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| # import gradio as gr | |
| # import torch | |
| # from PIL import Image | |
| # from model import CRM | |
| # from inference import generate3d | |
| # import numpy as np | |
| # # Load model | |
| # crm_path = "CRM.pth" # Make sure the model is uploaded to the Space | |
| # model = CRM(torch.load(crm_path, map_location="cpu")) | |
| # model = model.to("cuda:0" if torch.cuda.is_available() else "cpu") | |
| # def generate_3d(image_path, seed=1234, scale=5.5, step=30): | |
| # image = Image.open(image_path).convert("RGB") | |
| # np_img = np.array(image) | |
| # glb_path = generate3d(model, np_img, np_img, "cuda:0" if torch.cuda.is_available() else "cpu") | |
| # return glb_path | |
| # iface = gr.Interface( | |
| # fn=generate_3d, | |
| # inputs=gr.Image(type="filepath"), | |
| # outputs=gr.Model3D(), | |
| # title="Convolutional Reconstruction Model (CRM)", | |
| # description="Upload an image to generate a 3D model." | |
| # ) | |
| # iface.launch() | |
| #############2nd################3 | |
| # import os | |
| # import torch | |
| # import gradio as gr | |
| # from huggingface_hub import hf_hub_download | |
| # from model import CRM # Make sure this matches your model file structure | |
| # # Define model details | |
| # REPO_ID = "Mariam-Elz/CRM" # Hugging Face model repo | |
| # MODEL_FILES = { | |
| # "ccm-diffusion": "ccm-diffusion.pth", | |
| # "pixel-diffusion": "pixel-diffusion.pth", | |
| # "CRM": "CRM.pth" | |
| # } | |
| # DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| # # Download models from Hugging Face if not already present | |
| # MODEL_DIR = "./models" | |
| # os.makedirs(MODEL_DIR, exist_ok=True) | |
| # for name, filename in MODEL_FILES.items(): | |
| # model_path = os.path.join(MODEL_DIR, filename) | |
| # if not os.path.exists(model_path): | |
| # print(f"Downloading {filename}...") | |
| # hf_hub_download(repo_id=REPO_ID, filename=filename, local_dir=MODEL_DIR) | |
| # # Load the model | |
| # print("Loading CRM Model...") | |
| # model = CRM() | |
| # model.load_state_dict(torch.load(os.path.join(MODEL_DIR, MODEL_FILES["CRM"]), map_location=DEVICE)) | |
| # model.to(DEVICE) | |
| # model.eval() | |
| # print("✅ Model Loaded Successfully!") | |
| # # Define Gradio Interface | |
| # def predict(input_image): | |
| # with torch.no_grad(): | |
| # output = model(input_image.to(DEVICE)) # Modify based on model input format | |
| # return output.cpu() | |
| # demo = gr.Interface( | |
| # fn=predict, | |
| # inputs=gr.Image(type="pil"), | |
| # outputs=gr.Image(type="pil"), | |
| # title="Convolutional Reconstruction Model (CRM)", | |
| # description="Upload an image to generate a reconstructed output." | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| ########################3rd-MAIN######################3 | |
| # 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 | |
| # 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." | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| #################4th################## | |
| # import torch | |
| # import gradio as gr | |
| # import requests | |
| # import os | |
| # # Define model repo | |
| # model_repo = "Mariam-Elz/CRM" | |
| # # Define model files and download paths | |
| # model_files = { | |
| # "CRM.pth": "models/CRM.pth" | |
| # } | |
| # os.makedirs("models", exist_ok=True) | |
| # # Download model files only if they don't exist | |
| # for filename, output_path in model_files.items(): | |
| # if not os.path.exists(output_path): | |
| # url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}" | |
| # print(f"Downloading {filename}...") | |
| # response = requests.get(url) | |
| # with open(output_path, "wb") as f: | |
| # f.write(response.content) | |
| # # Load model with low memory usage | |
| # def load_model(): | |
| # model_path = "models/CRM.pth" | |
| # model = torch.load(model_path, map_location="cpu") # Load on CPU to reduce memory usage | |
| # 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(): | |
| # image_tensor = torch.tensor(image).unsqueeze(0) # Add batch dimension | |
| # image_tensor = image_tensor.to("cpu") # Keep on CPU to save memory | |
| # output = model(image_tensor) | |
| # return output.squeeze(0).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." | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| # ##############5TH################# | |
| # import torch | |
