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 kontext_pipeline import FluxKontextPipeline
+from diffusers import FluxTransformer2DModel
+from diffusers.utils import load_image
+
+from huggingface_hub import hf_hub_download
+
+
+kontext_path = hf_hub_download(repo_id="diffusers/kontext", filename="kontext.safetensors")
+
+MAX_SEED = np.iinfo(np.int32).max
+
+transformer = FluxTransformer2DModel.from_single_file(kontext_path, torch_dtype=torch.bfloat16)
+pipe = FluxKontextPipeline.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, progress=gr.Progress(track_tqdm=True)):
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    input_image = input_image.convert("RGB")
+
+    original_width, original_height = input_image.size
+    
+    if original_width >= original_height:
+        new_width = 1024
+        new_height = int(original_height * (new_width / original_width))
+    else:
+        new_height = 1024
+        new_width = int(original_width * (new_height / original_height))
+    
+    input_image_resized = input_image.resize((new_width, new_height), Image.LANCZOS)
+
+    image = pipe(
+        image=input_image_resized, # Use the resized image
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        generator=torch.Generator().manual_seed(seed),
+    ).images[0]
+    return image, seed
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 Kontext [dev]
+        """)
+
+        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)
+        
+        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=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,
+            )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale],
+        outputs = [result, seed]
+    )
+
+demo.launch()