Add SkyReels V2: Infinite-Length Film Generative Model

[tolgacangoz]

Thanks for the opportunity to fix #11374!

Original Work

Original repo: https://github.com/SkyworkAI/SkyReels-V2
Paper: https://huggingface.co/papers/2504.13074

SkyReels V2's main contributions are summarized as follow:
• Comprehensive video captioner that understand the shot language while capturing the general description of the video, which dramatically improve the prompt adherence.
• Motion-specific preference optimization enhances motion dynamics with a semi-automatic data collection pipeline.
• Effective Diffusion-forcing adaptation enables the generation of ultra-long videos and story generation capabilities, providing a robust framework for extending temporal coherence and narrative depth.
• SkyCaptioner-V1 and SkyReels-V2 series models including diffusion-forcing, text2video, image2video, camera director and elements2video models with various sizes (1.3B, 5B, 14B) are open-sourced.

TODOs:
✅ FlowMatchUniPCMultistepScheduler: just copy-pasted from the original repo
✅ SkyReelsV2Transformer3DModel: 90% WanTransformer3DModel
✅ SkyReelsV2DiffusionForcingPipeline
✅ SkyReelsV2DiffusionForcingImageToVideoPipeline: Includes FLF2V.
✅ SkyReelsV2DiffusionForcingVideoToVideoPipeline: Extends a given video.
✅ SkyReelsV2Pipeline
✅ SkyReelsV2ImageToVideoPipeline
✅ scripts/convert_skyreelsv2_to_diffusers.py

• tolgacangoz/SkyReels-V2-Diffusers

⏳ Did you make sure to update the documentation with your changes? Did you write any new necessary tests?: We will construct these during review.

T2V with Diffusion Forcing (OLD)

Skywork/SkyReels-V2-DF-1.3B-540P
seed 0 and num_frames 97
Original repo	diffusers integration
original_0_short.mp4	diffusers_0_short.mp4

seed 37 and num_frames 97
Original repo	diffusers integration
original_37_short.mp4	diffusers_37_short.mp4

seed 0 and num_frames 257
Original repo	diffusers integration
original_0_long.mp4	diffusers_0_long.mp4

seed 37 and num_frames 257
Original repo	diffusers integration
original_37_long.mp4	diffusers_37_long.mp4

!pip install git+https://github.com/tolgacangoz/diffusers.git@skyreels-v2 ftfy -q
import torch
from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingPipeline
from diffusers.utils import export_to_video

vae = AutoencoderKLWan.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			subfolder="vae",
			torch_dtype=torch.float32)
pipe = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			vae=vae,
			torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
pipe.transformer.set_ar_attention(causal_block_size=5)

prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."

output = pipe(
    prompt=prompt,
    num_inference_steps=30,
    height=544,
    width=960,
    num_frames=97,
    ar_step=5,  # Controls asynchronous inference (0 for synchronous mode)
    generator=torch.Generator(device="cpu").manual_seed(0),
    overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos; 17 for long
    addnoise_condition=20,  # Improves consistency in long video generation
).frames[0]
export_to_video(output, "T2V.mp4", fps=24, quality=8)

"""
You can set `ar_step=5` to enable asynchronous inference. When asynchronous inference,
`causal_block_size=5` is recommended while it is not supposed to be set for
synchronous generation. Asynchronous inference will take more steps to diffuse the
whole sequence which means it will be SLOWER than synchronous mode. In our
experiments, asynchronous inference may improve the instruction following and visual consistent performance.
"""

I2V with Diffusion Forcing (OLD)

prompt="A penguin dances."	diffusers integration
	i2v-short.mp4

#!pip uninstall diffusers -yq
#!pip install git+https://github.com/tolgacangoz/diffusers.git@skyreels-v2 ftfy -q
import torch
from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline
from diffusers.utils import export_to_video, load_image

vae = AutoencoderKLWan.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			subfolder="vae",
			torch_dtype=torch.float32)
pipe = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			vae=vae,
			torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
#pipe.transformer.set_ar_attention(causal_block_size=5)

image = load_image("Penguin from https://huggingface.co/tasks/image-to-video")
prompt = "A penguin dances."

output = pipe(
    image=image,
    prompt=prompt,
    num_inference_steps=50,
    height=544,
    width=960,
    num_frames=97,
    #ar_step=5,  # Controls asynchronous inference (0 for synchronous mode)
    generator=torch.Generator(device="cpu").manual_seed(0),
    overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos; 17 for long
    addnoise_condition=20,  # Improves consistency in long video generation
).frames[0]
export_to_video(output, "I2V.mp4", fps=24, quality=8)

"""
When I set `ar_step=5` and `causal_block_size=5`, then the results seem really bad.
"""

FLF2V with Diffusion Forcing (OLD)

Now, Houston, we have a problem.
I have been unable to produce good results with this task. I tried many hyperparameter combinations with the original code.
The first frame's latent (torch.Size([1, 16, 1, 68, 120])) is overwritten onto the first of 25 frame latents of latents (torch.Size([1, 16, 25, 68, 120])). Then, the last frame's latent is concatenated, thus latents is torch.Size([1, 16, 26, 68, 120]). After the denoising process, the length of the last frame latent is discarded at the end and then decoded by the VAE. I tried not concatenating the last frame but overwriting onto the latest frame of latents and not discarding the latest frame latent at the end, but still got bad results. Here are some results:

