Update app.py

app.py

@@ -1,299 +1,299 @@
-# # Not ready to use yet
-# import spaces
-# import argparse
-# import numpy as np
-# import gradio as gr
-# from omegaconf import OmegaConf
-# import torch
-# from PIL import Image
-# import PIL
-# from pipelines import TwoStagePipeline
-# from huggingface_hub import hf_hub_download
-# import os
-# import rembg
-# from typing import Any
-# import json
-# import os
-# import json
-# import argparse
-
-# from model import CRM
-# from inference import generate3d
-
-# pipeline = None
-# rembg_session = rembg.new_session()
-
-
-# def expand_to_square(image, bg_color=(0, 0, 0, 0)):
-#     # expand image to 1:1
-#     width, height = image.size
-#     if width == height:
-#         return image
-#     new_size = (max(width, height), max(width, height))
-#     new_image = Image.new("RGBA", new_size, bg_color)
-#     paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
-#     new_image.paste(image, paste_position)
-#     return new_image
-
-# def check_input_image(input_image):
-#     if input_image is None:
-#         raise gr.Error("No image uploaded!")
-
-
-# def remove_background(
-#     image: PIL.Image.Image,
-#     rembg_session: Any = None,
-#     force: bool = False,
-#     **rembg_kwargs,
-# ) -> PIL.Image.Image:
-#     do_remove = True
-#     if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
-#         # explain why current do not rm bg
-#         print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
-#         background = Image.new("RGBA", image.size, (0, 0, 0, 0))
-#         image = Image.alpha_composite(background, image)
-#         do_remove = False
-#     do_remove = do_remove or force
-#     if do_remove:
-#         image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
-#     return image
-
-# def do_resize_content(original_image: Image, scale_rate):
-#     # resize image content wile retain the original image size
-#     if scale_rate != 1:
-#         # Calculate the new size after rescaling
-#         new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
-#         # Resize the image while maintaining the aspect ratio
-#         resized_image = original_image.resize(new_size)
-#         # Create a new image with the original size and black background
-#         padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
-#         paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
-#         padded_image.paste(resized_image, paste_position)
-#         return padded_image
-#     else:
-#         return original_image
-
-# def add_background(image, bg_color=(255, 255, 255)):
-#     # given an RGBA image, alpha channel is used as mask to add background color
-#     background = Image.new("RGBA", image.size, bg_color)
-#     return Image.alpha_composite(background, image)
-
-
-# def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
-#     """
-#     input image is a pil image in RGBA, return RGB image
-#     """
-#     print(background_choice)
-#     if background_choice == "Alpha as mask":
-#         background = Image.new("RGBA", image.size, (0, 0, 0, 0))
-#         image = Image.alpha_composite(background, image)
-#     else:
-#         image = remove_background(image, rembg_session, force=True)
-#     image = do_resize_content(image, foreground_ratio)
-#     image = expand_to_square(image)
-#     image = add_background(image, backgroud_color)
-#     return image.convert("RGB")
-
-# @spaces.GPU
-# def gen_image(input_image, seed, scale, step):
-#     global pipeline, model, args
-#     pipeline.set_seed(seed)
-#     rt_dict = pipeline(input_image, scale=scale, step=step)
-#     stage1_images = rt_dict["stage1_images"]
-#     stage2_images = rt_dict["stage2_images"]
-#     np_imgs = np.concatenate(stage1_images, 1)
-#     np_xyzs = np.concatenate(stage2_images, 1)
-
-#     glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
-#     return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path#, obj_path
-
-
-# parser = argparse.ArgumentParser()
-# parser.add_argument(
-#     "--stage1_config",
-#     type=str,
-#     default="configs/nf7_v3_SNR_rd_size_stroke.yaml",
-#     help="config for stage1",
-# )
-# parser.add_argument(
-#     "--stage2_config",
