Update README.md

README.md

@@ -12,70 +12,4 @@ library_name: diffusers
 For detailed information, code, and documentation, please visit our GitHub repository:
 [Anonymize-Anyone](https://github.com/fh2c1/Anonymize-Anyone)
 
-## Anonymize Anyone
-
-![anonymiza-anyone demo images](./assets/Fig1.png)
-
-## Model
-
-![overall_structure](./assets/Fig2.png)
-
-**Anonymize Anyone** presents a novel approach to text-to-face synthesis using a Diffusion Model that considers Race Fairness. Our method uses facial segmentation masks to edit specific facial regions, and employs a Stable Diffusion v2 Inpainting model trained on a curated Asian dataset. We introduce two key losses: **ℒ𝐹𝐹𝐸** (Focused Feature Enhancement Loss) to enhance performance with limited data, and **ℒ𝑫𝑰𝑭𝑭** (Difference Loss) to address catastrophic forgetting. Finally, we apply **Simple Preference Optimization** (SimPO) for refined and enhanced image generation.
-
-## Model Checkpoints
-
-- [Anonymize-Anyone (Inpainting model with **ℒ𝐹𝐹𝐸** and **ℒ𝑫𝑰𝑭𝑭**)](https://huggingface.co/fh2c1/Anonymize-Anyone)
-- [SimPO-LoRA (Diffusion model with **Simple Preference Optimization**)](https://huggingface.co/fh2c1/SimPO-LoRA)
-
-### Using with Diffusers🧨
-
-You can use this model directly with the `diffusers` library:
-
-
-```python
-import torch
-from PIL import Image
-from diffusers import StableDiffusionInpaintPipeline
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-sd_pipe = StableDiffusionInpaintPipeline.from_pretrained(
-    "fh2c1/Anonymize-Anyone",
-    torch_dtype=torch.float16,
-    safety_checker=None,
-).to(device)
-sd_pipe.load_lora_weights("fh2c1/SimPO-LoRA", adapter_name="SimPO")
-sd_pipe.set_adapters(["SimPO"], adapter_weights=[0.5])
-
-def generate_image(image_path, mask_path, prompt, negative_prompt, pipe, seed):
-    try:
-        in_image = Image.open(image_path)
-        in_mask = Image.open(mask_path)
-    except IOError as e:
-        print(f"Loading error: {e}")
-        return None
-    generator = torch.Generator(device).manual_seed(seed)
-    result = pipe(image=in_image, mask_image=in_mask, prompt=prompt,
-                  negative_prompt=negative_prompt, generator=generator)
-    return result.images[0]
-
-image = '/content/Anonymize-Anyone/data/2.png'
-mask = "/content/Anonymize-Anyone/data/2_mask.png"
-prompt = "he is an asian man."
-seed = 38189219984105
-negative_prompt = "low resolution, ugly, disfigured, ugly, bad, immature, cartoon, anime, 3d, painting, b&w, deformed eyes, low quailty, noise"
-
-try:
-    generated_image = generate_image(image_path=image, mask_path=mask, prompt=prompt,
-                                     negative_prompt=negative_prompt, pipe=sd_pipe, seed=seed)
-except TypeError as e:
-    print(f"TypeError : {e}")
-
-generated_image
-```
-![result](./assets/Fig3.png)
-
-For more detailed usage instructions, including how to prepare segmentation masks and run inference, please refer to our [GitHub repository](https://github.com/fh2c1/Anonymize-Anyone).
-
-## Training
-
-For information on how to train the model, including the use of **ℒ𝐹𝐹𝐸** (Focused Feature Enhancement Loss) and **ℒ𝑫𝑰𝑭𝑭** (Difference Loss), please see our GitHub repository's [training section](https://github.com/fh2c1/Anonymize-Anyone#running_man-train).
+# We updated our model to [Anonymize Anyone v1.2](https://huggingface.co/fh2c1/SimPO-LoRA-1.2), improving race fairness in text-to-face synthesis using Simple Preference Optimization in diffusion models.