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A10G
| import os | |
| import torch | |
| import fire | |
| import gradio as gr | |
| from PIL import Image | |
| from functools import partial | |
| import cv2 | |
| import time | |
| import numpy as np | |
| from rembg import remove | |
| from segment_anything import sam_model_registry, SamPredictor | |
| import os | |
| import sys | |
| import numpy | |
| import torch | |
| import rembg | |
| import threading | |
| import urllib.request | |
| from PIL import Image | |
| from typing import Dict, Optional, Tuple, List | |
| from dataclasses import dataclass | |
| import huggingface_hub | |
| from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection | |
| from mvdiffusion.models.unet_mv2d_condition import UNetMV2DConditionModel | |
| from mvdiffusion.data.single_image_dataset import SingleImageDataset as MVDiffusionDataset | |
| from mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline | |
| from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler | |
| from einops import rearrange | |
| import numpy as np | |
| def save_image(tensor): | |
| ndarr = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy() | |
| # pdb.set_trace() | |
| im = Image.fromarray(ndarr) | |
| return ndarr | |
| weight_dtype = torch.float16 | |
| _TITLE = '''Wonder3D: Single Image to 3D using Cross-Domain Diffusion''' | |
| _DESCRIPTION = ''' | |
| <div> | |
| Generate consistent multi-view normals maps and color images. | |
| <a style="display:inline-block; margin-left: .5em" href='https://github.com/xxlong0/Wonder3D/'><img src='https://img.shields.io/github/stars/xxlong0/Wonder3D?style=social' /></a> | |
| </div> | |
| <div> | |
| The demo does not include the mesh reconstruction part, please visit <a href="https://github.com/xxlong0/Wonder3D/">our github repo</a> to get a textured mesh. | |
| </div> | |
| ''' | |
| _GPU_ID = 0 | |
| if not hasattr(Image, 'Resampling'): | |
| Image.Resampling = Image | |
| def sam_init(): | |
| sam_checkpoint = os.path.join(os.path.dirname(__file__), "sam_pt", "sam_vit_h_4b8939.pth") | |
| model_type = "vit_h" | |
| sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=f"cuda:{_GPU_ID}") | |
| predictor = SamPredictor(sam) | |
| return predictor | |
| def sam_segment(predictor, input_image, *bbox_coords): | |
| bbox = np.array(bbox_coords) | |
| image = np.asarray(input_image) | |
| start_time = time.time() | |
| predictor.set_image(image) | |
| masks_bbox, scores_bbox, logits_bbox = predictor.predict( | |
| box=bbox, | |
| multimask_output=True | |
| ) | |
| print(f"SAM Time: {time.time() - start_time:.3f}s") | |
| out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8) | |
| out_image[:, :, :3] = image | |
| out_image_bbox = out_image.copy() | |
| out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255 | |
| torch.cuda.empty_cache() | |
| return Image.fromarray(out_image_bbox, mode='RGBA') | |
| def expand2square(pil_img, background_color): | |
| width, height = pil_img.size | |
| if width == height: | |
| return pil_img | |
| elif width > height: | |
| result = Image.new(pil_img.mode, (width, width), background_color) | |
| result.paste(pil_img, (0, (width - height) // 2)) | |
| return result | |
| else: | |
| result = Image.new(pil_img.mode, (height, height), background_color) | |
| result.paste(pil_img, ((height - width) // 2, 0)) | |
| return result | |
| def preprocess(predictor, input_image, chk_group=None, segment=True, rescale=False): | |
| RES = 1024 | |
| input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS) | |
| if chk_group is not None: | |
| segment = "Background Removal" in chk_group | |
| rescale = "Rescale" in chk_group | |
| if segment: | |
| image_rem = input_image.convert('RGBA') | |
| image_nobg = remove(image_rem, alpha_matting=True) | |
| arr = np.asarray(image_nobg)[:,:,-1] | |
| x_nonzero = np.nonzero(arr.sum(axis=0)) | |
| y_nonzero = np.nonzero(arr.sum(axis=1)) | |
| x_min = int(x_nonzero[0].min()) | |
| y_min = int(y_nonzero[0].min()) | |
| x_max = int(x_nonzero[0].max()) | |
| y_max = int(y_nonzero[0].max()) | |
| input_image = sam_segment(predictor, input_image.convert('RGB'), x_min, y_min, x_max, y_max) | |
| # Rescale and recenter | |
| if rescale: | |
| image_arr = np.array(input_image) | |
| in_w, in_h = image_arr.shape[:2] | |
| out_res = min(RES, max(in_w, in_h)) | |
| ret, mask = cv2.threshold(np.array(input_image.split()[-1]), 0, 255, cv2.THRESH_BINARY) | |
| x, y, w, h = cv2.boundingRect(mask) | |
