docs: remove the images to reduce the repo size (#329)

* replace the assets

* move some assets to huggingface

* update

* revert back

* see if the attachment works

* use the attachment link

* remove all the images

README.md

@@ -11,7 +11,7 @@
 
 **Nunchaku** is a high-performance inference engine optimized for 4-bit neural networks, as introduced in our paper [SVDQuant](http://arxiv.org/abs/2411.05007). For the underlying quantization library, check out [DeepCompressor](https://github.com/mit-han-lab/deepcompressor).
 
-Join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q), [**Discord**](https://discord.gg/Wk6PnwX9Sm) and [**WeChat**](./assets/wechat.jpg) to engage in discussions with the community! More details can be found [here](https://github.com/mit-han-lab/nunchaku/issues/149). If you have any questions, run into issues, or are interested in contributing, don’t hesitate to reach out!
+Join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q), [**Discord**](https://discord.gg/Wk6PnwX9Sm) and [**WeChat**](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/wechat.jpg) to engage in discussions with the community! More details can be found [here](https://github.com/mit-han-lab/nunchaku/issues/149). If you have any questions, run into issues, or are interested in contributing, don’t hesitate to reach out!
 
 ## News
 
@@ -40,31 +40,29 @@ Join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_inv
 
 ## Overview
 
-![teaser](./assets/teaser.jpg)
+![teaser](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/teaser.jpg)
 SVDQuant is a post-training quantization technique for 4-bit weights and activations that well maintains visual fidelity. On 12B FLUX.1-dev, it achieves 3.6× memory reduction compared to the BF16 model. By eliminating CPU offloading, it offers 8.7× speedup over the 16-bit model when on a 16GB laptop 4090 GPU, 3× faster than the NF4 W4A16 baseline. On PixArt-∑, it demonstrates significantly superior visual quality over other W4A4 or even W4A8 baselines. "E2E" means the end-to-end latency including the text encoder and VAE decoder.
 
 **SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models**<br>
 [Muyang Li](https://lmxyy.me)\*, [Yujun Lin](https://yujunlin.com)\*, [Zhekai Zhang](https://hanlab.mit.edu/team/zhekai-zhang)\*, [Tianle Cai](https://www.tianle.website/#/), [Xiuyu Li](https://xiuyuli.com), [Junxian Guo](https://github.com/JerryGJX), [Enze Xie](https://xieenze.github.io), [Chenlin Meng](https://cs.stanford.edu/~chenlin/), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), and [Song Han](https://hanlab.mit.edu/songhan) <br>
 *MIT, NVIDIA, CMU, Princeton, UC Berkeley, SJTU, and Pika Labs* <br>
 
-<p align="center">
-  <img src="assets/demo.gif" width="100%"/>
-</p>
+https://github.com/user-attachments/assets/fdd4ab68-6489-4c65-8768-259bd866e8f8
 
 ## Method
 
 #### Quantization Method -- SVDQuant
 
-![intuition](./assets/intuition.gif)Overview of SVDQuant. Stage1: Originally, both the activation $\boldsymbol{X}$ and weights $\boldsymbol{W}$ contain outliers, making 4-bit quantization challenging.  Stage 2: We migrate the outliers from activations to weights, resulting in the updated activation $\hat{\boldsymbol{X}}$ and weights $\hat{\boldsymbol{W}}$. While $\hat{\boldsymbol{X}}$ becomes easier to quantize, $\hat{\boldsymbol{W}}$ now becomes more difficult. Stage 3: SVDQuant further decomposes $\hat{\boldsymbol{W}}$ into a low-rank component $\boldsymbol{L}_1\boldsymbol{L}_2$ and a residual $\hat{\boldsymbol{W}}-\boldsymbol{L}_1\boldsymbol{L}_2$ with SVD. Thus, the quantization difficulty is alleviated by the low-rank branch, which runs at 16-bit precision. 
+![intuition](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/intuition.gif)Overview of SVDQuant. Stage1: Originally, both the activation $\boldsymbol{X}$ and weights $\boldsymbol{W}$ contain outliers, making 4-bit quantization challenging.  Stage 2: We migrate the outliers from activations to weights, resulting in the updated activation $\hat{\boldsymbol{X}}$ and weights $\hat{\boldsymbol{W}}$. While $\hat{\boldsymbol{X}}$ becomes easier to quantize, $\hat{\boldsymbol{W}}$ now becomes more difficult. Stage 3: SVDQuant further decomposes $\hat{\boldsymbol{W}}$ into a low-rank component $\boldsymbol{L}_1\boldsymbol{L}_2$ and a residual $\hat{\boldsymbol{W}}-\boldsymbol{L}_1\boldsymbol{L}_2$ with SVD. Thus, the quantization difficulty is alleviated by the low-rank branch, which runs at 16-bit precision. 
 
