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@@ -206,3 +206,6 @@ o_voxel-0.0.1-cp311-cp311-linux_x86_64.whl filter=lfs diff=lfs merge=lfs -text
 spconv_cu130-2.3.8-cp311-cp311-linux_x86_64.whl filter=lfs diff=lfs merge=lfs -text
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 fairseq-0.12.3-cp310-cp310-win_amd64.whl filter=lfs diff=lfs merge=lfs -text
+SparkVSR-bf16/assets/inference_pipeline.png filter=lfs diff=lfs merge=lfs -text
+SparkVSR-bf16/assets/training_pipeline.png filter=lfs diff=lfs merge=lfs -text
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SparkVSR-bf16/LICENSE

@@ -0,0 +1,71 @@
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SparkVSR-bf16/README.md

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+---
+license: apache-2.0
+---
+<div align="center">
+  <p><img src="assets/logo2.png" width="360px"></p>
+  <h1>SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation</h1>
+  <p>
+    Jiongze Yu<sup>1</sup>, Xiangbo Gao<sup>1</sup>, Pooja Verlani<sup>2</sup>, Akshay Gadde<sup>2</sup>,
+    Yilin Wang<sup>2</sup>, Balu Adsumilli<sup>2</sup>, Zhengzhong Tu<sup>†,1</sup>
+  </p>
+  <p>
+    <sup>1</sup>Texas A&amp;M University &nbsp;&nbsp; <sup>2</sup>YouTube, Google
+    <br>
+    <sup>†</sup>Corresponding author
+  </p>
+  <p>
+    <a href="https://sparkvsr.github.io/"><img src="https://img.shields.io/badge/Project-Page-Green"></a>
+    &nbsp;
+    <a href="https://huggingface.co/JiongzeYu/SparkVSR"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue"></a>
+    &nbsp;
+    <a href="https://arxiv.org/abs/2603.16864"><img src="https://img.shields.io/badge/arXiv-2603.16864-b31b1b.svg"></a>
+  </p>
+</div>
+
+#### 📰 News
+
+- **2026.03.17:** This repo is released.🔥🔥🔥
+
+---
+
+> **Abstract:** Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference time: users cannot reliably correct unexpected artifacts, but instead can only accept whatever the model produces. 
+In this paper, we propose a novel interactive VSR framework dubbed SparkVSR that makes sparse keyframes a simple and expressive control signal. Specifically, users can first super-resolve or optionally a small set of keyframes using any off-the-shelf image super-resolution (ISR) model, then SparkVSR propagates the keyframe priors to the entire video sequence while remaining grounded by the original LR video motion.
+Concretely, we introduce a keyframe-conditioned latent-pixel two-stage training pipeline that fuses LR video latents with sparsely encoded HR keyframe latents to learn robust cross-space propagation and refine perceptual details. At inference time, SparkVSR supports flexible keyframe selection (manual specification, codec I-frame extraction, or random sampling) and a reference-free guidance mechanism that continuously balances keyframe adherence and blind restoration, ensuring robust performance even when reference keyframes are absent or imperfect. Experiments on multiple VSR benchmarks demonstrate improved temporal consistency and strong restoration quality, surpassing baselines by up to 24.6\%, 21.8\%, and 5.6\% on CLIP-IQA, DOVER, and MUSIQ, respectively, enabling controllable, keyframe-driven video super-resolution.
+Moreover, we demonstrate that SparkVSR is a generic interactive, keyframe-conditioned video processing framework as it can be applied out of the box to unseen tasks such as old-film restoration and video style transfer.
+
+---
+
+### Inference Pipeline
+
+<p align="center">
+  <img src="assets/inference_pipeline.png">
+</p>
+
+
+---
+
+### Training Pipeline
+
+<p align="center">
+  <img src="assets/training_pipeline.png">
+</p>
+
+
+
+
+## 🔖 TODO
+
+- ✅ Release inference code.
+- ✅ Release pre-trained models.
+- ✅ Release training code.
+- ✅ Release project page.
+- ⬜ Release ComfyUI.
