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| from functools import partial | |
| from PIL import Image | |
| import numpy as np | |
| import gradio as gr | |
| import torch | |
| import os | |
| import fire | |
| from omegaconf import OmegaConf | |
| from ldm.util import add_margin, instantiate_from_config | |
| from sam_utils import sam_init, sam_out_nosave | |
| _TITLE = '''SyncDreamer: Generating Multiview-consistent Images from a Single-view Image''' | |
| _DESCRIPTION = ''' | |
| <div> | |
| <a style="display:inline-block" href="https://liuyuan-pal.github.io/SyncDreamer/"><img src="https://img.shields.io/badge/SyncDremer-Homepage-blue"></a> | |
| <a style="display:inline-block; margin-left: .5em" href="https://arxiv.org/abs/2309.03453"><img src="https://img.shields.io/badge/2309.03453-f9f7f7?logo=data:image/png;base64,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"></a> | |
| <a style="display:inline-block; margin-left: .5em" href='https://github.com/liuyuan-pal/SyncDreamer'><img src='https://img.shields.io/github/stars/liuyuan-pal/SyncDreamer?style=social' /></a> | |
| </div> | |
| Given a single-view image, SyncDreamer is able to generate multiview-consistent images, which enables direct 3D reconstruction with NeuS or NeRF without SDS loss | |
| 1. Upload the image. | |
| 2. Predict the mask for the foreground object. | |
| 3. Crop the foreground object. | |
| 4. Generate multiview images. | |
| ''' | |
| _USER_GUIDE0 = "Step0: Please upload an image in the block above (or choose an example above). We use alpha values as object masks if given." | |
| _USER_GUIDE1 = "Step1: Please select a crop size using the glider." | |
| _USER_GUIDE2 = "Step2: Please choose a suitable elevation angle and then click the Generate button." | |
| _USER_GUIDE3 = "Generated multiview images are shown below!" | |
| deployed = True | |
| def resize_inputs(image_input, crop_size): | |
| alpha_np = np.asarray(image_input)[:, :, 3] | |
| coords = np.stack(np.nonzero(alpha_np), 1)[:, (1, 0)] | |
| min_x, min_y = np.min(coords, 0) | |
| max_x, max_y = np.max(coords, 0) | |
| ref_img_ = image_input.crop((min_x, min_y, max_x, max_y)) | |
| h, w = ref_img_.height, ref_img_.width | |
| scale = crop_size / max(h, w) | |
| h_, w_ = int(scale * h), int(scale * w) | |
| ref_img_ = ref_img_.resize((w_, h_), resample=Image.BICUBIC) | |
| results = add_margin(ref_img_, size=256) | |
| return results | |
| def generate(model, batch_view_num, sample_num, cfg_scale, seed, image_input, elevation_input): | |
| seed=int(seed) | |
| torch.random.manual_seed(seed) | |
| np.random.seed(seed) | |
| # prepare data | |
| image_input = np.asarray(image_input) | |
| image_input = image_input.astype(np.float32) / 255.0 | |
| alpha_values = image_input[:,:, 3:] | |
| image_input[:, :, :3] = alpha_values * image_input[:,:, :3] + 1 - alpha_values # white background | |
| image_input = image_input[:, :, :3] * 2.0 - 1.0 | |
| image_input = torch.from_numpy(image_input.astype(np.float32)) | |
| elevation_input = torch.from_numpy(np.asarray([np.deg2rad(elevation_input)], np.float32)) | |
| data = {"input_image": image_input, "input_elevation": elevation_input} | |
| for k, v in data.items(): | |
| if deployed: | |
| data[k] = v.unsqueeze(0).cuda() | |
| else: | |
| data[k] = v.unsqueeze(0) | |
| data[k] = torch.repeat_interleave(data[k], sample_num, dim=0) | |
| if deployed: | |
| x_sample = model.sample(data, cfg_scale, batch_view_num) | |
| else: | |
| x_sample = torch.zeros(sample_num, 16, 3, 256, 256) | |
| B, N, _, H, W = x_sample.shape | |
| x_sample = (torch.clamp(x_sample,max=1.0,min=-1.0) + 1) * 0.5 | |
| x_sample = x_sample.permute(0,1,3,4,2).cpu().numpy() * 255 | |
| x_sample = x_sample.astype(np.uint8) | |
| results = [] | |
| for bi in range(B): | |
| results.append(np.concatenate([x_sample[bi,ni] for ni in range(N)], 1)) | |
| results = np.concatenate(results, 0) | |
| return Image.fromarray(results) | |
| def run_demo(): | |
| # device = f"cuda:0" if torch.cuda.is_available() else "cpu" | |
| # models = None # init_model(device, os.path.join(code_dir, ckpt)) | |
| cfg = 'configs/syncdreamer.yaml' | |
