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--- |
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license: apache-2.0 |
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base_model: |
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- THUDM/CogVideoX-5b |
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language: |
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- en |
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tags: |
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- video-generation |
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- paddlemix |
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--- |
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Englishh | [简体中文](README_zh.md) |
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# VCtrl |
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<p style="text-align: center;"> |
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<p align="center"> |
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<a href="https://huggingface.co/PaddleMIX">🤗 Huggingface Space</a> | |
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<a href="https://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl">🌐 Github </a> | |
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<a href="">📜 arxiv </a> | |
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<a href="https://pp-vctrl.github.io/">📷 Project </a> |
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</p> |
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## Model Introduction |
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**VCtrl** is a versatile video generation control model that introduces an auxiliary conditional encoder to flexibly connect with various control modules while avoiding large-scale retraining of the original generator. The model efficiently transmits control signals through sparse residual connections and standardizes diverse control inputs into a unified representation via a consistent encoding process. Task-specific masks are further incorporated to enhance adaptability. Thanks to this unified and flexible design, VCtrl can be widely applied in **character animation**, **scene transition**, **video editing**, and other video generation scenarios. The table below provides detailed information about the video generation models we offer: |
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<table style="border-collapse: collapse; width: 100%;"> |
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<tr> |
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<th style="text-align: center;">Model Name</th> |
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<th style="text-align: center;">VCtrl-Canny</th> |
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<th style="text-align: center;">VCtrl-Mask</th> |
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<th style="text-align: center;">VCtrl-Pose</th> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Video Resolution</td> |
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<td colspan="1" style="text-align: center;">720 * 480</td> |
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<td colspan="1" style="text-align: center;"> 720 * 480 </td> |
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<td colspan="1 style="text-align: center;"> 720 * 480 & 480 * 720 </td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Inference Precision</td> |
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<td colspan="3" style="text-align: center;"><b>FP16(Recommended)</b></td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Single GPU VRAM Usage</td> |
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<td colspan="3" style="text-align: center;"><b>V100: 32GB minimum*</b></td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Inference Speed<br>(Step = 25, FP16)</td> |
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<td colspan="3" style="text-align: center;">Single A100: ~300s(49 frames)<br>Single V100: ~400s(49 frames)</td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Prompt Language</td> |
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<td colspan="5" style="text-align: center;">English*</td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Prompt Length Limit</td> |
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<td colspan="3" style="text-align: center;">224 Tokens</td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Video Length</td> |
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<td colspan="3" style="text-align: center;">T2V model supports only 49 frames, I2V model can extend to any frame count</td> |
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</tr> |
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<tr> |
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<td style="text-align: center;">Frame Rate</td> |
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<td colspan="3" style="text-align: center;">30 FPS </td> |
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</tr> |
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</table> |
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## Quick Start 🤗 |
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This model is now supported for deployment using the ppdiffusers library from paddlemix. Follow the steps below to get started. |
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**We recommend visiting our [github](https://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl) for a better experience.** |
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1. Install dependencies |
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```shell |
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# Clone the PaddleMIX repository |
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git clone https://github.com/PaddlePaddle/PaddleMIX.git |
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# Install paddlemix |
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cd PaddleMIX |
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pip install -e . |
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# Install ppdiffusers |
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pip install -e ppdiffusers |
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# Install paddlenlp |
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pip install paddlenlp==v3.0.0-beta2 |
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# Navigate to the vctrl directory |
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cd ppdiffusers/examples/ppvctrl |
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# Install other required dependencies |
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pip install -r requirements.txt |
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# Install paddlex |
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pip install paddlex==3.0.0b2 |
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``` |
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2. Run the code |
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```python |
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import os |
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import paddle |
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import numpy as np |
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from decord import VideoReader |
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from moviepy.editor import ImageSequenceClip |
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from PIL import Image |
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from ppdiffusers import ( |
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CogVideoXDDIMScheduler, |
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CogVideoXTransformer3DVCtrlModel, |
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CogVideoXVCtrlPipeline, |
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VCtrlModel, |
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) |
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def write_mp4(video_path, samples, fps=8): |
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clip = ImageSequenceClip(samples, fps=fps) |
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clip.write_videofile(video_path, audio_codec="aac") |
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def save_vid_side_by_side(batch_output, validation_control_images, output_folder, fps): |
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flattened_batch_output = [img for sublist in batch_output for img in sublist] |
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ori_video_path = output_folder + "/origin_predict.mp4" |
