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  4. README.md +257 -0
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  37. configs/inference_512_v1.0.yaml +103 -0
  38. configs/training_1024_v1.0/config.yaml +166 -0
  39. configs/training_1024_v1.0/run.sh +37 -0
  40. configs/training_512_v1.0/config.yaml +166 -0
  41. configs/training_512_v1.0/run.sh +37 -0
  42. gradio_app.py +82 -0
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  50. lvdm/data/webvid.py +202 -0
.gitattributes CHANGED
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+ .DS_Store
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+ .vscode
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+ __pycache__
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+ *.egg-info
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+ checkpoints
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+ backup
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+ LOG
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README.md ADDED
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+ ## ___***ToonCrafter: Generative Cartoon Interpolation***___
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+ <!-- ![](./assets/logo_long.png#gh-light-mode-only){: width="50%"} -->
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+ <!-- ![](./assets/logo_long_dark.png#gh-dark-mode-only=100x20) -->
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+ <div align="center">
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+
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+
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+
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+ </div>
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+
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+ ## 🔆 Introduction
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+
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+ ⚠️ Please check our [disclaimer](#disc) first.
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+
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+ 🤗 ToonCrafter can interpolate two cartoon images by leveraging the pre-trained image-to-video diffusion priors. Please check our project page and paper for more information. <br>
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+
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+
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+
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+
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+
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+
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+
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+ ### 1.1 Showcases (512x320)
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+ <table class="center">
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+ <tr style="font-weight: bolder;text-align:center;">
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+ <td>Input starting frame</td>
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+ <td>Input ending frame</td>
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+ <td>Generated video</td>
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+ </tr>
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+ <tr>
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+ <td>
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+ <img src=assets/72109_125.mp4_00-00.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/72109_125.mp4_00-01.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/00.gif width="250">
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+ </td>
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+ </tr>
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+
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+
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+ <tr>
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+ <td>
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+ <img src=assets/Japan_v2_2_062266_s2_frame1.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/Japan_v2_2_062266_s2_frame3.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/03.gif width="250">
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+ </td>
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+ </tr>
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+ <tr>
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+ <td>
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+ <img src=assets/Japan_v2_1_070321_s3_frame1.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/Japan_v2_1_070321_s3_frame3.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/02.gif width="250">
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+ </td>
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+ </tr>
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+ <tr>
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+ <td>
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+ <img src=assets/74302_1349_frame1.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/74302_1349_frame3.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/01.gif width="250">
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+ </td>
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+ </tr>
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+ </table>
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+
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+ ### 1.2 Sparse sketch guidance
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+ <table class="center">
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+ <tr style="font-weight: bolder;text-align:center;">
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+ <td>Input starting frame</td>
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+ <td>Input ending frame</td>
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+ <td>Input sketch guidance</td>
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+ <td>Generated video</td>
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+ </tr>
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+ <tr>
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+ <td>
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+ <img src=assets/72105_388.mp4_00-00.png width="200">
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+ </td>
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+ <td>
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+ <img src=assets/72105_388.mp4_00-01.png width="200">
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+ </td>
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+ <td>
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+ <img src=assets/06.gif width="200">
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+ </td>
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+ <td>
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+ <img src=assets/07.gif width="200">
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+ </td>
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+ </tr>
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+
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+ <tr>
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+ <td>
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+ <img src=assets/72110_255.mp4_00-00.png width="200">
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+ </td>
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+ <td>
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+ <img src=assets/72110_255.mp4_00-01.png width="200">
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+ </td>
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+ <td>
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+ <img src=assets/12.gif width="200">
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+ </td>
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+ <td>
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+ <img src=assets/13.gif width="200">
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+ </td>
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+ </tr>
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+
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+
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+ </table>
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+
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+
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+ ### 2. Applications
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+ #### 2.1 Cartoon Sketch Interpolation (see project page for more details)
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+ <table class="center">
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+ <tr style="font-weight: bolder;text-align:center;">
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+ <td>Input starting frame</td>
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+ <td>Input ending frame</td>
125
+ <td>Generated video</td>
126
+ </tr>
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+
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+ <tr>
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+ <td>
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+ <img src=assets/frame0001_10.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/frame0016_10.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/10.gif width="250">
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+ </td>
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+ </tr>
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+
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+
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+ <tr>
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+ <td>
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+ <img src=assets/frame0001_11.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/frame0016_11.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/11.gif width="250">
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+ </td>
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+ </tr>
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+
153
+ </table>
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+
155
+
156
+ #### 2.2 Reference-based Sketch Colorization
157
+ <table class="center">
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+ <tr style="font-weight: bolder;text-align:center;">
159
+ <td>Input sketch</td>
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+ <td>Input reference</td>
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+ <td>Colorization results</td>
162
+ </tr>
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+
164
+ <tr>
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+ <td>
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+ <img src=assets/04.gif width="250">
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+ </td>
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+ <td>
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+ <img src=assets/frame0001_05.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/05.gif width="250">
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+ </td>
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+ </tr>
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+
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+
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+ <tr>
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+ <td>
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+ <img src=assets/08.gif width="250">
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+ </td>
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+ <td>
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+ <img src=assets/frame0001_09.png width="250">
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+ </td>
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+ <td>
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+ <img src=assets/09.gif width="250">
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+ </td>
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+ </tr>
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+
189
+ </table>
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+
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+
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+
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+
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+
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+
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+
197
+ ## 📝 Changelog
198
+ - [ ] Add sketch control and colorization function.
199
+ - __[2024.05.29]__: 🔥🔥 Release code and model weights.
200
+ - __[2024.05.28]__: Launch the project page and update the arXiv preprint.
