initial app

app.py

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+import torch
+import spaces
+import gradio as gr
+from diffusers import FluxFillPipeline
+
+pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
+
+@spaces.GPU()
+def inpaint(
+    image,
+    mask,
+    prompt="",
+    num_inference_steps=28,
+    guidance_scale=50,
+):
+    background = image.convert("RGB")
+    mask = mask.convert("L")
+
+    result = pipe(
+        prompt=prompt,
+        height=background.height,
+        width=background.width,
+        image=background,
+        mask_image=mask,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+    ).images[0]
+
+    result = result.convert("RGBA")
+
+    return result
+
+
+demo = gr.Interface(
+    fn=inpaint,
+    inputs=[
+        gr.Image(label="image", type="pil"),
+        gr.Image(label="mask", type="pil"),
+        gr.Text(label="prompt"),
+        gr.Number(value=50, label="num_inference_steps"),
+        gr.Number(value=28, label="guidance_scale"),
+    ],
+    outputs=["image"],
+    api_name="inpaint",
+    examples=[[3, "./assets/rocket.png", "./assets/Inpainting mask.png"]],
+    cache_examples=False,
+    description="it is recommended that you use https://github.com/la-voliere/react-mask-editor when creating an image mask in JS and then inverse it before sending it to this space",
+)
+
+demo.launch()

requirements.txt

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+diffusers
+spaces