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Running
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A10G
| 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 streamlit as st | |
| 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 | |
| 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 | |
| img_example_counter = 0 | |
| iret_base = 'example_images' | |
| iret = [ | |
| dict(rimageinput=os.path.join(iret_base, x), dispi=os.path.join(iret_base, x)) | |
| for x in sorted(os.listdir(iret_base)) | |
| ] | |
| class SAMAPI: | |
| predictor = None | |
| def get_instance(sam_checkpoint=None): | |
| if SAMAPI.predictor is None: | |
| if sam_checkpoint is None: | |
| sam_checkpoint = "tmp/sam_vit_h_4b8939.pth" | |
| if not os.path.exists(sam_checkpoint): | |
| os.makedirs('tmp', exist_ok=True) | |
| urllib.request.urlretrieve( | |
| "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth", | |
| sam_checkpoint | |
| ) | |
| device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
| model_type = "default" | |
| from segment_anything import sam_model_registry, SamPredictor | |
| sam = sam_model_registry[model_type](checkpoint=sam_checkpoint) | |
| sam.to(device=device) | |
| predictor = SamPredictor(sam) | |
| SAMAPI.predictor = predictor | |
| return SAMAPI.predictor | |
| def segment_api(rgb, mask=None, bbox=None, sam_checkpoint=None): | |
| """ | |
| Parameters | |
| ---------- | |
| rgb : np.ndarray h,w,3 uint8 | |
| mask: np.ndarray h,w bool | |
| Returns | |
| ------- | |
| """ | |
| np = numpy | |
| predictor = SAMAPI.get_instance(sam_checkpoint) | |
| predictor.set_image(rgb) | |
| if mask is None and bbox is None: | |
| box_input = None | |
| else: | |
| # mask to bbox | |
| if bbox is None: | |
| y1, y2, x1, x2 = np.nonzero(mask)[0].min(), np.nonzero(mask)[0].max(), np.nonzero(mask)[1].min(), \ | |
| np.nonzero(mask)[1].max() | |
| else: | |
| x1, y1, x2, y2 = bbox | |
| box_input = np.array([[x1, y1, x2, y2]]) | |
| masks, scores, logits = predictor.predict( | |
| box=box_input, | |
| multimask_output=True, | |
| return_logits=False, | |
| ) | |
| mask = masks[-1] | |
| return mask | |
| def image_examples(samples, ncols, return_key=None, example_text="Examples"): | |
| global img_example_counter | |
| trigger = False | |
| with st.expander(example_text, True): | |
| for i in range(len(samples) // ncols): | |
| cols = st.columns(ncols) | |
| for j in range(ncols): | |
| idx = i * ncols + j | |
| if idx >= len(samples): | |
| continue | |
| entry = samples[idx] | |
| with cols[j]: | |
| st.image(entry['dispi']) | |
| img_example_counter += 1 | |
| with st.columns(5)[2]: | |
| this_trigger = st.button('\+', key='imgexuse%d' % img_example_counter) | |
| trigger = trigger or this_trigger | |
| if this_trigger: | |
| trigger = entry[return_key] | |
| return trigger | |
| def segment_img(img: Image): | |
| output = rembg.remove(img) | |
| mask = numpy.array(output)[:, :, 3] > 0 | |
| sam_mask = SAMAPI.segment_api(numpy.array(img)[:, :, :3], mask) | |
| segmented_img = Image.new("RGBA", img.size, (0, 0, 0, 0)) | |
| segmented_img.paste(img, mask=Image.fromarray(sam_mask)) | |
| return segmented_img | |
| def segment_6imgs(imgs): | |
| segmented_imgs = [] | |
| for i, img in enumerate(imgs): | |
| output = rembg.remove(img) | |
| mask = numpy.array(output)[:, :, 3] | |
| mask = SAMAPI.segment_api(numpy.array(img)[:, :, :3], mask) | |
| data = numpy.array(img)[:,:,:3] | |
| data[mask == 0] = [255, 255, 255] | |
| segmented_imgs.append(data) | |
| result = numpy.concatenate([ | |
| numpy.concatenate([segmented_imgs[0], segmented_imgs[1]], axis=1), | |
| numpy.concatenate([segmented_imgs[2], segmented_imgs[3]], axis=1), | |
| numpy.concatenate([segmented_imgs[4], segmented_imgs[5]], axis=1) | |
| ]) | |
| return Image.fromarray(result) | |
| def pack_6imgs(imgs): | |
| result = numpy.concatenate([ | |
| numpy.concatenate([imgs[0], imgs[1]], axis=1), | |
| numpy.concatenate([imgs[2], imgs[3]], axis=1), | |
| numpy.concatenate([imgs[4], imgs[5]], axis=1) | |
| ]) | |
| return Image.fromarray(result) | |
| 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 check_dependencies(): | |
| reqs = [] | |
| try: | |
| import diffusers | |
| except ImportError: | |