| ckpt = 'ckpt/syncdreamer-pretrain.ckpt' | |
| config = OmegaConf.load(cfg) | |
| # model = None | |
| if deployed: | |
| model = instantiate_from_config(config.model) | |
| print(f'loading model from {ckpt} ...') | |
| ckpt = torch.load(ckpt,map_location='cpu') | |
| model.load_state_dict(ckpt['state_dict'], strict=True) | |
| model = model.cuda().eval() | |
| del ckpt | |
| else: | |
| model = None | |
| # init sam model | |
| mask_predictor = sam_init() | |
| mask_predict_fn = lambda x: sam_out_nosave(mask_predictor, x) | |
| # with open('instructions_12345.md', 'r') as f: | |
| # article = f.read() | |
| # NOTE: Examples must match inputs | |
| example_folder = os.path.join(os.path.dirname(__file__), 'hf_demo', 'examples') | |
| example_fns = os.listdir(example_folder) | |
| example_fns.sort() | |
| examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')] | |
| # Compose demo layout & data flow. | |
| with gr.Blocks(title=_TITLE, css="hf_demo/style.css") as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown('# ' + _TITLE) | |
| # with gr.Column(scale=0): | |
| # gr.DuplicateButton(value='Duplicate Space for private use', elem_id='duplicate-button') | |
| gr.Markdown(_DESCRIPTION) | |
| with gr.Row(variant='panel'): | |
| with gr.Column(scale=1): | |
| image_block = gr.Image(type='pil', image_mode='RGBA', height=256, label='Input image', tool=None, interactive=True) | |
| guide_text = gr.Markdown(_USER_GUIDE0, visible=True) | |
| gr.Examples( | |
| examples=examples_full, # NOTE: elements must match inputs list! | |
| inputs=[image_block], | |
| outputs=[image_block], | |
| cache_examples=False, | |
| label='Examples (click one of the images below to start)', | |
| examples_per_page=40 | |
| ) | |
| with gr.Column(scale=1): | |
| sam_block = gr.Image(type='pil', image_mode='RGBA', label="SAM output", height=256, interactive=False) | |
| crop_size_slider = gr.Slider(120, 240, 200, step=10, label='Crop size', interactive=True) | |
| crop_btn = gr.Button('Crop the image', variant='primary', interactive=True) | |
| fig0 = gr.Image(value=Image.open('assets/crop_size.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False) | |
| with gr.Column(scale=1): | |
| input_block = gr.Image(type='pil', image_mode='RGBA', label="Input to SyncDreamer", height=256, interactive=False) | |
| elevation = gr.Slider(-10, 40, 30, step=5, label='Elevation angle', interactive=True) | |
| cfg_scale = gr.Slider(1.0, 5.0, 2.0, step=0.1, label='Classifier free guidance', interactive=True) | |
| # sample_num = gr.Slider(1, 2, 2, step=1, label='Sample Num', interactive=True, info='How many instance (16 images per instance)') | |
| # batch_view_num = gr.Slider(1, 16, 8, step=1, label='', interactive=True) | |
| seed = gr.Number(6033, label='Random seed', interactive=True) | |
| run_btn = gr.Button('Run Generation', variant='primary', interactive=True) | |
| fig1 = gr.Image(value=Image.open('assets/elevation.jpg'), type='pil', image_mode='RGB', height=256, show_label=False, tool=None, interactive=False) | |
| output_block = gr.Image(type='pil', image_mode='RGB', label="Outputs of SyncDreamer", height=256, interactive=False) | |
| update_guide = lambda GUIDE_TEXT: gr.update(value=GUIDE_TEXT) | |
| image_block.change(fn=mask_predict_fn, inputs=[image_block], outputs=[sam_block], queue=False)\ | |
| .success(fn=partial(update_guide, _USER_GUIDE1), outputs=[guide_text], queue=False) | |
| crop_size_slider.change(fn=resize_inputs, inputs=[sam_block, crop_size_slider], outputs=[input_block], queue=False)\ | |
| .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False) | |
| crop_btn.click(fn=resize_inputs, inputs=[sam_block, crop_size_slider], outputs=[input_block], queue=False)\ | |
| .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False) | |
| run_btn.click(partial(generate, model, 16, 1), inputs=[cfg_scale, seed, input_block, elevation], outputs=[output_block], queue=False)\ | |
| .success(fn=partial(update_guide, _USER_GUIDE3), outputs=[guide_text], queue=False) | |
| demo.queue().launch(share=False, max_threads=80) # auth=("admin", os.environ['PASSWD']) | |
| if __name__=="__main__": | |
| fire.Fire(run_demo) |