+
+## ⚙️ Dependencies
+
+- Python 3.10+
+- PyTorch >= 2.5.0
+- Diffusers
+- Other dependencies (see `requirements.txt`)
+
+```bash
+# Clone the github repo and go to the directory
+git clone https://github.com/taco-group/SparkVSR
+cd SparkVSR
+
+# Create and activate conda environment
+conda create -n sparkvsr python=3.10
+conda activate sparkvsr
+
+# Install all required dependencies
+pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cu124
+pip install -r requirements.txt
+```
+
+The installation command may need to be adjusted according to your platform, CUDA version, and desired PyTorch version. Please check the [official PyTorch previous versions page](https://pytorch.org/get-started/previous-versions/) for more options.
+
+## 📖 Contents
+
+1. [Datasets](#datasets)
+1. [Models](#models)
+1. [Training](#training)
+1. [Inference](#inference)
+1. [Citation](#citation)
+1. [Acknowledgements](#acknowledgements)
+
+## <a name="datasets"></a>📁 Datasets
+
+### 🗳️ Train Datasets
+
+Our model is trained on the same datasets as [DOVE](https://github.com/zhengchen1999/DOVE): **HQ-VSR** and **DIV2K-HR**. All datasets should be placed in the directory `datasets/train/`.
+
+| Dataset      | Type  | # Videos / Images | Download                                                     |
+| ------------ | ----- | ----------------- | ------------------------------------------------------------ |
+| **HQ-VSR**   | Video | 2,055             | [Google Drive](https://drive.google.com/file/d/1a4-n8WpV8rJar5qOFJ0GyivhZCv5bCQD/view?usp=sharing) |
+| **DIV2K-HR** | Image | 800               | [Official Link](http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip) |
+
+All datasets should follow this structure:
+
+```shell
+datasets/
+└── train/
+    ├── HQ-VSR/
+    └── DIV2K_train_HR/
+```
+
+
+
+### 🗳️ Test Datasets
+
+We use several real-world and synthetic test datasets for evaluation. All datasets follow a consistent directory structure:
+
+| Dataset |    Type    | # Videos | Average Frames |                           Download                           |
+| :------ | :--------: | :---: | :---: | :----------------------------------------------------------: |
+| UDM10   | Synthetic  |  10   | 32    | [Google Drive](https://drive.google.com/file/d/1AmGVSCwMm_OFPd3DKgNyTwj0GG2H-tG4/view?usp=drive_link) |
+| SPMCS   | Synthetic  |  30   | 32    | [Google Drive](https://drive.google.com/file/d/1b2uktCFPKS-R1fTecWcLFcOnmUFIBNWT/view?usp=drive_link) |
+| YouHQ40 | Synthetic  |  40   | 32    | [Google Drive](https://drive.google.com/file/d/1zO23UCStxL3htPJQcDUUnUeMvDrysLTh/view?usp=sharing) |
+| RealVSR | Real-world |  50   | 50    | [Google Drive](https://drive.google.com/file/d/1wr4tTiCvQlqdYPeU1dmnjb5KFY4VjGCO/view?usp=drive_link) |
+| MovieLQ | Old-movie | 10  | 192  | [Google Drive](https://drive.google.com/file/d/1qreKCO1Zn8bW_do8HxbPYiIWdPhXRE9G/view?usp=drive_link) |
+
+Make sure the path (`datasets/test/`) is correct before running inference.
+
+The directory structure is as follows:
+
+```shell
+datasets/
+└── test/
+    └── [DatasetName]/
+        ├── GT/         # Ground Truth: folder of high-quality frames (one per clip)
+        ├── GT-Video/   # Ground Truth (video version): lossless MKV format
+        ├── LQ/         # Low-quality Input: folder of degraded frames (one per clip)
+        └── LQ-Video/   # Low-Quality Input (video version): lossless MKV format
+```
+
+### 📊 Dataset Preparation (Path Lists)
+
+Before training or testing, you need to generate `.txt` files containing the relative paths of all valid video and image files in your dataset directories. These text lists act as the index for the dataloader during training and inference. Run the following commands:
+
+```bash
+# 🔹 Train dataset
+python finetune/scripts/prepare_dataset.py --dir datasets/train/HQ-VSR
+python finetune/scripts/prepare_dataset.py --dir datasets/train/DIV2K_train_HR
+
+# 🔹 Testing dataset
+python finetune/scripts/prepare_dataset.py --dir datasets/test/UDM10/GT-Video
+python finetune/scripts/prepare_dataset.py --dir datasets/test/UDM10/LQ-Video
+# (You may need to repeat the above for other test datasets as needed)
+```
+
+
+## <a name="models"></a>📦 Models
+
+Our model is built upon the **CogVideoX1.5-5B-I2V** base model. We provide pretrained weights for SparkVSR at different training stages.
