# SkyReels-V2: Infinite-length Film Generative model
[SkyReels-V2](https://huggingface.co/papers/2504.13074) by the SkyReels Team from Skywork AI.
*Recent advances in video generation have been driven by diffusion models and autoregressive frameworks, yet critical challenges persist in harmonizing prompt adherence, visual quality, motion dynamics, and duration: compromises in motion dynamics to enhance temporal visual quality, constrained video duration (5-10 seconds) to prioritize resolution, and inadequate shot-aware generation stemming from general-purpose MLLMs' inability to interpret cinematic grammar, such as shot composition, actor expressions, and camera motions. These intertwined limitations hinder realistic long-form synthesis and professional film-style generation. To address these limitations, we propose SkyReels-V2, an Infinite-length Film Generative Model, that synergizes Multi-modal Large Language Model (MLLM), Multi-stage Pretraining, Reinforcement Learning, and Diffusion Forcing Framework. Firstly, we design a comprehensive structural representation of video that combines the general descriptions by the Multi-modal LLM and the detailed shot language by sub-expert models. Aided with human annotation, we then train a unified Video Captioner, named SkyCaptioner-V1, to efficiently label the video data. Secondly, we establish progressive-resolution pretraining for the fundamental video generation, followed by a four-stage post-training enhancement: Initial concept-balanced Supervised Fine-Tuning (SFT) improves baseline quality; Motion-specific Reinforcement Learning (RL) training with human-annotated and synthetic distortion data addresses dynamic artifacts; Our diffusion forcing framework with non-decreasing noise schedules enables long-video synthesis in an efficient search space; Final high-quality SFT refines visual fidelity. All the code and models are available at [this https URL](https://github.com/SkyworkAI/SkyReels-V2).*
You can find all the original SkyReels-V2 checkpoints under the [Skywork](https://huggingface.co/collections/Skywork/skyreels-v2-6801b1b93df627d441d0d0d9) organization.
The following SkyReels-V2 models are supported in Diffusers:
- [SkyReels-V2 DF 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers)
- [SkyReels-V2 DF 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-540P-Diffusers)
- [SkyReels-V2 DF 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-DF-14B-720P-Diffusers)
- [SkyReels-V2 T2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-540P-Diffusers)
- [SkyReels-V2 T2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-T2V-14B-720P-Diffusers)
- [SkyReels-V2 I2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers)
- [SkyReels-V2 I2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P-Diffusers)
- [SkyReels-V2 I2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-720P-Diffusers)
- [SkyReels-V2 FLF2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-FLF2V-1.3B-540P-Diffusers)
> [!TIP]
> Click on the SkyReels-V2 models in the right sidebar for more examples of video generation.
### A _Visual_ Demonstration
The example below has the following parameters:
- `base_num_frames=97`
- `num_frames=97`
- `num_inference_steps=30`
- `ar_step=5`
- `causal_block_size=5`
With `vae_scale_factor_temporal=4`, expect `5` blocks of `5` frames each as calculated by:
`num_latent_frames: (97-1)//vae_scale_factor_temporal+1 = 25 frames -> 5 blocks of 5 frames each`
And the maximum context length in the latent space is calculated with `base_num_latent_frames`:
`base_num_latent_frames = (97-1)//vae_scale_factor_temporal+1 = 25 -> 25//5 = 5 blocks`
Asynchronous Processing Timeline:
```text
┌─────────────────────────────────────────────────────────────────┐
│ Steps: 1 6 11 16 21 26 31 36 41 46 50 │
│ Block 1: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
│ Block 2: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
│ Block 3: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
│ Block 4: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
│ Block 5: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
└─────────────────────────────────────────────────────────────────┘
```
For Long Videos (`num_frames` > `base_num_frames`):
`base_num_frames` acts as the "sliding window size" for processing long videos.