-#     type=str,
-#     default="configs/stage2-v2-snr.yaml",
-#     help="config for stage2",
-# )
+# Not ready to use yet
+import spaces
+import argparse
+import numpy as np
+import gradio as gr
+from omegaconf import OmegaConf
+import torch
+from PIL import Image
+import PIL
+from pipelines import TwoStagePipeline
+from huggingface_hub import hf_hub_download
+import os
+import rembg
+from typing import Any
+import json
+import os
+import json
+import argparse
+
+from model import CRM
+from inference import generate3d
+
+pipeline = None
+rembg_session = rembg.new_session()
+
+
+def expand_to_square(image, bg_color=(0, 0, 0, 0)):
+    # expand image to 1:1
+    width, height = image.size
+    if width == height:
+        return image
+    new_size = (max(width, height), max(width, height))
+    new_image = Image.new("RGBA", new_size, bg_color)
+    paste_position = ((new_size[0] - width) // 2, (new_size[1] - height) // 2)
+    new_image.paste(image, paste_position)
+    return new_image
+
+def check_input_image(input_image):
+    if input_image is None:
+        raise gr.Error("No image uploaded!")
+
+
+def remove_background(
+    image: PIL.Image.Image,
+    rembg_session: Any = None,
+    force: bool = False,
+    **rembg_kwargs,
+) -> PIL.Image.Image:
+    do_remove = True
+    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+        # explain why current do not rm bg
+        print("alhpa channl not enpty, skip remove background, using alpha channel as mask")
+        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
+        image = Image.alpha_composite(background, image)
+        do_remove = False
+    do_remove = do_remove or force
+    if do_remove:
+        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+    return image
+
+def do_resize_content(original_image: Image, scale_rate):
+    # resize image content wile retain the original image size
+    if scale_rate != 1:
+        # Calculate the new size after rescaling
+        new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
+        # Resize the image while maintaining the aspect ratio
+        resized_image = original_image.resize(new_size)
+        # Create a new image with the original size and black background
+        padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
+        paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
+        padded_image.paste(resized_image, paste_position)
+        return padded_image
+    else:
+        return original_image
+
+def add_background(image, bg_color=(255, 255, 255)):
+    # given an RGBA image, alpha channel is used as mask to add background color
+    background = Image.new("RGBA", image.size, bg_color)
+    return Image.alpha_composite(background, image)
+
+
+def preprocess_image(image, background_choice, foreground_ratio, backgroud_color):
+    """
+    input image is a pil image in RGBA, return RGB image
+    """
+    print(background_choice)
+    if background_choice == "Alpha as mask":
+        background = Image.new("RGBA", image.size, (0, 0, 0, 0))
+        image = Image.alpha_composite(background, image)
+    else:
+        image = remove_background(image, rembg_session, force=True)
+    image = do_resize_content(image, foreground_ratio)
+    image = expand_to_square(image)
+    image = add_background(image, backgroud_color)
+    return image.convert("RGB")
+
+@spaces.GPU
+def gen_image(input_image, seed, scale, step):
+    global pipeline, model, args
+    pipeline.set_seed(seed)
+    rt_dict = pipeline(input_image, scale=scale, step=step)
+    stage1_images = rt_dict["stage1_images"]
+    stage2_images = rt_dict["stage2_images"]
+    np_imgs = np.concatenate(stage1_images, 1)
+    np_xyzs = np.concatenate(stage2_images, 1)
+
+    glb_path = generate3d(model, np_imgs, np_xyzs, args.device)
+    return Image.fromarray(np_imgs), Image.fromarray(np_xyzs), glb_path#, obj_path
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--stage1_config",
+    type=str,
+    default="configs/nf7_v3_SNR_rd_size_stroke.yaml",
+    help="config for stage1",
+)
+parser.add_argument(
+    "--stage2_config",
+    type=str,
+    default="configs/stage2-v2-snr.yaml",
+    help="config for stage2",
+)
 