| max_size = max(w, h) | |
| ratio = 0.75 | |
| side_len = int(max_size / ratio) | |
| padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8) | |
| center = side_len//2 | |
| padded_image[center-h//2:center-h//2+h, center-w//2:center-w//2+w] = image_arr[y:y+h, x:x+w] | |
| rgba = Image.fromarray(padded_image).resize((out_res, out_res), Image.LANCZOS) | |
| rgba_arr = np.array(rgba) / 255.0 | |
| rgb = rgba_arr[...,:3] * rgba_arr[...,-1:] + (1 - rgba_arr[...,-1:]) | |
| input_image = Image.fromarray((rgb * 255).astype(np.uint8)) | |
| else: | |
| input_image = expand2square(input_image, (127, 127, 127, 0)) | |
| return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS) | |
| def load_wonder3d_pipeline(cfg): | |
| # Load scheduler, tokenizer and models. | |
| # noise_scheduler = DDPMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler") | |
| image_encoder = CLIPVisionModelWithProjection.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="image_encoder", revision=cfg.revision) | |
| feature_extractor = CLIPImageProcessor.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="feature_extractor", revision=cfg.revision) | |
| vae = AutoencoderKL.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="vae", revision=cfg.revision) | |
| unet = UNetMV2DConditionModel.from_pretrained_2d(cfg.pretrained_unet_path, subfolder="unet", revision=cfg.revision, **cfg.unet_from_pretrained_kwargs) | |
| unet.enable_xformers_memory_efficient_attention() | |
| # Move text_encode and vae to gpu and cast to weight_dtype | |
| image_encoder.to(dtype=weight_dtype) | |
| vae.to(dtype=weight_dtype) | |
| unet.to(dtype=weight_dtype) | |
| pipeline = MVDiffusionImagePipeline( | |
| image_encoder=image_encoder, feature_extractor=feature_extractor, vae=vae, unet=unet, safety_checker=None, | |
| scheduler=DDIMScheduler.from_pretrained(cfg.pretrained_model_name_or_path, subfolder="scheduler"), | |
| **cfg.pipe_kwargs | |
| ) | |
| if torch.cuda.is_available(): | |
| pipeline.to('cuda:0') | |
| # sys.main_lock = threading.Lock() | |
| return pipeline | |
| from mvdiffusion.data.single_image_dataset import SingleImageDataset | |
| def prepare_data(single_image, crop_size): | |
| dataset = SingleImageDataset( | |
| root_dir = None, | |
| num_views = 6, | |
| img_wh=[256, 256], | |
| bg_color='white', | |
| crop_size=crop_size, | |
| single_image=single_image | |
| ) | |
| return dataset[0] | |
| def run_pipeline(pipeline, cfg, single_image, guidance_scale, steps, seed, crop_size): | |
| import pdb | |
| # pdb.set_trace() | |
| batch = prepare_data(single_image, crop_size) | |
| pipeline.set_progress_bar_config(disable=True) | |
| seed = int(seed) | |
| generator = torch.Generator(device=pipeline.unet.device).manual_seed(seed) | |
| # repeat (2B, Nv, 3, H, W) | |
| imgs_in = torch.cat([batch['imgs_in']]*2, dim=0).to(weight_dtype) | |
| # (2B, Nv, Nce) | |
| camera_embeddings = torch.cat([batch['camera_embeddings']]*2, dim=0).to(weight_dtype) | |
| task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0).to(weight_dtype) | |
| camera_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1).to(weight_dtype) | |
| # (B*Nv, 3, H, W) | |
| imgs_in = rearrange(imgs_in, "Nv C H W -> (Nv) C H W") | |
| # (B*Nv, Nce) | |
| # camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce") | |
| out = pipeline( | |
| imgs_in, camera_embeddings, generator=generator, guidance_scale=guidance_scale, | |
| num_inference_steps=steps, | |
| output_type='pt', num_images_per_prompt=1, **cfg.pipe_validation_kwargs | |
| ).images | |
| bsz = out.shape[0] // 2 | |
| normals_pred = out[:bsz] | |
| images_pred = out[bsz:] | |
| normals_pred = [save_image(normals_pred[i]) for i in range(bsz)] | |
| images_pred = [save_image(images_pred[i]) for i in range(bsz)] | |
| out = images_pred + normals_pred | |
| return *out, images_pred, normals_pred | |
| class TestConfig: | |
| pretrained_model_name_or_path: str | |
| pretrained_unet_path:str | |
| revision: Optional[str] | |
| validation_dataset: Dict | |
| save_dir: str | |
| seed: Optional[int] | |
| validation_batch_size: int | |
| dataloader_num_workers: int | |
| local_rank: int | |
| pipe_kwargs: Dict | |
| pipe_validation_kwargs: Dict | |
| unet_from_pretrained_kwargs: Dict | |
| validation_guidance_scales: List[float] | |
| validation_grid_nrow: int | |
| camera_embedding_lr_mult: float | |
| num_views: int | |
| camera_embedding_type: str | |
| pred_type: str # joint, or ablation | |
| enable_xformers_memory_efficient_attention: bool | |
