Update app.py

app.py

@@ -336,105 +336,346 @@
 
 
 #############7tth################
-import torch
-import torch.nn as nn
-import gradio as gr
-import requests
-import os
-import torchvision.transforms as transforms
-import numpy as np
-from PIL import Image
+# import torch
+# import torch.nn as nn
+# import gradio as gr
+# import requests
+# import os
+# import torchvision.transforms as transforms
+# import numpy as np
+# from PIL import Image
+
+# # Hugging Face Model Repository
+# model_repo = "Mariam-Elz/CRM"
+
+# # Model File Path
+# model_path = "models/CRM.pth"
+# os.makedirs("models", exist_ok=True)
 
-# Hugging Face Model Repository
-model_repo = "Mariam-Elz/CRM"
-
-# Model File Path
-model_path = "models/CRM.pth"
-os.makedirs("models", exist_ok=True)
-
-# Download model weights if not present
-if not os.path.exists(model_path):
-    url = f"https://huggingface.co/{model_repo}/resolve/main/CRM.pth"
-    print(f"Downloading CRM.pth...")
-    response = requests.get(url)
-    with open(model_path, "wb") as f:
-        f.write(response.content)
-
-# Set Device
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# Define Model Architecture (Replace with your actual model)
-class CRMModel(nn.Module):
-    def __init__(self):
-        super(CRMModel, self).__init__()
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
-        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
-        self.relu = nn.ReLU()
+# # Download model weights if not present
+# if not os.path.exists(model_path):
+#     url = f"https://huggingface.co/{model_repo}/resolve/main/CRM.pth"
+#     print(f"Downloading CRM.pth...")
+#     response = requests.get(url)
+#     with open(model_path, "wb") as f:
+#         f.write(response.content)
+
+# # Set Device
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# # Define Model Architecture (Replace with your actual model)
+# class CRMModel(nn.Module):
+#     def __init__(self):
+#         super(CRMModel, self).__init__()
+#         self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
+#         self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
+#         self.relu = nn.ReLU()
     
-    def forward(self, x):
-        x = self.relu(self.conv1(x))
-        x = self.relu(self.conv2(x))
-        return x
-
-# Load Model
-def load_model():
-    print("Loading model...")
-    model = CRMModel()  # Use the correct architecture here
-    state_dict = torch.load(model_path, map_location=device)
-
-    if isinstance(state_dict, dict):  # Ensure it's a valid state_dict
-        model.load_state_dict(state_dict)
+#     def forward(self, x):
+#         x = self.relu(self.conv1(x))
+#         x = self.relu(self.conv2(x))
+#         return x
+
+# # Load Model
+# def load_model():
+#     print("Loading model...")
+#     model = CRMModel()  # Use the correct architecture here
+#     state_dict = torch.load(model_path, map_location=device)
+
+#     if isinstance(state_dict, dict):  # Ensure it's a valid state_dict
+#         model.load_state_dict(state_dict)
+#     else:
+#         raise ValueError("Error: The loaded state_dict is not in the correct format.")
+
+#     model.to(device)
+#     model.eval()
+#     print("Model loaded successfully!")
+#     return model
+
+# # Load the model
+# model = load_model()
+
+# # Define Inference Function
+# def infer(image):
+#     """Process input image and return a reconstructed 3D output."""
+#     try:
+#         print("Preprocessing image...")
+
+#         # Convert image to PyTorch tensor & normalize
+#         transform = transforms.Compose([
+#             transforms.Resize((256, 256)),  # Resize to fit model input
+#             transforms.ToTensor(),  # Converts to tensor (C, H, W)
+#             transforms.Normalize(mean=[0.5], std=[0.5]),  # Normalize
+#         ])
+#         image_tensor = transform(image).unsqueeze(0).to(device)  # Add batch dimension
+
+#         print("Running inference...")
+#         with torch.no_grad():
+#             output = model(image_tensor)  # Forward pass
+
+#         # Ensure output is a valid tensor
+#         if isinstance(output, torch.Tensor):
+#             output_image = output.squeeze(0).permute(1, 2, 0).cpu().numpy()
+#             output_image = np.clip(output_image * 255.0, 0, 255).astype(np.uint8)
+#             print("Inference complete! Returning output.")
+#             return output_image
+#         else:
+#             print("Error: Model output is not a tensor.")
+#             return None
+
+#     except Exception as e:
+#         print(f"Error during inference: {e}")
+#         return None
+
+# # Create Gradio UI
+# demo = gr.Interface(
+#     fn=infer,
+#     inputs=gr.Image(type="pil"),
+#     outputs=gr.Image(type="numpy"),
+#     title="Convolutional Reconstruction Model",
+#     description="Upload an image to get the reconstructed output."
+# )
+
+# if __name__ == "__main__":
+#     demo.launch()
+
+
+
+
+# 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:
-        raise ValueError("Error: The loaded state_dict is not in the correct format.")
-
-    model.to(device)
-    model.eval()
-    print("Model loaded successfully!")
-    return model
-
-# Load the model
-model = load_model()
-
-# Define Inference Function
-def infer(image):
-    """Process input image and return a reconstructed 3D output."""
-    try:
-        print("Preprocessing image...")
-
-        # Convert image to PyTorch tensor & normalize
-        transform = transforms.Compose([
-            transforms.Resize((256, 256)),  # Resize to fit model input
-            transforms.ToTensor(),  # Converts to tensor (C, H, W)
-            transforms.Normalize(mean=[0.5], std=[0.5]),  # Normalize
-        ])
-        image_tensor = transform(image).unsqueeze(0).to(device)  # Add batch dimension
-
-        print("Running inference...")
-        with torch.no_grad():
-            output = model(image_tensor)  # Forward pass
-
-        # Ensure output is a valid tensor
-        if isinstance(output, torch.Tensor):
-            output_image = output.squeeze(0).permute(1, 2, 0).cpu().numpy()
-            output_image = np.clip(output_image * 255.0, 0, 255).astype(np.uint8)
-            print("Inference complete! Returning output.")
-            return output_image
-        else:
-            print("Error: Model output is not a tensor.")
-            return None
-
-    except Exception as e:
-        print(f"Error during inference: {e}")
-        return None
-
-# Create Gradio UI
-demo = gr.Interface(
-    fn=infer,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="numpy"),
-    title="Convolutional Reconstruction Model",
-    description="Upload an image to get the reconstructed output."
+        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
 )
 
-if __name__ == "__main__":
-    demo.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()