201
+ <br>
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+
203
+
204
+ ## 🧰 Models
205
+
206
+ |Model|Resolution|GPU Mem. & Inference Time (A100, ddim 50steps)|Checkpoint|
207
+ |:---------|:---------|:--------|:--------|
208
+ |ToonCrafter_512|320x512| TBD (`perframe_ae=True`)|[Hugging Face](https://huggingface.co/Doubiiu/ToonCrafter/blob/main/model.ckpt)|
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+
210
+
211
+ Currently, our ToonCrafter can support generating videos of up to 16 frames with a resolution of 512x320. The inference time can be reduced by using fewer DDIM steps.
212
+
213
+
214
+
215
+ ## ⚙️ Setup
216
+
217
+ ### Install Environment via Anaconda (Recommended)
218
+ ```bash
219
+ conda create -n tooncrafter python=3.8.5
220
+ conda activate tooncrafter
221
+ pip install -r requirements.txt
222
+ ```
223
+
224
+
225
+ ## 💫 Inference
226
+ ### 1. Command line
227
+
228
+ Download pretrained ToonCrafter_512 and put the `model.ckpt` in `checkpoints/tooncrafter_512_interp_v1/model.ckpt`.
229
+ ```bash
230
+ sh scripts/run.sh
231
+ ```
232
+
233
+
234
+ ### 2. Local Gradio demo
235
+
236
+ Download the pretrained model and put it in the corresponding directory according to the previous guidelines.
237
+ ```bash
238
+ python gradio_app.py
239
+ ```
240
+
241
+
242
+
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+
244
+
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+
246
+ <!-- ## 🤝 Community Support -->
247
+
248
+
249
+
250
+ <a name="disc"></a>
251
+ ## 📢 Disclaimer
252
+ Calm down. Our framework opens up the era of generative cartoon interpolation, but due to the variaity of generative video prior, the success rate is not guaranteed.
253
+
254
+ ⚠️This is an open-source research exploration, instead of commercial products. It can't meet all your expectations.
255
+
256
+ This project strives to impact the domain of AI-driven video generation positively. Users are granted the freedom to create videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users.
257
+ ****
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Git LFS Details

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  • Pointer size: 132 Bytes
  • Size of remote file: 1.03 MB
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configs/inference_512_v1.0.yaml ADDED
@@ -0,0 +1,103 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ target: lvdm.models.ddpm3d.LatentVisualDiffusion
3
+ params:
4
+ rescale_betas_zero_snr: True
5
+ parameterization: "v"
6
+ linear_start: 0.00085
7
+ linear_end: 0.012
8
+ num_timesteps_cond: 1
9
+ timesteps: 1000
10
+ first_stage_key: video
11
+ cond_stage_key: caption
12
+ cond_stage_trainable: False
13
+ conditioning_key: hybrid
14
+ image_size: [40, 64]
15
+ channels: 4
16
+ scale_by_std: False
17
+ scale_factor: 0.18215
18
+ use_ema: False
19
+ uncond_type: 'empty_seq'
20
+ use_dynamic_rescale: true
21
+ base_scale: 0.7
22
+ fps_condition_type: 'fps'
23
+ perframe_ae: True
24
+ loop_video: true
25
+ unet_config:
26
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
27
+ params:
28
+ in_channels: 8
29
+ out_channels: 4
30
+ model_channels: 320
31
+ attention_resolutions:
32
+ - 4
33
+ - 2
34
+ - 1
35
+ num_res_blocks: 2
36
+ channel_mult:
37
+ - 1
38
+ - 2
39
+ - 4
40
+ - 4
41
+ dropout: 0.1
42
+ num_head_channels: 64
43
+ transformer_depth: 1
44
+ context_dim: 1024
45
+ use_linear: true
46
+ use_checkpoint: True
47
+ temporal_conv: True
48
+ temporal_attention: True
49
+ temporal_selfatt_only: true
50
+ use_relative_position: false
51
+ use_causal_attention: False
52
+ temporal_length: 16
53
+ addition_attention: true
54
+ image_cross_attention: true
55
+ default_fs: 24
56
+ fs_condition: true
57
+
58
+ first_stage_config:
59
+ target: lvdm.models.autoencoder.AutoencoderKL_Dualref
60
+ params:
61
+ embed_dim: 4
62
+ monitor: val/rec_loss
63
+ ddconfig:
64
+ double_z: True
65
+ z_channels: 4
66
+ resolution: 256
67
+ in_channels: 3
68
+ out_ch: 3
69
+ ch: 128
70
+ ch_mult:
71
+ - 1
72
+ - 2
73
+ - 4
74
+ - 4
75
+ num_res_blocks: 2
76
+ attn_resolutions: []
77
+ dropout: 0.0
78
+ lossconfig:
79
+ target: torch.nn.Identity
80
+
81
+ cond_stage_config:
82
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
83
+ params:
84
+ freeze: true
85
+ layer: "penultimate"
86
+
87
+ img_cond_stage_config:
88
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
89
+ params:
90
+ freeze: true
91
+
92
+ image_proj_stage_config:
93
+ target: lvdm.modules.encoders.resampler.Resampler
94
+ params:
95
+ dim: 1024
96
+ depth: 4
97
+ dim_head: 64
98
+ heads: 12
99
+ num_queries: 16
100
+ embedding_dim: 1280
101
+ output_dim: 1024
102