+
+| Model Name            |                   Description                    | HuggingFace |
+| :-------------------- | :----------------------------------------------: | :---------: |
+| CogVideoX1.5-5B-I2V | Base model used for initialization |    [zai-org/CogVideoX1.5-5B-I2V](https://huggingface.co/zai-org/CogVideoX1.5-5B-I2V)     |
+| SparkVSR (Stage-1) | SparkVSR Stage-1 trained weights |    [JiongzeYu/SparkVSR-S1](https://huggingface.co/JiongzeYu/SparkVSR-S1)     |
+| SparkVSR (Stage-2) | SparkVSR Stage-2 final weights |    [JiongzeYu/SparkVSR](https://huggingface.co/JiongzeYu/SparkVSR)     |
+
+> 💡 **Placement of Models:**
+> - Place the base model (`CogVideoX1.5-5B-I2V`) into the `pretrained_weights/` folder.
+> - Place the downloaded SparkVSR weights (Stage-1 and Stage-2) into the `checkpoints/` folder.
+
+## <a name="training"></a>🔧 Training
+
+> **Note:** Training requires 4×A100 GPUs.
+> ⚠️ **Important:** The Stage-1 weight is the intermediate result of our first training stage and is trained only in latent space. We release it mainly for training-time validation and comparison. The Stage-2 model is the final SparkVSR model.
+
+- 🔹 **Stage-1 (Latent-Space): Keyframe-Conditioned Adaptation.** Enter the `finetune/` directory and start training:
+
+  ```bash
+  cd finetune/
+  bash sparkvsr_train_s1_ref.sh
+  ```
+
+  This stage adapts the base model to VSR by learning to fuse LR video latents with sparse HR keyframe latents for robust cross-space propagation.
+
+- 🔹 **Stage-2 (Pixel-Space): Detail Refinement.** First, convert the Stage-1 checkpoint into a loadable SFT weight format:
+
+  ```bash
+  python scripts/prepare_sft_ckpt.py --checkpoint_dir ../checkpoint/SparkVSR-s1/checkpoint-10000
+  ```
+  *(Adjust the path and step number to match your actual training output).*
+
+  > You can skip Stage-1 by downloading our [SparkVSR Stage-1 weight](https://huggingface.co/JiongzeYu/SparkVSR-S1) as the starting point for Stage-2.
+
+  Then, run the second-stage fine-tuning:
+
+  ```bash
+  bash sparkvsr_train_s2_ref.sh
+  ```
+
+  This stage refines perceptual details in pixel space, ensuring adherence to provided keyframes while simultaneously maintaining strong **no-reference blind SR** capabilities when keyframes are absent or imperfect.
+
+- Finally, convert the Stage-2 checkpoint for inference:
+
+  ```bash
+  python scripts/prepare_sft_ckpt.py --checkpoint_dir ../checkpoint/SparkVSR-s2/checkpoint-500
+  ```
+
+## <a name="inference"></a>🔨 Inference
+
+- For a quick test, you can directly run SparkVSR on the sample videos in `test_input/`.
+- The example commands below are configured to use `test_input/` for fast testing.
+- Before running inference on benchmark datasets, make sure you have downloaded the corresponding pre-trained models and test datasets.
+- The full inference commands are provided in the shell script: `sparkvsr_inference.sh`.
+
+SparkVSR supports flexible keyframe propagation through three primary inference modes (`--ref_mode`).
+
+> ⚠️ **Important:** Always use the **Stage-2** checkpoint for inference. The Stage-1 checkpoint is only an intermediate latent-space result and is not our final model.
