Update README.md

README.md

@@ -12,82 +12,6 @@ pipeline_tag: text-to-image
 <!-- Provide a quick summary of what the model is/does. -->
 
 Diffusers version of MIGC adapter state dict. The actual values are identical to the original checkpoint file [MICG_SD14.ckpt](https://huggingface.co/limuloo1999/MIGC)
-Please see the details of MIGC in the [MIGC repositiory](https://github.com/limuloo/MIGC).
+Please see the details of MIGC in the [MIGC repositiory](https://github.com/limuloo/MIGC). 
 
-
-# How to use
-
-Please use modified pipeline class in `pipeline_stable_diffusion_migc.py` file.
-
-```python
-import random
-
-import numpy as np
-import safetensors.torch
-import torch
-from huggingface_hub import hf_hub_download
-
-from pipeline_stable_diffusion_migc import StableDiffusionMIGCPipeline
-
-
-DEVICE="cuda"
-SEED=42
-
-pipe = StableDiffusionMIGCPipeline.from_pretrained("CompVis/stable-diffusion-v1-4").to(DEVICE)
-adapter_path = hf_hub_download(repo_id="thisiswooyeol/MIGC-diffusers", filename="migc_adapter_weights.safetensors")
-
-# Load MIGC adapter to UNet attn2 layers
-state_dict = safetensors.torch.load_file(adapter_path)
-for name, module in pipe.unet.named_modules():
-    if hasattr(module, "migc"):
-        print(f"Found MIGC in {name}")
-
-        # Get the state dict with the incorrect keys
-        state_dict_to_load = {k: v for k, v in state_dict.items() if k.startswith(name)}
-
-        # Create a new state dict, removing the "attn2." prefix from each key
-        new_state_dict = {k.replace(f"{name}.migc.", "", 1): v for k, v in state_dict_to_load.items()}
-
-        # Load the corrected state dict
-        module.migc.load_state_dict(new_state_dict)
-        module.to(device=pipe.unet.device, dtype=pipe.unet.dtype)
-
-
-# Sample inference !
-prompt = "bestquality, detailed, 8k.a photo of a black potted plant and a yellow refrigerator and a brown surfboard"
-phrases = [
-  "a black potted plant",
-  "a brown surfboard",
-  "a yellow refrigerator",
-]
-bboxes = [
-  [0.5717187499999999, 0.0, 0.8179531250000001, 0.29807511737089204],
-  [0.85775, 0.058755868544600943, 0.9991875, 0.646525821596244],
-  [0.6041562500000001, 0.284906103286385, 0.799046875, 0.9898591549295774],
-]
-
-def seed_everything(seed):
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-seed_everything(SEED)
-
-image = pipe(
-    prompt=prompt,
-    phrases=phrases,
-    bboxes=bboxes,
-    negative_prompt="worst quality, low quality, bad anatomy",
-    generator=torch.Generator(DEVICE).manual_seed(SEED),
-).images[0]
-image.save("image.png")
-```
-
-This will generate following image:
-
-![image](assets/image.png)
-
-If we overlay the box layouts to the generated image, we can see the model successfully follows the layout instructions.
-![image_with_layout](assets/image_with_layout.png)
+How to use this modified MIGC adapter is described in [MIGC-diffusers](https://github.com/thisisWooyeol/MIGC-diffusers) github repository.