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README.md

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-license: apache-2.0
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+# 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>