+>
+> 💡 **Recommendation:** Among the three inference modes, we strongly recommend the two reference-guided settings: `api` mode (with `nano-banana-pro` as the reference generator) and `pisasr` mode (with PiSA-SR as the reference generator). In these modes, SparkVSR injects high-quality spatial details through the reference frames. By contrast, `no_ref` does not use external reference frames and should be treated mainly as a practical fallback and a comparison baseline, rather than the final showcase setting. If you do not have access to the `nano-banana-pro` API, we strongly recommend using `pisasr` as the reference source.
+
+### 🌟 Global Customization Flags
+
+Regardless of the mode you choose, you can customize the temporal propagation behavior using these flags:
+- **`--ref_indices`**: Specifies the indices of the keyframes you want to use as references (0-indexed). 
+  - *Example:* `--ref_indices 0 96`
+  - ⚠️ **Important:** The interval between any two reference frame indices must be strictly **greater than 4**.
+- **`--ref_guidance_scale`**: Controls the strength of the reference keyframe's influence on the output video (Default is `1.0`). Increasing this value forces the model to adhere more strictly to the provided keyframes.
+- For short video clips (for example, clips within 2 seconds or around 48 frames), we strongly recommend using only the first frame as the reference signal: `--ref_indices 0`.
+
+---
+
+### 1️⃣ API Mode (`--ref_mode api`)
+Uses keyframes restored by a commercial API as the condition signal. SparkVSR defaults to using the impressive `fal-ai/nano-banana-pro/edit` endpoint.
+
+> ⚠️ **Setup Requirement:**
+> 1. Open `finetune/utils/ref_utils.py`.
+> 2. Locate the configuration block at the top of the file.
+> 3. Replace `'your_fal_key'` with your actual API key.
+> 4. *(Optional)* Customize the `TASK_PROMPT` in the same file to better guide the restoration process.
+
+```shell
+MODEL_PATH="checkpoints/sparkvsr-s2/ckpt-500-sft" 
+
+CUDA_VISIBLE_DEVICES=0 python sparkvsr_inference_script.py \
+    --input_dir test_input \
+    --model_path $MODEL_PATH \
+    --output_path results/test_input/api_ref \
+    --is_vae_st \
+    --ref_mode api \
+    --ref_prompt_mode fixed \
+    --ref_guidance_scale 1.0 \
+    --upscale 4 \
+    --ref_indices 0
+```
+
+### 2️⃣ PiSA-SR Mode (`--ref_mode pisasr`)
+Uses keyframes restored by the open-source PiSA-SR model.
+
+> ⚠️ **Setup Requirement:**
+> 1. Clone the [PiSA-SR Repository](https://github.com/csslc/PiSA-SR) and follow their instructions to install dependencies in a separate Conda environment.
+> 2. Download their pre-trained weights (`stable-diffusion-2-1-base` and `pisa_sr.pkl`).
+> 3. Update the `--pisa_*` flags in `sparkvsr_inference.sh` to point to your actual cloned PiSA-SR directory, environment, and desired GPU.
+
+```shell
+MODEL_PATH="checkpoints/sparkvsr-s2/ckpt-500-sft" 
+
+CUDA_VISIBLE_DEVICES=0 python sparkvsr_inference_script.py \
+    --input_dir test_input \
+    --model_path $MODEL_PATH \
+    --output_path results/test_input/pisa_ref \
+    --is_vae_st \
+    --ref_mode pisasr \
+    --ref_prompt_mode fixed \
+    --ref_guidance_scale 1.0 \
+    --upscale 4 \
+    --ref_indices 0 \
+    --pisa_python_executable "path/to/your/pisasr/conda/env/bin/python" \
+    --pisa_script_path "path/to/your/PiSA-SR/test_pisasr.py" \
+    --pisa_sd_model_path "path/to/your/PiSA-SR/preset/models/stable-diffusion-2-1-base" \
+    --pisa_chkpt_path "path/to/your/PiSA-SR/preset/models/pisa_sr.pkl" \
+    --pisa_gpu "0"
+```
+
+### 3️⃣ Optional: No-Ref Mode (`--ref_mode no_ref`)
+Performs blind video super-resolution without any reference keyframes. This mode is useful as a practical fallback and baseline, but it is not the recommended setting for the best visual quality.