+ ff_mult: 4
103
+ video_length: 16
configs/training_1024_v1.0/config.yaml ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ pretrained_checkpoint: checkpoints/dynamicrafter_1024_v1/model.ckpt
3
+ base_learning_rate: 1.0e-05
4
+ scale_lr: False
5
+ target: lvdm.models.ddpm3d.LatentVisualDiffusion
6
+ params:
7
+ rescale_betas_zero_snr: True
8
+ parameterization: "v"
9
+ linear_start: 0.00085
10
+ linear_end: 0.012
11
+ num_timesteps_cond: 1
12
+ log_every_t: 200
13
+ timesteps: 1000
14
+ first_stage_key: video
15
+ cond_stage_key: caption
16
+ cond_stage_trainable: False
17
+ image_proj_model_trainable: True
18
+ conditioning_key: hybrid
19
+ image_size: [72, 128]
20
+ channels: 4
21
+ scale_by_std: False
22
+ scale_factor: 0.18215
23
+ use_ema: False
24
+ uncond_prob: 0.05
25
+ uncond_type: 'empty_seq'
26
+ rand_cond_frame: true
27
+ use_dynamic_rescale: true
28
+ base_scale: 0.3
29
+ fps_condition_type: 'fps'
30
+ perframe_ae: True
31
+
32
+ unet_config:
33
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
34
+ params:
35
+ in_channels: 8
36
+ out_channels: 4
37
+ model_channels: 320
38
+ attention_resolutions:
39
+ - 4
40
+ - 2
41
+ - 1
42
+ num_res_blocks: 2
43
+ channel_mult:
44
+ - 1
45
+ - 2
46
+ - 4
47
+ - 4
48
+ dropout: 0.1
49
+ num_head_channels: 64
50
+ transformer_depth: 1
51
+ context_dim: 1024
52
+ use_linear: true
53
+ use_checkpoint: True
54
+ temporal_conv: True
55
+ temporal_attention: True
56
+ temporal_selfatt_only: true
57
+ use_relative_position: false
58
+ use_causal_attention: False
59
+ temporal_length: 16
60
+ addition_attention: true
61
+ image_cross_attention: true
62
+ default_fs: 10
63
+ fs_condition: true
64
+
65
+ first_stage_config:
66
+ target: lvdm.models.autoencoder.AutoencoderKL
67
+ params:
68
+ embed_dim: 4
69
+ monitor: val/rec_loss
70
+ ddconfig:
71
+ double_z: True
72
+ z_channels: 4
73
+ resolution: 256
74
+ in_channels: 3
75
+ out_ch: 3
76
+ ch: 128
77
+ ch_mult:
78
+ - 1
79
+ - 2
80
+ - 4
81
+ - 4
82
+ num_res_blocks: 2
83
+ attn_resolutions: []
84
+ dropout: 0.0
85
+ lossconfig:
86
+ target: torch.nn.Identity
87
+
88
+ cond_stage_config:
89
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
90
+ params:
91
+ freeze: true
92
+ layer: "penultimate"
93
+
94
+ img_cond_stage_config:
95
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
96
+ params:
97
+ freeze: true
98
+
99
+ image_proj_stage_config:
100
+ target: lvdm.modules.encoders.resampler.Resampler
101
+ params:
102
+ dim: 1024
103
+ depth: 4
104
+ dim_head: 64
105
+ heads: 12
106
+ num_queries: 16
107
+ embedding_dim: 1280
108
+ output_dim: 1024
109
+ ff_mult: 4
110
+ video_length: 16
111
+
112
+ data:
113
+ target: utils_data.DataModuleFromConfig
114
+ params:
115
+ batch_size: 1
116
+ num_workers: 12
117
+ wrap: false
118
+ train:
119
+ target: lvdm.data.webvid.WebVid
120
+ params:
121
+ data_dir: <WebVid10M DATA>
122
+ meta_path: <.csv FILE>
123
+ video_length: 16
124
+ frame_stride: 6
125
+ load_raw_resolution: true
126
+ resolution: [576, 1024]
127
+ spatial_transform: resize_center_crop
128
+ random_fs: true ## if true, we uniformly sample fs with max_fs=frame_stride (above)
129
+
130
+ lightning:
131
+ precision: 16
132
+ # strategy: deepspeed_stage_2
133
+ trainer:
134
+ benchmark: True
135
+ accumulate_grad_batches: 2
136
+ max_steps: 100000
137
+ # logger
138
+ log_every_n_steps: 50
139
+ # val
140
+ val_check_interval: 0.5
141
+ gradient_clip_algorithm: 'norm'
142
+ gradient_clip_val: 0.5
143
+ callbacks:
144
+ model_checkpoint:
145
+ target: pytorch_lightning.callbacks.ModelCheckpoint
146
+ params:
147
+ every_n_train_steps: 9000 #1000
148
+ filename: "{epoch}-{step}"
149
+ save_weights_only: True
150
+ metrics_over_trainsteps_checkpoint:
151
+ target: pytorch_lightning.callbacks.ModelCheckpoint
152
+ params:
153
+ filename: '{epoch}-{step}'
154
+ save_weights_only: True
155
+ every_n_train_steps: 10000 #20000 # 3s/step*2w=
156
+ batch_logger:
157
+ target: callbacks.ImageLogger
158
+ params:
159
+ batch_frequency: 500
160
+ to_local: False
161
+ max_images: 8
162
+ log_images_kwargs:
163
+ ddim_steps: 50
164
+ unconditional_guidance_scale: 7.5
165
+ timestep_spacing: uniform_trailing
166
+ guidance_rescale: 0.7
configs/training_1024_v1.0/run.sh ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # NCCL configuration
2
+ # export NCCL_DEBUG=INFO
3
+ # export NCCL_IB_DISABLE=0
4
+ # export NCCL_IB_GID_INDEX=3
5
+ # export NCCL_NET_GDR_LEVEL=3
6
+ # export NCCL_TOPO_FILE=/tmp/topo.txt
7
+
8
+ # args
9
+ name="training_1024_v1.0"
10
+ config_file=configs/${name}/config.yaml
11
+
12
+ # save root dir for logs, checkpoints, tensorboard record, etc.