-# parser.add_argument("--device", type=str, default="cuda")
-# args = parser.parse_args()
-
-# crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
-# specs = json.load(open("configs/specs_objaverse_total.json"))
-# model = CRM(specs)
-# model.load_state_dict(torch.load(crm_path, map_location="cpu"), strict=False)
-# model = model.to(args.device)
-
-# stage1_config = OmegaConf.load(args.stage1_config).config
-# stage2_config = OmegaConf.load(args.stage2_config).config
-# stage2_sampler_config = stage2_config.sampler
-# stage1_sampler_config = stage1_config.sampler
-
-# stage1_model_config = stage1_config.models
-# stage2_model_config = stage2_config.models
-
-# xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
-# pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
-# stage1_model_config.resume = pixel_path
-# stage2_model_config.resume = xyz_path
-
-# pipeline = TwoStagePipeline(
-#     stage1_model_config,
-#     stage2_model_config,
-#     stage1_sampler_config,
-#     stage2_sampler_config,
-#     device=args.device,
-#     dtype=torch.float32
-# )
+parser.add_argument("--device", type=str, default="cuda")
+args = parser.parse_args()
+
+crm_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="CRM.pth")
+specs = json.load(open("configs/specs_objaverse_total.json"))
+model = CRM(specs)
+model.load_state_dict(torch.load(crm_path, map_location="cpu"), strict=False)
+model = model.to(args.device)
+
+stage1_config = OmegaConf.load(args.stage1_config).config
+stage2_config = OmegaConf.load(args.stage2_config).config
+stage2_sampler_config = stage2_config.sampler
+stage1_sampler_config = stage1_config.sampler
+
+stage1_model_config = stage1_config.models
+stage2_model_config = stage2_config.models
+
+xyz_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="ccm-diffusion.pth")
+pixel_path = hf_hub_download(repo_id="Zhengyi/CRM", filename="pixel-diffusion.pth")
+stage1_model_config.resume = pixel_path
+stage2_model_config.resume = xyz_path
+
+pipeline = TwoStagePipeline(
+    stage1_model_config,
+    stage2_model_config,
+    stage1_sampler_config,
+    stage2_sampler_config,
+    device=args.device,
+    dtype=torch.float32
+)
 