| cond_on_normals: bool | |
| cond_on_colors: bool | |
| def run_demo(): | |
| from utils.misc import load_config | |
| from omegaconf import OmegaConf | |
| # parse YAML config to OmegaConf | |
| cfg = load_config("./configs/mvdiffusion-joint-ortho-6views.yaml") | |
| # print(cfg) | |
| schema = OmegaConf.structured(TestConfig) | |
| cfg = OmegaConf.merge(schema, cfg) | |
| pipeline = load_wonder3d_pipeline(cfg) | |
| torch.set_grad_enabled(False) | |
| pipeline.to(f'cuda:{_GPU_ID}') | |
| predictor = sam_init() | |
| custom_theme = gr.themes.Soft(primary_hue="blue").set( | |
| button_secondary_background_fill="*neutral_100", | |
| button_secondary_background_fill_hover="*neutral_200") | |
| custom_css = '''#disp_image { | |
| text-align: center; /* Horizontally center the content */ | |
| }''' | |
| with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown('# ' + _TITLE) | |
| gr.Markdown(_DESCRIPTION) | |
| with gr.Row(variant='panel'): | |
| with gr.Column(scale=1): | |
| input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image', tool=None) | |
| example_folder = os.path.join(os.path.dirname(__file__), "./example_images") | |
| example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)] | |
| gr.Examples( | |
| examples=example_fns, | |
| inputs=[input_image], | |
| # outputs=[input_image], | |
| cache_examples=False, | |
| label='Examples (click one of the images below to start)', | |
| examples_per_page=30 | |
| ) | |
| with gr.Column(scale=1): | |
| processed_image = gr.Image(type='pil', label="Processed Image", interactive=False, height=320, tool=None, image_mode='RGBA', elem_id="disp_image") | |
| processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False, tool=None) | |
| with gr.Accordion('Advanced options', open=True): | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_processing = gr.CheckboxGroup(['Background Removal'], | |
| label='Input Image Preprocessing', | |
| value=['Background Removal'], | |
| info='untick this, if masked image with alpha channel') | |
| with gr.Column(): | |
| output_processing = gr.CheckboxGroup(['Background Removal'], label='Output Image Postprocessing', value=[]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| scale_slider = gr.Slider(1, 5, value=3, step=1, | |
| label='Classifier Free Guidance Scale') | |
| with gr.Column(): | |
| steps_slider = gr.Slider(15, 100, value=50, step=1, | |
| label='Number of Diffusion Inference Steps') | |
| with gr.Row(): | |
| with gr.Column(): | |
| seed = gr.Number(42, label='Seed') | |
| with gr.Column(): | |
| crop_size = gr.Number(192, label='Crop size') | |
| # crop_size = 192 | |
| run_btn = gr.Button('Generate', variant='primary', interactive=True) | |
| with gr.Row(): | |
| view_1 = gr.Image(interactive=False, height=240, show_label=False) | |
| view_2 = gr.Image(interactive=False, height=240, show_label=False) | |
| view_3 = gr.Image(interactive=False, height=240, show_label=False) | |
| view_4 = gr.Image(interactive=False, height=240, show_label=False) | |
| view_5 = gr.Image(interactive=False, height=240, show_label=False) | |
| view_6 = gr.Image(interactive=False, height=240, show_label=False) | |
| with gr.Row(): | |
| normal_1 = gr.Image(interactive=False, height=240, show_label=False) | |
| normal_2 = gr.Image(interactive=False, height=240, show_label=False) | |
| normal_3 = gr.Image(interactive=False, height=240, show_label=False) | |
| normal_4 = gr.Image(interactive=False, height=240, show_label=False) | |
| normal_5 = gr.Image(interactive=False, height=240, show_label=False) | |
| normal_6 = gr.Image(interactive=False, height=240, show_label=False) | |
| with gr.Row(): | |
| view_gallery = gr.Gallery(interactive=False, show_label=False, container=True, preview=True, allow_preview=False, height=1200) | |
| normal_gallery = gr.Gallery(interactive=False, show_label=False, container=True, preview=True, allow_preview=False, height=1200) | |
| run_btn.click(fn=partial(preprocess, predictor), | |
| inputs=[input_image, input_processing], | |
| outputs=[processed_image_highres, processed_image], queue=True | |
| ).success(fn=partial(run_pipeline, pipeline, cfg), | |
| inputs=[processed_image_highres, scale_slider, steps_slider, seed, crop_size], | |
| outputs=[view_1, view_2, view_3, view_4, view_5, view_6, | |
| normal_1, normal_2, normal_3, normal_4, normal_5, normal_6, | |
| view_gallery, normal_gallery] | |
| ) | |
| demo.queue().launch(share=True, max_threads=80) | |
| if __name__ == '__main__': | |
| fire.Fire(run_demo) |