Update app.py

app.py

@@ -1,7 +1,107 @@
+import torch
+import spaces
 import gradio as gr
+from diffusers import FluxInpaintPipeline
+import random
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+MARKDOWN = """
+# FLUX.1 Inpainting 🎨
+Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for 
+creating this amazing model, and a big thanks to [Gothos](https://github.com/Gothos) 
+for taking it to the next level by enabling inpainting with the FLUX.
+"""
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+MAX_SEED = np.iinfo(np.int32).max
+DEVICE = "cuda" #if torch.cuda.is_available() else "cpu"
+
+pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
+
+@spaces.GPU()
+def process(input_image_editor, input_text, strength, seed, randomize_seed, num_inference_steps, guidance_scale=3.5, progress=gr.Progress(track_tqdm=True)):
+    if not input_text:
+        raise gr.Error("Please enter a text prompt.")
+
+    image = input_image_editor['background']
+
+    if not image:
+        raise gr.Error("Please upload an image.")
+
+    width, height = image.size
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator(device=DEVICE).manual_seed(seed)
+
+    result = pipe(prompt=input_text, image=image, mask_image=mask_image, width=width, height=height,
+                  strength=strength, num_inference_steps=num_inference_steps, generator=generator,
+                  guidance_scale=guidance_scale).images[0]
+
+    return result, mask_image, seed
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(MARKDOWN)
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image_component = gr.ImageEditor(
+                label='Image',
+                type='pil',
+                sources=["upload", "webcam"],
+                image_mode='RGB',
+                layers=False,
+                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
+            input_text_component = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            with gr.Accordion("Advanced Settings", open=False):
+                strength_slider = gr.Slider(
+                    minimum=0.0,
+                    maximum=1.0,
+                    value=0.7,
+                    step=0.01,
+                    label="Strength"
+                )
+                num_inference_steps = gr.Slider(
+                    minimum=1,
+                    maximum=100,
+                    value=30,
+                    step=1,
+                    label="Number of inference steps"
+                )
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                )
+                seed_number = gr.Number(
+                    label="Seed", 
+                    value=42,
+                    precision=0
+                )
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            with gr.Accordion("Upload a mask", open=False):
+                uploaded_mask_component = gr.Image(label="Already made mask (black pixels will be preserved, white pixels will be redrawn)", sources=["upload"], type="pil")
+            submit_button_component = gr.Button(
+                value='Inpaint', variant='primary')
+        with gr.Column(scale=1):
+            output_image_component = gr.Image(
+                type='pil', image_mode='RGB', label='Generated Image')
+            output_mask_component = gr.Image(
+                type='pil', image_mode='RGB', label='Generated Mask')
+            with gr.Accordion("Debug Info", open=False):
+                output_seed = gr.Number(label="Used Seed")
+
+    submit_button_component.click(
+        fn=process,
+        inputs=[input_image_component, input_text_component, strength_slider, seed_number, randomize_seed, num_inference_steps, guidance_scale],
+        outputs=[output_image_component, output_mask_component, output_seed]
+    )
+
+demo.launch(debug=False, show_error=True)