13
+ save_root="<YOUR_SAVE_ROOT_DIR>"
14
+
15
+ mkdir -p $save_root/$name
16
+
17
+ ## run
18
+ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \
19
+ --nproc_per_node=$HOST_GPU_NUM --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
20
+ ./main/trainer.py \
21
+ --base $config_file \
22
+ --train \
23
+ --name $name \
24
+ --logdir $save_root \
25
+ --devices $HOST_GPU_NUM \
26
+ lightning.trainer.num_nodes=1
27
+
28
+ ## debugging
29
+ # CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \
30
+ # --nproc_per_node=4 --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
31
+ # ./main/trainer.py \
32
+ # --base $config_file \
33
+ # --train \
34
+ # --name $name \
35
+ # --logdir $save_root \
36
+ # --devices 4 \
37
+ # lightning.trainer.num_nodes=1
configs/training_512_v1.0/config.yaml ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ pretrained_checkpoint: checkpoints/dynamicrafter_512_v1/model.ckpt
3
+ base_learning_rate: 1.0e-05
4
+ scale_lr: False
5
+ target: lvdm.models.ddpm3d.LatentVisualDiffusion
6
+ params:
7
+ rescale_betas_zero_snr: True
8
+ parameterization: "v"
9
+ linear_start: 0.00085
10
+ linear_end: 0.012
11
+ num_timesteps_cond: 1
12
+ log_every_t: 200
13
+ timesteps: 1000
14
+ first_stage_key: video
15
+ cond_stage_key: caption
16
+ cond_stage_trainable: False
17
+ image_proj_model_trainable: True
18
+ conditioning_key: hybrid
19
+ image_size: [40, 64]
20
+ channels: 4
21
+ scale_by_std: False
22
+ scale_factor: 0.18215
23
+ use_ema: False
24
+ uncond_prob: 0.05
25
+ uncond_type: 'empty_seq'
26
+ rand_cond_frame: true
27
+ use_dynamic_rescale: true
28
+ base_scale: 0.7
29
+ fps_condition_type: 'fps'
30
+ perframe_ae: True
31
+
32
+ unet_config:
33
+ target: lvdm.modules.networks.openaimodel3d.UNetModel
34
+ params:
35
+ in_channels: 8
36
+ out_channels: 4
37
+ model_channels: 320
38
+ attention_resolutions:
39
+ - 4
40
+ - 2
41
+ - 1
42
+ num_res_blocks: 2
43
+ channel_mult:
44
+ - 1
45
+ - 2
46
+ - 4
47
+ - 4
48
+ dropout: 0.1
49
+ num_head_channels: 64
50
+ transformer_depth: 1
51
+ context_dim: 1024
52
+ use_linear: true
53
+ use_checkpoint: True
54
+ temporal_conv: True
55
+ temporal_attention: True
56
+ temporal_selfatt_only: true
57
+ use_relative_position: false
58
+ use_causal_attention: False
59
+ temporal_length: 16
60
+ addition_attention: true
61
+ image_cross_attention: true
62
+ default_fs: 10
63
+ fs_condition: true
64
+
65
+ first_stage_config:
66
+ target: lvdm.models.autoencoder.AutoencoderKL
67
+ params:
68
+ embed_dim: 4
69
+ monitor: val/rec_loss
70
+ ddconfig:
71
+ double_z: True
72
+ z_channels: 4
73
+ resolution: 256
74
+ in_channels: 3
75
+ out_ch: 3
76
+ ch: 128
77
+ ch_mult:
78
+ - 1
79
+ - 2
80
+ - 4
81
+ - 4
82
+ num_res_blocks: 2
83
+ attn_resolutions: []
84
+ dropout: 0.0
85
+ lossconfig:
86
+ target: torch.nn.Identity
87
+
88
+ cond_stage_config:
89
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPEmbedder
90
+ params:
91
+ freeze: true
92
+ layer: "penultimate"
93
+
94
+ img_cond_stage_config:
95
+ target: lvdm.modules.encoders.condition.FrozenOpenCLIPImageEmbedderV2
96
+ params:
97
+ freeze: true
98
+
99
+ image_proj_stage_config:
100
+ target: lvdm.modules.encoders.resampler.Resampler
101
+ params:
102
+ dim: 1024
103
+ depth: 4
104
+ dim_head: 64
105
+ heads: 12
106
+ num_queries: 16
107
+ embedding_dim: 1280
108
+ output_dim: 1024
109
+ ff_mult: 4
110
+ video_length: 16
111
+
112
+ data:
113
+ target: utils_data.DataModuleFromConfig
114
+ params:
115
+ batch_size: 2
116
+ num_workers: 12
117
+ wrap: false
118
+ train:
119
+ target: lvdm.data.webvid.WebVid
120
+ params:
121
+ data_dir: <WebVid10M DATA>
122
+ meta_path: <.csv FILE>
123
+ video_length: 16
124
+ frame_stride: 6
125
+ load_raw_resolution: true
126
+ resolution: [320, 512]
127
+ spatial_transform: resize_center_crop
128
+ random_fs: true ## if true, we uniformly sample fs with max_fs=frame_stride (above)
129
+
130
+ lightning:
131
+ precision: 16
132
+ # strategy: deepspeed_stage_2
133
+ trainer:
134
+ benchmark: True
135
+ accumulate_grad_batches: 2
136
+ max_steps: 100000
137
+ # logger
138
+ log_every_n_steps: 50
139
+ # val
140
+ val_check_interval: 0.5
141
+ gradient_clip_algorithm: 'norm'
142
+ gradient_clip_val: 0.5
143
+ callbacks:
144
+ model_checkpoint:
145
+ target: pytorch_lightning.callbacks.ModelCheckpoint
146
+ params:
147
+ every_n_train_steps: 9000 #1000
148
+ filename: "{epoch}-{step}"
149
+ save_weights_only: True
150
+ metrics_over_trainsteps_checkpoint:
151
+ target: pytorch_lightning.callbacks.ModelCheckpoint
152
+ params:
153
+ filename: '{epoch}-{step}'
154
+ save_weights_only: True
155
+ every_n_train_steps: 10000 #20000 # 3s/step*2w=
156
+ batch_logger:
157
+ target: callbacks.ImageLogger
158
+ params:
159
+ batch_frequency: 500
160
+ to_local: False
161
+ max_images: 8
162
+ log_images_kwargs:
163
+ ddim_steps: 50
164
+ unconditional_guidance_scale: 7.5
165
+ timestep_spacing: uniform_trailing
166
+ guidance_rescale: 0.7
configs/training_512_v1.0/run.sh ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # NCCL configuration
2
+ # export NCCL_DEBUG=INFO
3
+ # export NCCL_IB_DISABLE=0
4
+ # export NCCL_IB_GID_INDEX=3
5
+ # export NCCL_NET_GDR_LEVEL=3
6
+ # export NCCL_TOPO_FILE=/tmp/topo.txt
7
+
8
+ # args
9
+ name="training_512_v1.0"
10
+ config_file=configs/${name}/config.yaml
11
+
12
+ # save root dir for logs, checkpoints, tensorboard record, etc.
