Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import spaces
+import torch
+import random
+from PIL import Image
+
+from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig
+from diffusers.utils import load_image
+
+MAX_SEED = np.iinfo(np.int32).max
+
+# Load GGUF transformer model from Hugging Face
+gguf_path = "https://huggingface.co/bullerwins/FLUX.1-Kontext-dev-GGUF/resolve/main/flux1-kontext-dev-Q8_0.gguf"
+
+transformer = FluxTransformer2DModel.from_single_file(
+    gguf_path,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+
+# Create pipeline with GGUF transformer
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+@spaces.GPU
+def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
+    """
+    Perform image editing using the FLUX.1 Kontext pipeline.
+    
+    This function takes an input image and a text prompt to generate a modified version
+    of the image based on the provided instructions. It uses the FLUX.1 Kontext model
+    for contextual image editing tasks.
+    
+    Args:
+        input_image (PIL.Image.Image): The input image to be edited. Will be converted
+            to RGB format if not already in that format.
+        prompt (str): Text description of the desired edit to apply to the image.
+            Examples: "Remove glasses", "Add a hat", "Change background to beach".
+        seed (int, optional): Random seed for reproducible generation. Defaults to 42.
+            Must be between 0 and MAX_SEED (2^31 - 1).
+        randomize_seed (bool, optional): If True, generates a random seed instead of
+            using the provided seed value. Defaults to False.
+        guidance_scale (float, optional): Controls how closely the model follows the
+            prompt. Higher values mean stronger adherence to the prompt but may reduce
+            image quality. Range: 1.0-10.0. Defaults to 2.5.
+        steps (int, optional): Controls how many steps to run the diffusion model for.
+            Range: 1-30. Defaults to 28.
+        progress (gr.Progress, optional): Gradio progress tracker for monitoring
+            generation progress. Defaults to gr.Progress(track_tqdm=True).
+    
+    Returns:
+        tuple: A 3-tuple containing:
+            - PIL.Image.Image: The generated/edited image
+            - int: The seed value used for generation (useful when randomize_seed=True)
+            - gr.update: Gradio update object to make the reuse button visible
+    
+    Example:
+        >>> edited_image, used_seed, button_update = infer(
+        ...     input_image=my_image,
+        ...     prompt="Add sunglasses",
+        ...     seed=123,
+        ...     randomize_seed=False,
+        ...     guidance_scale=2.5
+        ... )
+    """
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    if input_image:
+        input_image = input_image.convert("RGB")
+        image = pipe(
+            image=input_image, 
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            width = input_image.size[0],
+            height = input_image.size[1],
+            num_inference_steps=steps,
+            generator=torch.Generator().manual_seed(seed),
+        ).images[0]
+    else:
+        image = pipe(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=steps,
+            generator=torch.Generator().manual_seed(seed),
+        ).images[0]
+    return image, seed, gr.Button(visible=True)
+
+@spaces.GPU
+def infer_example(input_image, prompt):
+    image, seed, _ = infer(input_image, prompt)
+    return image, seed
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 Kontext [dev]
+Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
+        """)
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(label="Upload the image for editing", type="pil")
+                with gr.Row():
+                    prompt = gr.Text(
+                        label="Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
+                        container=False,
+                    )
+                    run_button = gr.Button("Run", scale=0)
+                with gr.Accordion("Advanced Settings", open=False):
+                    
+                    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=1,
+                        maximum=10,
+                        step=0.1,
+                        value=2.5,
+                    )       
+                    
+                    steps = gr.Slider(
+                        label="Steps",
+                        minimum=1,
+                        maximum=30,
+                        value=28,
+                        step=1
+                    )
+                    
+            with gr.Column():
+                result = gr.Image(label="Result", show_label=False, interactive=False)
+                reuse_button = gr.Button("Reuse this image", visible=False)
+        
+            
+        examples = gr.Examples(
+            examples=[
+                ["flowers.png", "turn the flowers into sunflowers"],
+                ["monster.png", "make this monster ride a skateboard on the beach"],
+                ["cat.png", "make this cat happy"]
+            ],
+            inputs=[input_image, prompt],
+            outputs=[result, seed],
+            fn=infer_example,
+            cache_examples="lazy"
+        )
+            
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],
+        outputs = [result, seed, reuse_button]
+    )
+    reuse_button.click(
+        fn = lambda image: image,
+        inputs = [result],
+        outputs = [input_image]
+    )
+
+demo.launch(mcp_server=True)