upd

ICEdit

@@ -0,0 +1 @@
+Subproject commit 6e4f95590e5b56ca1313dc7f515a4d6bed49244c

app.py

@@ -1,94 +1,145 @@
+'''
+python scripts/gradio_demo.py 
+'''
+
+import sys
+import os
+workspace_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "ICEdit/icedit"))
+
+if workspace_dir not in sys.path:
+    sys.path.insert(0, workspace_dir)
+    
+from diffusers import FluxFillPipeline
 import gradio as gr
 import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+import spaces
+import argparse
+import random 
+from diffusers import FluxFillPipeline
+from PIL import Image
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+parser = argparse.ArgumentParser() 
+parser.add_argument("--port", type=int, default=7860, help="Port for the Gradio app")
+parser.add_argument("--output-dir", type=str, default="gradio_results", help="Directory to save the output image")
+parser.add_argument("--flux-path", type=str, default='black-forest-labs/flux.1-fill-dev', help="Path to the model")
+parser.add_argument("--lora-path", type=str, default='sanaka87/ICEdit-MoE-LoRA', help="Path to the LoRA weights")
+parser.add_argument("--enable-model-cpu-offload", action="store_true", help="Enable CPU offloading for the model")
+args = parser.parse_args()
+
+pipe = FluxFillPipeline.from_pretrained(args.flux_path, torch_dtype=torch.bfloat16)
+pipe.load_lora_weights(args.lora_path)
+
+if args.enable_model_cpu_offload:
+    pipe.enable_model_cpu_offload() 
+else:
+    pipe = pipe.to("cuda")
+
+@spaces.GPU
+def infer(edit_images, 
+          prompt, 
+          seed=666, 
+          randomize_seed=False, 
+          width=1024, 
+          height=1024, 
+          guidance_scale=50, 
+          num_inference_steps=28, 
+          progress=gr.Progress(track_tqdm=True)
 ):
+    
+    image = edit_images["background"]
+        
+    if image.size[0] != 512:
+        print("\033[93m[WARNING] We can only deal with the case where the image's width is 512.\033[0m")
+        new_width = 512
+        scale = new_width / image.size[0]
+        new_height = int(image.size[1] * scale)
+        new_height = (new_height // 8) * 8  
+        image = image.resize((new_width, new_height))
+        print(f"\033[93m[WARNING] Resizing the image to {new_width} x {new_height}\033[0m")
+        
+    image = image.convert("RGB")
+    width, height = image.size
+    image = image.resize((512, int(512 * height / width)))
+    combined_image = Image.new("RGB", (width * 2, height))
+    combined_image.paste(image, (0, 0)) 
+    mask_array = np.zeros((height, width * 2), dtype=np.uint8)
+    mask_array[:, width:] = 255 
+    mask = Image.fromarray(mask_array)
+    instruction = f'A diptych with two side-by-side images of the same scene. On the right, the scene is exactly the same as on the left but {prompt}'
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
-
     image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
+        prompt=instruction,
+        image=combined_image,
+        mask_image=mask,
+        height=height,
+        width=width*2,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
+        generator=torch.Generator("cpu").manual_seed(seed)
     ).images[0]
 
-    return image, seed
-
+    w,h = image.size
+    image = image.crop((w//2, 0, w, h))
 
+    os.makedirs(args.output_dir, exist_ok=True)
+        
+    index = len(os.listdir(args.output_dir))
+    image.save(f"{args.output_dir}/result_{index}.png")
+    
+    return image, seed
+    
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 1000px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown(f"""# IC-Edit
+A demo for [IC-Edit](https://arxiv.org/pdf/2504.20690).
+More **open-source**, with **lower costs**, **faster speed** (it takes about 9 seconds to process one image), and **powerful performance**.
+""")
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
+            with gr.Column():
+                edit_image = gr.ImageEditor(
+                    label='Upload and draw mask for inpainting',
+                    type='pil',
+                    sources=["upload", "webcam"],
+                    image_mode='RGB',
+                    layers=False,
+                    brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"),
+                    height=600
+                )
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                )
+                run_button = gr.Button("Run")
+                
+            result = gr.Image(label="Result", show_label=False)
+        
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
+            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -96,59 +147,52 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-
+            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
+            
             with gr.Row():
+                
                 width = gr.Slider(
                     label="Width",
-                    minimum=256,
+                    minimum=512,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
+                    visible=False
                 )
-
+                
                 height = gr.Slider(
                     label="Height",
-                    minimum=256,
+                    minimum=512,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
+                    visible=False
                 )
-
+            
             with gr.Row():
+
                 guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=50,
+                    step=0.5,
+                    value=50,
                 )
-
+  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=28,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        fn = infer,
+        inputs = [edit_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch(server_port=args.port)