13
+ save_root="<YOUR_SAVE_ROOT_DIR>"
14
+
15
+ mkdir -p $save_root/$name
16
+
17
+ ## run
18
+ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python3 -m torch.distributed.launch \
19
+ --nproc_per_node=$HOST_GPU_NUM --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
20
+ ./main/trainer.py \
21
+ --base $config_file \
22
+ --train \
23
+ --name $name \
24
+ --logdir $save_root \
25
+ --devices $HOST_GPU_NUM \
26
+ lightning.trainer.num_nodes=1
27
+
28
+ ## debugging
29
+ # CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \
30
+ # --nproc_per_node=4 --nnodes=1 --master_addr=127.0.0.1 --master_port=12352 --node_rank=0 \
31
+ # ./main/trainer.py \
32
+ # --base $config_file \
33
+ # --train \
34
+ # --name $name \
35
+ # --logdir $save_root \
36
+ # --devices 4 \
37
+ # lightning.trainer.num_nodes=1
gradio_app.py ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, argparse
2
+ import sys
3
+ import gradio as gr
4
+ from scripts.gradio.i2v_test_application import Image2Video
5
+ sys.path.insert(1, os.path.join(sys.path[0], 'lvdm'))
6
+
7
+
8
+ i2v_examples_interp_512 = [
9
+ ['prompts/512_interp/74906_1462_frame1.png', 'walking man', 50, 7.5, 1.0, 10, 123, 'prompts/512_interp/74906_1462_frame3.png'],
10
+ ['prompts/512_interp/Japan_v2_2_062266_s2_frame1.png', 'an anime scene', 50, 7.5, 1.0, 10, 789, 'prompts/512_interp/Japan_v2_2_062266_s2_frame3.png'],
11
+ ['prompts/512_interp/Japan_v2_3_119235_s2_frame1.png', 'an anime scene', 50, 7.5, 1.0, 10, 123, 'prompts/512_interp/Japan_v2_3_119235_s2_frame3.png'],
12
+ ]
13
+
14
+
15
+
16
+
17
+ def dynamicrafter_demo(result_dir='./tmp/', res=512):
18
+ if res == 1024:
19
+ resolution = '576_1024'
20
+ css = """#input_img {max-width: 1024px !important} #output_vid {max-width: 1024px; max-height:576px}"""
21
+ elif res == 512:
22
+ resolution = '320_512'
23
+ css = """#input_img {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px} #input_img2 {max-width: 512px !important} #output_vid {max-width: 512px; max-height: 320px}"""
24
+ elif res == 256:
25
+ resolution = '256_256'
26
+ css = """#input_img {max-width: 256px !important} #output_vid {max-width: 256px; max-height: 256px}"""
27
+ else:
28
+ raise NotImplementedError(f"Unsupported resolution: {res}")
29
+ image2video = Image2Video(result_dir, resolution=resolution)
30
+ with gr.Blocks(analytics_enabled=False, css=css) as dynamicrafter_iface:
31
+
32
+
33
+
34
+ with gr.Tab(label='ToonCrafter_320x512'):
35
+ with gr.Column():
36
+ with gr.Row():
37
+ with gr.Column():
38
+ with gr.Row():
39
+ i2v_input_image = gr.Image(label="Input Image1",elem_id="input_img")
40
+ with gr.Row():
41
+ i2v_input_text = gr.Text(label='Prompts')
42
+ with gr.Row():
43
+ i2v_seed = gr.Slider(label='Random Seed', minimum=0, maximum=50000, step=1, value=123)
44
+ i2v_eta = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, label='ETA', value=1.0, elem_id="i2v_eta")
45
+ i2v_cfg_scale = gr.Slider(minimum=1.0, maximum=15.0, step=0.5, label='CFG Scale', value=7.5, elem_id="i2v_cfg_scale")
46
+ with gr.Row():
47
+ i2v_steps = gr.Slider(minimum=1, maximum=60, step=1, elem_id="i2v_steps", label="Sampling steps", value=50)
48
+ i2v_motion = gr.Slider(minimum=5, maximum=30, step=1, elem_id="i2v_motion", label="FPS", value=10)
49
+ i2v_end_btn = gr.Button("Generate")
50
+ with gr.Column():
51
+ with gr.Row():
52
+ i2v_input_image2 = gr.Image(label="Input Image2",elem_id="input_img2")
53
+ with gr.Row():
54
+ i2v_output_video = gr.Video(label="Generated Video",elem_id="output_vid",autoplay=True,show_share_button=True)
55
+
56
+ gr.Examples(examples=i2v_examples_interp_512,
57
+ inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_input_image2],
58
+ outputs=[i2v_output_video],
59
+ fn = image2video.get_image,
60
+ cache_examples=False,
61
+ )
62
+ i2v_end_btn.click(inputs=[i2v_input_image, i2v_input_text, i2v_steps, i2v_cfg_scale, i2v_eta, i2v_motion, i2v_seed, i2v_input_image2],
63
+ outputs=[i2v_output_video],
64
+ fn = image2video.get_image
65
+ )
66
+
67
+
68
+ return dynamicrafter_iface
69
+
70
+ def get_parser():
71
+ parser = argparse.ArgumentParser()
72
+ return parser
73
+
74
+ if __name__ == "__main__":
75
+ parser = get_parser()
76
+ args = parser.parse_args()
77
+
78
+ result_dir = os.path.join('./', 'results')
79
+ dynamicrafter_iface = dynamicrafter_demo(result_dir)
80
+ dynamicrafter_iface.queue(max_size=12)
81
+ #dynamicrafter_iface.launch(max_threads=1)
82
+ dynamicrafter_iface.launch(server_name='0.0.0.0', server_port=8080, max_threads=1)
lvdm/__pycache__/basics.cpython-310.pyc ADDED
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lvdm/__pycache__/common.cpython-310.pyc ADDED
