_size, seq_len, -1, self.head_k_dim)
- value = value.reshape(batch_size, seq_len, -1, self.head_v_dim)
-
- beta = b.sigmoid()
- # float32 keeps exp(A_log) finite for half-precision weights.
- g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias)
- if self.num_v_heads > self.num_k_heads:
- repeats = self.num_v_heads // self.num_k_heads
- query = query.repeat_interleave(repeats, dim=2)
- key = key.repeat_interleave(repeats, dim=2)
-
- core_attn_out = chunk_gated_delta_rule(_l2norm(query), _l2norm(key), value, g=g, beta=beta)
-
- core_attn_out = self.norm(core_attn_out.reshape(-1, self.head_v_dim), z.reshape(-1, self.head_v_dim))
- core_attn_out = core_attn_out.reshape(batch_size, seq_len, -1)
- output, _ = self.out_proj(core_attn_out)
- return output
-
-
-class Qwen3_5RotaryEmbedding(nn.Module):
- """Text-only partial RoPE.
-
- Qwen3.5's interleaved M-RoPE collapses to standard RoPE when the temporal,
- height and width positions are identical, which holds for text tokens.
- """
-
- def __init__(self, config: Qwen3_5TextArchConfig) -> None:
- super().__init__()
- rope = config.resolved_rope_parameters()
- if rope["rope_type"] != "default":
- raise NotImplementedError(f"Qwen3.5 rope_type {rope['rope_type']!r} is not supported")
- rotary_dim = int(config.head_dim * rope["partial_rotary_factor"])
- inv_freq = 1.0 / (rope["rope_theta"]**(
- torch.arange(0, rotary_dim, 2, dtype=torch.float32, device="cpu") / rotary_dim))
- self.register_buffer("inv_freq", inv_freq, persistent=False)
-
- @torch.no_grad()
- def forward(self, hidden_states: torch.Tensor, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
- inv_freq = self.inv_freq.to(device=hidden_states.device, dtype=torch.float32)
- device_type = hidden_states.device.type if hidden_states.device.type != "mps" else "cpu"
- with torch.autocast(device_type=device_type, enabled=False):
- inv_freq = inv_freq[None, :, None].expand(position_ids.shape[0], -1, 1)
- freqs = (inv_freq @ position_ids[:, None, :].float()).transpose(1, 2)
- emb = torch.cat((freqs, freqs), dim=-1)
- cos = emb.cos()
- sin = emb.sin()
- return cos.to(hidden_states.dtype), sin.to(hidden_states.dtype)
-
-
-def _rotate_half(x: torch.Tensor) -> torch.Tensor:
- x1, x2 = x.chunk(2, dim=-1)
- return torch.cat((-x2, x1), dim=-1)
-
-
-def _repeat_kv(x: torch.Tensor, groups: int) -> torch.Tensor:
- batch_size, num_kv_heads, seq_len, head_dim = x.shape
- x = x[:, :, None].expand(batch_size, num_kv_heads, groups, seq_len, head_dim)
- return x.reshape(batch_size, num_kv_heads * groups, seq_len, head_dim)
-
-
-def _apply_partial_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
- """Rotate the leading ``cos.shape[-1]`` channels of ``[B, H, L, D]`` and pass the rest through."""
- rotary_dim = cos.shape[-1]
- cos = cos.unsqueeze(1)
- sin = sin.unsqueeze(1)
- x_rot, x_pass = x[..., :rotary_dim], x[..., rotary_dim:]
- x_rot = x_rot * cos + _rotate_half(x_rot) * sin
- return torch.cat([x_rot, x_pass], dim=-1)
-
-
-class Qwen3_5Attention(nn.Module):
- """Gated full attention with per-head QK-norm, partial RoPE and GQA."""
-
- def __init__(
- self,
- config: Qwen3_5TextConfig,
- quant_config: QuantizationConfig | None = None,
- prefix: str = "",
- ) -> None:
- super().__init__()
- arch = config.arch_config
- self.num_heads = arch.num_attention_heads
- self.num_kv_heads = arch.num_key_value_heads
- if self.num_heads % self.num_kv_heads:
- raise ValueError(f"Qwen3.5 attention heads ({self.num_heads}) must be a multiple of KV heads "
- f"({self.num_kv_heads})")
- self.head_dim = arch.head_dim
- self.scaling = self.head_dim**-0.5
- # q_proj packs, per head, the query followed by its output gate.
- self.q_proj = ReplicatedLinear(arch.hidden_size,
- self.num_heads * self.head_dim * 2,
- bias=arch.attention_bias,
- quant_config=quant_config,
- prefix=f"{prefix}.q_proj")
- self.k_proj = ReplicatedLinear(arch.hidden_size,
- self.num_kv_heads * self.head_dim,
- bias=arch.attention_bias,
- quant_config=quant_config,
- prefix=f"{prefix}.k_proj")
- self.v_proj = ReplicatedLinear(arch.hidden_size,
- self.num_kv_heads * self.head_dim,
- bias=arch.attention_bias,
- quant_config=quant_config,
- prefix=f"{prefix}.v_proj")
- self.o_proj = ReplicatedLinear(self.num_heads * self.head_dim,
- arch.hidden_size,
- bias=arch.attention_bias,
- quant_config=quant_config,
- prefix=f"{prefix}.o_proj")
- self.q_norm = Qwen3_5RMSNorm(self.head_dim, eps=arch.rms_norm_eps)
- self.k_norm = Qwen3_5RMSNorm(self.head_dim, eps=arch.rms_norm_eps)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- position_embeddings: tuple[torch.Tensor, torch.Tensor],
- padding_mask: torch.Tensor | None = None,
- ) -> torch.Tensor:
- batch_size, seq_len, _ = hidden_states.shape
- packed, _ = self.q_proj(hidden_states)
- query, gate = torch.chunk(packed.view(batch_size, seq_len, -1, self.head_dim * 2), 2, dim=-1)
- gate = gate.reshape(batch_size, seq_len, -1)
- key, _ = self.k_proj(hidden_states)
- value, _ = self.v_proj(hidden_states)
-
- query = self.q_norm(query).transpose(1, 2)
- key = self.k_norm(key.view(batch_size, seq_len, -1, self.head_dim)).transpose(1, 2)
- value = value.view(batch_size, seq_len, -1, self.head_dim).transpose(1, 2)
- cos, sin = position_embeddings
- query = _apply_partial_rotary(query, cos, sin)
- key = _apply_partial_rotary(key, cos, sin)
-
- groups = self.num_heads // self.num_kv_heads
- # Native GQA is only used where SDPA keeps a fused kernel (no mask, head_dim <= 256).
- native_gqa = padding_mask is None and self.head_dim <= 256
- if groups > 1 and not native_gqa:
- key = _repeat_kv(key, groups)
- value = _repeat_kv(value, groups)
- if padding_mask is None:
- attn_output = F.scaled_dot_product_attention(
- query,
- key,
- value,
- dropout_p=0.0,
- is_causal=seq_len > 1,
- scale=self.scaling,
- enable_gqa=groups > 1 and native_gqa,
- )
- else:
- causal = torch.ones(seq_len, seq_len, device=query.device, dtype=torch.bool).tril()
- mask = causal[None, None] & padding_mask.to(device=query.device, dtype=torch.bool)[:, None, None, :]
- attn_output = F.scaled_dot_product_attention(
- query,
- key,
- value,
- attn_mask=mask,
- dropout_p=0.0,
- is_causal=False,
- scale=self.scaling,
- )
-
- attn_output = attn_output.transpose(1, 2).reshape(batch_size, seq_len, -1)
- attn_output = attn_output * torch.sigmoid(gate)
- output, _ = self.o_proj(attn_output)
- return output
-
-
-class Qwen3_5MLP(nn.Module):
-
- def __init__(
- self,
- config: Qwen3_5TextArchConfig,
- quant_config: QuantizationConfig | None = None,
- prefix: str = "",
- ) -> None:
- super().__init__()
- if config.hidden_act != "silu":
- raise ValueError(f"Unsupported Qwen3.5 activation: {config.hidden_act}")
- self.gate_proj = ReplicatedLinear(config.hidden_size,
- config.intermediate_size,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.gate_proj")
- self.up_proj = ReplicatedLinear(config.hidden_size,
- config.intermediate_size,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.up_proj")
- self.down_proj = ReplicatedLinear(config.intermediate_size,
- config.hidden_size,
- bias=False,
- quant_config=quant_config,
- prefix=f"{prefix}.down_proj")
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- gate, _ = self.gate_proj(x)
- up, _ = self.up_proj(x)
- output, _ = self.down_proj(F.silu(gate) * up)
- return output
-
-
-class Qwen3_5DecoderLayer(nn.Module):
-
- def __init__(
- self,
- config: Qwen3_5TextConfig,
- layer_type: str,
- quant_config: QuantizationConfig | None = None,
- prefix: str = "",
- ) -> None:
- super().__init__()
- arch = config.arch_config
- self.layer_type = layer_type
- if layer_type == "linear_attention":
- self.linear_attn = Qwen3_5GatedDeltaNet(arch, quant_config=quant_config, prefix=f"{prefix}.linear_attn")
- else:
- self.self_attn = Qwen3_5Attention(config, quant_config=quant_config, prefix=f"{prefix}.self_attn")
- self.mlp = Qwen3_5MLP(arch, quant_config=quant_config, prefix=f"{prefix}.mlp")
- self.input_layernorm = Qwen3_5RMSNorm(arch.hidden_size, eps=arch.rms_norm_eps)
- self.post_attention_layernorm = Qwen3_5RMSNorm(arch.hidden_size, eps=arch.rms_norm_eps)
-
- def forward(
- self,
- hidden_states: torch.Tensor,
- position_embeddings: tuple[torch.Tensor, torch.Tensor],
- padding_mask: torch.Tensor | None = None,
- ) -> torch.Tensor:
- residual = hidden_states
- hidden_states = self.input_layernorm(hidden_states)
- if self.layer_type == "linear_attention":
- hidden_states = self.linear_attn(hidden_states, padding_mask)
- else:
- hidden_states = self.self_attn(hidden_states, position_embeddings, padding_mask)
- hidden_states = residual + hidden_states
-
- residual = hidden_states
- hidden_states = self.post_attention_layernorm(hidden_states)
- hidden_states = self.mlp(hidden_states)
- return residual + hidden_states
-
-
-class Qwen3_5TextModel(TextEncoder):
- """Qwen3.5 text tower used as a diffusion text encoder (e.g. PAN2).
-
- ``hidden_states`` follows the Transformers contract: entry 0 is the token
- embeddings, entry ``i`` is the output of layer ``i - 1``, and the last entry
- is the final-normed output (it replaces the raw last-layer output).
- """
-
- def __init__(self, config: Qwen3_5TextConfig) -> None:
- super().__init__(config)
- self.config = config
- arch = config.arch_config
- self.quant_config = getattr(config, "quant_config", None)
- self.layer_types = arch.resolved_layer_types()
-
- self.embed_tokens = VocabParallelEmbedding(
- arch.vocab_size,
- arch.hidden_size,
- org_num_embeddings=arch.vocab_size,
- quant_config=self.quant_config,
- )
- self.layers = nn.ModuleList([
- Qwen3_5DecoderLayer(
- config,
- layer_type,
- quant_config=self.quant_config,
- prefix=f"{config.prefix}.layers.{index}",
- ) for index, layer_type in enumerate(self.layer_types)
- ])
- self.norm = Qwen3_5RMSNorm(arch.hidden_size, eps=arch.rms_norm_eps)
- self.rotary_emb = Qwen3_5RotaryEmbedding(arch)
-
- def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
- return self.embed_tokens(input_ids)
-
- def forward(
- self,
- input_ids: torch.Tensor | None = None,
- position_ids: torch.Tensor | None = None,
- attention_mask: torch.Tensor | None = None,
- inputs_embeds: torch.Tensor | None = None,
- output_hidden_states: bool | None = None,
- **kwargs: Any,
- ) -> BaseEncoderOutput:
- output_hidden_states = (output_hidden_states
- if output_hidden_states is not None else self.config.arch_config.output_hidden_states)
- if (input_ids is None) == (inputs_embeds is None):
- raise ValueError("Specify exactly one of input_ids or inputs_embeds")
- if inputs_embeds is None:
- assert input_ids is not None
- if input_ids.ndim == 1:
- input_ids = input_ids.unsqueeze(0)
- inputs_embeds = self.get_input_embeddings(input_ids)
- hidden_states = inputs_embeds
- batch_size, seq_len = hidden_states.shape[:2]
-
- if position_ids is None:
- position_ids = torch.arange(seq_len, device=hidden_states.device).unsqueeze(0).expand(batch_size, -1)
- elif position_ids.ndim == 1:
- position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
- elif position_ids.ndim != 2:
- raise ValueError("Qwen3.5 text encoder expects text position_ids shaped [batch, seq]")
-
- # An all-ones mask is equivalent to no mask; only real padding takes the masked path.
- padding_mask = None
- if attention_mask is not None:
- if attention_mask.ndim != 2:
- raise ValueError("Qwen3.5 text encoder expects a 2D [batch, seq] padding mask")
- if not bool(attention_mask.to(torch.bool).all()):
- padding_mask = attention_mask.to(hidden_states.device)
-
- position_embeddings = self.rotary_emb(hidden_states, position_ids)
- all_hidden_states: tuple[torch.Tensor, ...] | None = () if output_hidden_states else None
- for layer in self.layers:
- if all_hidden_states is not None:
- all_hidden_states += (hidden_states, )
- hidden_states = layer(hidden_states, position_embeddings, padding_mask)
-
- hidden_states = self.norm(hidden_states)
- if all_hidden_states is not None:
- all_hidden_states += (hidden_states, )
-
- return BaseEncoderOutput(
- last_hidden_state=hidden_states,
- hidden_states=all_hidden_states,
- attention_mask=attention_mask,
- )
-
- def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
- params_dict = dict(self.named_parameters())
- loaded_params: set[str] = set()
- for name, loaded_weight in weights:
- if name.startswith(_SKIPPED_CHECKPOINT_PREFIXES) or "rotary_emb.inv_freq" in name:
- continue
- for checkpoint_prefix in _CHECKPOINT_PREFIXES:
- if name.startswith(checkpoint_prefix):
- name = name[len(checkpoint_prefix):]
- break
- if name not in params_dict:
- logger.debug("Skipping unexpected Qwen3.5 checkpoint tensor %s", name)
- continue
- param = params_dict[name]
- weight_loader = getattr(param, "weight_loader", default_weight_loader)
- weight_loader(param, loaded_weight)
- loaded_params.add(name)
- return loaded_params
-
-
-EntryClass = Qwen3_5TextModel
diff --git a/fastvideo/models/registry.py b/fastvideo/models/registry.py
index de6464b31..897e3f276 100644
--- a/fastvideo/models/registry.py
+++ b/fastvideo/models/registry.py
@@ -44,7 +44,6 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
"Kandinsky5Transformer3DModel": ("dits", "kandinsky5", "Kandinsky5Transformer3DModel"),
"Kandinsky6Transformer3DModel": ("dits", "kandinsky6", "Kandinsky6Transformer3DModel"),
"Flux2Transformer2DModel": ("dits", "flux_2", "Flux2Transformer2DModel"),
- "PAN2Transformer3DModel": ("dits", "pan2", "PAN2Transformer3DModel"),
}
_IMAGE_TO_VIDEO_DIT_MODELS = {
@@ -92,7 +91,6 @@ _TEXT_ENCODER_MODELS = {
"LTX2GemmaTextEncoderModel": ("encoders", "gemma", "LTX2GemmaTextEncoderModel"),
"Qwen3ForCausalLM": ("encoders", "qwen3", "Qwen3ForCausalLM"),
"Mistral3ForConditionalGeneration": ("encoders", "mistral3", "Mistral3ForConditionalGeneration"),
- "Qwen3_5TextModel": ("encoders", "qwen3_5", "Qwen3_5TextModel"),
}
_IMAGE_ENCODER_MODELS: dict[str, tuple] = {
diff --git a/fastvideo/pipelines/basic/pan2/__init__.py b/fastvideo/pipelines/basic/pan2/__init__.py
deleted file mode 100644
index 988131360..000000000
--- a/fastvideo/pipelines/basic/pan2/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
diff --git a/fastvideo/pipelines/basic/pan2/pan2_pipeline.py b/fastvideo/pipelines/basic/pan2/pan2_pipeline.py
deleted file mode 100644
index f3f7dc3f5..000000000
--- a/fastvideo/pipelines/basic/pan2/pan2_pipeline.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 video generation: text-to-video, and image-to-video from a first frame."""
-
-import torch
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.basic.pan2.stages import (
- PAN2ConditioningStage,
- PAN2DenoisingStage,
- PAN2InputValidationStage,
- PAN2TextEncodingStage,
- PAN2TimestepPreparationStage,
-)
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import DecodingStage, LatentPreparationStage
-
-
-class PAN2Pipeline(ComposedPipelineBase):
- """One stage chain serves both modes: a request with ``image_path`` (or a PIL ``pil_image``) runs image-to-video
- from that first frame, and one without runs text-to-video."""
-
- _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-
- def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
- self.add_stage(stage_name="input_validation_stage", stage=PAN2InputValidationStage())
- self.add_stage(stage_name="prompt_encoding_stage",
- stage=PAN2TextEncodingStage(
- text_encoders=[self.get_module("text_encoder")],
- tokenizers=[self.get_module("tokenizer")],
- ))
- self.add_stage(stage_name="timestep_preparation_stage",
- stage=PAN2TimestepPreparationStage(scheduler=self.get_module("scheduler")))
- self.add_stage(stage_name="latent_preparation_stage",
- stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
- transformer=self.get_module("transformer"),
- latent_dtype=torch.float32))
- self.add_stage(stage_name="conditioning_stage", stage=PAN2ConditioningStage(vae=self.get_module("vae")))
- self.add_stage(stage_name="denoising_stage",
- stage=PAN2DenoisingStage(transformer=self.get_module("transformer"),
- scheduler=self.get_module("scheduler"),
- pipeline=self))
- self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-
-class PAN2ModularPipeline(PAN2Pipeline):
- """The pipeline class PAN2 checkpoints declare in their ``modular_model_index.json``."""