+
+```shell
+MODEL_PATH="checkpoints/sparkvsr-s2/ckpt-500-sft" 
+
+CUDA_VISIBLE_DEVICES=0 python sparkvsr_inference_script.py \
+    --input_dir test_input \
+    --model_path $MODEL_PATH \
+    --output_path results/test_input/no_ref \
+    --is_vae_st \
+    --ref_mode no_ref \
+    --ref_prompt_mode fixed \
+    --ref_guidance_scale 1.0 \
+    --upscale 4
+```
+
+> 💡 **Note:** All three of the above inference modes and their complete execution commands are fully organized and ready to run in the [`sparkvsr_inference.sh`](./sparkvsr_inference.sh) script!
+
+### 📏 Metric Evaluation
+
+To quantitatively evaluate the super-resolved videos, we provide a unified evaluation script: [`run_eval_all.sh`](./run_eval_all.sh).
+
+> ⚠️ **Evaluation Setup Requirement:**
+> To calculate **DOVER** and **FastVQA/FasterVQA** scores, you must clone their respective repositories and place them (along with their weights) into the `metrics/` directory.
+> 1. Clone [VQAssessment/DOVER](https://github.com/VQAssessment/DOVER) into `metrics/DOVER`.
+> 2. Clone [VQAssessment/FAST-VQA-and-FasterVQA](https://github.com/VQAssessment/FAST-VQA-and-FasterVQA) into `metrics/FastVQA`.
+> 3. Download the pre-trained weights specified in their repositories to their respective nested algorithm folders.
+
+Once the metrics are set up, you can simply run the unified evaluation script [`run_eval_all.sh`](./run_eval_all.sh) to calculate the scores. The evaluation results will be saved as `all_metrics_results.json` in your specified output directory.
+
+## <a name="citation"></a>📎 Citation
+
+If you find the code helpful in your research or work, please cite the following paper(s).
+
+```bibtex
+@misc{yu2026sparkvsrinteractivevideosuperresolution,
+      title={SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation}, 
+      author={Jiongze Yu and Xiangbo Gao and Pooja Verlani and Akshay Gadde and Yilin Wang and Balu Adsumilli and Zhengzhong Tu},
+      year={2026},
+      eprint={2603.16864},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.16864}, 
+}
+```
+
+## <a name="acknowledgements"></a>💡 Acknowledgements
+
+Our work is built upon the solid foundations laid by [DOVE](https://github.com/zhengchen1999/DOVE) and [CogVideoX](https://github.com/THUDM/CogVideo). We sincerely thank the authors for their excellent open-source contributions.

SparkVSR-bf16/README_zh.md

@@ -0,0 +1,223 @@
+# CogVideoX1.5-5B-I2V
+
+<p style="text-align: center;">
+  <div align="center">
+  <img src=https://github.com/THUDM/CogVideo/raw/main/resources/logo.svg width="50%"/>
+  </div>
+  <p align="center">
+  <a href="https://huggingface.co/THUDM/CogVideoX1.5-5B-I2V/blob/main/README.md">📄 Read in English</a> | 
+  <a href="https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space">🤗 Huggingface Space</a> |
+  <a href="https://github.com/THUDM/CogVideo">🌐 Github </a> | 
+  <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
+</p>
+<p align="center">
+📍 前往<a href="https://chatglm.cn/video?fr=osm_cogvideox"> 清影</a> 和 <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9"> API平台</a> 体验商业版视频生成模型
+</p>
+
+## 模型介绍
+
+CogVideoX是 [清影](https://chatglm.cn/video?fr=osm_cogvideo) 同源的开源版本视频生成模型。下表展示我们在本代提供的视频生成模型列表相关信息:
+
