add README.md

README.md

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-license: apache-2.0
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+# CogVideoX-Fun-V1.1-Reward-LoRAs
+## Introduction
+We explore the Reward Backpropagation technique <sup>[1](#ref1) [2](#ref2)</sup> to optimized the generated videos by [CogVideoX-Fun-V1.1](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-V1.1-5b-InP-HPS2.1.safetensors | [CogVideoX-Fun-V1.1-5b](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP) | [HPS v2.1](https://github.com/tgxs002/HPSv2) | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-Reward-LoRAs/resolve/main/CogVideoX-Fun-V1.1-5b-InP-HPS2.1.safetensors) | Official HPS v2.1 reward LoRA (`rank=128` and `network_alpha=64`) for CogVideoX-Fun-V1.1-5b-InP. It is trained with a batch size of 8 for 1,500 steps.|
+| CogVideoX-Fun-V1.1-2b-InP-HPS2.1.safetensors | [CogVideoX-Fun-V1.1-2b](alibaba-pai/CogVideoX-Fun-V1.1-2b-InP) | [HPS v2.1](https://github.com/tgxs002/HPSv2) | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-Reward-LoRAs/resolve/main/CogVideoX-Fun-V1.1-2b-InP-HPS2.1.safetensors) | Official HPS v2.1 reward LoRA (`rank=128` and `network_alpha=64`) for CogVideoX-Fun-V1.1-2b-InP. It is trained with a batch size of 8 for 3,000 steps.|
+| CogVideoX-Fun-V1.1-5b-InP-MPS.safetensors | [CogVideoX-Fun-V1.1-5b](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP) | [MPS](https://github.com/Kwai-Kolors/MPS) | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-Reward-LoRAs/resolve/main/CogVideoX-Fun-V1.1-5b-InP-MPS.safetensors) | Official MPS reward LoRA (`rank=128` and `network_alpha=64`) for CogVideoX-Fun-V1.1-5b-InP. It is trained with a batch size of 8 for 5,500 steps.|
+| CogVideoX-Fun-V1.1-2b-InP-MPS.safetensors | [CogVideoX-Fun-V1.1-2b](alibaba-pai/CogVideoX-Fun-V1.1-2b-InP) | [MPS](https://github.com/Kwai-Kolors/MPS) | [🤗Link](https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-Reward-LoRAs/resolve/main/CogVideoX-Fun-V1.1-2b-InP-MPS.safetensors) | Official MPS reward LoRA (`rank=128` and `network_alpha=64`) for CogVideoX-Fun-V1.1-2b-InP. It is trained with a batch size of 8 for 16,000 steps.|
+
+## Demo
+### CogVideoX-Fun-V1.1-5B
+
+<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-V1.1-5B</th>
+            <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.1-5B <br> HPSv2.1 Reward LoRA</th>
+            <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.1-5B <br> MPS Reward LoRA</th>
+        </tr>
+    </thead>
+    <tr>
+        <td>
+            Pig with wings flying above a diamond mountain
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/6682f507-4ca2-45e9-9d76-86e2d709efb3" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/ec9219a2-96b3-44dd-b918-8176b2beb3b0" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/a75c6a6a-0b69-4448-afc0-fda3c7955ba0" width="100%" controls autoplay loop></video>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            A dog runs through a field while a cat climbs a tree
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/0392d632-2ec3-46b4-8867-0da1db577b6d" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/7d8c729d-6afb-408e-b812-67c40c3aaa96" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/dcd1343c-7435-4558-b602-9c0fa08cbd59" 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/af0df8e0-1edb-4e2c-9a87-70df2b564aef" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/59b840f7-d33c-4972-8024-11a097f1c419" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/4a1d0af0-54e3-455c-9930-0789e2346fa0" 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/99e44f9d-c770-48ce-8cc5-69fe36d757bc" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/9c106677-e4cb-4970-a1a2-a013fa6ce903" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/0a7b57ab-36a8-4fb6-bcfa-75e3878c55b7" width="100%" controls autoplay loop></video>
+        </td>
+    </tr>
+</table>
+
+### CogVideoX-Fun-V1.1-2B
+
+<table border="0" style="width: 100%; text-align: center; margin-top: 20px;">
+    <thead>
+        <tr>
+            <th style="text-align: center;" width="10%">Prompt</th>
+            <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.1-2B</th>
+            <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.1-2B <br> HPSv2.1 Reward LoRA</th>
+            <th style="text-align: center;" width="30%">CogVideoX-Fun-V1.1-2B <br> MPS Reward LoRA</th>
+        </tr>
+    </thead>
+    <tr>
+        <td>
+            A blue car drives past a white picket fence on a sunny day
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/274b0873-4fbd-4afa-94c0-22b23168f0a1" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/730f2ba3-4c54-44ce-ad5b-4eeca7ae844e" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/1b8eb777-0f17-46ef-9e7e-c8be7636e157" width="100%" controls autoplay loop></video>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            Blue jay swooping near a red maple tree
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/a14778d2-38ea-42c3-89a2-18164c48f3cf" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/90af433f-ab01-4341-9977-c675041d76d0" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/dafe8bf6-77ac-4934-8c9c-61c25088f80b" width="100%" controls autoplay loop></video>
+        </td>
+    </tr>
+    <tr>
+        <td>
+          Yellow curtains swaying near a blue sofa
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/e8a445a4-781b-4b3f-899b-2cc24201f247" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/318cfb00-8bd1-407f-aaee-8d4220573b82" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/6b90e8a4-1754-42f4-b454-73510ed0701d" width="100%" controls autoplay loop></video>
+        </td>
+    </tr>
+    <tr>
+        <td>
+            White tractor plowing near a green farmhouse
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/42d35282-e964-4c8b-aae9-a1592178493a" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/c9704bd4-d88d-41a1-8e5b-b7980df57a4a" width="100%" controls autoplay loop></video>
+        </td>
+        <td>
+            <video src="https://github.com/user-attachments/assets/7a785b34-4a5d-4491-9e03-c40cf953a1dc" width="100%" controls autoplay loop></video>
+        </td>
+    </tr>
+</table>
+
+> [!NOTE]
+> The above test prompts are from <a href="https://github.com/Vchitect/VBench/tree/master/prompts">VBench</a>. All videos are generated with lora weight 0.7.
+
+## Quick Start
+We provide a simple inference code to run CogVideoX-Fun-V1.1-5b-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-V1.1-5b-InP"
+lora_path = "alibaba-pai/CogVideoX-Fun-V1.1-Reward-LoRAs/CogVideoX-Fun-V1.1-5b-InP-HPS2.1.safetensors"
+lora_weight = 0.7
+
+prompt = "Pig with wings flying above a diamond mountain"
+sample_size = [512, 512]
+video_length = 49
+
+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, i.e., adversarial patches) to increase the reward.
+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>