# # 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()