 #### Nunchaku Engine Design
 
-![engine](./assets/engine.jpg) (a) Naïvely running low-rank branch with rank 32 will introduce 57% latency overhead due to extra read of 16-bit inputs in *Down Projection* and extra write of 16-bit outputs in *Up Projection*. Nunchaku optimizes this overhead with kernel fusion. (b) *Down Projection* and *Quantize* kernels use the same input, while *Up Projection* and *4-Bit Compute* kernels share the same output. To reduce data movement overhead, we fuse the first two and the latter two kernels together.
+![engine](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/engine.jpg) (a) Naïvely running low-rank branch with rank 32 will introduce 57% latency overhead due to extra read of 16-bit inputs in *Down Projection* and extra write of 16-bit outputs in *Up Projection*. Nunchaku optimizes this overhead with kernel fusion. (b) *Down Projection* and *Quantize* kernels use the same input, while *Up Projection* and *4-Bit Compute* kernels share the same output. To reduce data movement overhead, we fuse the first two and the latter two kernels together.
 
 
 ## Performance
 
-![efficiency](./assets/efficiency.jpg)SVDQuant reduces the 12B FLUX.1 model size by 3.6× and cuts the 16-bit model's memory usage by 3.5×. With Nunchaku, our INT4 model runs 3.0× faster than the NF4 W4A16 baseline on both desktop and laptop NVIDIA RTX 4090 GPUs. Notably, on the laptop 4090, it achieves a total 10.1× speedup by eliminating CPU offloading. Our NVFP4 model is also 3.1× faster than both BF16 and NF4 on the RTX 5090 GPU.
+![efficiency](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/efficiency.jpg)SVDQuant reduces the 12B FLUX.1 model size by 3.6× and cuts the 16-bit model's memory usage by 3.5×. With Nunchaku, our INT4 model runs 3.0× faster than the NF4 W4A16 baseline on both desktop and laptop NVIDIA RTX 4090 GPUs. Notably, on the laptop 4090, it achieves a total 10.1× speedup by eliminating CPU offloading. Our NVFP4 model is also 3.1× faster than both BF16 and NF4 on the RTX 5090 GPU.
 
 ## Installation
 We provide tutorial videos to help you install and use Nunchaku on Windows, available in both [**English**](https://youtu.be/YHAVe-oM7U8?si=cM9zaby_aEHiFXk0) and [**Chinese**](https://www.bilibili.com/video/BV1BTocYjEk5/?share_source=copy_web&vd_source=8926212fef622f25cc95380515ac74ee). You can also follow the corresponding step-by-step text guide at [`docs/setup_windows.md`](docs/setup_windows.md). If you run into issues, these resources are a good place to start.
@@ -219,7 +217,7 @@ For a complete example, refer to [`examples/flux.1-dev-offload.py`](examples/flu
 
 ## Customized LoRA
 
-![lora](./assets/lora.jpg)
+![lora](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/lora.jpg)
 
 [SVDQuant](http://arxiv.org/abs/2411.05007) seamlessly integrates with off-the-shelf LoRAs without requiring requantization. You can simply use your LoRA with:
 
@@ -279,7 +277,7 @@ You can specify individual strengths for each LoRA in the list. For a complete e
 
 Nunchaku supports both the [FLUX.1-tools](https://blackforestlabs.ai/flux-1-tools/) and the [FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro) models. Example scripts can be found in the [`examples`](examples) directory.
 
