Update app.py

app.py

@@ -1,237 +1,299 @@
-# Not ready to use yet
-import spaces
-import argparse
-import numpy as np
-import gradio as gr
-from omegaconf import OmegaConf
+# # 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
+# )
+
+# _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
-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 gradio as gr
+import requests
 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
+# 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",
+}
+
+
+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)
+
+# 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
+
+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()
+
+# 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."
 )
 
-_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()
+if __name__ == "__main__":
+    demo.launch()