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lvdm/__pycache__/distributions.cpython-310.pyc ADDED
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lvdm/__pycache__/ema.cpython-310.pyc ADDED
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lvdm/basics.py ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # adopted from
2
+ # https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
3
+ # and
4
+ # https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
5
+ # and
6
+ # https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
7
+ #
8
+ # thanks!
9
+
10
+ import torch.nn as nn
11
+ from utils.utils import instantiate_from_config
12
+
13
+
14
+ def disabled_train(self, mode=True):
15
+ """Overwrite model.train with this function to make sure train/eval mode
16
+ does not change anymore."""
17
+ return self
18
+
19
+ def zero_module(module):
20
+ """
21
+ Zero out the parameters of a module and return it.
22
+ """
23
+ for p in module.parameters():
24
+ p.detach().zero_()
25
+ return module
26
+
27
+ def scale_module(module, scale):
28
+ """
29
+ Scale the parameters of a module and return it.
30
+ """
31
+ for p in module.parameters():
32
+ p.detach().mul_(scale)
33
+ return module
34
+
35
+
36
+ def conv_nd(dims, *args, **kwargs):
37
+ """
38
+ Create a 1D, 2D, or 3D convolution module.
39
+ """
40
+ if dims == 1:
41
+ return nn.Conv1d(*args, **kwargs)
42
+ elif dims == 2:
43
+ return nn.Conv2d(*args, **kwargs)
44
+ elif dims == 3:
45
+ return nn.Conv3d(*args, **kwargs)
46
+ raise ValueError(f"unsupported dimensions: {dims}")
47
+
48
+
49
+ def linear(*args, **kwargs):
50
+ """
51
+ Create a linear module.
52
+ """
53
+ return nn.Linear(*args, **kwargs)
54
+
55
+
56
+ def avg_pool_nd(dims, *args, **kwargs):
57
+ """
58
+ Create a 1D, 2D, or 3D average pooling module.
59
+ """
60
+ if dims == 1:
61
+ return nn.AvgPool1d(*args, **kwargs)
62
+ elif dims == 2:
63
+ return nn.AvgPool2d(*args, **kwargs)
64
+ elif dims == 3:
65
+ return nn.AvgPool3d(*args, **kwargs)
66
+ raise ValueError(f"unsupported dimensions: {dims}")
67
+
68
+
69
+ def nonlinearity(type='silu'):
70
+ if type == 'silu':
71
+ return nn.SiLU()
72
+ elif type == 'leaky_relu':
73
+ return nn.LeakyReLU()
74
+
75
+
76
+ class GroupNormSpecific(nn.GroupNorm):
77
+ def forward(self, x):
78
+ return super().forward(x.float()).type(x.dtype)
79
+
80
+
81
+ def normalization(channels, num_groups=32):
82
+ """
83
+ Make a standard normalization layer.
84
+ :param channels: number of input channels.
85
+ :return: an nn.Module for normalization.
86
+ """
87
+ return GroupNormSpecific(num_groups, channels)
88
+
89
+
90
+ class HybridConditioner(nn.Module):
91
+
92
+ def __init__(self, c_concat_config, c_crossattn_config):
93
+ super().__init__()
94
+ self.concat_conditioner = instantiate_from_config(c_concat_config)
95
+ self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
96
+
97
+ def forward(self, c_concat, c_crossattn):
98
+ c_concat = self.concat_conditioner(c_concat)
99
+ c_crossattn = self.crossattn_conditioner(c_crossattn)
100
+ return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
lvdm/common.py ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import math
2
+ from inspect import isfunction
3
+ import torch
4
+ from torch import nn
5
+ import torch.distributed as dist
6
+
7
+
8
+ def gather_data(data, return_np=True):
9
+ ''' gather data from multiple processes to one list '''
10
+ data_list = [torch.zeros_like(data) for _ in range(dist.get_world_size())]
11
+ dist.all_gather(data_list, data) # gather not supported with NCCL
12
+ if return_np:
13
+ data_list = [data.cpu().numpy() for data in data_list]
14
+ return data_list
15
+
16
+ def autocast(f):
17
+ def do_autocast(*args, **kwargs):
18
+ with torch.cuda.amp.autocast(enabled=True,
19
+ dtype=torch.get_autocast_gpu_dtype(),
20
+ cache_enabled=torch.is_autocast_cache_enabled()):
21
+ return f(*args, **kwargs)
22
+ return do_autocast
23
+
24
+
25
+ def extract_into_tensor(a, t, x_shape):
26
+ b, *_ = t.shape
27
+ out = a.gather(-1, t)
28
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
29
+
30
+
31
+ def noise_like(shape, device, repeat=False):
32
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
33
+ noise = lambda: torch.randn(shape, device=device)
34
+ return repeat_noise() if repeat else noise()
35
+
36
+
37
+ def default(val, d):
38
+ if exists(val):
39
+ return val
40
+ return d() if isfunction(d) else d
41
+
42
+ def exists(val):
43
+ return val is not None
44
+
45
+ def identity(*args, **kwargs):
46
+ return nn.Identity()
47
+
48
+ def uniq(arr):
49
+ return{el: True for el in arr}.keys()
50
+
51
+ def mean_flat(tensor):
52
+ """
53
+ Take the mean over all non-batch dimensions.