-![control](./assets/control.jpg)
+![control](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/control.jpg)
 
 ## ComfyUI
 
@@ -313,7 +311,7 @@ We warmly welcome contributions from the community! To get started, please refer
 
 ## Troubleshooting
 
-Encountering issues while using Nunchaku? Start by browsing our [FAQ](docs/faq.md) for common solutions. If you still need help, feel free to [open an issue](https://github.com/mit-han-lab/nunchaku/issues). You’re also welcome to join our community discussions on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q), [**Discord**](https://discord.gg/Wk6PnwX9Sm), or [**WeChat**](./assets/wechat.jpg).
+Encountering issues while using Nunchaku? Start by browsing our [FAQ](docs/faq.md) for common solutions. If you still need help, feel free to [open an issue](https://github.com/mit-han-lab/nunchaku/issues). You’re also welcome to join our community discussions on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q), [**Discord**](https://discord.gg/Wk6PnwX9Sm), or [**WeChat**](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/wechat.jpg).
 
 ## Contact Us
 

README_ZH.md

@@ -11,7 +11,7 @@
 
 **Nunchaku**是一款专为4-bit神经网络优化的高性能推理引擎，基于我们的论文 [SVDQuant](http://arxiv.org/abs/2411.05007) 提出。底层量化库请参考 [DeepCompressor](https://github.com/mit-han-lab/deepcompressor)。
 
-欢迎加入我们的用户群：[**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q)、[**Discord**](https://discord.gg/Wk6PnwX9Sm) 和 [**微信**](./assets/wechat.jpg)，与社区交流！更多详情请见[此处](https://github.com/mit-han-lab/nunchaku/issues/149)。如有任何问题、建议或贡献意向，欢迎随时联系！
+欢迎加入我们的用户群：[**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q)、[**Discord**](https://discord.gg/Wk6PnwX9Sm) 和 [**微信**](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/wechat.jpg)，与社区交流！更多详情请见[此处](https://github.com/mit-han-lab/nunchaku/issues/149)。如有任何问题、建议或贡献意向，欢迎随时联系！
 
 ## 最新动态
 
@@ -39,30 +39,28 @@
 
 ## 项目概览
 
-![teaser](./assets/teaser.jpg)
+![teaser](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/teaser.jpg)
 SVDQuant 是一种支持4-bit权重和激活的后训练量化技术，能有效保持视觉质量。在12B FLUX.1-dev模型上，相比BF16模型实现了3.6倍内存压缩。通过消除CPU offloading，在16GB笔记本RTX 4090上比16位模型快8.7倍，比NF4 W4A16基线快3倍。在PixArt-∑模型上，其视觉质量显著优于其他W4A4甚至W4A8方案。"E2E"表示包含文本编码器和VAE解码器的端到端延迟。
 
 **SVDQuant: 通过低秩分量吸收异常值实现4-bit扩散模型量化**<br>
 [Muyang Li](https://lmxyy.me)\*, [Yujun Lin](https://yujunlin.com)\*, [Zhekai Zhang](https://hanlab.mit.edu/team/zhekai-zhang)\*, [Tianle Cai](https://www.tianle.website/#/), [Xiuyu Li](https://xiuyuli.com), [Junxian Guo](https://github.com/JerryGJX), [Enze Xie](https://xieenze.github.io), [Chenlin Meng](https://cs.stanford.edu/~chenlin/), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), [Song Han](https://hanlab.mit.edu/songhan) <br>
 *麻省理工学院、英伟达、卡内基梅隆大学、普林斯顿大学、加州大学伯克利分校、上海交通大学、pika实验室* <br>
 