-
-
-EntryClass = [PAN2Pipeline, PAN2ModularPipeline]
diff --git a/fastvideo/pipelines/basic/pan2/presets.py b/fastvideo/pipelines/basic/pan2/presets.py
deleted file mode 100644
index 10409a1d5..000000000
--- a/fastvideo/pipelines/basic/pan2/presets.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Inference presets for PAN2 text-to-video and image-to-video."""
-
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_DENOISE_STAGE = PresetStageSpec(
- name="denoise",
- kind="denoising",
- description="PAN2 flow-matching denoising",
- allowed_overrides=frozenset({"num_inference_steps", "guidance_scale"}),
-)
-
-# 704x1248, 313 frames (13 s at 24 fps), 50 steps, CFG 3. The preset sends no negative prompt (an empty one is encoded
-# as a single space); the reference sampler sends the release's structured negative caption, so pass it as the
-# request's ``negative_prompt`` to follow that recipe.
-_SHARED_DEFAULTS = {
- "height": 704,
- "width": 1248,
- "num_frames": 313,
- "fps": 24,
- "num_inference_steps": 50,
- "guidance_scale": 3.0,
- "negative_prompt": "",
-}
-
-PAN2_T2V = InferencePreset(
- name="pan2_t2v",
- version=1,
- model_family="pan2",
- description="PAN2 text-to-video",
- workload_type="t2v",
- stage_schemas=(_DENOISE_STAGE, ),
- defaults=dict(_SHARED_DEFAULTS),
-)
-
-PAN2_I2V = InferencePreset(
- name="pan2_i2v",
- version=1,
- model_family="pan2",
- description="PAN2 image-to-video from a first frame",
- workload_type="i2v",
- stage_schemas=(_DENOISE_STAGE, ),
- defaults=dict(_SHARED_DEFAULTS),
-)
-
-ALL_PRESETS = (PAN2_T2V, PAN2_I2V)
-
-__all__ = ["ALL_PRESETS", "PAN2_I2V", "PAN2_T2V"]
diff --git a/fastvideo/pipelines/basic/pan2/stages.py b/fastvideo/pipelines/basic/pan2/stages.py
deleted file mode 100644
index 6ef9001b2..000000000
--- a/fastvideo/pipelines/basic/pan2/stages.py
+++ /dev/null
@@ -1,210 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2-specific pipeline stages: prompt encoding, first-frame conditioning, sigma schedule and fp32 sampling."""
-
-from __future__ import annotations
-
-from collections.abc import Sequence
-from typing import Any
-
-import numpy as np
-import PIL.Image
-import torch
-import torchvision.transforms.functional as TF
-from torch.distributed.tensor import DTensor
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-from fastvideo.pipelines.stages.input_validation import InputValidationStage
-from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
-from fastvideo.pipelines.stages.timestep_preparation import TimestepPreparationStage
-from fastvideo.pipelines.stages.validators import StageValidators as V
-from fastvideo.pipelines.stages.validators import VerificationResult
-from fastvideo.utils import PRECISION_TO_TYPE
-
-_USER_HEADER = "<|im_start|>user\n"
-
-
-class PAN2InputValidationStage(InputValidationStage):
- """Rejects geometry the PAN2 VAE and patch embedding cannot represent."""
-
- def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
- batch = super().forward(batch, fastvideo_args)
- pipeline_config = fastvideo_args.pipeline_config
- vae_arch = pipeline_config.vae_config.arch_config
- spatial_multiple = vae_arch.spatial_compression_ratio * pipeline_config.dit_config.arch_config.patch_size[1]
- if batch.height % spatial_multiple != 0 or batch.width % spatial_multiple != 0:
- raise ValueError(f"PAN2 height and width must be divisible by {spatial_multiple}, "
- f"got {batch.height} and {batch.width}.")
- if (batch.num_frames - 1) % vae_arch.temporal_compression_ratio != 0:
- raise ValueError(f"PAN2 num_frames - 1 must be divisible by {vae_arch.temporal_compression_ratio}, "
- f"got {batch.num_frames}.")
- if batch.pil_image is not None and not isinstance(batch.pil_image, PIL.Image.Image):
- raise ValueError("PAN2 image-to-video expects the first frame as a PIL image or an image_path.")
- return batch
-
-
-class PAN2TextEncodingStage(TextEncodingStage):
- """Encodes a prompt with PAN2's chat template.
-
- PAN2 conditions on the tokens after the ``<|im_start|>user\\n`` header (the prompt and the assistant header),
- read from the text encoder's hidden states with its last ``text_encoder_skip_layers`` layers skipped.
- """
-
- @staticmethod
- def tokenize(tokenizer: Any, prompt: str, system_prompt: str, max_length: int) -> tuple[torch.Tensor, int]:
- """Token ids truncated to ``max_length`` tokens after the user header, and where that header ends."""
- # An empty prompt is encoded as a single space, as in training.
- messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": prompt or " "}]
- input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
- return_dict=True)["input_ids"]
- if isinstance(input_ids, torch.Tensor):
- input_ids = input_ids.tolist()
- if input_ids and isinstance(input_ids[0], list):
- input_ids = input_ids[0]
- header_ids = tokenizer(_USER_HEADER, add_special_tokens=False)["input_ids"]
- crop_start = next((i + len(header_ids)
- for i in range(len(input_ids)) if input_ids[i:i + len(header_ids)] == header_ids), None)
- if crop_start is None:
- raise ValueError(f"The PAN2 tokenizer's chat template has no {_USER_HEADER!r} header.")
- return torch.tensor([input_ids[:crop_start + max_length]], dtype=torch.long), crop_start
-
- @torch.no_grad()
- def encode_text(
- self,
- text: str | list[str],
- fastvideo_args: FastVideoArgs,
- encoder_index: int | list[int] | None = None,
- return_attention_mask: bool = False,
- return_type: str = "list",
- device: torch.device | str | None = None,
- dtype: torch.dtype | None = None,
- max_length: int | Sequence[int | None] | None = None,
- truncation: bool | None = None,
- padding: bool | str | None = None,
- ):
- pipeline_config = fastvideo_args.pipeline_config
- texts = [text] if isinstance(text, str) else list(text)
- if len(texts) != 1:
- raise ValueError(f"PAN2 encodes one prompt per request, got {len(texts)}.")
- if isinstance(max_length, Sequence):
- max_length = max_length[0]
- max_length = max_length or pipeline_config.text_max_sequence_length
-
- tokenizer = self.tokenizers[0]
- text_encoder = self.text_encoders[0]
- target_device = torch.device(device) if device is not None else get_local_torch_device()
- # A module parked on CPU by text_encoder_cpu_offload computes on the target device and goes back afterwards;
- # FSDP-wrapped (DTensor) encoders move their own shards.
- first_param = next(text_encoder.parameters(), None)
- input_device = first_param.device if first_param is not None else target_device
- moved_for_forward = False
- if (first_param is not None and not isinstance(first_param, DTensor)
- and input_device.type != target_device.type):
- text_encoder = text_encoder.to(target_device)
- input_device = target_device
- moved_for_forward = True
-
- input_ids, crop_start = self.tokenize(tokenizer, texts[0], pipeline_config.text_system_prompt, max_length)
- with set_forward_context(current_timestep=0, attn_metadata=None):
- outputs = text_encoder(input_ids=input_ids.to(input_device), output_hidden_states=True)
- prompt_embeds = outputs.hidden_states[-(pipeline_config.text_encoder_skip_layers + 1)][:, crop_start:]
- # fp32 is exact for the bf16 encoder output; the denoiser casts the embeddings to the DiT dtype.
- prompt_embeds = prompt_embeds.to(device=target_device, dtype=dtype or torch.float32, copy=True)
- attention_mask = torch.ones(prompt_embeds.shape[:2], dtype=torch.long, device=target_device)
- if moved_for_forward and fastvideo_args.text_encoder_cpu_offload:
- text_encoder.to("cpu")
- return self.return_embeds([prompt_embeds], [attention_mask], return_type, return_attention_mask, [0])
-
-
-class PAN2TimestepPreparationStage(TimestepPreparationStage):
- """PAN2 samples on uniformly spaced sigmas from 1 down to (excluding) 0; the scheduler applies the shift."""
-
- def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
- if batch.timesteps is None and batch.sigmas is None:
- batch.sigmas = np.linspace(1.0, 0.0, batch.num_inference_steps + 1)[:-1].tolist()
- return super().forward(batch, fastvideo_args)
-
-
-class PAN2ConditioningStage(PipelineStage):
- """Builds the conditioning latents and mask that PAN2 stacks after the noisy latents along channels.
-
- Text-to-video conditions on zeros. Image-to-video fills the first latent frame with the normalized VAE latents
- of the first frame, scaled to cover the output and center cropped, and sets that frame's mask channel to one.
- """
-
- def __init__(self, vae) -> None:
- super().__init__()
- self.vae = vae
-
- @staticmethod
- def preprocess_image(image: PIL.Image.Image, height: int, width: int) -> torch.Tensor:
- """``[1, 3, 1, H, W]`` pixels in ``[-1, 1]``: scale to cover the canvas, round the size, center crop."""
- image = image.convert("RGB")
- scale = max(width / image.width, height / image.height)
- image = image.resize((round(image.width * scale), round(image.height * scale)), resample=PIL.Image.LANCZOS)
- left, top = round((image.width - width) / 2), round((image.height - height) / 2)
- image = image.crop((left, top, left + width, top + height))
- return (TF.to_tensor(image) * 2.0 - 1.0)[None, :, None]
-
- def _encode_first_frame(self, image: PIL.Image.Image, batch: ForwardBatch,
- fastvideo_args: FastVideoArgs) -> torch.Tensor:
- device = get_local_torch_device()
- vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
- self.vae = self.vae.to(device)
- pixels = self.preprocess_image(image, batch.height, batch.width).to(device=device, dtype=vae_dtype)
- latents = self.vae.encode(pixels).mode().float()
- shift = self.vae.shift_factor.to(latents.device, latents.dtype)
- scale = self.vae.scaling_factor.to(latents.device, latents.dtype)
- if fastvideo_args.vae_cpu_offload:
- self.vae.to("cpu")
- return (latents - shift) * scale
-
- @torch.no_grad()
- def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
- latents = batch.latents
- batch_size, num_channels, num_frames, height, width = latents.shape
- condition = torch.zeros(batch_size,
- num_channels + 1,
- num_frames,
- height,
- width,
- device=latents.device,
- dtype=torch.float32)
- if batch.pil_image is not None:
- image_latents = self._encode_first_frame(batch.pil_image, batch, fastvideo_args)
- condition[:, :num_channels, :1] = image_latents.to(condition.device)
- condition[:, num_channels:, :1] = 1.0
- batch.image_latent = condition
- return batch
-
- def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
- result = VerificationResult()
- result.add_check("latents", batch.latents, [V.is_tensor, V.with_dims(5)])
- result.add_check("height", batch.height, V.positive_int)
- result.add_check("width", batch.width, V.positive_int)
- return result
-
- def verify_output(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
- result = VerificationResult()
- result.add_check("image_latent", batch.image_latent, [V.is_tensor, V.with_dims(5)])
- return result
-
-
-class PAN2DenoisingStage(DenoisingStage):
- """Keeps the latents in fp32 across the loop; they are cast to the DiT dtype for each forward.
-
- Image-to-video leaves the conditioning image's latent frame out of classifier-free guidance: that frame takes the
- positive prediction, as in the reference sampler.
- """
-
- def finish_guidance(self, noise_pred, noise_pred_text, batch, fastvideo_args) -> torch.Tensor:
- if batch.pil_image is not None and fastvideo_args.pipeline_config.i2v_skip_first_latent_cfg:
- noise_pred[:, :, :1] = noise_pred_text[:, :, :1]
- return noise_pred
-
- def prepare_scheduler_model_output(self, noise_pred, state) -> torch.Tensor:
- return noise_pred.float()
diff --git a/fastvideo/pipelines/stages/denoising.py b/fastvideo/pipelines/stages/denoising.py
index 7e20544a8..acdc478ae 100644
--- a/fastvideo/pipelines/stages/denoising.py
+++ b/fastvideo/pipelines/stages/denoising.py
@@ -472,9 +472,6 @@ class DenoisingStage(PipelineStage):
noise_pred_text,
guidance_rescale=batch.guidance_rescale,
)
-
- noise_pred = self.finish_guidance(noise_pred, noise_pred_text, batch, fastvideo_args)
- noise_pred = self.prepare_scheduler_model_output(noise_pred, state)
if scheduler_fp32:
# Diffusers-style: fp32 Euler update outside autocast avoids BF16 drift.
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
@@ -608,14 +605,6 @@ class DenoisingStage(PipelineStage):
def prepare_timestep(self, timestep, latent_model_input, batch, state) -> torch.Tensor:
return timestep.repeat(latent_model_input.shape[0])
- def finish_guidance(self, noise_pred, noise_pred_text, batch, fastvideo_args) -> torch.Tensor:
- """The guided prediction handed on to the scheduler; a model overrides it to keep part of the output out of CFG."""
- return noise_pred
-
- def prepare_scheduler_model_output(self, noise_pred, state) -> torch.Tensor:
- """The model output handed to the scheduler step, which returns the next latents in its dtype."""
- return noise_pred
-
def finish_step(self, latents, state) -> torch.Tensor:
return latents
diff --git a/fastvideo/pipelines/stages/latent_preparation.py b/fastvideo/pipelines/stages/latent_preparation.py
index ef0e94df8..e0116af9a 100644
--- a/fastvideo/pipelines/stages/latent_preparation.py
+++ b/fastvideo/pipelines/stages/latent_preparation.py
@@ -30,16 +30,11 @@ class LatentPreparationStage(PipelineStage):
denoised during the diffusion process.
"""
- def __init__(self,
- scheduler,
- transformer,
- use_btchw_layout: bool = False,
- latent_dtype: torch.dtype | None = None) -> None:
+ def __init__(self, scheduler, transformer, use_btchw_layout: bool = False) -> None:
super().__init__()
self.scheduler = scheduler
self.transformer = transformer
self.use_btchw_layout = use_btchw_layout
- self.latent_dtype = latent_dtype
def forward(
self,
@@ -93,7 +88,7 @@ class LatentPreparationStage(PipelineStage):
batch.prompt_embeds = [dummy_prompt]
batch.negative_prompt_embeds = []
batch.do_classifier_free_guidance = False
- dtype = self.latent_dtype or batch.prompt_embeds[0].dtype
+ dtype = batch.prompt_embeds[0].dtype
device = get_local_torch_device()
generator = batch.generator
latents = batch.latents
diff --git a/fastvideo/registry.py b/fastvideo/registry.py
index 38faade50..414183892 100644
--- a/fastvideo/registry.py
+++ b/fastvideo/registry.py
@@ -10,7 +10,6 @@ from __future__ import annotations
import dataclasses
import os
-import re
from collections.abc import Callable
from functools import lru_cache
from typing import TYPE_CHECKING, Any
@@ -43,7 +42,6 @@ from fastvideo.configs.pipelines.flux_2 import (
from fastvideo.configs.pipelines.matrixgame2 import MatrixGame2I2V480PConfig
from fastvideo.configs.pipelines.matrixgame3 import MatrixGame3I2V720PConfig
from fastvideo.configs.pipelines.minimax_h3 import MiniMaxH3PipelineConfig
-from fastvideo.configs.pipelines.pan2 import PAN2PipelineConfig
from fastvideo.configs.pipelines.mmaudio import (
MMAudioLarge44kV2PipelineConfig,
MMAudioLarge44kV2AConfig,
@@ -1180,22 +1178,6 @@ def _register_configs() -> None:
pipeline_cls_name="MiniMaxH3ModularPipeline",
)
- # PAN2: one pipeline runs text-to-video, and image-to-video when the request carries a first frame.
- def _is_pan2(name: str) -> bool:
- name = name.lower()
- return "pan2modularpipeline" in name or re.search(r"(^|[/_-])pan-?2([/_.-]|$)", name) is not None
-
- register_configs(
- sampling_param_cls=None,
- pipeline_config_cls=PAN2PipelineConfig,
- workload_types=(WorkloadType.T2V, WorkloadType.I2V),
- hf_model_paths=["IFM/PAN2"],
- model_detectors=[_is_pan2],
- model_family="pan2",
- default_preset="pan2_t2v",
- pipeline_cls_name="PAN2ModularPipeline",
- )
-
# SD3.5
register_configs(
sampling_param_cls=None,
@@ -1370,8 +1352,6 @@ def _register_presets() -> None:
ALL_PRESETS as MINIMAX_H3_PRESETS, )
from fastvideo.pipelines.basic.mmaudio.presets import (
ALL_PRESETS as MMAUDIO_PRESETS, )
- from fastvideo.pipelines.basic.pan2.presets import (
- ALL_PRESETS as PAN2_PRESETS, )
from fastvideo.pipelines.basic.sd35.presets import (
ALL_PRESETS as SD35_PRESETS, )
from fastvideo.pipelines.basic.stable_audio.presets import (
@@ -1404,7 +1384,6 @@ def _register_presets() -> None:
MATRIXGAME3_PRESETS,
MINIMAX_H3_PRESETS,
MMAUDIO_PRESETS,
- PAN2_PRESETS,
SD35_PRESETS,
STABLE_AUDIO_PRESETS,
WAN_PRESETS,
diff --git a/fastvideo/tests/entrypoints/test_serving_family_baselines.py b/fastvideo/tests/entrypoints/test_serving_family_baselines.py
index be67249f3..bb7cf8b24 100644
--- a/fastvideo/tests/entrypoints/test_serving_family_baselines.py
+++ b/fastvideo/tests/entrypoints/test_serving_family_baselines.py
@@ -25,7 +25,6 @@ from fastvideo.entrypoints.openai.request_adapter import (
prepare_reference_media,
)
from fastvideo.pipelines.basic.hunyuan15.presets import HUNYUAN15_I2V_480P_DISTILLED
-from fastvideo.pipelines.basic.pan2.presets import PAN2_T2V
ROOT = Path(__file__).resolve().parents[3]
@@ -38,7 +37,6 @@ BASELINES = {
"openai_kandinsky6_ti2va.yaml": "t2v",
"openai_kandinsky6_ti2va_distilled.yaml": "t2v",
"openai_ltx2_distilled.yaml": "t2v",
- "openai_pan2.yaml": "t2v",
"openai_flux1_dev.yaml": "t2i",
"openai_flux2_dev.yaml": "t2i",
"openai_flux2_klein.yaml": "t2i",
@@ -114,32 +112,6 @@ def test_hunyuan_i2v_480p_keeps_the_registered_checkpoint_semantics(tmp_path: Pa
assert adapted.output.output_path.endswith(".mp4")
-def test_pan2_serving_matches_its_preset_and_runs_image_to_video_from_a_reference(tmp_path: Path) -> None:
- config = _load("openai_pan2.yaml")
- sampling = config.default_request.sampling
- assert config.generator.model_path == "IFM/PAN2"
- assert config.generator.engine.offload.dit is False
- assert config.generator.engine.offload.dit_layerwise is False
- for field in ("height", "width", "num_frames", "fps", "num_inference_steps", "guidance_scale"):
- assert getattr(sampling, field) == PAN2_T2V.defaults[field], field
-
- image = tmp_path / "first-frame.png"
- image.write_bytes(b"not-decoded-at-admission")
- for reference in (None, {"image_url": str(image)}):
- request = VideoGenerationRequest(prompt="a red fox", image_reference=reference)
- adapted = build_generation_request(
- "pan2_test",
- request,
- _args(config, "t2v"),
- served_model_name=config.server.served_model_name or "",
- output_dir=str(tmp_path),
- default_request=config.default_request,
- )
- assert adapted.sampling.num_frames == 313
- assert adapted.inputs.image_path == (str(image) if reference else None)
- assert adapted.output.output_path.endswith(".mp4")
-
-
def test_stable_audio_seconds_override_produces_wav(tmp_path: Path) -> None:
config = _load("openai_stable_audio.yaml")
request = VideoGenerationRequest(prompt="rain on a metal roof", seconds=6)
diff --git a/fastvideo/tests/stages/test_pan2_first_latent_cfg.py b/fastvideo/tests/stages/test_pan2_first_latent_cfg.py
deleted file mode 100644
index e74314091..000000000
--- a/fastvideo/tests/stages/test_pan2_first_latent_cfg.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 image-to-video keeps the first latent frame out of classifier-free guidance; no weights or CUDA needed."""
