Update app.py

app.py

@@ -1,6 +1,5 @@
 import spaces
 from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
-#from diffusers.utils import load_image
 from PIL import Image
 import torch
 import numpy as np
@@ -17,10 +16,10 @@ pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
     "briaai/BRIA-2.2",
     controlnet=controlnet,
     torch_dtype=torch.float16,
-    #device_map='auto',
     low_cpu_mem_usage=True,
     offload_state_dict=True,
 ).to('cuda').to(torch.float16)
+
 pipe.scheduler = EulerAncestralDiscreteScheduler(
     beta_start=0.00085,
     beta_end=0.012,
@@ -28,8 +27,6 @@ pipe.scheduler = EulerAncestralDiscreteScheduler(
     num_train_timesteps=1000,
     steps_offset=1
 )
-# pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
-# pipe.enable_xformers_memory_efficient_attention()
 pipe.force_zeros_for_empty_prompt = False
 
 def resize_image(image):
@@ -42,52 +39,50 @@ def resize_image(image):
     resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
     return resized_image
 
-
 @spaces.GPU
 def generate_(prompt, negative_prompt, grayscale_image, num_steps, controlnet_conditioning_scale, seed):
     generator = torch.Generator("cuda").manual_seed(seed)    
     images = pipe(
-    prompt, negative_prompt=negative_prompt, image=grayscale_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-    generator=generator,
+        prompt, 
+        negative_prompt=negative_prompt, 
+        image=grayscale_image, 
+        num_inference_steps=num_steps, 
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+        generator=generator,
     ).images
     return images
 
 @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)
-
     grayscale_image = input_image.convert('L').convert('RGB')
     images = generate_(prompt, negative_prompt, grayscale_image, num_steps, controlnet_conditioning_scale, seed)
+    return grayscale_image, images[0]
 
-    # Return both grayscale and output images in a list for the gallery
-    return [grayscale_image, images[0]]
-    
 block = gr.Blocks().queue()
 
 with block:
     gr.Markdown("## BRIA 2.2 ControlNet Recoloring")
     gr.HTML('''
       <p style="margin-bottom: 10px; font-size: 94%">
-        This is a demo for ControlNet Recoloring that using
+        This is a demo for ControlNet Recoloring that uses
         <a href="https://huggingface.co/briaai/BRIA-2.2" target="_blank">BRIA 2.2 text-to-image model</a> as backbone. 
-        Trained on licensed data, BRIA 2.2 provide full legal liability coverage for copyright and privacy infringement.
+        Trained on licensed data, BRIA 2.2 provides 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
+            input_image = gr.Image(sources=None, type="pil")
             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,)
+            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')
+            preview = gr.Image(label="Control (Grayscale)", type="pil")
+            result = gr.Image(label="Output Image", type="pil")
     ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
-    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+    run_button.click(fn=process, inputs=ips, outputs=[preview, result])
 
-block.launch(debug = True)
+block.launch(debug=True)