Example: `257`-frame video with `base_num_frames=97`, `overlap_history=17`
```text
┌──── Iteration 1 (frames 1-97) ────┐
│ Processing window: 97 frames │ → 5 blocks,
│ Generates: frames 1-97 │ async processing
└───────────────────────────────────┘
┌────── Iteration 2 (frames 81-177) ──────┐
│ Processing window: 97 frames │
│ Overlap: 17 frames (81-97) from prev │ → 5 blocks,
│ Generates: frames 98-177 │ async processing
└─────────────────────────────────────────┘
┌────── Iteration 3 (frames 161-257) ──────┐
│ Processing window: 97 frames │
│ Overlap: 17 frames (161-177) from prev │ → 5 blocks,
│ Generates: frames 178-257 │ async processing
└──────────────────────────────────────────┘
```
Each iteration independently runs the asynchronous processing with its own `5` blocks.
`base_num_frames` controls:
1. Memory usage (larger window = more VRAM)
2. Model context length (must match training constraints)
3. Number of blocks per iteration (`base_num_latent_frames // causal_block_size`)
Each block takes `30` steps to complete denoising.
Block N starts at step: `1 + (N-1) x ar_step`
Total steps: `30 + (5-1) x 5 = 50` steps
Synchronous mode (`ar_step=0`) would process all blocks/frames simultaneously:
```text
┌──────────────────────────────────────────────┐
│ Steps: 1 ... 30 │
│ All blocks: [■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■] │
└──────────────────────────────────────────────┘
```
Total steps: `30` steps
An example on how the step matrix is constructed for asynchronous processing:
Given the parameters: (`num_inference_steps=30, flow_shift=8, num_frames=97, ar_step=5, causal_block_size=5`)
```
- num_latent_frames = (97 frames - 1) // (4 temporal downsampling) + 1 = 25
- step_template = [999, 995, 991, 986, 980, 975, 969, 963, 956, 948,
941, 932, 922, 912, 901, 888, 874, 859, 841, 822,
799, 773, 743, 708, 666, 615, 551, 470, 363, 216]
```
The algorithm creates a `50x25` `step_matrix` where:
```
- Row 1: [999×5, 999×5, 999×5, 999×5, 999×5]
- Row 2: [995×5, 999×5, 999×5, 999×5, 999×5]
- Row 3: [991×5, 999×5, 999×5, 999×5, 999×5]
- ...
- Row 7: [969×5, 995×5, 999×5, 999×5, 999×5]
- ...
- Row 21: [799×5, 888×5, 941×5, 975×5, 999×5]
- ...
- Row 35: [ 0×5, 216×5, 666×5, 822×5, 901×5]
- ...
- Row 42: [ 0×5, 0×5, 0×5, 551×5, 773×5]
- ...
- Row 50: [ 0×5, 0×5, 0×5, 0×5, 216×5]
```
Detailed Row `6` Analysis:
```
- step_matrix[5]: [ 975×5, 999×5, 999×5, 999×5, 999×5]
- step_index[5]: [ 6×5, 1×5, 0×5, 0×5, 0×5]
- step_update_mask[5]: [True×5, True×5, False×5, False×5, False×5]
- valid_interval[5]: (0, 25)
```
Key Pattern: Block `i` lags behind Block `i-1` by exactly `ar_step=5` timesteps, creating the
staggered "diffusion forcing" effect where later blocks condition on cleaner earlier blocks.
### Text-to-Video Generation
The example below demonstrates how to generate a video from text.
Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.
From the original repo:
>You can use --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.
```py
import torch
from diffusers import AutoModel, SkyReelsV2DiffusionForcingPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video
model_id = "Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers"
vae = AutoModel.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipeline = SkyReelsV2DiffusionForcingPipeline.from_pretrained(
model_id,
vae=vae,
torch_dtype=torch.bfloat16,
)
pipeline.to("cuda")
flow_shift = 8.0 # 8.0 for T2V, 5.0 for I2V
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
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 = pipeline(
prompt=prompt,
num_inference_steps=30,
height=544, # 720 for 720P
width=960, # 1280 for 720P
num_frames=97,
base_num_frames=97, # 121 for 720P
ar_step=5, # Controls asynchronous inference (0 for synchronous mode)
causal_block_size=5, # Number of frames in each block for asynchronous processing
overlap_history=None, # Number of frames to overlap for smooth transitions in long videos; 17 for long video generations
addnoise_condition=20, # Improves consistency in long video generation
).frames[0]
export_to_video(output, "video.mp4", fps=24, quality=8)
```
### First-Last-Frame-to-Video Generation
The example below demonstrates how to use the image-to-video pipeline to generate a video using a text description, a starting frame, and an ending frame.