-
-import pytest
-import torch
-
-from fastvideo.tests.stages._denoising_fixtures import (
- NullProgressBar, TinyDenoiser, TinyScheduler, _patch_denoising_module, _tiny_args, _tiny_batch,
-)
-
-
-def _one_step(monkeypatch, env_overrides, *, image_to_video: bool, skip_first_latent: bool, guidance_rescale=0.0,
- frame_values=(0.0, 0.0, 0.0)):
- _patch_denoising_module(monkeypatch, env_overrides, None)
- from fastvideo.pipelines.basic.pan2.stages import PAN2DenoisingStage
-
- stage = PAN2DenoisingStage(TinyDenoiser(), TinyScheduler())
- stage.progress_bar = lambda iterable=None, total=None: NullProgressBar()
- batch = _tiny_batch()
- batch.latents = torch.tensor(frame_values).reshape(1, 1, 3, 1, 1)
- batch.raw_latent_shape = (1, 1, 3, 1, 1)
- batch.timesteps = torch.tensor([0.0])
- batch.num_inference_steps = 1
- batch.pil_image = object() if image_to_video else None
- batch.guidance_rescale = guidance_rescale
- args = _tiny_args()
- args.pipeline_config.i2v_skip_first_latent_cfg = skip_first_latent
- args.pipeline_config.scheduler_step_in_fp32 = True
- return stage.forward(batch, args).latents.flatten().tolist()
-
-
-def test_image_to_video_takes_the_positive_prediction_for_the_first_latent_frame(monkeypatch, env_overrides):
- # With a zero latent and timestep the cond prediction is 0.5 and the uncond -0.5, so CFG 2 guides to 1.5.
- latents = _one_step(monkeypatch, env_overrides, image_to_video=True, skip_first_latent=True)
-
- assert latents == pytest.approx([-0.005, -0.015, -0.015])
-
-
-@pytest.mark.parametrize("image_to_video,skip_first_latent", [(True, False), (False, True)])
-def test_every_frame_is_guided_without_a_conditioning_image_or_with_the_skip_off(monkeypatch, env_overrides,
- image_to_video, skip_first_latent):
- latents = _one_step(monkeypatch, env_overrides, image_to_video=image_to_video, skip_first_latent=skip_first_latent)
-
- assert latents == pytest.approx([-0.015, -0.015, -0.015])
-
-
-def test_first_latent_frame_keeps_the_positive_prediction_after_guidance_rescale(monkeypatch, env_overrides):
- # Frames 0, 1, 2 predict 0.5, 0.7, 0.9 (cond) and 1.5, 1.7, 1.9 (CFG 2), so the rescale factor is 1 and only the
- # first frame's replacement by 0.5 remains. Replacing before the rescale would scale it by std(cond)/std(mixed).
- latents = _one_step(monkeypatch,
- env_overrides,
- image_to_video=True,
- skip_first_latent=True,
- guidance_rescale=1.0,
- frame_values=(0.0, 1.0, 2.0))
-
- assert latents == pytest.approx([-0.005, 0.983, 1.981])
diff --git a/fastvideo/tests/stages/test_pan2_latent_preparation.py b/fastvideo/tests/stages/test_pan2_latent_preparation.py
deleted file mode 100644
index 80c09dd4d..000000000
--- a/fastvideo/tests/stages/test_pan2_latent_preparation.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 draws its initial noise in fp32 whatever dtype the prompt embeddings arrive in; no weights or CUDA needed."""
-
-import types
-
-import pytest
-import torch
-
-from fastvideo.pipelines.basic.pan2 import pan2_pipeline
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages import LatentPreparationStage, latent_preparation
-
-
-@pytest.fixture()
-def cpu_device(monkeypatch):
- monkeypatch.setattr(latent_preparation, "get_local_torch_device", lambda: torch.device("cpu"))
-
-
-def _args() -> types.SimpleNamespace:
- arch = types.SimpleNamespace(temporal_compression_ratio=4, spatial_compression_ratio=16)
- return types.SimpleNamespace(pipeline_config=types.SimpleNamespace(vae_config=types.SimpleNamespace(arch_config=arch)))
-
-
-def _noise(stage, embeds_dtype: torch.dtype) -> torch.Tensor:
- batch = ForwardBatch(data_type="video",
- prompt="a cat",
- prompt_embeds=[torch.zeros(1, 4, 8, dtype=embeds_dtype)],
- generator=torch.Generator("cpu").manual_seed(7),
- height=32,
- width=32,
- num_frames=5)
- return stage.forward(batch, _args()).latents
-
-
-def test_noise_is_fp32_and_independent_of_the_prompt_embedding_dtype(cpu_device):
- transformer = types.SimpleNamespace(num_channels_latents=3)
- stage = LatentPreparationStage(scheduler=types.SimpleNamespace(),
- transformer=transformer,
- latent_dtype=torch.float32)
-
- from_bf16, from_fp32 = _noise(stage, torch.bfloat16), _noise(stage, torch.float32)
-
- assert from_bf16.dtype == torch.float32
- assert torch.equal(from_bf16, from_fp32)
- assert _noise(LatentPreparationStage(types.SimpleNamespace(), transformer), torch.bfloat16).dtype == torch.bfloat16
-
-
-def test_pan2_pipeline_requests_fp32_latents(monkeypatch):
- seen = {}
-
- class _Stage:
-
- def __init__(self, *args, **kwargs):
- seen.setdefault("kwargs", []).append(kwargs)
-
- for name in ("PAN2InputValidationStage", "PAN2TextEncodingStage", "PAN2TimestepPreparationStage",
- "PAN2ConditioningStage", "PAN2DenoisingStage", "DecodingStage"):
- monkeypatch.setattr(pan2_pipeline, name, _Stage)
- monkeypatch.setattr(pan2_pipeline, "LatentPreparationStage", _Stage)
- pipeline = pan2_pipeline.PAN2Pipeline.__new__(pan2_pipeline.PAN2Pipeline)
- stages = {}
- pipeline.add_stage = lambda stage_name, stage: stages.__setitem__(stage_name, stage)
- pipeline.get_module = lambda name: name
-
- pipeline.create_pipeline_stages(None)
-
- assert any(kwargs.get("latent_dtype") is torch.float32 for kwargs in seen["kwargs"])
diff --git a/tests/local_tests/encoders/test_pan2_qwen3_5_parity.py b/tests/local_tests/encoders/test_pan2_qwen3_5_parity.py
deleted file mode 100644
index 35a835d82..000000000
--- a/tests/local_tests/encoders/test_pan2_qwen3_5_parity.py
+++ /dev/null
@@ -1,339 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Qwen3.5 text-encoder parity for PAN2 against Transformers' ``Qwen3_5TextModel``.
-
-Coverage scope: both. The checkpoint test loads the public tiny PAN2 text
-encoder through FastVideo's production ``TextEncoderLoader`` and feeds both
-models the exact PAN2 chat-templated token ids. The random-config tests use a
-larger hybrid stack (GQA, partial RoPE, more value than key heads in the Gated
-DeltaNet, sequences that cross several 64-token delta-rule chunks) and also
-cover bf16 and padded batches.
-
-Everything runs on CPU. Set ``PAN2_TINY_MODEL_DIR`` to a local copy of
-``wuqing157/tiny-pan2-modular-pipe``; otherwise the HF cache is used and the
-checkpoint test skips when it is absent.
-"""
-
-from __future__ import annotations
-
-import copy
-import os
-import socket
-from pathlib import Path
-from types import SimpleNamespace
-from typing import Any
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-from fastvideo.configs.models.encoders.qwen3_5 import Qwen3_5TextConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.models.loader.utils import set_default_torch_dtype
-
-PARITY_SCOPE = "both"
-TINY_REPO_ID = "wuqing157/tiny-pan2-modular-pipe"
-PAN2_SYSTEM_MESSAGE = "You are a helpful assistant. Generate the video with the following instructions."
-PAN2_TEXT_ENCODER_LAYERS_TO_SKIP = 2
-PAN2_PROMPTS = (
- "A red fox crosses fresh snow at sunrise.",
- "A slow dolly shot glides through a rain-soaked neon market at night. Steam rises from food stalls while "
- "vendors in bright raincoats shout over the hum of generators. Reflections of pink, teal and amber signs ripple "
- "across puddles as a cyclist weaves between crowds, her umbrella catching droplets that sparkle in the light. "
- "In the background, a tram rattles past an old clock tower, its windows glowing warm yellow. The camera rises to "
- "reveal rooftops crowded with antennas and laundry lines, then tilts toward a thunderstorm rolling in over the "
- "harbor, lightning briefly silhouetting cranes and container ships. Cinematic lighting, shallow depth of field, "
- "35mm film grain, realistic motion blur.",
-)
-FP32_TOLERANCE = {"atol": 1e-5, "rtol": 1e-5}
-BF16_TOLERANCE = {"atol": 2e-2, "rtol": 2e-2}
-
-
-def _hf_qwen3_5() -> tuple[Any, Any]:
- module = pytest.importorskip("transformers.models.qwen3_5", reason="transformers without Qwen3.5 support")
- return module.Qwen3_5TextConfig, module.Qwen3_5TextModel
-
-
-def _tiny_model_dir() -> Path | None:
- local = os.getenv("PAN2_TINY_MODEL_DIR")
- if local:
- return Path(local)
- try:
- from huggingface_hub import snapshot_download
-
- return Path(snapshot_download(TINY_REPO_ID, local_files_only=True))
- except Exception:
- return None
-
-
-def _free_port() -> int:
- with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
- sock.bind(("127.0.0.1", 0))
- return sock.getsockname()[1]
-
-
-@pytest.fixture(scope="module")
-def cpu_single_rank():
- """Single-rank CPU runtime; the loader would otherwise target ``mps`` on CPU-only Linux hosts."""
- import fastvideo.distributed.parallel_state as parallel_state
- import fastvideo.models.loader.component_loader as component_loader
- from fastvideo.distributed import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
-
- with pytest.MonkeyPatch.context() as patch:
- patch.setattr(parallel_state, "get_local_torch_device", lambda: torch.device("cpu"))
- patch.setattr(component_loader, "get_local_torch_device", lambda: torch.device("cpu"))
- for name, value in (("MASTER_ADDR", "127.0.0.1"), ("MASTER_PORT", str(_free_port())), ("RANK", "0"),
- ("WORLD_SIZE", "1"), ("LOCAL_RANK", "0")):
- patch.setenv(name, value)
- maybe_init_distributed_environment_and_model_parallel(1, 1)
- yield
- cleanup_dist_env_and_memory()
-
-
-def _loader_args(config: Qwen3_5TextConfig, precision: str = "fp32") -> SimpleNamespace:
- return SimpleNamespace(
- pipeline_config=SimpleNamespace(text_encoder_configs=(config, ), text_encoder_precisions=(precision, )),
- text_encoder_cpu_offload=False,
- override_text_encoder_quant=None,
- override_text_encoder_safetensors=None,
- pin_cpu_memory=False,
- disable_offload_on_unified_memory=lambda device_id, offload_flag=None: False,
- )
-
-
-def _pan2_input_ids(tokenizer: Any, prompt: str) -> tuple[torch.Tensor, int]:
- """Tokenize exactly like PAN2's ``encode_prompt`` and return ids plus the user-header crop."""
- messages = [{"role": "system", "content": PAN2_SYSTEM_MESSAGE}, {"role": "user", "content": prompt or " "}]
- input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
- return_dict=True).input_ids
- header_ids = tokenizer("<|im_start|>user\n", add_special_tokens=False).input_ids
- crop_start = next(
- (i + len(header_ids) for i in range(len(input_ids)) if input_ids[i:i + len(header_ids)] == header_ids),
- None,
- )
- assert crop_start is not None, "PAN2 chat template has no user header"
- return torch.tensor([input_ids]), crop_start
-
-
-def _run_both(reference: torch.nn.Module, native: torch.nn.Module, **inputs: Any) -> tuple[Any, Any]:
- with torch.inference_mode():
- expected = reference(**inputs, output_hidden_states=True)
- with set_forward_context(current_timestep=0, attn_metadata=None):
- actual = native(**inputs, output_hidden_states=True)
- return expected, actual
-
-
-def _assert_hidden_states_close(
- expected: tuple[torch.Tensor, ...],
- actual: tuple[torch.Tensor, ...] | None,
- label: str,
- tolerance: dict[str, float],
- valid: torch.Tensor | None = None,
-) -> float:
- assert actual is not None, f"{label}: native encoder returned no hidden_states"
- assert len(actual) == len(expected), f"{label}: {len(actual)} hidden states, reference has {len(expected)}"
- worst = 0.0
- for index, (reference_state, native_state) in enumerate(zip(expected, actual, strict=True)):
- assert native_state.shape == reference_state.shape, f"{label} hidden_states[{index}] shape"
- assert torch.isfinite(native_state).all(), f"{label} hidden_states[{index}] has non-finite values"
- if valid is not None:
- reference_state = reference_state[valid]
- native_state = native_state[valid]
- drift = (native_state.float() - reference_state.float()).abs().max().item()
- worst = max(worst, drift)
- print(f"{label} hidden_states[{index}]: max_abs={drift:.3e}", flush=True)
- assert_close(native_state, reference_state, **tolerance, msg=lambda m: f"{label} hidden_states[{index}]: {m}")
- return worst
-
-
-def test_pan2_qwen3_5_arch_config_matches_transformers_rope_and_layer_types() -> None:
- """Config.json fields resolve to the same RoPE settings and layer schedule as Transformers."""
- hf_config_cls, _ = _hf_qwen3_5()
- cases = (
- {},
- {
- "rope_theta": 1_000_000.0
- },
- {
- "partial_rotary_factor": 0.5,
- "rope_parameters": {
- "rope_type": "default",
- "rope_theta": 5_000_000.0
- }
- },
- {
- "partial_rotary_factor": 0.5,
- "rope_parameters": {
- "rope_type": "default",
- "rope_theta": 5_000_000.0,
- "partial_rotary_factor": 0.25
- }
- },
- {
- "num_hidden_layers": 6,
- "full_attention_interval": 3
- },
- {
- "num_hidden_layers": 3,
- "layer_types": ["mamba", "attention", "linear_attention"]
- },
- )
- for case in cases:
- reference = hf_config_cls.from_dict(dict(case))
- config = Qwen3_5TextConfig()
- config.update_model_arch(dict(case))
- rope = config.arch_config.resolved_rope_parameters()
- for key in ("rope_type", "rope_theta", "partial_rotary_factor"):
- assert rope[key] == reference.rope_parameters[key], f"{case}: {key}"
- assert config.arch_config.resolved_layer_types() == list(reference.layer_types), case
-
-
-def test_pan2_qwen3_5_tiny_checkpoint_loader_parity(cpu_single_rank) -> None:
- """Production-loader encoder matches every HF hidden state on real PAN2 prompts (fp32 CPU)."""
