Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import random
+import os
+
+from diffusers import QwenImageEditInpaintPipeline
+from PIL import Image
+
+# Set environment variable for parallel loading
+os.environ["HF_ENABLE_PARALLEL_LOADING"] = "YES"
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+# Initialize Qwen Image Edit pipeline
+pipe = QwenImageEditInpaintPipeline.from_pretrained("Qwen/Qwen-Image-Edit", torch_dtype=torch.bfloat16).to("cuda")
+
+@spaces.GPU
+def infer(edit_images, prompt, negative_prompt="", seed=42, randomize_seed=False, strength=1.0, num_inference_steps=35, true_cfg_scale=4.0, progress=gr.Progress(track_tqdm=True)):
+    image = edit_images["background"]
+    mask = edit_images["layers"][0]
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    # Generate image using Qwen pipeline
+    result_image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=image,
+        mask_image=mask,
+        strength=strength,
+        num_inference_steps=num_inference_steps,
+        true_cfg_scale=true_cfg_scale,
+        generator=torch.Generator(device="cuda").manual_seed(seed)
+    ).images[0]
+    
+    return result_image, seed
+    
+examples = [
+    "change the hat to red",
+    "make the background a beautiful sunset",
+    "replace the object with a flower vase",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 1000px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# Qwen Image Edit Inpainting
+Advanced image inpainting using Qwen's Image Edit model  
+[[model](https://huggingface.co/Qwen/Qwen-Image-Edit)] [[paper](https://arxiv.org/abs/2412.20710)]
+        """)
+        with gr.Row():
+            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 (e.g., 'change the hat to red')",
+                    container=False,
+                )
+                negative_prompt = gr.Text(
+                    label="Negative Prompt",
+                    show_label=True,
+                    max_lines=1,
+                    placeholder="Enter what you don't want (optional)",
+                    container=False,
+                    value=""
+                )
+                run_button = gr.Button("Run")
+                
+            result = gr.Image(label="Result", show_label=False)
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=42,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+            with gr.Row():
+                strength = gr.Slider(
+                    label="Strength",
+                    minimum=0.0,
+                    maximum=2.0,
+                    step=0.1,
+                    value=1.0,
+                    info="Controls how much the inpainted region should change"
+                )
+                
+                true_cfg_scale = gr.Slider(
+                    label="True CFG Scale",
+                    minimum=1.0,
+                    maximum=20.0,
+                    step=0.5,
+                    value=4.0,
+                    info="Classifier-free guidance scale"
+                )
+  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=10,
+                    maximum=100,
+                    step=1,
+                    value=35,
+                )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [edit_image, prompt, negative_prompt, seed, randomize_seed, strength, num_inference_steps, true_cfg_scale],
+        outputs = [result, seed]
+    )
+
+demo.launch()