+<table  style="border-collapse: collapse; width: 100%;">
+  <tr>
+    <th style="text-align: center;">模型名</th>
+    <th style="text-align: center;">CogVideoX1.5-5B</th>
+    <th style="text-align: center;">CogVideoX1.5-5B-I2V (当前仓库)</th>
+  </tr>
+  <tr>
+    <td style="text-align: center;">视频分辨率</td>
+    <td colspan="1" style="text-align: center;">1360 * 768</td>
+    <td colspan="1" style="text-align: center;"> Min(W, H) = 768 <br> 768 ≤ Max(W, H) ≤ 1360 <br> Max(W, H) % 16 = 0 </td>
+    </tr>
+  <tr>
+    <td style="text-align: center;">推理精度</td>
+    <td colspan="2" style="text-align: center;"><b>BF16(推荐)</b>, FP16, FP32，FP8*，INT8，不支持INT4</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">单GPU显存消耗</td>
+    <td colspan="2"  style="text-align: center;"><b>BF16: 9GB minimum*</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">多GPU显存消耗</td>
+    <td colspan="2" style="text-align: center;"><b>BF16: 24GB* using diffusers</b><br></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">推理速度<br>(Step = 50, FP/BF16)</td>
+    <td colspan="2" style="text-align: center;">单卡A100: ~1000秒(5秒视频)<br>单卡H100: ~550秒(5秒视频)</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">提示词语言</td>
+    <td colspan="5" style="text-align: center;">English*</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">提示词长度上限</td>
+    <td colspan="2" style="text-align: center;">224 Tokens</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">视频长度</td>
+    <td colspan="2" style="text-align: center;">5 秒 或 10 秒</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">帧率</td>
+    <td colspan="2" style="text-align: center;">16 帧 / 秒 </td>
+  </tr>
+</table>
+
+**数据解释**
+
++ 使用 diffusers 库进行测试时，启用了全部`diffusers`库自带的优化，该方案未测试在非**NVIDIA A100 / H100**
+  外的设备上的实际显存 / 内存占用。通常，该方案可以适配于所有 **NVIDIA 安培架构**
+  以上的设备。若关闭优化，显存占用会成倍增加，峰值显存约为表格的3倍。但速度提升3-4倍左右。你可以选择性的关闭部分优化，这些优化包括:
+
+```
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_slicing()
+pipe.vae.enable_tiling()
+```
+
++ 多GPU推理时，需要关闭 `enable_sequential_cpu_offload()` 优化。
++ 使用 INT8 模型会导致推理速度降低，此举是为了满足显存较低的显卡能正常推理并保持较少的视频质量损失，推理速度大幅降低。
++ [PytorchAO](https://github.com/pytorch/ao) 和 [Optimum-quanto](https://github.com/huggingface/optimum-quanto/)
+  可以用于量化文本编码器、Transformer 和 VAE 模块，以降低 CogVideoX 的内存需求。这使得在较小显存的 GPU
+  上运行模型成为可能！同样值得注意的是，TorchAO 量化完全兼容 `torch.compile`，这可以显著提高推理速度。在 `NVIDIA H100`
+  及以上设备上必须使用 `FP8` 精度，这需要源码安装 `torch`、`torchao`、`diffusers` 和 `accelerate` Python
+  包。建议使用 `CUDA 12.4`。
++ 推理速度测试同样采用了上述显存优化方案，不采用显存优化的情况下，推理速度提升约10%。 只有`diffusers`版本模型支持量化。
++ 模型仅支持英语输入，其他语言可以通过大模型润色时翻译为英语。
+
+**提醒**
+
++ 使用 [SAT](https://github.com/THUDM/SwissArmyTransformer) 推理和微调SAT版本模型。欢迎前往我们的github查看。
+
+## 快速上手 🤗
+
+本模型已经支持使用 huggingface 的 diffusers 库进行部署，你可以按照以下步骤进行部署。
+
+**我们推荐您进入我们的 [github](https://github.com/THUDM/CogVideo) 并查看相关的提示词优化和转换，以获得更好的体验。**
+
+1. 安装对应的依赖
+
+```shell
+# diffusers>=0.32.0dev (or from source)
+# transformers>=4.46.2
+# accelerate>=1.1.1
+# imageio-ffmpeg>=0.5.1
+pip install --upgrade transformers accelerate diffusers imageio-ffmpeg 
+```
+
+2. 运行代码
+
+```python 
+import torch
+from diffusers import CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+
+prompt = "A little girl is riding a bicycle at high speed. Focused, detailed, realistic."