- _, hf_model_cls = _hf_qwen3_5()
- model_dir = _tiny_model_dir()
- if model_dir is None or not (model_dir / "text_encoder" / "config.json").is_file():
- pytest.skip(f"{TINY_REPO_ID} is not available locally; set PAN2_TINY_MODEL_DIR")
- if not (model_dir / "tokenizer").is_dir():
- pytest.skip(f"{TINY_REPO_ID} tokenizer is not available locally")
- from transformers import AutoTokenizer
-
- from fastvideo.models.encoders.qwen3_5 import Qwen3_5TextModel
- from fastvideo.models.loader.component_loader import TextEncoderLoader
-
- text_encoder_dir = model_dir / "text_encoder"
- tokenizer = AutoTokenizer.from_pretrained(model_dir / "tokenizer", local_files_only=True)
- reference, loading_info = hf_model_cls.from_pretrained(
- text_encoder_dir,
- local_files_only=True,
- dtype=torch.float32,
- output_loading_info=True,
- )
- load_errors = {
- name: loading_info.get(name)
- for name in ("missing_keys", "unexpected_keys", "mismatched_keys", "error_msgs") if loading_info.get(name)
- }
- assert not load_errors, f"Reference Qwen3.5 checkpoint did not load strictly: {load_errors}"
- reference = reference.eval()
-
- native = TextEncoderLoader().load(str(text_encoder_dir), _loader_args(Qwen3_5TextConfig()))
- assert isinstance(native, Qwen3_5TextModel)
- assert native.layer_types == list(reference.config.layer_types)
- assert next(native.parameters()).dtype == torch.float32
- assert_close(native.rotary_emb.inv_freq, reference.rotary_emb.inv_freq.float(), atol=0.0, rtol=0.0)
-
- for prompt_index, prompt in enumerate(PAN2_PROMPTS):
- input_ids, crop_start = _pan2_input_ids(tokenizer, prompt)
- label = f"tiny prompt{prompt_index} (L={input_ids.shape[1]})"
- expected, actual = _run_both(reference, native, input_ids=input_ids)
- worst = _assert_hidden_states_close(expected.hidden_states, actual.hidden_states, label, FP32_TOLERANCE)
- assert_close(actual.last_hidden_state, expected.last_hidden_state, **FP32_TOLERANCE)
-
- skip = PAN2_TEXT_ENCODER_LAYERS_TO_SKIP + 1
- reference_embeds = expected.hidden_states[-skip][:, crop_start:]
- native_embeds = actual.hidden_states[-skip][:, crop_start:]
- assert_close(native_embeds, reference_embeds, **FP32_TOLERANCE)
- print(
- f"{label} PAN2 prompt_embeds hidden_states[-{skip}][:, {crop_start}:] "
- f"max_abs={(native_embeds - reference_embeds).abs().max().item():.3e} all_layers_max_abs={worst:.3e}",
- flush=True,
- )
-
-
-@pytest.fixture(scope="module")
-def random_pair(cpu_single_rank):
- """A randomly initialized 8-layer hybrid reference and the native model loaded from its state dict."""
- hf_config_cls, hf_model_cls = _hf_qwen3_5()
- from fastvideo.models.encoders.qwen3_5 import Qwen3_5TextModel
-
- torch.manual_seed(0)
- hf_config = hf_config_cls(
- vocab_size=1024,
- hidden_size=128,
- intermediate_size=256,
- num_hidden_layers=8,
- num_attention_heads=4,
- num_key_value_heads=2,
- head_dim=32,
- linear_conv_kernel_dim=4,
- linear_key_head_dim=16,
- linear_value_head_dim=24,
- linear_num_key_heads=2,
- linear_num_value_heads=4,
- layer_types=[
- "linear_attention",
- "linear_attention",
- "full_attention",
- "linear_attention",
- "full_attention",
- "linear_attention",
- "linear_attention",
- "full_attention",
- ],
- rope_parameters={
- "rope_type": "default",
- "rope_theta": 1_000_000.0,
- "partial_rotary_factor": 0.25
- },
- initializer_range=0.08,
- )
- reference = hf_model_cls(hf_config).eval()
- with torch.no_grad():
- # Move the zero-centered norms, gates and decay parameters off their
- # init values so every term contributes.
- for name, parameter in reference.named_parameters():
- if name.endswith("norm.weight") or name.endswith(("A_log", "dt_bias")):
- parameter.add_(0.2 * torch.randn_like(parameter))
-
- config = Qwen3_5TextConfig()
- config.update_model_arch(hf_config.to_dict())
- with set_default_torch_dtype(torch.float32):
- native = Qwen3_5TextModel(config).eval()
- loaded = native.load_weights(reference.state_dict().items())
- expected_names = {name for name, _ in native.named_parameters()}
- assert loaded == expected_names, f"unloaded native parameters: {sorted(expected_names - loaded)}"
- assert set(reference.state_dict()) == expected_names
- return reference, native
-
-
-def test_pan2_qwen3_5_random_config_fp32_parity(random_pair) -> None:
- """Multi-chunk delta rule (L=301), GQA and partial RoPE match HF in fp32."""
- reference, native = random_pair
- generator = torch.Generator().manual_seed(1)
- input_ids = torch.randint(0, reference.config.vocab_size, (2, 301), generator=generator)
- expected, actual = _run_both(reference, native, input_ids=input_ids)
- worst = _assert_hidden_states_close(expected.hidden_states, actual.hidden_states, "random fp32", FP32_TOLERANCE)
- print(f"random fp32 all_layers_max_abs={worst:.3e}", flush=True)
- # Each layer must move the residual stream, or the comparison is vacuous.
- for index in range(1, len(expected.hidden_states) - 1):
- change = (expected.hidden_states[index] - expected.hidden_states[index - 1]).abs().max().item()
- assert change > 0.1, f"layer {index - 1} barely changes the hidden state ({change:.3e})"
-
-
-def test_pan2_qwen3_5_random_config_padding_parity(random_pair) -> None:
- """Right- and left-padded rows match HF on every non-padded position."""
- reference, native = random_pair
- generator = torch.Generator().manual_seed(2)
- input_ids = torch.randint(0, reference.config.vocab_size, (2, 200), generator=generator)
- attention_mask = torch.ones_like(input_ids)
- attention_mask[0, 150:] = 0
- attention_mask[1, :37] = 0
- expected, actual = _run_both(reference, native, input_ids=input_ids, attention_mask=attention_mask)
- worst = _assert_hidden_states_close(
- expected.hidden_states,
- actual.hidden_states,
- "random padded",
- FP32_TOLERANCE,
- valid=attention_mask.bool(),
- )
- print(f"random padded all_layers_max_abs={worst:.3e}", flush=True)
-
-
-def test_pan2_qwen3_5_random_config_bf16_parity(random_pair) -> None:
- """Half-precision dtype handling (fp32 norms, gates and delta rule) follows HF."""
- reference, native = (copy.deepcopy(model).to(torch.bfloat16) for model in random_pair)
- generator = torch.Generator().manual_seed(3)
- input_ids = torch.randint(0, reference.config.vocab_size, (1, 173), generator=generator)
- expected, actual = _run_both(reference, native, input_ids=input_ids)
- assert actual.hidden_states is not None and actual.hidden_states[-1].dtype == torch.bfloat16
- worst = _assert_hidden_states_close(expected.hidden_states, actual.hidden_states, "random bf16", BF16_TOLERANCE)
- print(f"random bf16 all_layers_max_abs={worst:.3e}", flush=True)
diff --git a/tests/local_tests/pan2/PORT_STATUS.md b/tests/local_tests/pan2/PORT_STATUS.md
deleted file mode 100644
index 7c5b53a3b..000000000
--- a/tests/local_tests/pan2/PORT_STATUS.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# PAN2 Port Status
-
-## Summary
-- model_family: pan2
-- workload_types: T2V, I2V (one pipeline; I2V when the request carries a first frame)
-- official_ref: vllm-project/vllm-omni#8602 at `e0c4c73158a5e4f44eda8eaa8879f7fd9cedbd74` (the diffusers integration is private)
-- official_ref_dir: none; rebuilt in `tests/local_tests/pan2/reference.py`
-- hf_weights_path: `IFM/PAN2` (not public); tests use `wuqing157/tiny-pan2-modular-pipe@3ff3130b`
-- local_weights_dir: HF cache, or `PAN2_TINY_MODEL_DIR`
-- source_layout: Modular Diffusers (`modular_model_index.json`), diffusers component subfolders
-- local_tests_readme: `tests/local_tests/pan2/README.md`
-
-## Current Phase
-- phase: 11 (post-parity review and handoff)
-- status: in_progress (GPU and real-weight validation pending, see I001 and I002)
-- owner: orchestrator
-- last_updated: 2026-10-20
-
-## Component Matrix
-| Component | Type | Reuse/Port | Official Definition | Official Instantiation | FastVideo Target | Prototype | Conversion | Parity | Open Issues |
-|---|---|---|---|---|---|---|---|---|---|
-| transformer | DiT | port | vLLM-Omni `pan2_transformer.py` (diffusers `PAN2Transformer3DModel`) | `transformer/config.json` | `fastvideo/models/dits/pan2.py`, `fastvideo/configs/models/dits/pan2.py` | done | load-time key mapping (Q/K/V fused) | non_skip_pass | I002 |
-| text_encoder | encoder | port | transformers `Qwen3_5TextModel` | `text_encoder/config.json` | `fastvideo/models/encoders/qwen3_5.py`, `fastvideo/configs/models/encoders/qwen3_5.py` | done | passthrough | non_skip_pass | I002 |
-| tokenizer | tokenizer | reuse | `Qwen2Tokenizer` | `tokenizer/` | AutoTokenizer | n/a | passthrough | covered by pipeline parity | — |
-| vae | VAE | reuse | diffusers `AutoencoderKLWan` (Wan2.2, z_dim 48) | `vae/config.json` | `fastvideo/models/wan/vae.py` | done | passthrough | non_skip_pass | — |
-| scheduler | scheduler | reuse | diffusers `FlowMatchEulerDiscreteScheduler` | `scheduler/scheduler_config.json` (shift 7) | `fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py` | done | passthrough | non_skip_pass | — |
-
-## Conversion State
-- conversion_script: none; the transformer maps diffusers keys at load time (`PAN2ArchConfig.param_names_mapping`)
-- converted_weights_dir: n/a
-- source_layout: diffusers
-- strict_load_status: tiny checkpoint loads strictly (no missing or unexpected keys)
-- passthrough_components: text_encoder, tokenizer, vae, scheduler
-- retry_history: none
-
-## Parity Commands
-| Scope | Command | Last Result | Notes |
-|---|---|---|---|
-| DiT | `pytest tests/local_tests/transformers/test_pan2_transformer_parity.py` | 13 passed (CPU) | SP=2/4 vs one rank over gloo, T2V/I2V, one/two pad tokens; max abs 7.7e-6 |
-| Text encoder | `pytest tests/local_tests/encoders/test_pan2_qwen3_5_parity.py` | 5 passed (CPU) | exact |
-| VAE | `pytest tests/local_tests/vaes/test_pan2_vae_parity.py` | 9 passed (CPU) | exact |
-| Scheduler | `pytest tests/local_tests/schedulers/test_pan2_scheduler_parity.py` | 9 passed (CPU) | exact |
-| Pipeline | `pytest tests/local_tests/pipelines/test_pan2_pipeline_parity.py` | 3 passed (CPU) | T2V and I2V latents exact |
-
-## Open Questions
-| ID | Question | Owner | Needed By Phase | Status | Resolution |
-|---|---|---|---|---|---|
-| Q001 | Which reference, with the diffusers integration private? | orchestrator | 2 | resolved | vLLM-Omni #8602, rebuilt from diffusers/transformers blocks in `reference.py` |
-| Q002 | Port the `pan2-guardrail` prompt/video guardrails? | orchestrator | 7 | resolved | Not ported: FastVideo has no guardrail stage. |
-| Q003 | Port Cache-DiT `quality` and CFG-parallel? | orchestrator | 7 | resolved | Not ported: vLLM-Omni serving features. Ulysses SP is supported through `DistributedAttention`. |
-
-## Issues And Blockers
-| ID | Phase | Component | Severity | Issue | Evidence | Owner | Status | Resolution |
-|---|---|---|---|---|---|---|---|---|
-| I001 | 9-10 | all | medium | No real-weight parity or SSIM regression | `IFM/PAN2` is not public | orchestrator | open | Add `fastvideo/tests/ssim/test_pan2_similarity.py` once weights are public. |
-| I002 | 9 | transformer, text_encoder | medium | No GPU parity run yet (bf16 + FlashAttention, NCCL SP, 9B encoder speed) | Local parity tests ran on a CPU login node | orchestrator | open | Run the same tests on a GPU node. |
-| I003 | 7 | pipeline | low | The shared denoiser casts latents to the model-output dtype in the scheduler step; PAN2 samples on fp32 latents | pipeline parity fails (8e-2) without the fix | orchestrator | resolved | `DenoisingStage.prepare_scheduler_model_output` hook; `PAN2DenoisingStage` returns `noise_pred.float()`. |
-
-## Escape Hatches
-| ID | Phase | Decision Type | Question | Recommended Option | Status | Resolution |
-|---|---|---|---|---|---|---|
-
-## Decisions
-| Date | Decision | Rationale | Impact |
-|---|---|---|---|
-| 2026-10-15 | Native Qwen3.5 encoder with a PyTorch chunked gated delta rule | No `fla`/`causal_conv1d` dependency; encoder runs once per prompt | Slower than fused kernels for long prompts |
-| 2026-10-15 | Fuse the per-stream Q/K/V projections at load time | One GEMM per stream instead of three | Mapping in `PAN2ArchConfig.param_names_mapping` |
-| 2026-10-15 | Text embeddings kept in fp32 after encoding | The shared latent stage draws noise in the embedding dtype; PAN2 uses fp32 noise and latents | Denoiser casts embeddings to the DiT dtype (`cast_prompt_embeds_to_dit_dtype`) |
-
-## Handoff Notes
-- The remaining work is I001 and I002. Both need hardware or weights, not code changes.
diff --git a/tests/local_tests/pan2/README.md b/tests/local_tests/pan2/README.md
deleted file mode 100644
index 2fac8e256..000000000
--- a/tests/local_tests/pan2/README.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# PAN2 Local Tests
-
-Runbook for validating the PAN2 port (text-to-video and first-frame image-to-video). These tests run manually and are
-not part of the default CI suite.
-
-## Reference
-
-PAN2's diffusers integration (`PAN2Transformer3DModel`, `PAN2ModularPipeline`) is not public yet, and the full
-`IFM/PAN2` weights are not released. The reference is the vLLM-Omni port,
-[vllm-project/vllm-omni#8602](https://github.com/vllm-project/vllm-omni/pull/8602) (head `e0c4c731`), which reports
-parity with the diffusers implementation. `reference.py` rebuilds it from diffusers and transformers building blocks:
-the DiT from `AdaLayerNormZero`, `AdaLayerNormContinuous`, `FeedForward` (gelu-approximate and swiglu), `Timesteps`,
-`TimestepEmbedding` and the diffusers RoPE helpers, using the checkpoint's module names so the checkpoint loads
-strictly. The pipeline is `reference_generate`, a step-by-step copy of the vLLM-Omni `PAN2Pipeline.forward`
-using `Qwen3_5TextModel`, `AutoencoderKLWan` and `FlowMatchEulerDiscreteScheduler`.
-
-The checkpoint is the public random-weight [`wuqing157/tiny-pan2-modular-pipe`](https://huggingface.co/wuqing157/tiny-pan2-modular-pipe)
-(revision `3ff3130b`), the same one vLLM-Omni tests against. It has two DiT blocks, a four-layer Qwen3.5 text model
-(three Gated DeltaNet layers and one full-attention layer) and a small Wan2.2-layout VAE.
-
-## Setup
-
-```bash
-huggingface-cli download wuqing157/tiny-pan2-modular-pipe --revision 3ff3130b4527f61aaab7bde01465c9884dc84cdd
-# or point the tests at a local copy:
-export PAN2_TINY_MODEL_DIR=/path/to/tiny-pan2-modular-pipe
-```
-
-The tests need `diffusers` with `AutoencoderKLWan` and `transformers` with `Qwen3_5TextModel` (5.x). They run on CPU
-when no GPU is present. On a GPU-less host, `import fastvideo` also needs the `fastvideo_kernel` stub described in
-`fastvideo/tests/AGENTS.md`.
-
-## Tests
-
-| Area | Command | Result (CPU, fp32 unless noted) |
-|---|---|---|
-| DiT: tiny checkpoint, strict load + forward | `pytest tests/local_tests/transformers/test_pan2_transformer_parity.py -k tiny_checkpoint` | max abs 6.0e-7 |
-| DiT: random weights, batch 1 and 2 | `pytest tests/local_tests/transformers/test_pan2_transformer_parity.py -k random_weights` | max abs 0 and 1.9e-5 (outputs ~16) |
-| DiT: Ulysses SP=2/4 vs one rank, T2V/I2V with 15/18 tokens | `pytest tests/local_tests/transformers/test_pan2_transformer_parity.py -k sequence_parallel` | max abs 4.8e-6 / 7.7e-6 (gloo on CPU; NCCL on 2/4 GPUs) |
-| Qwen3.5 text encoder | `pytest tests/local_tests/encoders/test_pan2_qwen3_5_parity.py` | all hidden states exact (fp32, bf16, padded batch) |
-| Wan2.2 VAE encode/decode + normalization | `pytest tests/local_tests/vaes/test_pan2_vae_parity.py` | exact; normalization 2.4e-7 |
-| Flow-match scheduler, PAN2 sigmas + shift | `pytest tests/local_tests/schedulers/test_pan2_scheduler_parity.py` | exact |
-| Pipeline, T2V and I2V (bf16 DiT) | `pytest tests/local_tests/pipelines/test_pan2_pipeline_parity.py` | final latents and video exact |
-
-The pipeline test builds the FastVideo pipeline through the registry and component loaders and compares the final
-latents (relative L2 < 2e-2, cosine > 0.9995) and the decoded video (PSNR > 40 dB) with `reference_generate`. To
-confirm the threshold catches real deviations, three injected changes were each run against it: conditioning on
-`hidden_states[-2]`, letting the latents fall to bf16 between steps, and shifting the first-frame crop by one pixel.
-Each moved the latents by about 8e-2 relative L2.
-
-Upstream revision `e0c4c731` always masks video padding, fixing Ulysses=4 drift when padding was left unmasked.
-FastVideo already excludes this padding by passing the original video length to `DistributedAttention`, which trims
-it before RoPE and joint attention, then pads the output for the reverse all-to-all. The distributed test covers one
-pad token (15 tokens at SP=2/4), two pad tokens (18 at SP=4, matching 704x1248x313 modulo 4), and an unpadded
-control (18 at SP=2), with both zero T2V and first-frame I2V conditioning. No masking toggle is required.
-The pipeline math and checkpoint layout are unchanged between upstream revisions `59dd8c9a` and `e0c4c731`.
-
-On 2026-10-20, all 39 PAN2 local tests passed on CPU (Torch 2.12.0, Transformers 5.15.0, Diffusers 0.39.0).
