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| # # 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 | |
| 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() | |