Update app.py

app.py

@@ -1,136 +1,133 @@
-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
-import cv2
-import gradio as gr
-from torchvision import transforms 
-import fire
-import os
-
-controlnet = ControlNetModel.from_pretrained(
-    "geyongtao/HumanWild",
-    torch_dtype=torch.float16
-).to('cuda')
-
-vae = AutoencoderKL.from_pretrained(
-    "madebyollin/sdxl-vae-fp16-fix", 
-    torch_dtype=torch.float16).to("cuda")
-
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    controlnet=controlnet,
-    vae=vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    low_cpu_mem_usage=True,
-    offload_state_dict=True,
-).to('cuda')
-pipe.controlnet.to(memory_format=torch.channels_last)
-
-# pipe.enable_xformers_memory_efficient_attention()
-pipe.force_zeros_for_empty_prompt = False
-
-
-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_normal_map(image):
-    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
-    with torch.no_grad(), torch.autocast("cuda"):
-        depth_map = depth_estimator(image).predicted_depth
-    image = transforms.functional.center_crop(image, min(image.shape[-2:]))
-    depth_map = torch.nn.functional.interpolate(
-        depth_map.unsqueeze(1),
-        size=(1024, 1024),
-        mode="bicubic",
-        align_corners=False,
-    )
-    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
-    image = torch.cat([depth_map] * 3, dim=1)
-    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
-    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
-    return image
-
-
-@spaces.GPU
-def generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed):
-    generator = torch.Generator("cuda").manual_seed(seed)    
-    images = pipe(
-        prompt, 
-        negative_prompt=negative_prompt, 
-        image=normal_image, 
-        num_inference_steps=num_steps, 
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-        num_images_per_prompt=2,
-        generator=generator,
-    ).images
-    return images
-
-@spaces.GPU
-def process(normal_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
-    # resize input_image to 1024x1024
-    normal_image = resize_image(normal_image)
-    # depth_image = get_depth_map(input_image)
-    images = generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed)
-
-    return [images[0], images[1]]
-
-
-def run_demo():
-    
-    _TITLE = '''3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models'''
-
-    block = gr.Blocks().queue()
-    
-    with block:
-        gr.Markdown("# 3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models ")
-        gr.HTML('''
-          <p style="margin-bottom: 10px; font-size: 94%">
-            This is a demo for Surface Normal ControlNet that using
-            <a href="https://huggingface.co/geyongtao/HumanWild" target="_blank"> HumanWild model</a> pretrained weight. 
-            <a style="display:inline-block; margin-left: .5em" href='https://github.com/YongtaoGe/WildHuman/'><img src='https://img.shields.io/github/stars/YongtaoGe/WildHuman?style=social' /></a>
-          </p>
-        ''')
-        with gr.Row():
-            with gr.Column():
-                input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
-
-                example_folder = os.path.join(os.path.dirname(__file__), "./assets")
-                example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
-                gr.Examples(
-                    examples=example_fns,
-                    inputs=[input_image],
-                    cache_examples=False,
-                    label='Examples (click one of the images below to start)',
-                    examples_per_page=30
-                )
-            
-                prompt = gr.Textbox(label="Prompt", value="a person, in the wild")
-                negative_prompt = gr.Textbox(visible=False, 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=50, value=30, step=1)
-                controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=1.0, value=0.95, 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)
-
-if __name__ == '__main__':
+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
+import cv2
+import gradio as gr
+from torchvision import transforms 
+import fire
+import os
+
+controlnet = ControlNetModel.from_pretrained(
+    "geyongtao/HumanWild",
+    torch_dtype=torch.float16
+).to('cuda')
+
+vae = AutoencoderKL.from_pretrained(
+    "madebyollin/sdxl-vae-fp16-fix", 
+    torch_dtype=torch.float16).to("cuda")
+
+pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    controlnet=controlnet,
+    vae=vae,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    low_cpu_mem_usage=True,
+    offload_state_dict=True,
+).to('cuda')
+pipe.controlnet.to(memory_format=torch.channels_last)
+
+# pipe.enable_xformers_memory_efficient_attention()
+pipe.force_zeros_for_empty_prompt = False
+
+
+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_normal_map(image):
+    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
+    with torch.no_grad(), torch.autocast("cuda"):
+        depth_map = depth_estimator(image).predicted_depth
+    image = transforms.functional.center_crop(image, min(image.shape[-2:]))
+    depth_map = torch.nn.functional.interpolate(
+        depth_map.unsqueeze(1),
+        size=(1024, 1024),
+        mode="bicubic",
+        align_corners=False,
+    )
+    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
+    image = torch.cat([depth_map] * 3, dim=1)
+    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
+    return image
+
+
+@spaces.GPU
+def generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed):
+    generator = torch.Generator("cuda").manual_seed(seed)    
+    images = pipe(
+        prompt, 
+        negative_prompt=negative_prompt, 
+        image=normal_image, 
+        num_inference_steps=num_steps, 
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+        num_images_per_prompt=2,
+        generator=generator,
+    ).images
+    return images
+
+@spaces.GPU
+def process(normal_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
+    # resize input_image to 1024x1024
+    normal_image = resize_image(normal_image)
+    # depth_image = get_depth_map(input_image)
+    images = generate_(prompt, negative_prompt, normal_image, num_steps, controlnet_conditioning_scale, seed)
+
+    return [images[0], images[1]]
+
+
+def run_demo():
+    
+    _TITLE = '''3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models'''
+
+    block = gr.Blocks().queue()
+    
+    with block:
+        gr.Markdown("# 3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models ")
+        gr.HTML('''
+          <p style="margin-bottom: 10px; font-size: 94%">
+            This is a demo for Surface Normal ControlNet 
+        ''')
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
+
+                example_folder = os.path.join(os.path.dirname(__file__), "./assets")
+                example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
+                gr.Examples(
+                    examples=example_fns,
+                    inputs=[input_image],
+                    cache_examples=False,
+                    label='Examples (click one of the images below to start)',
+                    examples_per_page=30
+                )
+            
+                prompt = gr.Textbox(label="Prompt", value="a person, in the wild")
+                negative_prompt = gr.Textbox(visible=False, 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=50, value=30, step=1)
+                controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=1.0, value=0.95, 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)
+
+if __name__ == '__main__':
     fire.Fire(run_demo)