SkyReels-I2V-720P

📑 Technical Report · 👋 Playground · 💬 Discord · 🤗 Hugging Face · 🤖 ModelScope · 🌐 GitHub

Welcome to the SkyReels V2 repository! Here, you'll find the model weights for our infinite-length film generative models. To the best of our knowledge, it represents the first open-source video generative model employing AutoRegressive Diffusion-Forcing architecture that achieves the SOTA performance among publicly available models.

🔥🔥🔥 News!!

• Apr 24, 2025: 🔥 We release the 720P models, SkyReels-V2-DF-14B-720P and SkyReels-V2-I2V-14B-720P. The former facilitates infinite-length autoregressive video generation, and the latter focuses on Image2Video synthesis.
• Apr 21, 2025: 👋 We release the inference code and model weights of SkyReels-V2 Series Models and the video captioning model SkyCaptioner-V1 .
• Apr 3, 2025: 🔥 We also release SkyReels-A2. This is an open-sourced controllable video generation framework capable of assembling arbitrary visual elements.
• Feb 18, 2025: 🔥 we released SkyReels-A1. This is an open-sourced and effective framework for portrait image animation.
• Feb 18, 2025: 🔥 We released SkyReels-V1. This is the first and most advanced open-source human-centric video foundation model.

🎥 Demos

The demos above showcase 30-second videos generated using our SkyReels-V2 Diffusion Forcing model.

📑 TODO List

• [x] Technical Report
• [x] Checkpoints of the 14B and 1.3B Models Series
• [x] Single-GPU & Multi-GPU Inference Code
• [x] SkyCaptioner-V1: A Video Captioning Model
• [x] Prompt Enhancer
• [ ] Diffusers integration
• [ ] Checkpoints of the 5B Models Series
• [ ] Checkpoints of the Camera Director Models
• [ ] Checkpoints of the Step & Guidance Distill Model

🚀 Quickstart

Installation

# clone the repository.
git clone https://github.com/SkyworkAI/SkyReels-V2
cd SkyReels-V2
# Install dependencies. Test environment uses Python 3.10.12.
pip install -r requirements.txt

Model Download

You can download our models from Hugging Face:

After downloading, set the model path in your generation commands:

Single GPU Inference

• Diffusion Forcing for Long Video Generation

The Diffusion Forcing version model allows us to generate Infinite-Length videos. This model supports both text-to-video (T2V) and image-to-video (I2V) tasks, and it can perform inference in both synchronous and asynchronous modes. Here we demonstrate 2 running scripts as examples for long video generation. If you want to adjust the inference parameters, e.g., the duration of video, inference mode, read the Note below first.

synchronous generation for 10s video

model_id=Skywork/SkyReels-V2-DF-14B-540P
# synchronous inference
python3 generate_video_df.py \
  --model_id ${model_id} \
  --resolution 540P \
  --ar_step 0 \
  --base_num_frames 97 \
  --num_frames 257 \
  --overlap_history 17 \
  --prompt "A graceful white swan with a curved neck and delicate feathers swimming in a serene lake at dawn, its reflection perfectly mirrored in the still water as mist rises from the surface, with the swan occasionally dipping its head into the water to feed." \
  --addnoise_condition 20 \
  --offload \
  --teacache \
  --use_ret_steps \
  --teacache_thresh 0.3

asynchronous generation for 30s video

model_id=Skywork/SkyReels-V2-DF-14B-540P
# asynchronous inference
python3 generate_video_df.py \
  --model_id ${model_id} \
  --resolution 540P \
  --ar_step 5 \
  --causal_block_size 5 \
  --base_num_frames 97 \
  --num_frames 737 \
  --overlap_history 17 \
  --prompt "A graceful white swan with a curved neck and delicate feathers swimming in a serene lake at dawn, its reflection perfectly mirrored in the still water as mist rises from the surface, with the swan occasionally dipping its head into the water to feed." \
  --addnoise_condition 20 \
  --offload

Note:

• If you want to run the image-to-video (I2V) task, add --image ${image_path} to your command and it is also better to use text-to-video (T2V)-like prompt which includes some descriptions of the first-frame image.
• For long video generation, you can just switch the --num_frames, e.g., --num_frames 257 for 10s video, --num_frames 377 for 15s video, --num_frames 737 for 30s video, --num_frames 1457 for 60s video. The number is not strictly aligned with the logical frame number for specified time duration, but it is aligned with some training parameters, which means it may perform better. When you use asynchronous inference with causal_block_size > 1, the --num_frames should be carefully set.
• 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. REMEMBER that the frame latent number inputted into the model in every iteration, e.g., base frame latent number (e.g., (97-1)//4+1=25 for base_num_frames=97) and (e.g., (237-97-(97-17)x1+17-1)//4+1=20 for base_num_frames=97, num_frames=237, overlap_history=17) for the last iteration, MUST be divided by causal_block_size. If you find it too hard to calculate and set proper values, just use our recommended setting above :). 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.
• To reduce peak VRAM, just lower the --base_num_frames, e.g., to 77 or 57, while keeping the same generative length --num_frames y