Create app.py

app.py

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+import spaces
+import cv2
+import gradio as gr
+import torch
+from diffusers import (
+    AutoencoderKL,
+    EulerAncestralDiscreteScheduler,
+)
+from diffusers.utils import load_image
+from replace_bg.model.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline
+from replace_bg.model.controlnet import ControlNetModel
+from replace_bg.utilities import resize_image, remove_bg_from_image, paste_fg_over_image, get_control_image_tensor
+
+controlnet = ControlNetModel.from_pretrained("briaai/BRIA-2.3-ControlNet-BG-Gen", torch_dtype=torch.float16) 
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16, vae=vae).to('cuda:0')
+pipe.scheduler = EulerAncestralDiscreteScheduler(
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    num_train_timesteps=1000,
+    steps_offset=1
+)
+
+
+@spaces.GPU
+def generate_(prompt, negative_prompt, control_tensor, num_steps, controlnet_conditioning_scale, seed):
+    generator = torch.Generator("cuda").manual_seed(seed)    
+    gen_img = pipe(
+        negative_prompt=negative_prompt, 
+        prompt=prompt,     
+        controlnet_conditioning_scale=controlnet_conditioning_scale, 
+        num_inference_steps=num_steps,
+        image = control_tensor,
+        generator=generator
+    ).images[0]
+    result_image = paste_fg_over_image(gen_img, image, mask)
+    return result_image
+
+@spaces.GPU
+def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
+    
+    # resize input_image to 1024x1024
+    input_image = resize_image(input_image)
+    image = resize_image(image)
+    mask = remove_bg_from_image(image_path)
+    control_tensor = get_control_image_tensor(pipe.vae, image, mask)    
+  
+    images = generate_(prompt, negative_prompt, control_tensor, num_steps, controlnet_conditioning_scale, seed)
+
+    return [depth_image, images[0]]
+
+
+
+block = gr.Blocks().queue()
+
+with block:
+    gr.Markdown("## BRIA Generate Background")
+    gr.HTML('''
+      <p style="margin-bottom: 10px; font-size: 94%">
+        This is a demo for ControlNet Depth that using
+        <a href="briaai/BRIA-2.3-ControlNet-BG-Gen" target="_blank">BRIA 2.3 text-to-image model</a> as backbone. 
+        Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement.
+      </p>
+    ''')
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
+            prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(label="Negative prompt", value="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")
+            num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
+            controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
+            run_button = gr.Button(value="Run")
+            
+            
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
+    ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+block.launch(debug = True)