-<p align="center">
-  <img src="assets/demo.gif" width="100%"/>
-</p>
+https://github.com/user-attachments/assets/fdd4ab68-6489-4c65-8768-259bd866e8f8
 
 ## 方法原理
 
 #### 量化方法 -- SVDQuant
 
-![intuition](./assets/intuition.gif)SVDQuant三阶段示意图。阶段1：原始激活 $\boldsymbol{X}$ 和权重 $\boldsymbol{W}$ 均含异常值，4-bit量化困难。阶段2：将激活异常值迁移至权重，得到更易量化的激活 $\hat{\boldsymbol{X}}$ 和更难量化的权重 $\hat{\boldsymbol{W}}$ 。阶段3：通过SVD将 $\hat{\boldsymbol{W}}$ 分解为低秩分量 $\boldsymbol{L}_1\boldsymbol{L}_2$ 和残差 $\hat{\boldsymbol{W}}-\boldsymbol{L}_1\boldsymbol{L}_2$ ，低秩分支以16位精度运行缓解量化难度。
+![intuition](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/intuition.gif)SVDQuant三阶段示意图。阶段1：原始激活 $\boldsymbol{X}$ 和权重 $\boldsymbol{W}$ 均含异常值，4-bit量化困难。阶段2：将激活异常值迁移至权重，得到更易量化的激活 $\hat{\boldsymbol{X}}$ 和更难量化的权重 $\hat{\boldsymbol{W}}$ 。阶段3：通过SVD将 $\hat{\boldsymbol{W}}$ 分解为低秩分量 $\boldsymbol{L}_1\boldsymbol{L}_2$ 和残差 $\hat{\boldsymbol{W}}-\boldsymbol{L}_1\boldsymbol{L}_2$ ，低秩分支以16位精度运行缓解量化难度。
 
 #### Nunchaku引擎设计
 
-![engine](./assets/engine.jpg) (a) 原始低秩分支（秩32）因额外读写16位数据引入57%的延迟。Nunchaku通过核融合优化。(b) 将下投影与量化、上投影与4-bit计算分别融合，减少数据搬运。
+![engine](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/engine.jpg) (a) 原始低秩分支（秩32）因额外读写16位数据引入57%的延迟。Nunchaku通过核融合优化。(b) 将下投影与量化、上投影与4-bit计算分别融合，减少数据搬运。
 
 ## 性能表现
 
-![efficiency](./assets/efficiency.jpg)SVDQuant 将12B FLUX.1模型的体积压缩了3.6倍，同时将原始16位模型的显存占用减少了3.5倍。借助Nunchaku，我们的INT4模型在桌面和笔记本的NVIDIA RTX 4090 GPU上比NF4 W4A16基线快了3.0倍。值得一提的是，在笔记本4090上，通过消除CPU offloading，总体加速达到了10.1倍。我们的NVFP4模型在RTX 5090 GPU上也比BF16和NF4快了3.1倍。
+![efficiency](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/efficiency.jpg)SVDQuant 将12B FLUX.1模型的体积压缩了3.6倍，同时将原始16位模型的显存占用减少了3.5倍。借助Nunchaku，我们的INT4模型在桌面和笔记本的NVIDIA RTX 4090 GPU上比NF4 W4A16基线快了3.0倍。值得一提的是，在笔记本4090上，通过消除CPU offloading，总体加速达到了10.1倍。我们的NVFP4模型在RTX 5090 GPU上也比BF16和NF4快了3.1倍。
 
 ## 安装指南
 
@@ -215,7 +213,7 @@ pipeline.enable_sequential_cpu_offload()
 
 ## 自定义LoRA
 
-![lora](./assets/lora.jpg)
+![lora](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/lora.jpg)
 
 [SVDQuant](http://arxiv.org/abs/2411.05007) 可以无缝集成现有的 LoRA，而无需重新量化。你可以简单地通过以下方式使用你的 LoRA：
 
@@ -275,7 +273,7 @@ transformer.update_lora_params(composed_lora)
 