| import traceback | |
| traceback.print_exc() | |
| print("Error: `diffusers` not found.", file=sys.stderr) | |
| reqs.append("diffusers==0.20.2") | |
| else: | |
| if not diffusers.__version__.startswith("0.20"): | |
| print( | |
| f"Warning: You are using an unsupported version of diffusers ({diffusers.__version__}), which may lead to performance issues.", | |
| file=sys.stderr | |
| ) | |
| print("Recommended version is `diffusers==0.20.2`.", file=sys.stderr) | |
| try: | |
| import transformers | |
| except ImportError: | |
| import traceback | |
| traceback.print_exc() | |
| print("Error: `transformers` not found.", file=sys.stderr) | |
| reqs.append("transformers==4.29.2") | |
| if torch.__version__ < '2.0': | |
| try: | |
| import xformers | |
| except ImportError: | |
| print("Warning: You are using PyTorch 1.x without a working `xformers` installation.", file=sys.stderr) | |
| print("You may see a significant memory overhead when running the model.", file=sys.stderr) | |
| if len(reqs): | |
| print(f"Info: Fix all dependency errors with `pip install {' '.join(reqs)}`.") | |
| 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) | |
| weight_dtype = torch.float16 | |
| # 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 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.load(args.config) | |
| cfg = OmegaConf.merge(schema, cfg) | |
| check_dependencies() | |
| pipeline = load_wonder3d_pipeline(cfg) | |
| SAMAPI.get_instance() | |
| torch.set_grad_enabled(False) | |
| st.title("Wonder3D Demo") | |
| # st.caption("For faster inference without waiting in queue, you may clone the space and run it yourself.") | |
| prog = st.progress(0.0, "Idle") | |
| pic = st.file_uploader("Upload an Image", key='imageinput', type=['png', 'jpg', 'webp']) | |
| left, right = st.columns(2) | |
| with left: | |
| rem_input_bg = st.checkbox("Remove Input Background") | |
| with right: | |
| rem_output_bg = st.checkbox("Remove Output Background") | |
| num_inference_steps = st.slider("Number of Inference Steps", 15, 100, 75) | |
| st.caption("Diffusion Steps. For general real or synthetic objects, around 28 is enough. For objects with delicate details such as faces (either realistic or illustration), you may need 75 or more steps.") | |
| cfg_scale = st.slider("Classifier Free Guidance Scale", 1.0, 10.0, 4.0) | |
| seed = st.text_input("Seed", "42") | |
| submit = False | |
| if st.button("Submit"): | |
| submit = True | |
| results_container = st.container() | |
| sample_got = image_examples(iret, 4, 'rimageinput') | |
| if sample_got: | |
| pic = sample_got | |
| with results_container: | |
| if sample_got or submit: | |
| prog.progress(0.03, "Waiting in Queue...") | |
| with sys.main_lock: | |
| seed = int(seed) | |
| torch.manual_seed(seed) | |
| img = Image.open(pic) | |
| if max(img.size) > 1280: | |
| w, h = img.size | |
| w = round(1280 / max(img.size) * w) | |
| h = round(1280 / max(img.size) * h) | |
| img = img.resize((w, h)) | |
| left, right = st.columns(2) | |
| with left: | |
| st.image(img) | |
| st.caption("Input Image") | |
| prog.progress(0.1, "Preparing Inputs") | |
| if rem_input_bg: | |
| with right: | |
| img = segment_img(img) | |
| st.image(img) | |
| st.caption("Input (Background Removed)") | |
| img = expand2square(img, (127, 127, 127, 0)) | |
| pipeline.set_progress_bar_config(disable=True) | |
| result = pipeline( | |
| img, | |
| num_inference_steps=num_inference_steps, | |
| guidance_scale=cfg_scale, | |
| generator=torch.Generator(pipeline.device).manual_seed(seed), | |
| callback=lambda i, t, latents: prog.progress(0.1 + 0.8 * i / num_inference_steps, "Diffusion Step %d" % i) | |
| ).images | |
| bsz = result.shape[0] // 2 | |
| normals_pred = result[:bsz] | |
| images_pred = result[bsz:] | |
| prog.progress(0.9, "Post Processing") | |
| left, right = st.columns(2) | |
| with left: | |
| st.image(pack_6imgs(normals_pred)) | |
| st.image(pack_6imgs(images_pred)) | |
| st.caption("Result") | |
| if rem_output_bg: | |
| normals_pred = segment_6imgs(normals_pred) | |
| images_pred = segment_6imgs(images_pred) | |
| with right: | |
| st.image(normals_pred) | |
| st.image(images_pred) | |
| st.caption("Result (Background Removed)") | |
| prog.progress(1.0, "Idle") |