-The broader API/contract suite had 499 passes and two failures also reproduced on clean `main` at `cdda1b753`:
-the schema inventory lacks MiniMax-H3's `audio_scheduler_shift`/`video_scheduler_shift`, and
-`test_load_kandinsky5.py` reads `FASTVIDEO_ATTENTION_BACKEND` outside the environment registry.
-Importing the PAN2 tests and running their fixtures preserves both an unset and a pre-existing attention backend.
-
-## Not covered
-
-- Real-weight parity and an SSIM regression: `IFM/PAN2` is not public.
-- GPU runs: everything above ran on CPU. The bf16 GPU path, FlashAttention, and SP over NCCL still need a GPU node.
diff --git a/tests/local_tests/pan2/reference.py b/tests/local_tests/pan2/reference.py
deleted file mode 100644
index 41a207580..000000000
--- a/tests/local_tests/pan2/reference.py
+++ /dev/null
@@ -1,302 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 reference implementation for local parity tests.
-
-The diffusers ``PAN2Transformer3DModel`` and ``PAN2ModularPipeline`` are not public yet. The reference here is
-assembled from diffusers' and transformers' own building blocks (``AdaLayerNormZero``, ``AdaLayerNormContinuous``,
-``FeedForward`` with ``gelu-approximate``/``swiglu``, ``Timesteps``/``TimestepEmbedding``,
-``get_1d_rotary_pos_embed``/``apply_rotary_emb``, ``Qwen3_5TextModel``, ``AutoencoderKLWan``,
-``FlowMatchEulerDiscreteScheduler``) and follows the vLLM-Omni PAN2 port (vllm-project/vllm-omni#8602), which was
-validated against the diffusers implementation (upstream revision ``e0c4c731``). Module names are the checkpoint's,
-so checkpoints load strictly. Its unconditional SP padding mask is equivalent to unpadded reference attention.
-"""
-
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from diffusers.models.attention import FeedForward
-from diffusers.models.embeddings import TimestepEmbedding, Timesteps, apply_rotary_emb, get_1d_rotary_pos_embed
-from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm
-
-TINY_REPO_ID = "wuqing157/tiny-pan2-modular-pipe"
-TINY_REVISION = "3ff3130b4527f61aaab7bde01465c9884dc84cdd"
-SYSTEM_PROMPT = "You are a helpful assistant. Generate the video with the following instructions."
-TEXT_ENCODER_LAYERS_TO_SKIP = 2
-MAX_SEQUENCE_LENGTH = 1536
-
-
-def tiny_model_dir() -> Path | None:
- """``PAN2_TINY_MODEL_DIR``, else the pinned tiny checkpoint in the local HF cache."""
- local = os.getenv("PAN2_TINY_MODEL_DIR")
- if local:
- return Path(local)
- try:
- from huggingface_hub import snapshot_download
-
- return Path(snapshot_download(TINY_REPO_ID, revision=TINY_REVISION, local_files_only=True))
- except Exception:
- return None
-
-
-def _sdpa(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
- out = F.scaled_dot_product_attention(query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2))
- return out.transpose(1, 2).flatten(2)
-
-
-class _RefRefinerAttention(nn.Module):
-
- def __init__(self, dim: int, heads: int, dim_head: int, eps: float):
- super().__init__()
- self.heads = heads
- self.to_q = nn.Linear(dim, heads * dim_head, bias=False)
- self.to_k = nn.Linear(dim, heads * dim_head, bias=False)
- self.to_v = nn.Linear(dim, heads * dim_head, bias=False)
- self.norm_q = RMSNorm(dim_head, eps=eps)
- self.norm_k = RMSNorm(dim_head, eps=eps)
- self.to_out = nn.ModuleList([nn.Linear(heads * dim_head, dim, bias=False)])
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- query = self.norm_q(self.to_q(x).unflatten(-1, (self.heads, -1)))
- key = self.norm_k(self.to_k(x).unflatten(-1, (self.heads, -1)))
- value = self.to_v(x).unflatten(-1, (self.heads, -1))
- return self.to_out[0](_sdpa(query, key, value))
-
-
-class _RefRefinerBlock(nn.Module):
-
- def __init__(self, dim: int, heads: int, dim_head: int, mlp_ratio: float, eps: float):
- super().__init__()
- self.norm1 = RMSNorm(dim, eps=eps)
- self.attn = _RefRefinerAttention(dim, heads, dim_head, eps)
- self.norm2 = RMSNorm(dim, eps=eps)
- self.ff = FeedForward(dim, inner_dim=int(dim * mlp_ratio), activation_fn="swiglu", bias=False)
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = x + self.attn(self.norm1(x))
- return x + self.ff(self.norm2(x))
-
-
-class _RefJointAttention(nn.Module):
-
- def __init__(self, dim: int, heads: int, dim_head: int, eps: float):
- super().__init__()
- self.heads = heads
- self.to_q = nn.Linear(dim, heads * dim_head)
- self.to_k = nn.Linear(dim, heads * dim_head)
- self.to_v = nn.Linear(dim, heads * dim_head)
- self.norm_q = RMSNorm(dim_head, eps=eps)
- self.norm_k = RMSNorm(dim_head, eps=eps)
- self.to_out = nn.ModuleList([nn.Linear(heads * dim_head, dim)])
- self.add_q_proj = nn.Linear(dim, heads * dim_head)
- self.add_k_proj = nn.Linear(dim, heads * dim_head)
- self.add_v_proj = nn.Linear(dim, heads * dim_head)
- self.norm_added_q = RMSNorm(dim_head, eps=eps)
- self.norm_added_k = RMSNorm(dim_head, eps=eps)
- self.to_add_out = nn.Linear(heads * dim_head, dim)
-
- def forward(self, x: torch.Tensor, context: torch.Tensor,
- rope: tuple[torch.Tensor, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor]:
- query = self.norm_q(self.to_q(x).unflatten(-1, (self.heads, -1)))
- key = self.norm_k(self.to_k(x).unflatten(-1, (self.heads, -1)))
- value = self.to_v(x).unflatten(-1, (self.heads, -1))
- # Interleaved (GPT-J) rotary embedding on the video tokens only.
- query = apply_rotary_emb(query, rope, use_real=True, use_real_unbind_dim=-1, sequence_dim=1)
- key = apply_rotary_emb(key, rope, use_real=True, use_real_unbind_dim=-1, sequence_dim=1)
-
- context_query = self.norm_added_q(self.add_q_proj(context).unflatten(-1, (self.heads, -1)))
- context_key = self.norm_added_k(self.add_k_proj(context).unflatten(-1, (self.heads, -1)))
- context_value = self.add_v_proj(context).unflatten(-1, (self.heads, -1))
-
- out = _sdpa(torch.cat([query, context_query], dim=1), torch.cat([key, context_key], dim=1),
- torch.cat([value, context_value], dim=1))
- out, context_out = out.split([x.shape[1], context.shape[1]], dim=1)
- return self.to_out[0](out), self.to_add_out(context_out)
-
-
-class _RefBlock(nn.Module):
-
- def __init__(self, dim: int, heads: int, dim_head: int, mlp_ratio: float, context_mlp_ratio: float):
- super().__init__()
- eps = 1e-6
- self.norm1 = AdaLayerNormZero(dim)
- self.norm1_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
- self.scale_shift_table_context = nn.Parameter(torch.zeros(6 * dim))
- self.attn = _RefJointAttention(dim, heads, dim_head, eps)
- self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
- self.ff = FeedForward(dim, inner_dim=int(dim * mlp_ratio), activation_fn="gelu-approximate")
- self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
- self.ff_context = FeedForward(dim, inner_dim=int(dim * context_mlp_ratio), activation_fn="gelu-approximate")
-
- def forward(self, x, context, temb, rope):
- norm_x, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(x, emb=temb)
- c_shift_msa, c_scale_msa, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = (
- self.scale_shift_table_context.chunk(6, dim=-1))
- norm_context = self.norm1_context(context) * (1 + c_scale_msa) + c_shift_msa
- attn_out, context_attn_out = self.attn(norm_x, norm_context, rope)
- x = x + attn_out * gate_msa.unsqueeze(1)
- context = context + context_attn_out * c_gate_msa
- norm_x = self.norm2(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
- norm_context = self.norm2_context(context) * (1 + c_scale_mlp) + c_shift_mlp
- x = x + self.ff(norm_x) * gate_mlp.unsqueeze(1)
- context = context + self.ff_context(norm_context) * c_gate_mlp
- return x, context
-
-
-class ReferencePAN2Transformer(nn.Module):
-
- def __init__(self, patch_size=(1, 2, 2), in_channels=97, out_channels=48, num_attention_heads=32,
- attention_head_dim=128, num_layers=44, num_refiner_layers=4, mlp_ratio=8.0, context_mlp_ratio=4.0,
- refiner_mlp_ratio=4.0, text_embed_dim=4096, rope_axes_dim=(16, 56, 56), rope_theta=256.0,
- rope_temporal_scale=16 / 24, **_):
- super().__init__()
- self.patch_size = tuple(patch_size)
- self.rope_axes_dim = tuple(rope_axes_dim)
- self.rope_theta = rope_theta
- self.rope_temporal_scale = rope_temporal_scale
- inner_dim = num_attention_heads * attention_head_dim
- self.x_embedder = nn.Conv3d(in_channels, inner_dim, kernel_size=self.patch_size, stride=self.patch_size)
- self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
- self.time_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=inner_dim)
- self.context_embedder = nn.Sequential(RMSNorm(text_embed_dim, eps=1e-5), nn.Linear(text_embed_dim, inner_dim))
- self.context_refiner = nn.ModuleList([
- _RefRefinerBlock(inner_dim, num_attention_heads, attention_head_dim, refiner_mlp_ratio, eps=1e-5)
- for _ in range(num_refiner_layers)
- ])
- self.context_type_embedding = nn.Parameter(torch.zeros(inner_dim))
- self.transformer_blocks = nn.ModuleList([
- _RefBlock(inner_dim, num_attention_heads, attention_head_dim, mlp_ratio, context_mlp_ratio)
- for _ in range(num_layers)
- ])
- self.norm_out = AdaLayerNormContinuous(inner_dim, inner_dim, elementwise_affine=False, eps=1e-6)
- self.proj_out = nn.Linear(inner_dim, self.patch_size[0] * self.patch_size[1] * self.patch_size[2] * out_channels)
-
- def _rope(self, num_frames: int, height: int, width: int, device) -> tuple[torch.Tensor, torch.Tensor]:
- axes = [torch.arange(size, device=device, dtype=torch.float32) for size in (num_frames, height, width)]
- grid = torch.stack(torch.meshgrid(*axes, indexing="ij"), dim=0)
- grid[0] = grid[0] * self.rope_temporal_scale
- cos, sin = zip(*[
- get_1d_rotary_pos_embed(dim, grid[i].reshape(-1), self.rope_theta, use_real=True,
- repeat_interleave_real=True) for i, dim in enumerate(self.rope_axes_dim)
- ])
- return torch.cat(cos, dim=1).float(), torch.cat(sin, dim=1).float()
-
- def forward(self, hidden_states, timestep, encoder_hidden_states):
- batch_size, _, num_frames, height, width = hidden_states.shape
- p_t, p_h, p_w = self.patch_size
- t, h, w = num_frames // p_t, height // p_h, width // p_w
- rope = self._rope(t, h, w, hidden_states.device)
- x = self.x_embedder(hidden_states).flatten(2).transpose(1, 2)
- temb = self.time_embedder(self.time_proj(timestep).to(x.dtype))
- context = self.context_embedder(encoder_hidden_states)
- for block in self.context_refiner:
- context = block(context)
- context = context + self.context_type_embedding
- for block in self.transformer_blocks:
- x, context = block(x, context, temb, rope)
- x = self.proj_out(self.norm_out(x, temb))
- x = x.reshape(batch_size, t, h, w, -1, p_t, p_h, p_w).permute(0, 4, 1, 5, 2, 6, 3, 7)
- return x.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-
-
-def reference_encode_prompt(tokenizer, text_encoder, prompt: str, device) -> torch.Tensor:
- messages = [{"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": prompt or " "}]
- input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
- return_dict=True).input_ids
- header_ids = tokenizer("<|im_start|>user\n", add_special_tokens=False).input_ids
- crop_start = next(i + len(header_ids) for i in range(len(input_ids))
- if input_ids[i:i + len(header_ids)] == header_ids)
- input_ids = torch.tensor([input_ids[:crop_start + MAX_SEQUENCE_LENGTH]], device=device)
- hidden_states = text_encoder(input_ids=input_ids, output_hidden_states=True).hidden_states
- return hidden_states[-(TEXT_ENCODER_LAYERS_TO_SKIP + 1)][:, crop_start:]
-
-
-def reference_condition_latents(vae, latents: torch.Tensor, image: PIL.Image.Image | None, height: int,
- width: int) -> torch.Tensor:
- batch_size, num_channels, num_frames, latent_height, latent_width = latents.shape
- condition = latents.new_zeros(batch_size, num_channels + 1, num_frames, latent_height, latent_width)
- if image is None:
- return condition
- from diffusers.video_processor import VideoProcessor
-
- image = image.convert("RGB")
- scale = max(width / image.width, height / image.height)
- image = image.resize((round(image.width * scale), round(image.height * scale)), resample=PIL.Image.LANCZOS)
- left, top = round((image.width - width) / 2), round((image.height - height) / 2)
- image = image.crop((left, top, left + width, top + height))
- pixels = VideoProcessor(vae_scale_factor=16).preprocess(image, height=height, width=width)
- pixels = pixels.unsqueeze(2).to(device=latents.device, dtype=vae.dtype)
- image_latents = vae.encode(pixels).latent_dist.mode()
- mean = torch.tensor(vae.config.latents_mean).view(1, -1, 1, 1, 1).to(image_latents)
- std = torch.tensor(vae.config.latents_std).view(1, -1, 1, 1, 1).to(image_latents)
- condition[:, :num_channels, :1] = (image_latents - mean) / std
- condition[:, num_channels:, :1] = 1.0
- return condition
-
-
-@torch.no_grad()
-def reference_generate(components: dict, *, prompt: str, negative_prompt: str, image: PIL.Image.Image | None,
- height: int, width: int, num_frames: int, num_inference_steps: int, guidance_scale: float,
- seed: int, dit_dtype: torch.dtype, device="cpu",
- skip_first_latent_cfg: bool = True) -> tuple[torch.Tensor, torch.Tensor]:
- """Final latents and the decoded video in ``[0, 1]``, following the vLLM-Omni pipeline step by step.
-
- ``skip_first_latent_cfg`` is the reference sampler's ``--i2v_skip_first_latent_cfg``: with an image, the first
- latent frame takes the positive prediction instead of the guided one.
- """
- from diffusers.utils.torch_utils import randn_tensor
-
- tokenizer, text_encoder = components["tokenizer"], components["text_encoder"]
- transformer, vae, scheduler = components["transformer"], components["vae"], components["scheduler"]
- do_cfg = guidance_scale > 1.0
- prompt_embeds = reference_encode_prompt(tokenizer, text_encoder, prompt, device).to(dit_dtype)
- negative_embeds = (reference_encode_prompt(tokenizer, text_encoder, negative_prompt, device).to(dit_dtype)
- if do_cfg else None)
-
- shape = (1, vae.config.z_dim, (num_frames - 1) // vae.config.scale_factor_temporal + 1,
- height // vae.config.scale_factor_spatial, width // vae.config.scale_factor_spatial)
- generator = [torch.Generator("cpu").manual_seed(seed)]
- latents = randn_tensor(shape, generator=generator, device=torch.device(device), dtype=torch.float32)
- condition = reference_condition_latents(vae, latents, image, height, width).to(dit_dtype)
-
- scheduler.set_timesteps(sigmas=np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1], device=device)
- for t in scheduler.timesteps:
- model_input = torch.cat([latents.to(dit_dtype), condition], dim=1)
- timestep = t.expand(model_input.shape[0])
- noise_pred = transformer(model_input, timestep, prompt_embeds)
- if do_cfg:
- noise_uncond = transformer(model_input, timestep, negative_embeds)
- guided = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
- if image is not None and skip_first_latent_cfg:
- guided[:, :, :1] = noise_pred[:, :, :1]
- noise_pred = guided
- latents = scheduler.step(noise_pred.float(), t, latents, return_dict=False)[0]
-
- mean = torch.tensor(vae.config.latents_mean).view(1, -1, 1, 1, 1).to(latents)
- std = torch.tensor(vae.config.latents_std).view(1, -1, 1, 1, 1).to(latents)
- video = vae.decode((latents * std + mean).to(vae.dtype), return_dict=False)[0]
- return latents, (video.float() / 2 + 0.5).clamp(0, 1)
-
-
-def load_reference_components(model_dir: Path, dit_dtype: torch.dtype) -> dict:
- import json
-
- from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler
- from safetensors.torch import load_file
- from transformers import AutoTokenizer, Qwen3_5TextModel
-
- transformer_dir = model_dir / "transformer"
- transformer = ReferencePAN2Transformer(**json.loads((transformer_dir / "config.json").read_text()))
- transformer.load_state_dict(load_file(str(transformer_dir / "diffusion_pytorch_model.safetensors")), strict=True)
- return {
- "tokenizer": AutoTokenizer.from_pretrained(model_dir / "tokenizer"),
- "text_encoder": Qwen3_5TextModel.from_pretrained(model_dir / "text_encoder", torch_dtype=torch.float32).eval(),
- "transformer": transformer.to(dit_dtype).eval(),
- "vae": AutoencoderKLWan.from_pretrained(model_dir / "vae", torch_dtype=torch.float32).eval(),
- "scheduler": FlowMatchEulerDiscreteScheduler.from_pretrained(model_dir / "scheduler"),
- }
diff --git a/tests/local_tests/pipelines/test_pan2_pipeline_parity.py b/tests/local_tests/pipelines/test_pan2_pipeline_parity.py
deleted file mode 100644
index daf608fb2..000000000
--- a/tests/local_tests/pipelines/test_pan2_pipeline_parity.py
+++ /dev/null
@@ -1,159 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 end-to-end pipeline parity on the public tiny checkpoint.