-# _DESCRIPTION = '''
-# * Our [official implementation](https://github.com/thu-ml/CRM) uses UV texture instead of vertex color. It has better texture than this online demo.
-# * Project page of CRM: https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/
-# * If you find the output unsatisfying, try using different seeds:)
-# '''
-
-# with gr.Blocks() as demo:
-#     gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
-#     gr.Markdown(_DESCRIPTION)
-#     with gr.Row():
-#         with gr.Column():
-#             with gr.Row():
-#                 image_input = gr.Image(
-#                     label="Image input",
-#                     image_mode="RGBA",
-#                     sources="upload",
-#                     type="pil",
-#                 )
-#                 processed_image = gr.Image(label="Processed Image", interactive=False, type="pil", image_mode="RGB")
-#             with gr.Row():
-#                 with gr.Column():
-#                     with gr.Row():
-#                         background_choice = gr.Radio([
-#                                 "Alpha as mask",
-#                                 "Auto Remove background"
-#                             ], value="Auto Remove background",
-#                             label="backgroud choice")
-#                         # do_remove_background = gr.Checkbox(label=, value=True)
-#                         # force_remove = gr.Checkbox(label=, value=False)
-#                     back_groud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=False)
-#                     foreground_ratio = gr.Slider(
-#                         label="Foreground Ratio",
-#                         minimum=0.5,
-#                         maximum=1.0,
-#                         value=1.0,
-#                         step=0.05,
-#                     )
-
-#                 with gr.Column():
-#                     seed = gr.Number(value=1234, label="seed", precision=0)
-#                     guidance_scale = gr.Number(value=5.5, minimum=3, maximum=10, label="guidance_scale")
-#                     step = gr.Number(value=30, minimum=30, maximum=100, label="sample steps", precision=0)
-#             text_button = gr.Button("Generate 3D shape")
-#             gr.Examples(
-#                 examples=[os.path.join("examples", i) for i in os.listdir("examples")],
-#                 inputs=[image_input],
-#                 examples_per_page = 20,
-#             )
-#         with gr.Column():
-#             image_output = gr.Image(interactive=False, label="Output RGB image")
-#             xyz_ouput = gr.Image(interactive=False, label="Output CCM image")
-
-#             output_model = gr.Model3D(
-#                 label="Output OBJ",
-#                 interactive=False,
-#             )
-#             gr.Markdown("Note: Ensure that the input image is correctly pre-processed into a grey background, otherwise the results will be unpredictable.")
-
-#     inputs = [
-#         processed_image,
-#         seed,
-#         guidance_scale,
-#         step,
-#     ]
-#     outputs = [
-#         image_output,
-#         xyz_ouput,
-#         output_model,
-#         # output_obj,
-#     ]
-
-
-#     text_button.click(fn=check_input_image, inputs=[image_input]).success(
-#         fn=preprocess_image,
-#         inputs=[image_input, background_choice, foreground_ratio, back_groud_color],
-#         outputs=[processed_image],
-#     ).success(
-#         fn=gen_image,
-#         inputs=inputs,
-#         outputs=outputs,
-#     )
-#     demo.queue().launch()
+_DESCRIPTION = '''
+* Our [official implementation](https://github.com/thu-ml/CRM) uses UV texture instead of vertex color. It has better texture than this online demo.
+* Project page of CRM: https://ml.cs.tsinghua.edu.cn/~zhengyi/CRM/
+* If you find the output unsatisfying, try using different seeds:)
+'''
+
+with gr.Blocks() as demo:
+    gr.Markdown("# CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model")
+    gr.Markdown(_DESCRIPTION)
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                image_input = gr.Image(
+                    label="Image input",
+                    image_mode="RGBA",
+                    sources="upload",
+                    type="pil",
+                )
+                processed_image = gr.Image(label="Processed Image", interactive=False, type="pil", image_mode="RGB")
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        background_choice = gr.Radio([
+                                "Alpha as mask",
+                                "Auto Remove background"
+                            ], value="Auto Remove background",
+                            label="backgroud choice")
+                        # do_remove_background = gr.Checkbox(label=, value=True)
+                        # force_remove = gr.Checkbox(label=, value=False)
+                    back_groud_color = gr.ColorPicker(label="Background Color", value="#7F7F7F", interactive=False)
+                    foreground_ratio = gr.Slider(
+                        label="Foreground Ratio",
+                        minimum=0.5,
+                        maximum=1.0,
+                        value=1.0,
+                        step=0.05,
+                    )
+
+                with gr.Column():
+                    seed = gr.Number(value=1234, label="seed", precision=0)
+                    guidance_scale = gr.Number(value=5.5, minimum=3, maximum=10, label="guidance_scale")
+                    step = gr.Number(value=30, minimum=30, maximum=100, label="sample steps", precision=0)
+            text_button = gr.Button("Generate 3D shape")
+            gr.Examples(
+                examples=[os.path.join("examples", i) for i in os.listdir("examples")],
+                inputs=[image_input],
+                examples_per_page = 20,
+            )
+        with gr.Column():
+            image_output = gr.Image(interactive=False, label="Output RGB image")
+            xyz_ouput = gr.Image(interactive=False, label="Output CCM image")
+
+            output_model = gr.Model3D(
+                label="Output OBJ",
+                interactive=False,
+            )
+            gr.Markdown("Note: Ensure that the input image is correctly pre-processed into a grey background, otherwise the results will be unpredictable.")
+
+    inputs = [
+        processed_image,
+        seed,
+        guidance_scale,
+        step,
+    ]
+    outputs = [
+        image_output,
+        xyz_ouput,
+        output_model,
+        # output_obj,
+    ]
+
+
+    text_button.click(fn=check_input_image, inputs=[image_input]).success(
+        fn=preprocess_image,
+        inputs=[image_input, background_choice, foreground_ratio, back_groud_color],
+        outputs=[processed_image],
+    ).success(
+        fn=gen_image,
+        inputs=inputs,
+        outputs=outputs,
+    )
+    demo.queue().launch()
 