+image = load_image(image="input.jpg")
+pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+    "THUDM/CogVideoX1.5-5B-I2V",
+    torch_dtype=torch.bfloat16
+)
+
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+video = pipe(
+    prompt=prompt,
+    image=image,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=81,
+    guidance_scale=6,
+    generator=torch.Generator(device="cuda").manual_seed(42),
+).frames[0]
+
+export_to_video(video, "output.mp4", fps=8)
+```
+
+## Quantized Inference
+
+[PytorchAO](https://github.com/pytorch/ao) 和 [Optimum-quanto](https://github.com/huggingface/optimum-quanto/)
+可以用于对文本编码器、Transformer 和 VAE 模块进行量化，从而降低 CogVideoX 的内存需求。这使得在免费的 T4 Colab 或较小 VRAM 的
+GPU 上运行该模型成为可能！值得注意的是，TorchAO 量化与 `torch.compile` 完全兼容，这可以显著加快推理速度。
+
+```python
+# To get started, PytorchAO needs to be installed from the GitHub source and PyTorch Nightly.
+# Source and nightly installation is only required until the next release.
+
+import torch
+from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import T5EncoderModel
+from torchao.quantization import quantize_, int8_weight_only
+
+quantization = int8_weight_only
+
+text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX1.5-5B-I2V", subfolder="text_encoder", torch_dtype=torch.bfloat16)
+quantize_(text_encoder, quantization())
+
+transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX1.5-5B-I2V",subfolder="transformer", torch_dtype=torch.bfloat16)
+quantize_(transformer, quantization())
+
+vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX1.5-5B-I2V", subfolder="vae", torch_dtype=torch.bfloat16)
+quantize_(vae, quantization())
+
+# Create pipeline and run inference
+pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+    "THUDM/CogVideoX1.5-5B-I2V",
+    text_encoder=text_encoder,
+    transformer=transformer,
+    vae=vae,
+    torch_dtype=torch.bfloat16,
+)
+
+pipe.enable_model_cpu_offload()
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+prompt = "A little girl is riding a bicycle at high speed. Focused, detailed, realistic."
+image = load_image(image="input.jpg")
+video = pipe(
+    prompt=prompt,
+    image=image,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=81,
+    guidance_scale=6,
+    generator=torch.Generator(device="cuda").manual_seed(42),
+).frames[0]
+
+export_to_video(video, "output.mp4", fps=8)
+```
+
+此外，这些模型可以通过使用PytorchAO以量化数据类型序列化并存储，从而节省磁盘空间。你可以在以下链接中找到示例和基准测试。
+
+- [torchao](https://gist.github.com/a-r-r-o-w/4d9732d17412888c885480c6521a9897)
+- [quanto](https://gist.github.com/a-r-r-o-w/31be62828b00a9292821b85c1017effa)
+
+## 深入研究
+
+欢迎进入我们的 [github](https://github.com/THUDM/CogVideo)，你将获得：
+
+1. 更加详细的技术细节介绍和代码解释。
+2. 提示词的优化和转换。
+3. 模型推理和微调的详细代码。
+4. 项目更新日志动态，更多互动机会。
+5. CogVideoX 工具链，帮助您更好的使用模型。
+6. INT8 模型推理代码。
+
+## 模型协议
+
+该模型根据 [CogVideoX LICENSE](LICENSE) 许可证发布。
+
+## 引用
+
+```
+@article{yang2024cogvideox,
+  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
+  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
+  journal={arXiv preprint arXiv:2408.06072},
+  year={2024}
+}
+```

SparkVSR-bf16/model_index.json

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+{
+  "_class_name": "CogVideoXImageToVideoPipeline",
+  "_diffusers_version": "0.32.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "CogVideoXDDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "CogVideoXTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLCogVideoX"
+  ]
+}

SparkVSR-bf16/scheduler/scheduler_config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "CogVideoXDDIMScheduler",
+  "_diffusers_version": "0.32.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": true,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": true,
+  "snr_shift_scale": 1.0,
+  "steps_offset": 0,
+  "timestep_spacing": "trailing",
+  "trained_betas": null
+}

SparkVSR-bf16/text_encoder/config.json

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+  "architectures": [
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+  "is_gated_act": true,
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+  "model_type": "t5",
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+  "num_layers": 24,
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+  "torch_dtype": "float32",
+  "transformers_version": "4.46.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

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SparkVSR-bf16/tokenizer/tokenizer_config.json

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