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video_path = output_folder + "/test_1.mp4" |
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ori_final_images = [] |
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final_images = [] |
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outputs = [] |
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def get_concat_h(im1, im2): |
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dst = Image.new("RGB", (im1.width + im2.width, max(im1.height, im2.height))) |
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dst.paste(im1, (0, 0)) |
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dst.paste(im2, (im1.width, 0)) |
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return dst |
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for image_list in zip(validation_control_images, flattened_batch_output): |
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predict_img = image_list[1].resize(image_list[0].size) |
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result = get_concat_h(image_list[0], predict_img) |
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ori_final_images.append(np.array(image_list[1])) |
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final_images.append(np.array(result)) |
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outputs.append(np.array(predict_img)) |
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write_mp4(ori_video_path, ori_final_images, fps=fps) |
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write_mp4(video_path, final_images, fps=fps) |
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output_path = output_folder + "/output.mp4" |
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write_mp4(output_path, outputs, fps=fps) |
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def load_images_from_folder_to_pil(folder): |
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images = [] |
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valid_extensions = {".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"} |
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def frame_number(filename): |
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new_pattern_match = re.search("frame_(\\d+)_7fps", filename) |
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if new_pattern_match: |
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return int(new_pattern_match.group(1)) |
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matches = re.findall("\\d+", filename) |
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if matches: |
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if matches[-1] == "0000" and len(matches) > 1: |
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return int(matches[-2]) |
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return int(matches[-1]) |
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return float("inf") |
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sorted_files = sorted(os.listdir(folder), key=frame_number) |
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for filename in sorted_files: |
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ext = os.path.splitext(filename)[1].lower() |
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if ext in valid_extensions: |
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img = Image.open(os.path.join(folder, filename)).convert("RGB") |
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images.append(img) |
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return images |
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def load_images_from_video_to_pil(video_path): |
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images = [] |
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vr = VideoReader(video_path) |
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length = len(vr) |
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for idx in range(length): |
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frame = vr[idx].asnumpy() |
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images.append(Image.fromarray(frame)) |
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return images |
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validation_control_images = load_images_from_video_to_pil('your_path') |
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prompt = 'Group of fishes swimming in aquarium.' |
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vctrl = VCtrlModel.from_pretrained( |
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paddlemix/vctrl-5b-t2v-canny, |
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low_cpu_mem_usage=True, |
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paddle_dtype=paddle.float16 |
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) |
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pipeline = CogVideoXVCtrlPipeline.from_pretrained( |
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paddlemix/cogvideox-5b-vctrl, |
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vctrl=vctrl, |
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paddle_dtype=paddle.float16, |
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low_cpu_mem_usage=True, |
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map_location="cpu", |
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) |
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pipeline.scheduler = CogVideoXDDIMScheduler.from_config(pipeline.scheduler.config, timestep_spacing="trailing") |
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pipeline.vae.enable_tiling() |
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pipeline.vae.enable_slicing() |
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task='canny' |
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final_result=[] |
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video = pipeline( |
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prompt=prompt, |
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num_inference_steps=25, |
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num_frames=49, |
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guidance_scale=35, |
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generator=paddle.Generator().manual_seed(42), |
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conditioning_frames=validation_control_images[:num_frames], |
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conditioning_frame_indices=list(range(num_frames)), |
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conditioning_scale=1.0, |
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width=720, |
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height=480, |
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task='canny', |
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conditioning_masks=validation_mask_images[:num_frames] if task == "mask" else None, |
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vctrl_layout_type='spacing', |
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).frames[0] |
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final_result.append(video) |
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save_vid_side_by_side(final_result, validation_control_images[:num_frames], 'save.mp4', fps=30) |
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``` |
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## In-Depth Exploration |
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Welcome to our[github]("https://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl")， where you will find： |
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1. More detailed technical explanations and code walkthroughs. |
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2. Algorithm details for extracting control conditions. |
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3. Detailed code for model inference. |
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4. Project update logs and more interactive opportunities. |
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5. PaddleMix toolchain to help you better utilize the model. |
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## Citation |
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``` |
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@article{yang2024cogvideox, |
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title={VCtrl: Enabling Versatile Controls for Video Diffusion Models}, |
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year={2025} |
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} |
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``` |