 
 
-import torch
-import gradio as gr
-import requests
-import os
+# import torch
+# import gradio as gr
+# import requests
+# import os
 
-# Download model weights from Hugging Face model repo (if not already present)
-model_repo = "Mariam-Elz/CRM"  # Your Hugging Face model repo
+# # Download model weights from Hugging Face model repo (if not already present)
+# model_repo = "Mariam-Elz/CRM"  # Your Hugging Face model repo
 
-model_files = {
-    "ccm-diffusion.pth": "ccm-diffusion.pth",
-    "pixel-diffusion.pth": "pixel-diffusion.pth",
-    "CRM.pth": "CRM.pth",
-}
+# model_files = {
+#     "ccm-diffusion.pth": "ccm-diffusion.pth",
+#     "pixel-diffusion.pth": "pixel-diffusion.pth",
+#     "CRM.pth": "CRM.pth",
+# }
 
 
-os.makedirs("models", exist_ok=True)
+# os.makedirs("models", exist_ok=True)
 
 
 
-for filename, output_path in model_files.items():
-    file_path = f"models/{output_path}"
-    if not os.path.exists(file_path):
-        url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
-        print(f"Downloading {filename}...")
-        response = requests.get(url)
-        with open(file_path, "wb") as f:
-            f.write(response.content)
+# for filename, output_path in model_files.items():
+#     file_path = f"models/{output_path}"
+#     if not os.path.exists(file_path):
+#         url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}"
+#         print(f"Downloading {filename}...")
+#         response = requests.get(url)
+#         with open(file_path, "wb") as f:
+#             f.write(response.content)
 
-# Load model (This part depends on how the model is defined)
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# # Load model (This part depends on how the model is defined)
+# device = "cuda" if torch.cuda.is_available() else "cpu"
 
-def load_model():
-    model_path = "models/CRM.pth"
-    model = torch.load(model_path, map_location=device)
-    model.eval()
-    return model
+# def load_model():
+#     model_path = "models/CRM.pth"
+#     model = torch.load(model_path, map_location=device)
+#     model.eval()
+#     return model
 
-model = load_model()
+# model = load_model()
 
-# Define inference function
-def infer(image):
-    """Process input image and return a reconstructed image."""
-    with torch.no_grad():
-        # Assuming model expects a tensor input
-        image_tensor = torch.tensor(image).to(device)
-        output = model(image_tensor)
-        return output.cpu().numpy()
+# # Define inference function
+# def infer(image):
+#     """Process input image and return a reconstructed image."""
+#     with torch.no_grad():
+#         # Assuming model expects a tensor input
+#         image_tensor = torch.tensor(image).to(device)
+#         output = model(image_tensor)
+#         return output.cpu().numpy()
 
-# Create Gradio UI
-demo = gr.Interface(
-    fn=infer,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Image(type="numpy"),
-    title="Convolutional Reconstruction Model",
-    description="Upload an image to get the reconstructed output."
-)
+# # Create Gradio UI
+# demo = gr.Interface(
+#     fn=infer,
+#     inputs=gr.Image(type="numpy"),
+#     outputs=gr.Image(type="numpy"),
+#     title="Convolutional Reconstruction Model",
+#     description="Upload an image to get the reconstructed output."
+# )
 
-if __name__ == "__main__":
-    demo.launch()
+# if __name__ == "__main__":
+#     demo.launch()