Update app_canny.py

app_canny.py

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+#!/usr/bin/env python
+
+import gradio as gr
+
+from settings import (
+    DEFAULT_IMAGE_RESOLUTION,
+    DEFAULT_NUM_IMAGES,
+    MAX_IMAGE_RESOLUTION,
+    MAX_NUM_IMAGES,
+    MAX_SEED,
+)
+from utils import randomize_seed_fn
+
+
+def create_demo(process):
+    with gr.Blocks() as demo:
+        gr.Markdown("## BRIA 2.2 ControlNet Canny")
+        gr.HTML('''
+          <p style="margin-bottom: 10px; font-size: 94%">
+            This is a demo for ControlNet Canny that using
+            <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.
+          </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=inputs,
+            outputs=result,
+            api_name="canny",
+        )
+    return demo
+
+
+if __name__ == "__main__":
+    from model import Model
+
+    model = Model(task_name="Canny")
+    demo = create_demo(model.process_canny)
+    demo.queue().launch()
+
+
+
+
+
+
+
+-----------------------------------------------------------------------
+
+
+
+
+# from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL, EulerAncestralDiscreteScheduler
+# from diffusers.utils import load_image
+# from PIL import Image
+# import torch
+# import numpy as np
+# import cv2
+# import gradio as gr
+# from torchvision import transforms 
+
+# controlnet = ControlNetModel.from_pretrained(
+#     "briaai/BRIA-2.2-ControlNet-Canny",
+#     torch_dtype=torch.float16
+# ).to('cuda')
+
+# 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')
+# pipe.scheduler = EulerAncestralDiscreteScheduler(
+#     beta_start=0.00085,
+#     beta_end=0.012,
+#     beta_schedule="scaled_linear",
+#     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
+
+# low_threshold = 100
+# high_threshold = 200
+
+# def resize_image(image):
+#     image = image.convert('RGB')
+#     current_size = image.size
+#     if current_size[0] > current_size[1]:
+#         center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
+#     else:
+#         center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
+#     resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
+#     return resized_image
+
+# 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, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
+#     generator = torch.manual_seed(seed)
+    
+#     # resize input_image to 1024x1024
+#     input_image = resize_image(input_image)
+    
+#     canny_image = get_canny_filter(input_image)
+  
+#     images = pipe(
+#         prompt, negative_prompt=negative_prompt, image=canny_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+#         generator=generator,
+#         ).images
+
+#     return [canny_image,images[0]]
+    
+# block = gr.Blocks().queue()
+
+# with block:
+#     gr.Markdown("## BRIA 2.2 ControlNet Canny")
+#     gr.HTML('''
+#       <p style="margin-bottom: 10px; font-size: 94%">
+#         This is a demo for ControlNet Canny that using
+#         <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.
+#       </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)