-
-The FastVideo pipeline (built through the real registry and component loaders) is compared against
-``tests/local_tests/pan2/reference.py::reference_generate``, which follows the vLLM-Omni ``PAN2Pipeline.forward``
-step by step with transformers/diffusers components: prompt template and crop, fp32 noise, first-frame
-conditioning, the uniform sigma schedule with shift 7, sequential CFG, fp32 Euler steps and the VAE decode.
-
-Both sides run the DiT in bf16 (FastVideo's denoiser always feeds it bf16), so the comparison allows bf16 rounding.
-Runs on CPU when no GPU is present. Set ``PAN2_TINY_MODEL_DIR`` to a local copy of the tiny checkpoint.
-"""
-
-from __future__ import annotations
-
-import socket
-from contextlib import ExitStack
-
-import PIL.Image
-import pytest
-import torch
-
-from fastvideo import envs
-from tests.local_tests.pan2.reference import load_reference_components, reference_generate, tiny_model_dir
-
-HEIGHT, WIDTH, NUM_FRAMES = 64, 96, 9
-NUM_INFERENCE_STEPS = 3
-# A large scale makes the guided steps visible through the tiny random-weight VAE.
-GUIDANCE_SCALE = 30.0
-SEED = 42
-PROMPT = "a cat walks on the grass"
-NEGATIVE_PROMPT = "blurry"
-# Not the output aspect ratio, so the cover-resize and center crop are exercised.
-CONDITION_IMAGE = PIL.Image.radial_gradient("L").resize((120, 64)).convert("RGB")
-
-
-def _free_port() -> int:
- with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
- sock.bind(("127.0.0.1", 0))
- return sock.getsockname()[1]
-
-
-@pytest.fixture(scope="module")
-def pan2_runtime():
- model_dir = tiny_model_dir()
- if model_dir is None or not (model_dir / "modular_model_index.json").is_file():
- pytest.skip("wuqing157/tiny-pan2-modular-pipe is not available locally; set PAN2_TINY_MODEL_DIR")
- from fastvideo.configs.pipelines.pan2 import PAN2PipelineConfig
- from fastvideo.distributed import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
- import fastvideo.distributed.parallel_state as parallel_state
- from fastvideo.fastvideo_args import FastVideoArgs
- from fastvideo.pipelines import build_pipeline
-
- with ExitStack() as stack:
- stack.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override("TORCH_SDPA"))
- monkeypatch = stack.enter_context(pytest.MonkeyPatch.context())
- if not torch.cuda.is_available():
- # Reach modules that imported the device helper by name on a GPU-less Linux host.
- def _cpu_device() -> torch.device:
- return torch.device("cpu")
-
- monkeypatch.setattr(parallel_state.get_local_torch_device, "__code__", _cpu_device.__code__)
- for name, value in (("MASTER_ADDR", "127.0.0.1"), ("MASTER_PORT", str(_free_port())), ("RANK", "0"),
- ("WORLD_SIZE", "1"), ("LOCAL_RANK", "0")):
- stack.enter_context(envs.override_external(name, value))
- stack.callback(cleanup_dist_env_and_memory)
- maybe_init_distributed_environment_and_model_parallel(1, 1)
-
- pipeline_config = PAN2PipelineConfig(dit_precision="bf16",
- vae_precision="fp32",
- text_encoder_precisions=("fp32", ))
- args = FastVideoArgs(model_path=str(model_dir),
- num_gpus=1,
- pipeline_config=pipeline_config,
- dit_cpu_offload=False,
- dit_layerwise_offload=False,
- text_encoder_cpu_offload=False,
- vae_cpu_offload=False,
- use_fsdp_inference=False)
- pipeline = build_pipeline(args)
- device = parallel_state.get_local_torch_device()
- yield pipeline, args, model_dir, device
-
-
-def _fastvideo_generate(pipeline, args, image, guidance_scale: float = GUIDANCE_SCALE):
- from fastvideo.pipelines import ForwardBatch
-
- batch = ForwardBatch(data_type="video",
- prompt=PROMPT,
- negative_prompt=NEGATIVE_PROMPT,
- seed=SEED,
- height=HEIGHT,
- width=WIDTH,
- num_frames=NUM_FRAMES,
- num_inference_steps=NUM_INFERENCE_STEPS,
- guidance_scale=guidance_scale,
- pil_image=image)
- with torch.inference_mode():
- batch = pipeline.forward(batch, args)
- return batch.latents.float().cpu(), batch.output.float().cpu()
-
-
-def _psnr(a: torch.Tensor, b: torch.Tensor) -> float:
- mse = (a - b).pow(2).mean().clamp_min(1e-12)
- return float(10 * torch.log10(1.0 / mse))
-
-
-@pytest.mark.parametrize(
- ("image", "skip_first_latent_cfg"),
- [(None, True), (CONDITION_IMAGE, True), (CONDITION_IMAGE, False)],
- ids=["t2v", "i2v", "i2v-guided-first-frame"],
-)
-def test_pan2_pipeline_matches_reference(pan2_runtime, monkeypatch, image, skip_first_latent_cfg):
- pipeline, args, model_dir, device = pan2_runtime
- monkeypatch.setattr(args.pipeline_config, "i2v_skip_first_latent_cfg", skip_first_latent_cfg)
- latents, video = _fastvideo_generate(pipeline, args, image)
-
- components = load_reference_components(model_dir, torch.bfloat16)
- components = {
- name: module.to(device) if isinstance(module, torch.nn.Module) else module
- for name, module in components.items()
- }
- ref_latents, ref_video = reference_generate(components,
- prompt=PROMPT,
- negative_prompt=NEGATIVE_PROMPT,
- image=image,
- height=HEIGHT,
- width=WIDTH,
- num_frames=NUM_FRAMES,
- num_inference_steps=NUM_INFERENCE_STEPS,
- guidance_scale=GUIDANCE_SCALE,
- seed=SEED,
- dit_dtype=torch.bfloat16,
- device=device,
- skip_first_latent_cfg=skip_first_latent_cfg)
- ref_latents, ref_video = ref_latents.float().cpu(), ref_video.float().cpu()
-
- assert latents.dtype == torch.float32
- assert latents.shape == ref_latents.shape == (1, 48, 3, HEIGHT // 16, WIDTH // 16)
- assert video.shape == ref_video.shape == (1, 3, NUM_FRAMES, HEIGHT, WIDTH)
- latent_rel = float((latents - ref_latents).norm() / ref_latents.norm())
- cosine = float(torch.nn.functional.cosine_similarity(latents.flatten(), ref_latents.flatten(), dim=0))
- psnr = _psnr(video, ref_video)
- print(f"latents rel_l2={latent_rel:.3e} cosine={cosine:.6f}; video psnr={psnr:.1f} dB")
- assert latent_rel < 2e-2
- assert cosine > 0.9995
- assert psnr > 40.0
-
-
-def test_pan2_image_conditioning_and_guidance_change_every_frame(pan2_runtime):
- pipeline, args, _, _ = pan2_runtime
- _, t2v = _fastvideo_generate(pipeline, args, None)
- _, i2v = _fastvideo_generate(pipeline, args, CONDITION_IMAGE)
- _, unguided = _fastvideo_generate(pipeline, args, None, guidance_scale=1.0)
-
- def per_frame_max_diff(a, b):
- return (a - b).abs().amax(dim=(0, 1, 3, 4))
-
- assert per_frame_max_diff(t2v, i2v).max() > 1e-3
- assert per_frame_max_diff(t2v, unguided).min() > 1e-3
diff --git a/tests/local_tests/schedulers/test_pan2_scheduler_parity.py b/tests/local_tests/schedulers/test_pan2_scheduler_parity.py
deleted file mode 100644
index 3fcdb7497..000000000
--- a/tests/local_tests/schedulers/test_pan2_scheduler_parity.py
+++ /dev/null
@@ -1,200 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 flow-matching Euler schedule on FastVideo's FlowMatchEulerDiscreteScheduler.
-
-Coverage scope: both. PAN2 loads Diffusers' ``FlowMatchEulerDiscreteScheduler``
-from ``/scheduler`` (shift 7.0, no dynamic shifting), optionally
-overrides the shift with ``flow_shift``, calls
-``set_timesteps(sigmas=np.linspace(1.0, 0.0, N + 1)[:-1])`` and steps float32
-latents. The FastVideo side is loaded by the production ``SchedulerLoader`` and
-its schedule is built by the stock ``TimestepPreparationStage`` from
-``batch.sigmas``. Timesteps, sigmas, and every Euler update must be bit-exact.
-
-The default asset is the tiny PAN2 checkpoint (config only), so this runs on a
-CPU-only host::
-
- pytest tests/local_tests/schedulers/test_pan2_scheduler_parity.py -v -s
-
-Set ``PAN2_MODEL_PATH`` to a local PAN2 root containing ``scheduler/`` to use
-another checkpoint.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-from pathlib import Path
-from types import SimpleNamespace
-
-import numpy as np
-import pytest
-import torch
-from torch.testing import assert_close
-
-try:
- from diffusers import FlowMatchEulerDiscreteScheduler as DiffusersFlowMatchEulerDiscreteScheduler
-except ImportError: # pragma: no cover - environment dependent
- pytest.skip("diffusers with FlowMatchEulerDiscreteScheduler is required", allow_module_level=True)
-
-from fastvideo.models.loader.component_loader import SchedulerLoader
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
- FlowMatchEulerDiscreteScheduler as FastVideoFlowMatchEulerDiscreteScheduler, )
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.timestep_preparation import TimestepPreparationStage
-
-PARITY_SCOPE = "both"
-TINY_PAN2_REPO = "wuqing157/tiny-pan2-modular-pipe"
-TINY_PAN2_REVISION = "3ff3130b4527f61aaab7bde01465c9884dc84cdd"
-MODEL_PATH_ENV = "PAN2_MODEL_PATH"
-LATENT_SHAPE = (1, 48, 3, 4, 6)
-
-
-def _pan2_root(subfolder: str) -> Path:
- """Resolve a PAN2 root that contains ``subfolder``; skip when unavailable."""
- override = os.getenv(MODEL_PATH_ENV)
- if override:
- root = Path(override)
- else:
- try:
- from huggingface_hub import snapshot_download
- except ImportError:
- pytest.skip("huggingface_hub is required to resolve the tiny PAN2 checkpoint")
- root = None
- last_error: Exception | None = None
- for local_files_only in (True, False):
- try:
- root = Path(
- snapshot_download(
- TINY_PAN2_REPO,
- revision=TINY_PAN2_REVISION,
- allow_patterns=[f"{subfolder}/*"],
- local_files_only=local_files_only,
- ))
- break
- except Exception as exc: # noqa: BLE001 - offline/missing cache must skip.
- last_error = exc
- if root is None:
- pytest.skip(f"Tiny PAN2 checkpoint unavailable: {last_error}")
- if not (root / subfolder).is_dir():
- pytest.skip(f"PAN2 {subfolder}/ missing under {root}")
- return root
-
-
-@pytest.fixture(scope="module")
-def pan2_root() -> Path:
- root = _pan2_root("scheduler")
- if not (root / "scheduler" / "scheduler_config.json").is_file():
- pytest.skip(f"PAN2 scheduler config missing under {root}")
- return root
-
-
-@pytest.fixture
-def cpu_runtime(monkeypatch: pytest.MonkeyPatch) -> None:
- """Pin FastVideo's device helper to CPU; it reports ``mps`` on CPU-only Linux."""
- monkeypatch.setattr("fastvideo.pipelines.stages.timestep_preparation.get_local_torch_device",
- lambda: torch.device("cpu"))
-
-
-def _pan2_sigmas(num_steps: int) -> np.ndarray:
- """PAN2's pre-shift grid: uniform from 1 down to, but excluding, 0."""
- return np.linspace(1.0, 0.0, num_steps + 1)[:-1]
-
-
-def _reference(root: Path, flow_shift: float | None = None) -> DiffusersFlowMatchEulerDiscreteScheduler:
- """Mirror the upstream PAN2 pipeline's scheduler construction."""
- scheduler = DiffusersFlowMatchEulerDiscreteScheduler.from_pretrained(str(root),
- subfolder="scheduler",
- local_files_only=True)
- if flow_shift is not None:
- scheduler._shift = flow_shift
- return scheduler
-
-
-def _fastvideo(root: Path, flow_shift: float | None = None) -> FastVideoFlowMatchEulerDiscreteScheduler:
- """Load through the production SchedulerLoader, which applies ``flow_shift``."""
- fastvideo_args = SimpleNamespace(pipeline_config=SimpleNamespace(flow_shift=flow_shift))
- scheduler = SchedulerLoader().load(str(root / "scheduler"), fastvideo_args)
- assert type(scheduler) is FastVideoFlowMatchEulerDiscreteScheduler
- return scheduler
-
-
-def _prepare_fastvideo_timesteps(scheduler: FastVideoFlowMatchEulerDiscreteScheduler,
- num_steps: int) -> ForwardBatch:
- """Drive the stock TimestepPreparationStage with PAN2's custom sigmas."""
- batch = ForwardBatch(data_type="video", num_inference_steps=num_steps, sigmas=_pan2_sigmas(num_steps).tolist())
- stage = TimestepPreparationStage(scheduler)
- assert stage.verify_input(batch, SimpleNamespace()).is_valid()
- batch = stage.forward(batch, SimpleNamespace())
- assert batch.timesteps is scheduler.timesteps
- return batch
-
-
-def test_pan2_scheduler_loader_keeps_checkpoint_config(pan2_root: Path) -> None:
- """Without ``flow_shift`` the loader keeps every released config value, including shift 7.0."""
- raw = json.loads((pan2_root / "scheduler" / "scheduler_config.json").read_text())
- assert raw["_class_name"] == "FlowMatchEulerDiscreteScheduler"
- scheduler = _fastvideo(pan2_root)
- for key, value in raw.items():
- if not key.startswith("_"):
- assert scheduler.config[key] == value, key
- assert scheduler.shift == raw["shift"] == 7.0
- assert not scheduler.config.use_dynamic_shifting
-
-
-@pytest.mark.parametrize("num_steps", [1, 4, 8, 30, 50])
-def test_pan2_scheduler_schedule_matches_diffusers(num_steps: int, pan2_root: Path, cpu_runtime: None) -> None:
- """Timesteps and sigmas from ``batch.sigmas`` are bit-exact for short and long schedules."""
- reference = _reference(pan2_root)
- reference.set_timesteps(sigmas=_pan2_sigmas(num_steps), device="cpu")
- fastvideo = _fastvideo(pan2_root)
- batch = _prepare_fastvideo_timesteps(fastvideo, num_steps)
-
- print(f"[pan2-scheduler] N={num_steps} timesteps[:4]={reference.timesteps[:4].tolist()} "
- f"sigmas[-3:]={reference.sigmas[-3:].tolist()}")
- assert len(batch.timesteps) == num_steps
- assert_close(batch.timesteps, reference.timesteps, atol=0, rtol=0)
- assert_close(fastvideo.sigmas, reference.sigmas, atol=0, rtol=0)
- assert fastvideo.sigmas[0].item() == 1.0 and fastvideo.sigmas[-1].item() == 0.0
-
-
-@pytest.mark.parametrize("flow_shift", [None, 5.0], ids=["checkpoint_shift", "flow_shift_override"])
-def test_pan2_scheduler_steps_match_diffusers(flow_shift: float | None, pan2_root: Path, cpu_runtime: None) -> None:
- """Five float32 Euler updates are bit-exact, with and without a shift override."""
- num_steps = 5
- reference = _reference(pan2_root, flow_shift)
- reference.set_timesteps(sigmas=_pan2_sigmas(num_steps), device="cpu")
- fastvideo = _fastvideo(pan2_root, flow_shift)
- _prepare_fastvideo_timesteps(fastvideo, num_steps)
- assert fastvideo.shift == reference.shift == (flow_shift or 7.0)
- assert_close(fastvideo.timesteps, reference.timesteps, atol=0, rtol=0)
-
- generator = torch.Generator().manual_seed(2026)
- reference_latents = torch.randn(LATENT_SHAPE, generator=generator)
- fastvideo_latents = reference_latents.clone()
- max_diff = 0.0
- for index in range(num_steps):
- noise_pred = torch.randn(LATENT_SHAPE, generator=generator)
- reference_latents = reference.step(noise_pred, reference.timesteps[index], reference_latents).prev_sample
- fastvideo_latents = fastvideo.step(noise_pred, fastvideo.timesteps[index], fastvideo_latents).prev_sample
- assert fastvideo_latents.dtype == torch.float32
- max_diff = max(max_diff, (fastvideo_latents - reference_latents).abs().max().item())
- assert_close(fastvideo_latents, reference_latents, atol=0, rtol=0)
- print(f"[pan2-scheduler] flow_shift={flow_shift} steps={num_steps} max_abs={max_diff:.3e}")
- assert fastvideo.step_index == reference.step_index == num_steps
-
-
-def test_pan2_scheduler_step_returns_model_output_dtype(pan2_root: Path, cpu_runtime: None) -> None:
- """Both implementations cast ``prev_sample`` to the model output dtype.
-
- PAN2 keeps float32 latents by stepping with ``noise_pred.float()``; a bf16
- noise prediction would silently drop the latents to bf16 on either side.