| # import torch.nn as nn | |
| # import gradio as gr | |
| # import requests | |
| # import os | |
| # # Define model repo | |
| # model_repo = "Mariam-Elz/CRM" | |
| # # Define model files and download paths | |
| # model_files = { | |
| # "CRM.pth": "models/CRM.pth" | |
| # } | |
| # os.makedirs("models", exist_ok=True) | |
| # # Download model files only if they don't exist | |
| # for filename, output_path in model_files.items(): | |
| # if not os.path.exists(output_path): | |
| # url = f"https://huggingface.co/{model_repo}/resolve/main/{filename}" | |
| # print(f"Downloading {filename}...") | |
| # response = requests.get(url) | |
| # with open(output_path, "wb") as f: | |
| # f.write(response.content) | |
| # # Define the model architecture (you MUST replace this with your actual model) | |
| # class CRM_Model(nn.Module): | |
| # def __init__(self): | |
| # super(CRM_Model, self).__init__() | |
| # self.layer1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) | |
| # self.relu = nn.ReLU() | |
| # self.layer2 = nn.Conv2d(64, 3, kernel_size=3, padding=1) | |
| # def forward(self, x): | |
| # x = self.layer1(x) | |
| # x = self.relu(x) | |
| # x = self.layer2(x) | |
| # return x | |
| # # Load model with proper architecture | |
| # def load_model(): | |
| # model = CRM_Model() # Instantiate the model architecture | |
| # model_path = "models/CRM.pth" | |
| # model.load_state_dict(torch.load(model_path, map_location="cpu")) # Load weights | |
| # model.eval() # Set to evaluation mode | |
| # return model | |
| # model = load_model() | |
| # # Define inference function | |
| # def infer(image): | |
| # """Process input image and return a reconstructed image.""" | |
| # with torch.no_grad(): | |
| # image_tensor = torch.tensor(image).unsqueeze(0).permute(0, 3, 1, 2).float() / 255.0 # Convert to tensor | |
| # output = model(image_tensor) # Run through model | |
| # output = output.squeeze(0).permute(1, 2, 0).numpy() * 255.0 # Convert back to image | |
| # return output.astype("uint8") | |
| # # 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() | |
| #############6th-worked-proc################## | |
| # import torch | |
| # import gradio as gr | |
| # import requests | |
| # import os | |
| # import numpy as np | |
| # # Hugging Face Model Repository | |
| # model_repo = "Mariam-Elz/CRM" | |
| # # Download Model Weights (Only CRM.pth to Save Memory) | |
| # model_path = "models/CRM.pth" | |
| # os.makedirs("models", exist_ok=True) | |
| # 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 (Use CPU to Reduce RAM Usage) | |
| # device = "cpu" | |
| # # Load Model Efficiently | |
| # def load_model(): | |
| # model = torch.load(model_path, map_location=device) | |
| # if isinstance(model, torch.nn.Module): | |
| # model.eval() # Ensure model is in inference mode | |
| # return model | |
| # # Load model only when needed (saves memory) | |
| # model = load_model() | |
| # # Define Inference Function with Memory Optimizations | |
| # def infer(image): | |
| # """Process input image and return a reconstructed image.""" | |
| # with torch.no_grad(): | |
| # # Convert image to torch tensor & normalize (float16 to save RAM) | |
| # image_tensor = torch.tensor(image, dtype=torch.float16).unsqueeze(0).permute(0, 3, 1, 2) / 255.0 | |
| # image_tensor = image_tensor.to(device) | |
| # # Model Inference | |
| # output = model(image_tensor) | |
| # # Convert back to numpy image format | |
| # output_image = output.squeeze(0).permute(1, 2, 0).cpu().numpy() * 255.0 | |
| # output_image = np.clip(output_image, 0, 255).astype(np.uint8) | |
| # # Free Memory | |
| # del image_tensor, output | |
| # torch.cuda.empty_cache() | |
| # return output_image | |
| # # Create Gradio UI | |
| # demo = gr.Interface( | |
| # fn=infer, | |
| # inputs=gr.Image(type="numpy"), | |
| # outputs=gr.Image(type="numpy"), | |
| # title="Optimized Convolutional Reconstruction Model", | |
| # description="Upload an image to get the reconstructed output with reduced memory usage." | |
| # ) | |
| # if __name__ == "__main__": | |
| # demo.launch() | |
| #############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 | |
| # # 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() | |
| # 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: | |
| 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") | |
| 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() | |