 Nunchaku 支持 [FLUX.1-tools](https://blackforestlabs.ai/flux-1-tools/) 和 [FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro) 模型。示例脚本可以在 [`examples`](examples) 目录中找到。
 
-![control](./assets/control.jpg)
+![control](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/control.jpg)
 
 ## ComfyUI
 
@@ -309,7 +307,7 @@ Nunchaku 支持 [FLUX.1-tools](https://blackforestlabs.ai/flux-1-tools/) 和 [FL
 
 ## 问题排查
 
-使用 Nunchaku 时遇到问题？请先查阅我们的[常见问题解答](docs/faq_ZH.md)寻找解决方案。若仍需要帮助，可通过[open an issue](https://github.com/mit-han-lab/nunchaku/issues)联系我们。也欢迎您通过 [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q)、[**Discord**](https://discord.gg/Wk6PnwX9Sm) 或 [**微信**](./assets/wechat.jpg) 加入我们的社区讨论。
+使用 Nunchaku 时遇到问题？请先查阅我们的[常见问题解答](docs/faq_ZH.md)寻找解决方案。若仍需要帮助，可通过[open an issue](https://github.com/mit-han-lab/nunchaku/issues)联系我们。也欢迎您通过 [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q)、[**Discord**](https://discord.gg/Wk6PnwX9Sm) 或 [**微信**](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/assets/wechat.jpg) 加入我们的社区讨论。
 
 ## 联系我们
 

app/flux.1/depth_canny/README.md

 # Nunchaku INT4 FLUX.1 Depth/Canny-to-Image Demo
 
-![demo](./assets/demo.jpg)
+![demo](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/app/flux.1/depth_canny/assets/demo.jpg)
 
 This interactive Gradio application transforms your uploaded image into a different style based on a text prompt. The generated image preserves either the depth map or Canny edge of the original image, depending on the selected model.
 

app/flux.1/fill/README.md

 # Nunchaku INT4 FLUX.1 Inpainting Demo
 
-![demo](./assets/demo.jpg)
+![demo](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/app/flux.1/fill/assets/demo.jpg)
 
 This interactive Gradio application allows you to interactively inpaint an uploaded image based on a text prompt. The base model is [FLUX.1-Fill-dev](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev). To launch the application, run:
 

app/flux.1/redux/README.md

 # Nunchaku INT4 FLUX.1 Redux Demo
 
-![demo](./assets/demo.png)
+![demo](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/app/flux.1/redux/assets/demo.jpg)
 
 This interactive Gradio application allows you to interactively generate image variations. The base model is [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). We use [FLUX.1-Redux-dev](https://huggingface.co/black-forest-labs/FLUX.1-Redux-dev) to preprocess the image before inputting it into Flux.1-dev. To launch the application, run:
 

app/flux.1/sketch/README.md

 # Nunchaku INT4 FLUX.1 Sketch-to-Image Demo
 
-![demo](./assets/demo.jpg)
+![demo](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/app/flux.1/sketch/assets/demo.jpg)
 
 This interactive Gradio application transforms your drawing scribbles into realistic images given a text prompt. The base model is [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) with the [pix2pix-Turbo](https://github.com/GaParmar/img2img-turbo) sketch LoRA.
 

app/flux.1/t2i/README.md

@@ -2,7 +2,7 @@
 
 ## Text-to-Image Gradio Demo
 
-![demo](./assets/demo.jpg)
+![demo](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/app/flux.1/t2i/assets/demo.jpg)
 
 This interactive Gradio application can generate an image based on your provided text prompt. The base model can be either [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell) or [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
 

app/sana/t2i/README.md

@@ -2,7 +2,7 @@
 
 ## Text-to-Image Gradio Demo
 
-![demo](./assets/demo.jpg)
+![demo](https://huggingface.co/mit-han-lab/nunchaku-artifacts/resolve/main/nunchaku/app/sana/t2i/assets/demo.jpg)
 
 This interactive Gradio application can generate an image based on your provided text prompt. The base model is [SANA-1.6B](https://huggingface.co/Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers).