Create app.py

app.py

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+from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
+from diffusers.utils import load_image
+from PIL import Image
+import torch
+import numpy as np
+import cv2
+import gradio as gr 
+
+controlnet_conditioning_scale = 0.5  # recommended for good generalization
+
+controlnet = ControlNetModel.from_pretrained(
+    "briaai/ControlNet-Canny",
+    torch_dtype=torch.float16
+)
+
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "briaai/BRIA-2.0",
+    controlnet=controlnet,
+    vae=vae,
+    torch_dtype=torch.float16,
+)
+pipe.enable_model_cpu_offload()
+
+low_threshold = 100
+high_threshold = 200
+
+def get_canny_filter(image):
+    
+    if not isinstance(image, np.ndarray):
+        image = np.array(image) 
+        
+    image = cv2.Canny(image, low_threshold, high_threshold)
+    image = image[:, :, None]
+    image = np.concatenate([image, image, image], axis=2)
+    canny_image = Image.fromarray(image)
+    return canny_image
+
+def process(input_image, prompt):
+    canny_image = get_canny_filter(input_image)
+    images = pipe(
+        prompt,image=canny_image, controlnet_conditioning_scale=controlnet_conditioning_scale,
+        ).images
+
+    return [canny_image,images[0]]
+    
+block = gr.Blocks().queue()
+
+with block:
+    gr.Markdown("## BRIA 2.0 ControlNet Canny")
+    gr.HTML('''
+     <p style="margin-bottom: 10px; font-size: 94%">
+                This is a demo for BRIA 2.0 ControlNet Canny, a fully legal and safe T2I model. 
+              </p>
+              ''')
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(source='upload', type="numpy")
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button(label="Run")
+            
+            
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid_cols=2, height='auto')
+    ips = [input_image, prompt]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+block.launch(debug = True)