- """
- reference = _reference(pan2_root)
- reference.set_timesteps(sigmas=_pan2_sigmas(2), device="cpu")
- fastvideo = _fastvideo(pan2_root)
- _prepare_fastvideo_timesteps(fastvideo, 2)
- latents = torch.randn(LATENT_SHAPE, generator=torch.Generator().manual_seed(5))
- noise_pred = torch.randn(LATENT_SHAPE, generator=torch.Generator().manual_seed(6)).to(torch.bfloat16)
- expected = reference.step(noise_pred, reference.timesteps[0], latents).prev_sample
- actual = fastvideo.step(noise_pred, fastvideo.timesteps[0], latents).prev_sample
- assert expected.dtype == actual.dtype == torch.bfloat16
- assert_close(actual, expected, atol=0, rtol=0)
diff --git a/tests/local_tests/test_cookbook_serving.py b/tests/local_tests/test_cookbook_serving.py
index 2f4e37822..f0a2e11e8 100644
--- a/tests/local_tests/test_cookbook_serving.py
+++ b/tests/local_tests/test_cookbook_serving.py
@@ -264,23 +264,3 @@ def test_kandinsky6_cookbook_recipes_match_maintained_examples():
assert all("INPUT_VIDEO" in by_id[recipe_id]["command"] for recipe_id in ("kandinsky6-vsr", "kandinsky6-vsr-distilled"))
assert "VSR-distilled2steps" in by_id["kandinsky6-vsr-distilled"]["command"]
validate_cookbook()
-
-
-def test_pan2_recipe_serves_text_and_image_from_one_deployment():
- recipes = json.loads(COOKBOOK_DATA.read_text())["recipes"]
- recipe = next(item for item in recipes if item["family"] == "pan2")
- assert recipe["id"] == "pan2-ti2v"
- assert recipe["model"] == "IFM/PAN2"
- assert recipe["evidence"] == "Source-backed"
- assert recipe["hardware"] == {"platform": "cuda", "gpu_count": 1, "evidence": "source-configured"}
- profile = cookbook_serving_profile(recipe)
- assert profile["command"] == "fastvideo serve --config examples/serving/openai_pan2.yaml --server.host 127.0.0.1"
- assert profile["model"] == "pan2"
- assert profile["playground_url"] is None
- assert profile["sampling"]["num_frames"] == 313
- assert any("image_reference" in item for item in recipe["serving"]["limitations"])
- manifest = yaml.safe_load((ROOT / "docs/cookbook/recipes/pan2.yaml").read_text())
- deployment = manifest["deployments"]["cuda-rest"]
- assert deployment["workload"] == "t2v"
- assert deployment["defaults"] == recipe["serving"]["source"]
- validate_cookbook()
diff --git a/tests/local_tests/transformers/test_pan2_transformer_parity.py b/tests/local_tests/transformers/test_pan2_transformer_parity.py
deleted file mode 100644
index 4df532f06..000000000
--- a/tests/local_tests/transformers/test_pan2_transformer_parity.py
+++ /dev/null
@@ -1,328 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 transformer weight-schema and forward parity.
-
-The reference is ``tests/local_tests/pan2/reference.py``: diffusers building blocks arranged as the (not yet public)
-diffusers ``PAN2Transformer3DModel``, following the vLLM-Omni port. Its module names are the checkpoint's, so the
-public tiny checkpoint loads into it strictly.
-
-Set ``PAN2_TINY_MODEL_DIR`` to a local copy of ``wuqing157/tiny-pan2-modular-pipe``; otherwise the HF cache is
-used and the checkpoint test skips when it is absent.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import os
-import socket
-import subprocess
-import sys
-from contextlib import ExitStack
-from dataclasses import fields
-from pathlib import Path
-
-import pytest
-import torch
-import torch.nn as nn
-from torch.testing import assert_close
-
-from fastvideo import envs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.models.loader.fsdp_load import load_model_from_full_model_state_dict
-from fastvideo.models.loader.utils import get_param_names_mapping
-from tests.local_tests.pan2.reference import TINY_REPO_ID, ReferencePAN2Transformer, tiny_model_dir
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-SP_WORLD_SIZES = (2, 4)
-
-
-def _free_port() -> int:
- with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
- sock.bind(("127.0.0.1", 0))
- return sock.getsockname()[1]
-
-
-def _small_kwargs() -> dict:
- """Larger than the tiny checkpoint: several heads, asymmetric RoPE axes, more blocks, distinct MLP ratios."""
- return {
- "patch_size": (1, 2, 2),
- "in_channels": 9,
- "out_channels": 4,
- "num_attention_heads": 4,
- "attention_head_dim": 32,
- "num_layers": 3,
- "num_refiner_layers": 2,
- "mlp_ratio": 3.0,
- "context_mlp_ratio": 2.0,
- "refiner_mlp_ratio": 1.5,
- "text_embed_dim": 48,
- "rope_axes_dim": (8, 12, 12),
- "rope_theta": 256.0,
- "rope_temporal_scale": 16 / 24,
- }
-
-
-@pytest.fixture
-def single_rank_dist(monkeypatch):
- from fastvideo.distributed import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
- import fastvideo.distributed.parallel_state as parallel_state
-
- if not torch.cuda.is_available():
- def _cpu_device() -> torch.device:
- return torch.device("cpu")
-
- monkeypatch.setattr(parallel_state.get_local_torch_device, "__code__", _cpu_device.__code__)
- with ExitStack() as stack:
- stack.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override("TORCH_SDPA"))
- for name, value in (("MASTER_ADDR", "127.0.0.1"), ("MASTER_PORT", str(_free_port())), ("RANK", "0"),
- ("WORLD_SIZE", "1"), ("LOCAL_RANK", "0")):
- stack.enter_context(envs.override_external(name, value))
- try:
- maybe_init_distributed_environment_and_model_parallel(1, 1)
- yield
- finally:
- cleanup_dist_env_and_memory()
-
-
-def _build_native(arch_kwargs: dict):
- from fastvideo.configs.models.dits.pan2 import PAN2VideoConfig
- from fastvideo.models.dits.pan2 import PAN2Transformer3DModel
-
- config = PAN2VideoConfig()
- config.update_model_arch(arch_kwargs)
- return PAN2Transformer3DModel(config=config, hf_config=arch_kwargs).float().eval()
-
-
-def _load_into_native(native, weights) -> None:
- incompatible = load_model_from_full_model_state_dict(
- native,
- iter(weights),
- device=torch.device("cpu"),
- param_dtype=torch.float32,
- strict=True,
- param_names_mapping=get_param_names_mapping(native.param_names_mapping),
- training_mode=False,
- )
- assert incompatible.missing_keys == []
- assert incompatible.unexpected_keys == []
-
-
-def _randomize(module: nn.Module, seed: int) -> None:
- generator = torch.Generator().manual_seed(seed)
- with torch.no_grad():
- for param in module.parameters():
- param.copy_(torch.randn(param.shape, generator=generator) * 0.2)
-
-
-def _make_inputs(in_channels: int, text_dim: int, batch_size: int = 2, seed: int = 0) -> dict[str, torch.Tensor]:
- generator = torch.Generator().manual_seed(seed)
- return {
- # 9 frames at 64x96 in latent space: 3 latent frames of 4x6.
- "hidden_states": torch.randn(batch_size, in_channels, 3, 4, 6, generator=generator),
- "timestep": torch.tensor([700.0, 125.5][:batch_size]),
- "encoder_hidden_states": torch.randn(batch_size, 13, text_dim, generator=generator),
- }
-
-
-def _compare(native, reference, inputs: dict[str, torch.Tensor], scope: str) -> None:
- with torch.inference_mode():
- expected = reference(**inputs)
- with set_forward_context(current_timestep=0, attn_metadata=None):
- actual = native(**inputs)
- assert actual.shape == expected.shape
- diff = (actual - expected).abs()
- print(f"{scope}: shape={tuple(actual.shape)} ref_abs_mean={expected.abs().mean().item():.3e} "
- f"diff_max={diff.max().item():.3e} diff_mean={diff.mean().item():.3e}")
- assert torch.isfinite(expected).all() and expected.abs().mean() > 1e-3
- assert_close(actual, expected, atol=2e-5, rtol=2e-5)
-
-
-def test_pan2_transformer_registry_resolves_native_class():
- from fastvideo.models.dits.pan2 import PAN2Transformer3DModel
- from fastvideo.models.registry import ModelRegistry
-
- model_cls, architecture = ModelRegistry.resolve_model_cls("PAN2Transformer3DModel")
- assert model_cls is PAN2Transformer3DModel
- assert architecture == "PAN2Transformer3DModel"
-
-
-@pytest.mark.parametrize(("module_name", "expected"), [
- ("transformer_blocks.0", True),
- ("transformer_blocks.43", True),
- ("context_refiner.3", True),
- ("transformer_blocks.0.attn.to_qkv", False),
- ("transformer_blocks.0.ff.fc_in", False),
-])
-def test_pan2_fsdp_shard_conditions_match_only_blocks(module_name: str, expected: bool):
- from fastvideo.configs.models.dits.pan2 import PAN2ArchConfig
-
- conditions = PAN2ArchConfig()._fsdp_shard_conditions
- assert any(condition(module_name, None) for condition in conditions) is expected
-
-
-def test_pan2_default_arch_config_is_the_29b_model():
- from fastvideo.configs.models.dits.pan2 import PAN2ArchConfig
-
- config = PAN2ArchConfig()
- assert config.hidden_size == 4096
- assert config.num_channels_latents == 48
- assert config.in_channels == 2 * 48 + 1
- assert sum(config.rope_axes_dim) == config.attention_head_dim
-
-
-def test_pan2_tiny_checkpoint_config_fields_are_arch_fields():
- model_dir = tiny_model_dir()
- if model_dir is None or not (model_dir / "transformer" / "config.json").is_file():
- pytest.skip(f"{TINY_REPO_ID} is not available locally; set PAN2_TINY_MODEL_DIR")
- from fastvideo.configs.models.dits.pan2 import PAN2ArchConfig
-
- checkpoint_config = json.loads((model_dir / "transformer" / "config.json").read_text())
- assert checkpoint_config.pop("_class_name") == "PAN2Transformer3DModel"
- checkpoint_config.pop("_diffusers_version", None)
- assert set(checkpoint_config) <= {field.name for field in fields(PAN2ArchConfig)}
-
-
-def test_pan2_tiny_checkpoint_strict_load_and_forward_parity(single_rank_dist):
- model_dir = tiny_model_dir()
- if model_dir is None or not (model_dir / "transformer").is_dir():
- pytest.skip(f"{TINY_REPO_ID} is not available locally; set PAN2_TINY_MODEL_DIR")
- from safetensors.torch import load_file
-
- transformer_dir = model_dir / "transformer"
- arch_kwargs = json.loads((transformer_dir / "config.json").read_text())
- state_dict = load_file(str(transformer_dir / "diffusion_pytorch_model.safetensors"))
-
- reference = ReferencePAN2Transformer(**arch_kwargs).float().eval()
- reference.load_state_dict(state_dict, strict=True)
- native = _build_native(arch_kwargs)
- _load_into_native(native, state_dict.items())
-
- inputs = _make_inputs(arch_kwargs["in_channels"], arch_kwargs["text_embed_dim"])
- _compare(native, reference, inputs, "tiny checkpoint")
-
-
-def test_pan2_layerwise_offload_hooks_the_main_blocks(single_rank_dist):
- native = _build_native(_small_kwargs())
-
- first_module_list = next(child for child in native.children() if isinstance(child, torch.nn.ModuleList))
-
- assert first_module_list is native.transformer_blocks
-
-
-@pytest.mark.parametrize("batch_size", [1, 2])
-def test_pan2_random_weights_forward_parity(single_rank_dist, batch_size: int):
- kwargs = _small_kwargs()
- reference = ReferencePAN2Transformer(**kwargs).float().eval()
- # Zero-initialized tables (text modulation, type embedding) must be exercised too.
- _randomize(reference, seed=1234)
- native = _build_native(kwargs)
- _load_into_native(native, reference.state_dict().items())
-
- inputs = _make_inputs(kwargs["in_channels"], kwargs["text_embed_dim"], batch_size=batch_size, seed=batch_size)
- _compare(native, reference, inputs, f"random weights, batch {batch_size}")
-
-
-def _run_sp_worker(sp_size: int, output_path: Path) -> None:
- from fastvideo.configs.models.dits.pan2 import PAN2VideoConfig
- from fastvideo.distributed import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
- from fastvideo.models.dits.pan2 import PAN2Transformer3DModel
- import torch.distributed as dist
-
- if torch.cuda.is_available():
- device = torch.device(f"cuda:{int(os.environ.get('LOCAL_RANK', '0'))}")
- torch.cuda.set_device(device)
- torch.backends.cuda.matmul.allow_tf32 = False
- torch.backends.cudnn.allow_tf32 = False
- else:
- # GPU-less host: gloo ranks on CPU. fastvideo resolves the local device to mps there; swapping the code
- # object reaches every module that imported get_local_torch_device by name.
- import fastvideo.distributed.parallel_state as parallel_state
-
- def _cpu_device() -> torch.device:
- return torch.device("cpu")
-
- parallel_state.get_local_torch_device.__code__ = _cpu_device.__code__
- device = torch.device("cpu")
- try:
- maybe_init_distributed_environment_and_model_parallel(1, sp_size)
- kwargs = _small_kwargs()
- reference = ReferencePAN2Transformer(**kwargs).float()
- _randomize(reference, seed=1234)
- config = PAN2VideoConfig()
- config.update_model_arch(kwargs)
- native = PAN2Transformer3DModel(config=config, hf_config=kwargs).float().eval()
- _load_into_native(native, reference.state_dict().items())
- native = native.to(device)
- outputs = {}
- # 15 tokens need one pad at SP=2/4; 18 need two at SP=4, as at 704x1248x313.
- for latent_frames in (5, 6):
- for image_conditioned in (False, True):
- inputs = _make_inputs(kwargs["in_channels"], kwargs["text_embed_dim"])
- video = torch.randn(2, kwargs["in_channels"], latent_frames, 2, 6,
- generator=torch.Generator().manual_seed(5))
- video[:, kwargs["out_channels"]:] = 0
- if image_conditioned:
- video[:, kwargs["out_channels"]:-1, :1] = torch.randn(
- 2, kwargs["out_channels"], 1, 2, 6, generator=torch.Generator().manual_seed(6))
- video[:, -1:, :1] = 1
- inputs["hidden_states"] = video
- inputs = {name: value.to(device) for name, value in inputs.items()}
- with torch.inference_mode(), set_forward_context(current_timestep=0, attn_metadata=None):
- output = native(**inputs)
- outputs[f"{latent_frames}_{image_conditioned}"] = output.detach().cpu()
- if int(os.environ.get("RANK", "0")) == 0:
- torch.save(outputs, output_path)
- dist.barrier()
- finally:
- cleanup_dist_env_and_memory()
-
-
-def _run_torchrun(nproc_per_node: int, output_path: Path) -> None:
- command = [
- sys.executable, "-m", "torch.distributed.run", "--nnodes", "1", "--nproc_per_node",
- str(nproc_per_node), "--master_port",
- str(_free_port()),
- str(Path(__file__).resolve()), "--pan2-sp-worker", "--sp-size", str(nproc_per_node), "--output",
- str(output_path)
- ]
- environment = os.environ.copy()
- environment["PYTHONPATH"] = os.pathsep.join(filter(None, [str(REPO_ROOT), environment.get("PYTHONPATH")]))
- environment["FASTVIDEO_ATTENTION_BACKEND"] = "TORCH_SDPA"
- process = subprocess.run(command, capture_output=True, text=True, env=environment, timeout=180)
- if process.returncode != 0:
- raise RuntimeError(f"SP={nproc_per_node} worker failed with code {process.returncode}\n"
- f"STDOUT:\n{process.stdout}\nSTDERR:\n{process.stderr}")
-
-
-@pytest.fixture(scope="module")
-def single_rank_sp_outputs(tmp_path_factory):
- path = tmp_path_factory.mktemp("pan2-single") / "single.pt"
- _run_torchrun(1, path)
- return torch.load(path, map_location="cpu", weights_only=True)
-
-
-@pytest.mark.parametrize("sp_size", SP_WORLD_SIZES)
-def test_pan2_sequence_parallel_matches_single_rank(tmp_path: Path, single_rank_sp_outputs, sp_size: int):
- """T2V and I2V joint attention excludes padding at Ulysses SP=2/4. Uses gloo on CPU or NCCL on 2/4 GPUs."""
- if torch.cuda.is_available() and torch.cuda.device_count() < sp_size:
- pytest.skip(f"PAN2 SP parity requires {sp_size} CUDA devices.")
- sp_path = tmp_path / f"sp{sp_size}.pt"
- _run_torchrun(sp_size, sp_path)
- sp_outputs = torch.load(sp_path, map_location="cpu", weights_only=True)
- assert sp_outputs.keys() == single_rank_sp_outputs.keys()
- for case, single in single_rank_sp_outputs.items():
- sp = sp_outputs[case]
- diff = (sp - single).abs()
- print(f"sp{sp_size}-vs-single {case} diff_max={diff.max().item():.3e}")
- assert_close(sp, single, atol=1e-5, rtol=1e-5)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--pan2-sp-worker", action="store_true")
- parser.add_argument("--sp-size", type=int, choices=[1, *SP_WORLD_SIZES], required=True)
- parser.add_argument("--output", type=str, required=True)
- args = parser.parse_args()
- if not args.pan2_sp_worker:
- raise SystemExit("This module is intended to be run by pytest.")
- _run_sp_worker(args.sp_size, Path(args.output))
diff --git a/tests/local_tests/vaes/test_pan2_vae_parity.py b/tests/local_tests/vaes/test_pan2_vae_parity.py
deleted file mode 100644
index 3f56210f3..000000000
--- a/tests/local_tests/vaes/test_pan2_vae_parity.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PAN2 reuse of FastVideo's Wan2.2 (48-channel) VAE.
-
-Coverage scope: both. PAN2 ships a Diffusers ``AutoencoderKLWan`` in the
-Wan2.2 layout (z_dim 48, residual blocks, 2x2 patchify, 16x spatial and 4x
-temporal compression). The FastVideo side loads it through ``VAELoader``, the
-path a pipeline uses, and is compared on CPU fp32 against
-``diffusers.AutoencoderKLWan.from_pretrained``:
-
-* posterior mode/logvar for a single first frame (PAN2's I2V conditioning),
-* posterior mode/logvar for a 9-frame clip (4x temporal downsampling + cache),
-* decode of 3 latent frames (first-chunk handling + temporal upsampling cache).
-
-The normalization tests use a copy of the checkpoint with non-trivial
-``latents_mean``/``latents_std`` (the tiny checkpoint ships 0/1, which would
-hide a missing or doubled normalization) and pin where it happens: the raw
-``encode``/``decode`` never normalize, ``PAN2ConditioningStage`` applies
-PAN2's ``(z - mean) / std`` once, and ``DecodingStage`` applies
-``z * std + mean`` once.
-
-The default asset is the random-weight tiny PAN2 checkpoint, so this runs on a
-CPU-only host::
-
- pytest tests/local_tests/vaes/test_pan2_vae_parity.py -v -s
-
-Set ``PAN2_MODEL_PATH`` to a local PAN2 root containing ``vae/`` to use other
-weights.
