Merge branch 'main' of https://huggingface.co/IrohXu/stable-diffusion-3-inpainting into main

README.md

@@ -1,4 +1,6 @@
-# Stable Diffusion 3 Inpaint Pipeline
+# Stable Diffusion 3 Inpainting Pipeline
+
+This is the implementation of `Stable Diffusion 3 Inpainting Pipeline`.
 
 | input image | input mask image | output |
 |:-------------------------:|:-------------------------:|:-------------------------:|
@@ -8,7 +10,27 @@
 
 **Please ensure that the version of diffusers >= 0.29.1**
 
-# Demo
+## Model
+
+[Stable Diffusion 3 Medium](https://stability.ai/news/stable-diffusion-3-medium) is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
+
+For more technical details, please refer to the [Research paper](https://stability.ai/news/stable-diffusion-3-research-paper).
+
+Please note: this model is released under the Stability Non-Commercial Research Community License. For a Creator License or an Enterprise License visit Stability.ai or [contact us](https://stability.ai/license) for commercial licensing details.
+
+
+### Model Description
+
+- **Developed by:** Stability AI
+- **Model type:** MMDiT text-to-image generative model
+- **Model Description:** This is a model that can be used to generate images based on text prompts. It is a Multimodal Diffusion Transformer
+(https://arxiv.org/abs/2403.03206) that uses three fixed, pretrained text encoders 
+([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip), [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) and [T5-xxl](https://huggingface.co/google/t5-v1_1-xxl))
+
+## Demo
+
+Make sure you upgrade to the latest version of diffusers: pip install -U diffusers. And then you can run:
+
 ```python
 import torch
 from torchvision import transforms
@@ -20,7 +42,6 @@ def preprocess_image(image):
     image = image.convert("RGB")
     image = transforms.CenterCrop((image.size[1] // 64 * 64, image.size[0] // 64 * 64))(image)
     image = transforms.ToTensor()(image)
-    image = image * 2 - 1
     image = image.unsqueeze(0).to("cuda")
     return image