| # CogVideoX-Fun-V1.5-Reward-LoRAs | |
| ## Introduction | |
| We explore the Reward Backpropagation technique <sup>[1](#ref1) [2](#ref2)</sup> to optimized the generated videos by [CogVideoX-Fun-V1.5](https://github.com/aigc-apps/CogVideoX-Fun) for better alignment with human preferences. | |
| We provide the following pre-trained models (i.e. LoRAs) along with [the training script](https://github.com/aigc-apps/CogVideoX-Fun/blob/main/scripts/train_reward_lora.py). You can use these LoRAs to enhance the corresponding base model as a plug-in or train your own reward LoRA. | |
| For more details, please refer to our [GitHub repo](https://github.com/aigc-apps/CogVideoX-Fun). | |
| | Name | Base Model | Reward Model | Hugging Face | Description | | |
| |--|--|--|--|--| | |
| | CogVideoX-Fun-V1.5-5b-InP-HPS2.1.safetensors | [CogVideoX-Fun-V1.5-5b](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.5-5b-InP) | [HPS v2.1](https://github.com/tgxs002/HPSv2) | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.5-Reward-LoRAs/resolve/main/CogVideoX-Fun-V1.5-5b-InP-HPS2.1.safetensors) | Official HPS v2.1 reward LoRA (`rank=128` and `network_alpha=64`) for CogVideoX-Fun-V1.5-5b-InP. It is trained with a batch size of 8 for 1,500 steps.| | |
| | CogVideoX-Fun-V1.5-5b-InP-MPS.safetensors | [CogVideoX-Fun-V1.5-5b](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.5-5b-InP) | [MPS](https://github.com/Kwai-Kolors/MPS) | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.5-Reward-LoRAs/resolve/main/CogVideoX-Fun-V1.5-5b-InP-MPS.safetensors) | Official MPS reward LoRA (`rank=128` and `network_alpha=64`) for CogVideoX-Fun-V1.5-5b-InP. It is trained with a batch size of 8 for 5,500 steps.| | |
| ## Demo | |
| ### CogVideoX-Fun-V1.5-5B | |
| <table border="0" style="width: 100%; text-align: center; margin-top: 20px;"> | |
| <thead> | |
| <tr> | |
| <th style="text-align: center;" width="10%">Prompt</sup></th> | |
| <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.5-5B</th> | |
| <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.5-5B <br> HPSv2.1 Reward LoRA</th> | |
| <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.5-5B <br> MPS Reward LoRA</th> | |
| </tr> | |
| </thead> | |
| <tr> | |
| <td> | |
| A panda eats bamboo while a monkey swings from branch to branch | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/ec752b06-cb13-4f9d-9c47-260536deba49" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/537a923c-fb64-474d-bbfb-c8ddf502a212" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/6bb3b860-57d3-4ac3-8898-b72b40753f2f" width="100%" controls autoplay loop></video> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td> | |
| A penguin waddles on the ice, a camel treks by | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/ad551233-5acf-4974-91cc-cd18591acbf4" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/2763fe09-436b-4407-9e6d-385518e1720c" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/19b93c29-5e7b-414f-914d-ae010f8faf29" width="100%" controls autoplay loop></video> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td> | |
| Elderly artist with a white beard painting on a white canvas | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/3560f91f-c68f-4567-a880-e3297464fb89" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/abbf827c-41e3-4e8b-9771-2f3b788985ca" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/328c85ce-1d22-428d-bf6d-1152d0457563" width="100%" controls autoplay loop></video> | |
| </td> | |
| </tr> | |
| <tr> | |
| <td> | |
| Crystal cake shimmering beside a metal apple | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/a94c74d3-8b75-41c3-9b21-0d53f9c67781" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/c9509e81-8bf7-4023-b8dd-1a3f7e5def3a" width="100%" controls autoplay loop></video> | |
| </td> | |
| <td> | |
| <video src="https://github.com/user-attachments/assets/37157443-0cc7-4371-9f24-ec228124c206" width="100%" controls autoplay loop></video> | |
| </td> | |
| </tr> | |
| </table> | |
| > [!NOTE] | |
| > The above test prompts are from <a href="https://github.com/KaiyueSun98/T2V-CompBench">T2V-CompBench</a>. All videos are generated with lora weight 0.7. | |
| ## Quick Start | |
| We provide a simple inference code to run CogVideoX-Fun-V1.5-5b-InP with its HPS2.1 reward LoRA. | |
| ```python | |
| import torch | |
| from diffusers import CogVideoXDDIMScheduler | |
| from cogvideox.models.transformer3d import CogVideoXTransformer3DModel | |
| from cogvideox.pipeline.pipeline_cogvideox_inpaint import CogVideoX_Fun_Pipeline_Inpaint | |
| from cogvideox.utils.lora_utils import merge_lora | |
| from cogvideox.utils.utils import get_image_to_video_latent, save_videos_grid | |
| model_path = "alibaba-pai/CogVideoX-Fun-V1.5-5b-InP" | |
| lora_path = "alibaba-pai/CogVideoX-Fun-V1.5-Reward-LoRAs/CogVideoX-Fun-V1.5-5b-InP-HPS2.1.safetensors" | |
| lora_weight = 0.7 | |
| prompt = "Pig with wings flying above a diamond mountain" | |
| sample_size = [512, 512] | |
| video_length = 85 | |
| transformer = CogVideoXTransformer3DModel.from_pretrained_2d(model_path, subfolder="transformer").to(torch.bfloat16) | |
| scheduler = CogVideoXDDIMScheduler.from_pretrained(model_path, subfolder="scheduler") | |
| pipeline = CogVideoX_Fun_Pipeline_Inpaint.from_pretrained( | |
| model_path, transformer=transformer, scheduler=scheduler, torch_dtype=torch.bfloat16 | |
| ) | |
| pipeline.enable_model_cpu_offload() | |
| pipeline = merge_lora(pipeline, lora_path, lora_weight) | |
| generator = torch.Generator(device="cuda").manual_seed(42) | |
| input_video, input_video_mask, _ = get_image_to_video_latent(None, None, video_length=video_length, sample_size=sample_size) | |
| sample = pipeline( | |
| prompt, | |
| num_frames = video_length, | |
| negative_prompt = "bad detailed", | |
| height = sample_size[0], | |
| width = sample_size[1], | |
| generator = generator, | |
| guidance_scale = 7.0, | |
| num_inference_steps = 50, | |
| video = input_video, | |
| mask_video = input_video_mask, | |
| ).videos | |
| save_videos_grid(sample, "samples/output.mp4", fps=8) | |
| ``` | |
| ## Limitations | |
| 1. We observe after training to a certain extent, the reward continues to increase, but the quality of the generated videos does not further improve. | |
| The model trickly learns some shortcuts (by adding artifacts in the background) to increase the reward (i.e., reward hacking). | |
| 2. Currently, there is still a lack of suitable preference models for video generation. Directly using image preference models cannot | |
| evaluate preferences along the temporal dimension (such as dynamism and consistency). Further more, We find using image preference models leads to a decrease | |
| in the dynamism of generated videos. Although this can be mitigated by computing the reward using only the first frame of the decoded video, the impact still persists. | |
| ## Reference | |
| <ol> | |
| <li id="ref1">Clark, Kevin, et al. "Directly fine-tuning diffusion models on differentiable rewards.". In ICLR 2024.</li> | |
| <li id="ref2">Prabhudesai, Mihir, et al. "Aligning text-to-image diffusion models with reward backpropagation." arXiv preprint arXiv:2310.03739 (2023).</li> | |
| </ol> |