-"""
-
-from __future__ import annotations
-
-import json
-import os
-from pathlib import Path
-from types import SimpleNamespace
-
-import pytest
-import torch
-from safetensors.torch import load_file
-from torch.testing import assert_close
-
-try:
- from diffusers import AutoencoderKLWan as DiffusersAutoencoderKLWan
-except ImportError: # pragma: no cover - environment dependent
- pytest.skip("diffusers with AutoencoderKLWan is required", allow_module_level=True)
-
-from fastvideo.models.loader.component_loader import VAELoader
-from fastvideo.models.wan.vae import AutoencoderKLWan as FastVideoAutoencoderKLWan
-from fastvideo.models.wan.vae_config import WanVAEConfig
-
-PARITY_SCOPE = "both"
-TINY_PAN2_REPO = "wuqing157/tiny-pan2-modular-pipe"
-TINY_PAN2_REVISION = "3ff3130b4527f61aaab7bde01465c9884dc84cdd"
-MODEL_PATH_ENV = "PAN2_MODEL_PATH"
-
-# H and W must be multiples of 32 (16x spatial compression times the 2x2
-# patchify inside the VAE).
-HEIGHT, WIDTH = 64, 96
-VIDEO_FRAMES = 9
-LATENT_FRAMES = (VIDEO_FRAMES - 1) // 4 + 1
-ATOL = RTOL = 1e-5
-
-# Structural fields of Wan-AI/Wan2.2-TI2V-5B-Diffusers vae/config.json. The
-# statistics do not affect parameter shapes, so placeholders keep this inline.
-WAN22_TI2V_VAE_CONFIG = {
- "attn_scales": [],
- "base_dim": 160,
- "clip_output": False,
- "decoder_base_dim": 256,
- "dim_mult": [1, 2, 4, 4],
- "dropout": 0.0,
- "in_channels": 12,
- "is_residual": True,
- "latents_mean": [0.0] * 48,
- "latents_std": [1.0] * 48,
- "num_res_blocks": 2,
- "out_channels": 12,
- "patch_size": 2,
- "scale_factor_spatial": 16,
- "scale_factor_temporal": 4,
- "temperal_downsample": [False, True, True],
- "z_dim": 48,
-}
-
-
-def _pan2_root(subfolder: str) -> Path:
- """Resolve a PAN2 root that contains ``subfolder``; skip when unavailable."""
- override = os.getenv(MODEL_PATH_ENV)
- if override:
- root = Path(override)
- else:
- try:
- from huggingface_hub import snapshot_download
- except ImportError:
- pytest.skip("huggingface_hub is required to resolve the tiny PAN2 checkpoint")
- root = None
- last_error: Exception | None = None
- for local_files_only in (True, False):
- try:
- root = Path(
- snapshot_download(
- TINY_PAN2_REPO,
- revision=TINY_PAN2_REVISION,
- allow_patterns=[f"{subfolder}/*"],
- local_files_only=local_files_only,
- ))
- break
- except Exception as exc: # noqa: BLE001 - offline/missing cache must skip.
- last_error = exc
- if root is None:
- pytest.skip(f"Tiny PAN2 checkpoint unavailable: {last_error}")
- if not (root / subfolder).is_dir():
- pytest.skip(f"PAN2 {subfolder}/ missing under {root}")
- return root
-
-
-@pytest.fixture(scope="module")
-def vae_dir() -> Path:
- path = _pan2_root("vae") / "vae"
- for name in ("config.json", "diffusion_pytorch_model.safetensors"):
- if not (path / name).is_file():
- pytest.skip(f"PAN2 VAE asset missing: {path / name}")
- return path
-
-
-@pytest.fixture
-def cpu_runtime(monkeypatch: pytest.MonkeyPatch) -> None:
- """Pin FastVideo's device helper to CPU; it reports ``mps`` on CPU-only Linux."""
- for module in (
- "fastvideo.models.loader.component_loader",
- "fastvideo.pipelines.stages.decoding",
- "fastvideo.pipelines.basic.pan2.stages",
- ):
- monkeypatch.setattr(f"{module}.get_local_torch_device", lambda: torch.device("cpu"))
-
-
-def _pan2_vae_config() -> WanVAEConfig:
- """The VAE config a PAN2 pipeline config would carry (arch filled by the loader)."""
- config = WanVAEConfig()
- config.load_encoder = True
- config.load_decoder = True
- return config
-
-
-def _load_fastvideo(vae_dir: Path) -> FastVideoAutoencoderKLWan:
- """Load through the production VAELoader, exactly as a pipeline does."""
- fastvideo_args = SimpleNamespace(
- model_paths={},
- pipeline_config=SimpleNamespace(vae_config=_pan2_vae_config(), vae_precision="fp32"),
- vae_cpu_offload=False,
- )
- vae = VAELoader().load(str(vae_dir), fastvideo_args)
- assert isinstance(vae, FastVideoAutoencoderKLWan)
- assert fastvideo_args.model_paths["vae"] == str(vae_dir)
- assert all(param.dtype == torch.float32 and param.device.type == "cpu" for param in vae.parameters())
- return vae
-
-
-def _load_diffusers(vae_dir: Path) -> DiffusersAutoencoderKLWan:
- model, info = DiffusersAutoencoderKLWan.from_pretrained(
- str(vae_dir),
- torch_dtype=torch.float32,
- local_files_only=True,
- output_loading_info=True,
- )
- problems = {key: value for key, value in info.items() if value}
- assert not problems, f"Diffusers reference did not load strictly: {problems}"
- return model.eval()
-
-
-def _pixels(num_frames: int, seed: int) -> torch.Tensor:
- generator = torch.Generator().manual_seed(seed)
- return torch.rand((1, 3, num_frames, HEIGHT, WIDTH), generator=generator) * 2.0 - 1.0
-
-
-def _latents(seed: int) -> torch.Tensor:
- generator = torch.Generator().manual_seed(seed)
- return torch.randn((1, 48, LATENT_FRAMES, HEIGHT // 16, WIDTH // 16), generator=generator)
-
-
-def _report(name: str, actual: torch.Tensor, expected: torch.Tensor, clamp_bound: float | None = None) -> None:
- assert actual.shape == expected.shape, f"{name}: {tuple(actual.shape)} != {tuple(expected.shape)}"
- diff = (actual.float() - expected.float()).abs()
- message = (f"[pan2-vae] {name}: shape={tuple(actual.shape)} max_abs={diff.max().item():.3e} "
- f"mean_abs={diff.mean().item():.3e} ref_absmax={expected.abs().max().item():.3e}")
- if clamp_bound is not None:
- # Clamped pixels compare trivially; report how many there are.
- message += f" clamped={(expected.abs() >= clamp_bound).float().mean().item():.2%}"
- print(message)
-
-
-def _nontrivial_stats() -> tuple[list[float], list[float]]:
- mean = [round(0.05 * (index - 24), 4) for index in range(48)]
- std = [round(0.4 + 0.03 * index, 4) for index in range(48)]
- return mean, std
-
-
-@pytest.fixture
-def stats_vae_dir(vae_dir: Path, tmp_path: Path) -> Path:
- """Checkpoint copy with non-trivial latent statistics (weights symlinked)."""
- config = json.loads((vae_dir / "config.json").read_text())
- config["latents_mean"], config["latents_std"] = _nontrivial_stats()
- target = tmp_path / "vae"
- target.mkdir()
- (target / "config.json").write_text(json.dumps(config))
- (target / "diffusion_pytorch_model.safetensors").symlink_to(
- (vae_dir / "diffusion_pytorch_model.safetensors").resolve())
- return target
-
-
-def _diffusers_stats(model: DiffusersAutoencoderKLWan, like: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
- mean = torch.tensor(model.config.latents_mean).view(1, -1, 1, 1, 1).to(like)
- std = torch.tensor(model.config.latents_std).view(1, -1, 1, 1, 1).to(like)
- return mean, std
-
-
-def test_pan2_vae_production_loader_matches_checkpoint(vae_dir: Path, cpu_runtime: None) -> None:
- """The loader fills the tiny Wan2.2 arch from config.json and covers every weight.
-
- ``VAELoader`` loads ``AutoencoderKLWan`` with ``strict=False``, so key
- coverage is checked here instead of trusting the loader.
- """
- raw = json.loads((vae_dir / "config.json").read_text())
- vae = _load_fastvideo(vae_dir)
- arch = vae.config.arch_config
- for key in ("base_dim", "decoder_base_dim", "z_dim", "num_res_blocks", "in_channels", "out_channels",
- "is_residual", "patch_size", "scale_factor_spatial", "scale_factor_temporal", "dropout"):
- assert getattr(arch, key) == raw[key], key
- for key in ("dim_mult", "attn_scales", "temperal_downsample", "latents_mean", "latents_std"):
- assert list(getattr(arch, key)) == list(raw[key]), key
- assert vae.config.use_feature_cache and not vae.config.use_tiling
- assert arch.spatial_compression_ratio == 16 and arch.temporal_compression_ratio == 4
- assert tuple(vae.scaling_factor.shape) == tuple(vae.shift_factor.shape) == (1, 48, 1, 1, 1)
- assert_close(vae.scaling_factor.flatten(), 1.0 / torch.tensor(raw["latents_std"]))
- assert_close(vae.shift_factor.flatten(), torch.tensor(raw["latents_mean"]))
-
- checkpoint = load_file(str(vae_dir / "diffusion_pytorch_model.safetensors"))
- state = vae.state_dict()
- assert set(state) == set(checkpoint), (sorted(set(checkpoint) - set(state)), sorted(set(state) - set(checkpoint)))
- for key, tensor in checkpoint.items():
- assert torch.equal(state[key], tensor.float()), key
-
-
-@pytest.mark.parametrize("variant", ["wan22_ti2v_5b", "tiny_pan2"])
-def test_pan2_vae_config_shapes_match_diffusers(variant: str, request: pytest.FixtureRequest) -> None:
- """``WanVAEConfig`` expresses both the real Wan2.2 and the tiny PAN2 VAE configs.
-
- Both models are built on the meta device, so the full-size config costs no
- memory; parameter names and shapes must match one-to-one.
- """
- if variant == "tiny_pan2":
- raw = json.loads((request.getfixturevalue("vae_dir") / "config.json").read_text())
- config = {key: value for key, value in raw.items() if not key.startswith("_")}
- else:
- config = WAN22_TI2V_VAE_CONFIG
- fastvideo_config = _pan2_vae_config()
- fastvideo_config.update_model_arch(config)
- with torch.device("meta"):
- fastvideo = FastVideoAutoencoderKLWan(fastvideo_config)
- reference = DiffusersAutoencoderKLWan.from_config(config)
- fastvideo_shapes = {key: tuple(value.shape) for key, value in fastvideo.state_dict().items()}
- reference_shapes = {key: tuple(value.shape) for key, value in reference.state_dict().items()}
- assert fastvideo_shapes == reference_shapes
-
-
-def test_pan2_vae_wan22_topology_random_weight_parity() -> None:
- """Numerically cover the Wan2.2 paths the tiny config skips.
-
- The tiny checkpoint uses ``dim_mult`` [1, 1, 1, 1] and no separate decoder
- width, so channel-changing shortcuts and resamplers never run. This narrow
- random-weight model keeps the real topology (``dim_mult`` [1, 2, 4, 4], two
- res blocks, ``decoder_base_dim`` > ``base_dim``).
- """
- config = dict(WAN22_TI2V_VAE_CONFIG, base_dim=8, decoder_base_dim=16)
- torch.manual_seed(0)
- reference = DiffusersAutoencoderKLWan.from_config(config).eval()
- fastvideo_config = _pan2_vae_config()
- fastvideo_config.update_model_arch(config)
- fastvideo = FastVideoAutoencoderKLWan(fastvideo_config).eval()
- fastvideo.load_state_dict(reference.state_dict(), strict=True)
-
- video = _pixels(VIDEO_FRAMES, seed=11)
- latents = _latents(seed=12)
- with torch.inference_mode():
- expected_mode = reference.encode(video).latent_dist.mode()
- actual_mode = fastvideo.encode(video).mode()
- expected_video = reference.decode(latents).sample
- actual_video = fastvideo.decode(latents)
- _report("wan22-topology encode mode", actual_mode, expected_mode)
- _report("wan22-topology decode", actual_video, expected_video, clamp_bound=1.0)
- assert_close(actual_mode, expected_mode, atol=ATOL, rtol=RTOL)
- assert_close(actual_video, expected_video, atol=ATOL, rtol=RTOL)
-
-
-@pytest.mark.parametrize("num_frames", [1, VIDEO_FRAMES], ids=["first_frame", "video_9f"])
-def test_pan2_vae_encode_matches_diffusers(num_frames: int, vae_dir: Path, cpu_runtime: None) -> None:
- """Posterior mode (PAN2 uses ``latent_dist.mode()``) and logvar match Diffusers.
-
- FastVideo's ``encode`` returns the ``DiagonalGaussianDistribution`` itself,
- so the port calls ``vae.encode(x).mode()`` instead of ``.latent_dist.mode()``.
- """
- reference = _load_diffusers(vae_dir)
- fastvideo = _load_fastvideo(vae_dir)
- pixels = _pixels(num_frames, seed=100 + num_frames)
- with torch.inference_mode():
- expected = reference.encode(pixels).latent_dist
- actual = fastvideo.encode(pixels)
- assert tuple(actual.mode().shape) == (1, 48, (num_frames - 1) // 4 + 1, HEIGHT // 16, WIDTH // 16)
- _report(f"encode mode ({num_frames}f)", actual.mode(), expected.mode())
- _report(f"encode logvar ({num_frames}f)", actual.logvar, expected.logvar)
- assert_close(actual.mode(), expected.mode(), atol=ATOL, rtol=RTOL)
- assert_close(actual.logvar, expected.logvar, atol=ATOL, rtol=RTOL)
-
-
-def test_pan2_vae_decode_matches_diffusers(vae_dir: Path, cpu_runtime: None) -> None:
- """Decode three latent frames so first-chunk and temporal cache paths run."""
- reference = _load_diffusers(vae_dir)
- fastvideo = _load_fastvideo(vae_dir)
- latents = _latents(seed=7)
- with torch.inference_mode():
- expected = reference.decode(latents).sample
- actual = fastvideo.decode(latents)
- assert tuple(actual.shape) == (1, 3, VIDEO_FRAMES, HEIGHT, WIDTH)
- _report("decode", actual, expected, clamp_bound=1.0)
- assert_close(actual, expected, atol=ATOL, rtol=RTOL)
-
-
-def test_pan2_conditioning_stage_matches_reference(stats_vae_dir: Path, cpu_runtime: None) -> None:
- """``PAN2ConditioningStage`` builds the reference I2V condition: cover-resize and center crop the first frame,
- encode it, apply ``(z - mean) / std`` once, and set the mask channel on the first latent frame only.
-
- The raw ``encode`` is unnormalized, so the stage is the only place the statistics apply.
- """
- import PIL.Image
-
- from fastvideo.pipelines.basic.pan2.stages import PAN2ConditioningStage
- from tests.local_tests.pan2.reference import reference_condition_latents
-
- reference = _load_diffusers(stats_vae_dir)
- fastvideo = _load_fastvideo(stats_vae_dir)
- # Not the output aspect ratio, so the resize and crop are exercised.
- image = PIL.Image.radial_gradient("L").resize((120, 64)).convert("RGB")
- latents = torch.zeros(1, 48, LATENT_FRAMES, HEIGHT // 16, WIDTH // 16)
- with torch.inference_mode():
- pixels = PAN2ConditioningStage.preprocess_image(image, HEIGHT, WIDTH)
- raw = reference.encode(pixels).latent_dist.mode()
- assert_close(fastvideo.encode(pixels).mode(), raw, atol=ATOL, rtol=RTOL)
- expected = reference_condition_latents(reference, latents, image, HEIGHT, WIDTH)
-
- batch = SimpleNamespace(latents=latents, pil_image=image, height=HEIGHT, width=WIDTH, image_latent=None)
- stage_args = SimpleNamespace(pipeline_config=SimpleNamespace(vae_precision="fp32"), vae_cpu_offload=False)
- actual = PAN2ConditioningStage(fastvideo).forward(batch, stage_args).image_latent
- _report("PAN2 I2V condition", actual, expected)
- assert actual.shape == (1, 49, LATENT_FRAMES, HEIGHT // 16, WIDTH // 16)
- assert_close(actual, expected, atol=ATOL, rtol=RTOL)
- assert torch.equal(actual[:, 48, 0], torch.ones_like(actual[:, 48, 0]))
- assert not actual[:, :, 1:].any()
- assert not torch.allclose(raw, expected[:, :48, :1], atol=1e-2), "statistics too close to identity"
-
-
-def test_pan2_decoding_stage_denormalizes_once(stats_vae_dir: Path, cpu_runtime: None) -> None:
- """``DecodingStage`` performs PAN2's ``z * std + mean`` itself.
-
- The PAN2 denoising loop must hand normalized latents to ``DecodingStage``.
- Denormalizing beforehand (as the upstream pipeline does inline) would apply
- the statistics twice, which the negative check below detects.
- """
- from fastvideo.pipelines.stages.decoding import DecodingStage
-
- reference = _load_diffusers(stats_vae_dir)
- fastvideo = _load_fastvideo(stats_vae_dir)
- latents = _latents(seed=31)
- # vae_tiling=True is the PipelineConfig default; the feature-cache decode ignores tiling, so it must not matter.
- decode_args = SimpleNamespace(
- pipeline_config=SimpleNamespace(vae_precision="fp32", vae_decode_precision="fp32", vae_tiling=True),
- disable_autocast=False,
- )
- stage = DecodingStage(fastvideo)
- with torch.inference_mode():
- mean, std = _diffusers_stats(reference, latents)
- expected = reference.decode(latents * std + mean).sample
- expected = (expected * 0.5 + 0.5).clamp(0, 1)
- actual = stage.decode(latents, decode_args)
- doubled = stage.decode(latents * std + mean, decode_args)
- _report("DecodingStage output", actual, expected)
- assert_close(actual, expected, atol=ATOL, rtol=RTOL)
- assert (doubled - expected).abs().max().item() > 1e-3, "double denormalization went undetected"