First Frame	Last Frame
0.mp4	1.mp4
2.mp4	3.mp4
4.mp4	5.mp4
6.mp4	7.mp4

#!pip uninstall diffusers -yq
#!pip install git+https://github.com/tolgacangoz/diffusers.git@skyreels-v2 ftfy -q
import torch
from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline
from diffusers.utils import export_to_video, load_image

vae = AutoencoderKLWan.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			subfolder="vae",
			torch_dtype=torch.float32)
pipe = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			vae=vae,
			torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
#pipe.transformer.set_ar_attention(causal_block_size=5)

prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
first_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png")
last_frame = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png")

output = pipe(
    image=first_frame,
    last_image=last_frame,
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=50,
    height=544,
    width=960,
    num_frames=97,
    #ar_step=5,  # Controls asynchronous inference (0 for synchronous mode)
    generator=torch.Generator(device="cpu").manual_seed(0),
    overlap_history=None,  # Number of frames to overlap for smooth transitions in long videos; 17 for long
    addnoise_condition=20,  # Improves consistency in long video generation
).frames[0]
export_to_video(output, "FLF2V.mp4", fps=24, quality=8)

V2V with Diffusion Forcing (OLD)

This pipeline extends a given video.

Input Video	diffusers integration
video1.mp4	v2v.mp4

#!pip uninstall diffusers -yq
#!pip install git+https://github.com/tolgacangoz/diffusers.git@skyreels-v2 ftfy -q
import torch
from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingVideoToVideoPipeline
from diffusers.utils import export_to_video, load_video

vae = AutoencoderKLWan.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			subfolder="vae",
			torch_dtype=torch.float32)
pipe = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained(
			"tolgacangoz/SkyReels-V2-DF-1.3B-540P-Diffusers",
			vae=vae,
			torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
#pipe.transformer.set_ar_attention(causal_block_size=5)

prompt = "CG animation style, a small blue bird flaps its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its continuing flight and the vastness of the sky from a close-up, low-angle perspective."
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
video = load_video("Input video.mp4")

output = pipe(
    video=video,
    prompt=prompt,
    num_inference_steps=50,
    height=544,
    width=960,
    num_frames=120,
    base_num_frames=97,
    ar_step=0,  # Controls asynchronous inference (0 for synchronous mode)
    generator=torch.Generator(device="cpu").manual_seed(0),
    overlap_history=17,  # Number of frames to overlap for smooth transitions in long videos
    addnoise_condition=20,  # Improves consistency in long video generation
).frames[0]
export_to_video(output, "V2V.mp4", fps=24, quality=8)

Firstly, I want to congratulate you on this great work, and thanks for open-sourcing it, SkyReels Team! This PR proposes an integration of your model.

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR.

@yiyixuxu @a-r-r-o-w @linoytsaban @yjp999 @Howe2018 @RoseRollZhu @pftq @Langdx @guibinchen @qiudi0127 @nitinmukesh @tin2tin @ukaprch @okaris

[ukaprch]

It's about time. Thanks.

[DN6]

Thank you @tolgacangoz @a-r-r-o-w Could you take a look please

[tolgacangoz]

Hi @nitinmukesh @tin2tin. You can make tests, reviews for this PR just as you have done in other PRs, if you want.

[nitinmukesh]

Thank you @tolgacangoz for making the feature available in diffusers.

I will test it now.

[HuggingFaceDocBuilderDev]

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

[a-r-r-o-w]

Thank you for the awesome work here @tolgacangoz! The PR looks great. I just have some nits and changes that will help keep consistent implementations across our other model/processors, and cleanup the pipelines a bit.

It is a massive PR to review, but not the reason why it took so long for me. I'll have to admit the idea of diffusion forcing is new to me and I couldn't fully wrap my head around it until going through some different implementations. Don't know how you did it so fast :)

Also great work on figuring out the numerical precision matching!

Regarding hosting the models, we will try to establish contact with SkyReels team (if not already) and see if they can host the weights.

[a-r-r-o-w]

cc @yiyixuxu Do we have contact with the SkyReels team and do we know if they would be okay with hosting the weights? If it's not possible, we could maintain skyreels-community org similar to hunyuan

[a-r-r-o-w]

Nice examples, great work!

[a-r-r-o-w]

I think this part is adapted from the Wan VACE documentation, no? IIUC SkyReels does not support all these tasks though, so let's remove this

[a-r-r-o-w]

Suggested change

	- Try lower `shift` values (`2.0` to `5.0`) for lower resolution videos and higher `shift` values (`7.0` to `12.0`) for higher resolution images.
	- Try lower `shift` values (`2.0` to `5.0`) for lower resolution videos and higher `shift` values (`7.0` to `12.0`) for higher resolution videos.

[a-r-r-o-w]

Since we have a completely new transformer implementation (in the sense that we have new file, but similar as Wan), let's create a new lora loader mixin

[a-r-r-o-w]

I don't fully understand the total num_frames logic (we set 9 above but expect 21 here). Could you explain it a bit and provide a small example?

[a-r-r-o-w]

From reading through the code paths, I don't think there is something that could potentially break easily when handling last image. If you think there is, we can keep the test. Otherwise, to the above test suite, let's just add a simple extension test_inference_with_last_image for minimal testing

[a-r-r-o-w]

(nit): would give this a better name like enable_diffusion_forcing

[a-r-r-o-w]

same comments as above diffusion forcing pipeline. We could probably wrap the repeated logic into a helper function and call that directly

[a-r-r-o-w]

same comments as above about shift and helper function for repeated logic