Update app.py

app.py

@@ -1,32 +1,32 @@
-#!/usr/bin/env python
+# #!/usr/bin/env python
 
-from __future__ import annotations
+# from __future__ import annotations
 
-import gradio as gr
-import torch
+# import gradio as gr
+# import torch
 
-from app_canny import create_demo as create_demo_canny
-# from app_depth import create_demo as create_demo_depth
-# from app_recoloring import create_demo as create_demo_recoloring
-from model import Model
+# from app_canny import create_demo as create_demo_canny
+# # from app_depth import create_demo as create_demo_depth
+# # from app_recoloring import create_demo as create_demo_recoloring
+# from model import Model
 
-DESCRIPTION = "# BRIA 2.2 ControlNets"
+# DESCRIPTION = "# BRIA 2.2 ControlNets"
 
-model = Model(base_model_id='briaai/BRIA-2.2', task_name="Canny")
+# model = Model(base_model_id='briaai/BRIA-2.2', task_name="Canny")
 
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION)
+# with gr.Blocks(css="style.css") as demo:
+#     gr.Markdown(DESCRIPTION)
 
-    with gr.Tabs():
-        with gr.TabItem("Canny"):
-            create_demo_canny(model.process_canny)
-        # with gr.TabItem("Depth (Future)"):
-        #     create_demo_canny(model.process_mlsd)
-        # with gr.TabItem("Recoloring (Future)"):
-        #     create_demo_canny(model.process_scribble)
+#     with gr.Tabs():
+#         with gr.TabItem("Canny"):
+#             create_demo_canny(model.process_canny)
+#         # with gr.TabItem("Depth (Future)"):
+#         #     create_demo_canny(model.process_mlsd)
+#         # with gr.TabItem("Recoloring (Future)"):
+#         #     create_demo_canny(model.process_scribble)
 
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+# if __name__ == "__main__":
+#     demo.queue(max_size=20).launch()
 
 
 
@@ -35,103 +35,103 @@ if __name__ == "__main__":
 
 
 
-# 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):
+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) 
+    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)
+    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)
+    # resize input_image to 1024x1024
+    input_image = resize_image(input_image)
     
-#     canny_image = get_canny_filter(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
+    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]]
+    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")
+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])
+        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)
+block.launch(debug = True)