54
+ """
55
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
56
+
57
+ def ismap(x):
58
+ if not isinstance(x, torch.Tensor):
59
+ return False
60
+ return (len(x.shape) == 4) and (x.shape[1] > 3)
61
+
62
+ def isimage(x):
63
+ if not isinstance(x,torch.Tensor):
64
+ return False
65
+ return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
66
+
67
+ def max_neg_value(t):
68
+ return -torch.finfo(t.dtype).max
69
+
70
+ def shape_to_str(x):
71
+ shape_str = "x".join([str(x) for x in x.shape])
72
+ return shape_str
73
+
74
+ def init_(tensor):
75
+ dim = tensor.shape[-1]
76
+ std = 1 / math.sqrt(dim)
77
+ tensor.uniform_(-std, std)
78
+ return tensor
79
+
80
+ ckpt = torch.utils.checkpoint.checkpoint
81
+ def checkpoint(func, inputs, params, flag):
82
+ """
83
+ Evaluate a function without caching intermediate activations, allowing for
84
+ reduced memory at the expense of extra compute in the backward pass.
85
+ :param func: the function to evaluate.
86
+ :param inputs: the argument sequence to pass to `func`.
87
+ :param params: a sequence of parameters `func` depends on but does not
88
+ explicitly take as arguments.
89
+ :param flag: if False, disable gradient checkpointing.
90
+ """
91
+ if flag:
92
+ return ckpt(func, *inputs, use_reentrant=False)
93
+ else:
94
+ return func(*inputs)
lvdm/data/base.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from abc import abstractmethod
2
+ from torch.utils.data import IterableDataset
3
+
4
+
5
+ class Txt2ImgIterableBaseDataset(IterableDataset):
6
+ '''
7
+ Define an interface to make the IterableDatasets for text2img data chainable
8
+ '''
9
+ def __init__(self, num_records=0, valid_ids=None, size=256):
10
+ super().__init__()
11
+ self.num_records = num_records
12
+ self.valid_ids = valid_ids
13
+ self.sample_ids = valid_ids
14
+ self.size = size
15
+
16
+ print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
17
+
18
+ def __len__(self):
19
+ return self.num_records
20
+
21
+ @abstractmethod
22
+ def __iter__(self):
23
+ pass
lvdm/data/webvid.py ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import random
3
+ from tqdm import tqdm
4
+ import pandas as pd
5
+ from decord import VideoReader, cpu
6
+
7
+ import torch
8
+ from torch.utils.data import Dataset
9
+ from torch.utils.data import DataLoader
10
+ from torchvision import transforms
11
+
12
+
13
+ class WebVid(Dataset):
14
+ """
15
+ WebVid Dataset.
16
+ Assumes webvid data is structured as follows.
17
+ Webvid/
18
+ videos/
19
+ 000001_000050/ ($page_dir)
20
+ 1.mp4 (videoid.mp4)
21
+ ...
22
+ 5000.mp4
23
+ ...
24
+ """
25
+ def __init__(self,
26
+ meta_path,
27
+ data_dir,
28
+ subsample=None,
29
+ video_length=16,
30
+ resolution=[256, 512],
31
+ frame_stride=1,
32
+ frame_stride_min=1,
33
+ spatial_transform=None,
34
+ crop_resolution=None,
35
+ fps_max=None,
36
+ load_raw_resolution=False,
37
+ fixed_fps=None,
38
+ random_fs=False,
39
+ ):
40
+ self.meta_path = meta_path
41
+ self.data_dir = data_dir
42
+ self.subsample = subsample
43
+ self.video_length = video_length
44
+ self.resolution = [resolution, resolution] if isinstance(resolution, int) else resolution
45
+ self.fps_max = fps_max
46
+ self.frame_stride = frame_stride
47
+ self.frame_stride_min = frame_stride_min
48
+ self.fixed_fps = fixed_fps
49
+ self.load_raw_resolution = load_raw_resolution
50
+ self.random_fs = random_fs
51
+ self._load_metadata()
52
+ if spatial_transform is not None:
53
+ if spatial_transform == "random_crop":
54
+ self.spatial_transform = transforms.RandomCrop(crop_resolution)
55
+ elif spatial_transform == "center_crop":
56
+ self.spatial_transform = transforms.Compose([
57
+ transforms.CenterCrop(resolution),
58
+ ])
59
+ elif spatial_transform == "resize_center_crop":
60
+ # assert(self.resolution[0] == self.resolution[1])
61
+ self.spatial_transform = transforms.Compose([
62
+ transforms.Resize(min(self.resolution)),
63
+ transforms.CenterCrop(self.resolution),
64
+ ])
65
+ elif spatial_transform == "resize":
66
+ self.spatial_transform = transforms.Resize(self.resolution)
67
+ else:
68
+ raise NotImplementedError
69
+ else:
70
+ self.spatial_transform = None
71
+
72
+ def _load_metadata(self):
73
+ metadata = pd.read_csv(self.meta_path)
74
+ print(f'>>> {len(metadata)} data samples loaded.')
