Update README.md

README.md

@@ -1,95 +1,32 @@
 ---
+base_model: black-forest-labs/FLUX.1-dev
+base_model_relation: quantized
+datasets:
+- mit-han-lab/svdquant-datasets
+language:
+- en
+library_name: diffusers
 license: other
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
 license_name: flux-1-dev-non-commercial-license
+pipeline_tag: text-to-image
 tags:
 - text-to-image
 - SVDQuant
 - FLUX.1-dev
-- INT4
 - FLUX.1
 - Diffusion
 - Quantization
 - ICLR2025
-language:
-- en
-base_model:
-- black-forest-labs/FLUX.1-dev
-base_model_relation: quantized
-pipeline_tag: text-to-image
-datasets:
-- mit-han-lab/svdquant-datasets
-library_name: diffusers
+
 ---
+**This repository has been deprecated and will be hidden in December 2025. Please use https://huggingface.co/nunchaku-tech/nunchaku-flux.1-dev.**
 
 <p align="center" style="border-radius: 10px">
-  <img src="https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/assets/logo.svg" width="50%" alt="logo"/>
+  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
 </p>
-<h4 style="display: flex; justify-content: center; align-items: center; text-align: center;">Quantization Library:&nbsp;<a href='https://github.com/mit-han-lab/deepcompressor'>DeepCompressor</a> &ensp; Inference Engine:&nbsp;<a href='https://github.com/mit-han-lab/nunchaku'>Nunchaku</a>
-</h4>
-
-
-<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
-  <a href="https://arxiv.org/abs/2411.05007">[Paper]</a>&ensp;
-  <a href='https://github.com/mit-han-lab/nunchaku'>[Code]</a>&ensp;
-  <a href='https://svdquant.mit.edu'>[Demo]</a>&ensp;
-  <a href='https://hanlab.mit.edu/projects/svdquant'>[Website]</a>&ensp;
-  <a href='https://hanlab.mit.edu/blog/svdquant'>[Blog]</a>
-</div>
-
-![teaser](https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/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.
-
-## Method
-#### Quantization Method -- SVDQuant
-
-![intuition](https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/assets/intuition.gif)
-Overview of SVDQuant. Stage1: Originally, both the activation ***X*** and weights ***W*** contain outliers, making 4-bit quantization challenging.  Stage 2: We migrate the outliers from activations to weights, resulting in the updated activation and weight. While the activation becomes easier to quantize, the weight now becomes more difficult. Stage 3: SVDQuant further decomposes the weight into a low-rank component and a residual with SVD. Thus, the quantization difficulty is alleviated by the low-rank branch, which runs at 16-bit precision. 
 
-#### Nunchaku Engine Design
-
-![engine](https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/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.
-
-## Model Description
-
-- **Developed by:** MIT, NVIDIA, CMU, Princeton, UC Berkeley, SJTU and Pika Labs
-- **Model type:** INT W4A4 model
-- **Model size:** 6.64GB
-- **Model resolution:** The number of pixels need to be a multiple of 65,536.
-- **License:** Apache-2.0
-
-## Usage
-
-### Diffusers
-
-Please follow the instructions in [mit-han-lab/nunchaku](https://github.com/mit-han-lab/nunchaku) to set up the environment. Then you can run the model with
-
-```python
-import torch
-from diffusers import FluxPipeline
-
-from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
-
-transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
-pipeline = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
-).to("cuda")
-image = pipeline("A cat holding a sign that says hello world", num_inference_steps=50, guidance_scale=3.5).images[0]
-image.save("example.png")
-```
-
-### Comfy UI
-
-![comfyui](https://github.com/mit-han-lab/nunchaku/blob/main/assets/comfyui.jpg?raw=true)
-Please check [comfyui/README.md](comfyui/README.md) for the usage.
-
-## Limitations
-
-- The model is only runnable on NVIDIA GPUs with architectures sm_86 (Ampere: RTX 3090, A6000), sm_89 (Ada: RTX 4090), and sm_80 (A100). See this [issue](https://github.com/mit-han-lab/nunchaku/issues/1) for more details.
-- You may observe some slight differences from the BF16 models in details.
-
-### Citation
-
-If you find this model useful or relevant to your research, please cite
+## Citation
 
 ```bibtex
 @inproceedings{
@@ -99,4 +36,8 @@ If you find this model useful or relevant to your research, please cite
   booktitle={The Thirteenth International Conference on Learning Representations},
   year={2025}
 }
-```
+```
+
+## Attribution Notice
+
+The FLUX.1 [dev] Model is licensed by Black Forest Labs Inc. under the FLUX.1 [dev] Non-Commercial License. Copyright Black Forest Labs Inc. IN NO EVENT SHALL BLACK FOREST LABS INC. BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.