Update app.py

app.py

@@ -1,100 +1,137 @@
-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
+#!/usr/bin/env python
+
+from __future__ import annotations
+
 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):
+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
+
+DESCRIPTION = "# BRIA 2.2 ControlNets"
+
+model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="Canny")
+
+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)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).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) 
+#     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)