Updated app.py

app.py

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+import gradio as gr
+import spaces
+import random
+import torch
+from huggingface_hub import snapshot_download
+from kolors.pipelines.pipeline_stable_diffusion_xl_chatglm_256_inpainting import StableDiffusionXLInpaintPipeline
+from kolors.models.modeling_chatglm import ChatGLMModel
+from kolors.models.tokenization_chatglm import ChatGLMTokenizer
+from diffusers import AutoencoderKL, EulerDiscreteScheduler, UNet2DConditionModel
+from groundingdino.util.inference import load_model, predict
+from segment_anything import SamAutomaticMaskGenerator
+from PIL import Image
+import numpy as np
+import os
+
+# Download model checkpoints
+device = "cuda"
+ckpt_dir = snapshot_download(repo_id="Kwai-Kolors/Kolors-Inpainting")
+
+# Inpainting setup
+text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder',torch_dtype=torch.float16).half().to(device)
+tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder')
+vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device)
+scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler")
+unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device)
+
+pipe = StableDiffusionXLInpaintPipeline(
+    vae=vae,
+    text_encoder=text_encoder,
+    tokenizer=tokenizer,
+    unet=unet,
+    scheduler=scheduler
+)
+
+pipe.to(device)
+pipe.enable_attention_slicing()
+
+# GroundingDINO and SAM setup
+model_dino = load_model("path/to/groundingdino/config.yaml", "path/to/groundingdino/model.pth")
+sam = SamAutomaticMaskGenerator(model_type="vit_h", checkpoint="model/sam_vit_h_4b8939.pth")
+
+# Constants
+MAX_SEED = np.iinfo(np.int32).max
+
+def generate_mask(image: Image):
+    boxes, logits, phrases = predict(model_dino, image, "prompt")  # Provide the proper prompt for detection
+    masks = sam.generate(image)
+    mask = masks[0]["segmentation"]  # Use the first detected mask as an example
+    return Image.fromarray(mask)
+
+@spaces.GPU
+def infer(prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    # Generate mask using GroundingDINO + SAM
+    mask_image = generate_mask(image)
+    
+    generator = torch.Generator().manual_seed(seed)
+    result = pipe(
+        prompt=prompt,
+        image=image,
+        mask_image=mask_image,
+        height=image.height,
+        width=image.width,
+        guidance_scale=guidance_scale,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        negative_prompt=negative_prompt,
+        num_images_per_prompt=1,
+        strength=0.999
+    ).images[0]
+    
+    return result
+
+css="""
+#col-left {
+    margin: 0 auto;
+    max-width: 600px;
+}
+#col-right {
+    margin: 0 auto;
+    max-width: 700px;
+}
+"""
+
+def load_description(fp):
+    with open(fp, 'r', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+with gr.Blocks(css=css) as Kolors:
+    gr.HTML(load_description("assets/title.md"))
+    
+    with gr.Row():
+        with gr.Column(elem_id="col-left"):
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt", lines=2)
+            image = gr.ImageEditor(label="Image", type="pil", image_mode='RGB')
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt = gr.Textbox(label="Negative prompt", value="low quality, bad anatomy")
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=6.0)
+                num_inference_steps = gr.Slider(label="Number of inference steps", minimum=10, maximum=50, step=1, value=25)
+            
+            run_button = gr.Button("Run")
+        
+        with gr.Column(elem_id="col-right"):
+            result = gr.Image(label="Result", show_label=False)
+    
+    run_button.click(
+        fn=infer,
+        inputs=[prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps],
+        outputs=[result]
+    )
+
+Kolors.queue().launch(debug=True)