```python
import numpy as np
import torch
import torchvision.transforms.functional as TF
from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingImageToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video, load_image
model_id = "Skywork/SkyReels-V2-DF-1.3B-720P-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipeline = SkyReelsV2DiffusionForcingImageToVideoPipeline.from_pretrained(
model_id, vae=vae, torch_dtype=torch.bfloat16
)
pipeline.to("cuda")
flow_shift = 5.0 # 8.0 for T2V, 5.0 for I2V
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
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")
def aspect_ratio_resize(image, pipeline, max_area=720 * 1280):
aspect_ratio = image.height / image.width
mod_value = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size[1]
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
image = image.resize((width, height))
return image, height, width
def center_crop_resize(image, height, width):
# Calculate resize ratio to match first frame dimensions
resize_ratio = max(width / image.width, height / image.height)
# Resize the image
width = round(image.width * resize_ratio)
height = round(image.height * resize_ratio)
size = [width, height]
image = TF.center_crop(image, size)
return image, height, width
first_frame, height, width = aspect_ratio_resize(first_frame, pipeline)
if last_frame.size != first_frame.size:
last_frame, _, _ = center_crop_resize(last_frame, height, width)
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."
output = pipeline(
image=first_frame, last_image=last_frame, prompt=prompt, height=height, width=width, guidance_scale=5.0
).frames[0]
export_to_video(output, "video.mp4", fps=24, quality=8)
```
### Video-to-Video Generation
`SkyReelsV2DiffusionForcingVideoToVideoPipeline` extends a given video.
```python
import numpy as np
import torch
import torchvision.transforms.functional as TF
from diffusers import AutoencoderKLWan, SkyReelsV2DiffusionForcingVideoToVideoPipeline, UniPCMultistepScheduler
from diffusers.utils import export_to_video, load_video
model_id = "Skywork/SkyReels-V2-DF-1.3B-720P-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipeline = SkyReelsV2DiffusionForcingVideoToVideoPipeline.from_pretrained(
model_id, vae=vae, torch_dtype=torch.bfloat16
)
pipeline.to("cuda")
flow_shift = 5.0 # 8.0 for T2V, 5.0 for I2V
pipeline.scheduler = UniPCMultistepScheduler.from_config(pipeline.scheduler.config, flow_shift=flow_shift)
video = load_video("input_video.mp4")
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."
output = pipeline(
video=video, prompt=prompt, height=720, width=1280, guidance_scale=5.0, overlap_history=17,
num_inference_steps=30, num_frames=257, base_num_frames=121#, ar_step=5, causal_block_size=5,
).frames[0]
export_to_video(output, "video.mp4", fps=24, quality=8)
# Total frames will be the number of frames of the given video + 257
```
## Notes
- SkyReels-V2 supports LoRAs with [`~loaders.SkyReelsV2LoraLoaderMixin.load_lora_weights`].
`SkyReelsV2Pipeline` and `SkyReelsV2ImageToVideoPipeline` are also available without Diffusion Forcing framework applied.
## SkyReelsV2DiffusionForcingPipeline
[[autodoc]] SkyReelsV2DiffusionForcingPipeline
- all
- __call__
## SkyReelsV2DiffusionForcingImageToVideoPipeline
[[autodoc]] SkyReelsV2DiffusionForcingImageToVideoPipeline
- all
- __call__
## SkyReelsV2DiffusionForcingVideoToVideoPipeline
[[autodoc]] SkyReelsV2DiffusionForcingVideoToVideoPipeline
- all
- __call__
## SkyReelsV2Pipeline
[[autodoc]] SkyReelsV2Pipeline
- all
- __call__
## SkyReelsV2ImageToVideoPipeline
[[autodoc]] SkyReelsV2ImageToVideoPipeline
- all
- __call__
## SkyReelsV2PipelineOutput
[[autodoc]] pipelines.skyreels_v2.pipeline_output.SkyReelsV2PipelineOutput
