Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
+import spaces
+from PIL import Image
+import numpy as np
+
+# Load the ControlNet model and pipeline
+controlnet = ControlNetModel.from_pretrained(
+    "briaai/BRIA-2.2-ControlNet-Recoloring",
+    torch_dtype=torch.float16
+)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "briaai/BRIA-2.2",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+).to("cuda")
+
+# Function to transform the image based on a prompt
+@spaces.GPU(enable_queue=True)
+def generate_image(image, prompt):
+    # Prepare the image for processing
+    image = image.convert("RGB")
+    recoloring_image = Image.fromarray(np.array(image)).convert('L').convert('RGB')
+    
+    # Define the negative prompt
+    negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
+    
+    # Generate the transformed image
+    results = pipe(prompt=prompt, negative_prompt=negative_prompt, image=recoloring_image, controlnet_conditioning_scale=1.0, height=1024, width=1024)
+    return results.images[0]
+
+# Gradio Interface
+description = """
+Anything to Anything, a workflow by Angrypenguinpng using the Bria Recolor ControlNet, check it out here: https://huggingface.co/briaai/BRIA-2.2-ControlNet-Recoloring
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1><center>Image Transformation with Bria Recolor ControlNet</center></h1>")
+    gr.Markdown(description)
+    with gr.Group():
+        with gr.Row():
+            image = gr.Image(label='Upload your image')
+            prompt = gr.Textbox(label='Enter your prompt', placeholder="A portrait of a beautiful and playful ethereal singer, golden designs, highly detailed, blurry background")
+            submit = gr.Button('Transform Image')
+    output_image = gr.Image(label='Transformed Image')
+
+    submit.click(fn=generate_image, inputs=[image, prompt], outputs=output_image)
+
+demo.launch()