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--- 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. 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. > **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

Video generation
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--- 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. 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. > **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

Video generation
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Visual QA
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> IMPORTANT: > This model was trained using the decontaminated version of the original Skywork Reward Preference dataset, now referred to as **v0.2**. The updated dataset, Skywork-Reward-Preference-80K-v0.2, removes 4,957 contaminated pairs from the magpie-ultra-v0.1 subset, which had significant n-gram overlap with the evaluation prompts in RewardBench. You can find the set of removed pairs here. For more detailed information, please refer to this GitHub gist. > > **If your task involves evaluation on RewardBench, we strongly recommend using v0.2 of both the dataset and the models instead of v0.1, to ensure proper decontamination and avoid any contamination issues.** **Skywork-Reward-Gemma-2-27B-v0.2** and **Skywork-Reward-Llama-3.1-8B-v0.2** are two advanced reward models built on the gemma-2-27b-it and Llama-3.1-8B-Instruct architectures, respectively. Both models were trained using the Skywork Reward Data Collection containing only 80K high-quality preference pairs sourced from publicly available data. We include only public data in an attempt to demonstrate that high-performance reward models can be achieved with a relatively small dataset and straightforward data curation techniques, without further algorithmic or architectural modifications. The sources of data used in the Skywork Reward Data Collection are detailed in the Data Mixture section below.

Text analysis
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> IMPORTANT: > This model was trained using the decontaminated version of the original Skywork Reward Preference dataset, now referred to as **v0.2**. The updated dataset, Skywork-Reward-Preference-80K-v0.2, removes 4,957 contaminated pairs from the magpie-ultra-v0.1 subset, which had significant n-gram overlap with the evaluation prompts in RewardBench. You can find the set of removed pairs here. For more detailed information, please refer to this GitHub gist. > > **If your task involves evaluation on RewardBench, we strongly recommend using v0.2 of both the dataset and the models instead of v0.1, to ensure proper decontamination and avoid any contamination issues.** We include only public data in an attempt to demonstrate that high-performance reward models can be achieved with a relatively small dataset and straightforward data curation techniques, without further algorithmic or architectural modifications. The sources of data used in the Skywork Reward Data Collection are detailed in the Data Mixture section below. The resulting reward models excel at handling preferences in complex scenarios, including challenging preference pairs, and span various domains such as mathematics, coding, and safety.

Text analysis
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