75
+ if self.subsample is not None:
76
+ metadata = metadata.sample(self.subsample, random_state=0)
77
+
78
+ metadata['caption'] = metadata['name']
79
+ del metadata['name']
80
+ self.metadata = metadata
81
+ self.metadata.dropna(inplace=True)
82
+
83
+ def _get_video_path(self, sample):
84
+ rel_video_fp = os.path.join(sample['page_dir'], str(sample['videoid']) + '.mp4')
85
+ full_video_fp = os.path.join(self.data_dir, 'videos', rel_video_fp)
86
+ return full_video_fp
87
+
88
+ def __getitem__(self, index):
89
+ if self.random_fs:
90
+ frame_stride = random.randint(self.frame_stride_min, self.frame_stride)
91
+ else:
92
+ frame_stride = self.frame_stride
93
+
94
+ ## get frames until success
95
+ while True:
96
+ index = index % len(self.metadata)
97
+ sample = self.metadata.iloc[index]
98
+ video_path = self._get_video_path(sample)
99
+ ## video_path should be in the format of "....../WebVid/videos/$page_dir/$videoid.mp4"
100
+ caption = sample['caption']
101
+
102
+ try:
103
+ if self.load_raw_resolution:
104
+ video_reader = VideoReader(video_path, ctx=cpu(0))
105
+ else:
106
+ video_reader = VideoReader(video_path, ctx=cpu(0), width=530, height=300)
107
+ if len(video_reader) < self.video_length:
108
+ print(f"video length ({len(video_reader)}) is smaller than target length({self.video_length})")
109
+ index += 1
110
+ continue
111
+ else:
112
+ pass
113
+ except:
114
+ index += 1
115
+ print(f"Load video failed! path = {video_path}")
116
+ continue
117
+
118
+ fps_ori = video_reader.get_avg_fps()
119
+ if self.fixed_fps is not None:
120
+ frame_stride = int(frame_stride * (1.0 * fps_ori / self.fixed_fps))
121
+
122
+ ## to avoid extreme cases when fixed_fps is used
123
+ frame_stride = max(frame_stride, 1)
124
+
125
+ ## get valid range (adapting case by case)
126
+ required_frame_num = frame_stride * (self.video_length-1) + 1
127
+ frame_num = len(video_reader)
128
+ if frame_num < required_frame_num:
129
+ ## drop extra samples if fixed fps is required
130
+ if self.fixed_fps is not None and frame_num < required_frame_num * 0.5:
131
+ index += 1
132
+ continue
133
+ else:
134
+ frame_stride = frame_num // self.video_length
135
+ required_frame_num = frame_stride * (self.video_length-1) + 1
136
+
137
+ ## select a random clip
138
+ random_range = frame_num - required_frame_num
139
+ start_idx = random.randint(0, random_range) if random_range > 0 else 0
140
+
141
+ ## calculate frame indices
142
+ frame_indices = [start_idx + frame_stride*i for i in range(self.video_length)]
143
+ try:
144
+ frames = video_reader.get_batch(frame_indices)
145
+ break
146
+ except:
147
+ print(f"Get frames failed! path = {video_path}; [max_ind vs frame_total:{max(frame_indices)} / {frame_num}]")
148
+ index += 1
149
+ continue
150
+
151
+ ## process data
152
+ assert(frames.shape[0] == self.video_length),f'{len(frames)}, self.video_length={self.video_length}'
153
+ frames = torch.tensor(frames.asnumpy()).permute(3, 0, 1, 2).float() # [t,h,w,c] -> [c,t,h,w]
154
+
155
+ if self.spatial_transform is not None:
156
+ frames = self.spatial_transform(frames)
157
+
158
+ if self.resolution is not None:
159
+ assert (frames.shape[2], frames.shape[3]) == (self.resolution[0], self.resolution[1]), f'frames={frames.shape}, self.resolution={self.resolution}'
160
+
161
+ ## turn frames tensors to [-1,1]
162
+ frames = (frames / 255 - 0.5) * 2
163
+ fps_clip = fps_ori // frame_stride
164
+ if self.fps_max is not None and fps_clip > self.fps_max:
165
+ fps_clip = self.fps_max
166
+
167
+ data = {'video': frames, 'caption': caption, 'path': video_path, 'fps': fps_clip, 'frame_stride': frame_stride}
168
+ return data
169
+
170
+ def __len__(self):
171
+ return len(self.metadata)
172
+
173
+
174
+ if __name__== "__main__":
175
+ meta_path = "" ## path to the meta file
176
+ data_dir = "" ## path to the data directory
177
+ save_dir = "" ## path to the save directory
178
+ dataset = WebVid(meta_path,
179
+ data_dir,
180
+ subsample=None,
181
+ video_length=16,
182
+ resolution=[256,448],
183
+ frame_stride=4,
184
+ spatial_transform="resize_center_crop",
185
+ crop_resolution=None,
186
+ fps_max=None,
187
+ load_raw_resolution=True
188
+ )
189
+ dataloader = DataLoader(dataset,
190
+ batch_size=1,
191
+ num_workers=0,
192
+ shuffle=False)
193
+
194
+
195
+ import sys
196
+ sys.path.insert(1, os.path.join(sys.path[0], '..', '..'))
197
+ from utils.save_video import tensor_to_mp4
198
+ for i, batch in tqdm(enumerate(dataloader), desc="Data Batch"):
199
+ video = batch['video']
200
+ name = batch['path'][0].split('videos/')[-1].replace('/','_')
201
+ tensor_to_mp4(video, save_dir+'/'+name, fps=8)
202
+