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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.models.diffusion.sync_dreamer import SyncDDIMSampler, SyncMultiviewDiffusion | |
| from ldm.util import add_margin, instantiate_from_config | |
| from sam_utils import sam_init, sam_out_nosave | |
| import torch | |
| _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 </br> | |
| Procedure: </br> | |
| **Step 1**. Upload an image or select an example. ==> The foreground is masked out by SAM and we crop it as inputs. </br> | |
| **Step 2**. Select "Elevation angle "and click "Run generation". ==> Generate multiview images. The **Elevation angle** is the elevation of the input image. (This costs about 30s.) </br> | |
| You may adjust the **Crop size** and **Elevation angle** to get a better result! <br> | |
| To reconstruct a NeRF or a 3D mesh from the generated images, please refer to our [github repository](https://github.com/liuyuan-pal/SyncDreamer). <br> | |
| We have heavily borrowed codes from [One-2-3-45](https://huggingface.co/spaces/One-2-3-45/One-2-3-45), which is also an amazing single-view reconstruction method. | |
| ''' | |
| _USER_GUIDE0 = "Step1: Please upload an image in the block above (or choose an example shown in the left)." | |
| # _USER_GUIDE1 = "Step1: Please select a **Crop size** and click **Crop it**." | |
| _USER_GUIDE2 = "Step2: Please choose a **Elevation angle** and click **Run Generate**. The **Elevation angle** is the elevation of the input image. This costs about 30s." | |
| _USER_GUIDE3 = "Generated multiview images are shown below! (You may adjust the **Crop size** and **Elevation angle** to get a better result!)" | |
| others = '''**Step 1**. Select "Crop size" and click "Crop it". ==> The foreground object is centered and resized. </br>''' | |
| deployed = True | |
| if deployed: | |
| print(f"Is CUDA available: {torch.cuda.is_available()}") | |
| print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}") | |
| class BackgroundRemoval: | |
| def __init__(self, device='cuda'): | |
| from carvekit.api.high import HiInterface | |
| self.interface = HiInterface( | |
| object_type="object", # Can be "object" or "hairs-like". | |
| batch_size_seg=5, | |
| batch_size_matting=1, | |
| device=device, | |
| seg_mask_size=640, # Use 640 for Tracer B7 and 320 for U2Net | |
| matting_mask_size=2048, | |
| trimap_prob_threshold=231, | |
| trimap_dilation=30, | |
| trimap_erosion_iters=5, | |
| fp16=True, | |
| ) | |
| def __call__(self, image): | |
| # image: [H, W, 3] array in [0, 255]. | |
| image = self.interface([image])[0] | |
| return image | |
| def resize_inputs(image_input, crop_size): | |
| if image_input is None: return None | |
| 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, sample_steps, batch_view_num, sample_num, cfg_scale, seed, image_input, elevation_input): | |
| if deployed: | |
| assert isinstance(model, SyncMultiviewDiffusion) | |
| 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: | |
| sampler = SyncDDIMSampler(model, sample_steps) | |
| x_sample = model.sample(sampler, 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) | |
| else: | |
| return Image.fromarray(np.zeros([sample_num*256,16*256,3],np.uint8)) | |
| def sam_predict(predictor, removal, raw_im): | |
| if raw_im is None: return None | |
| if deployed: | |
| raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS) | |
| image_nobg = removal(raw_im.convert('RGB')) | |
| 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()) | |
| # image_nobg.save('./nobg.png') | |
| image_nobg.thumbnail([512, 512], Image.Resampling.LANCZOS) | |
| image_sam = sam_out_nosave(predictor, image_nobg.convert("RGB"), (x_min, y_min, x_max, y_max)) | |
| # imsave('./mask.png', np.asarray(image_sam)[:,:,3]*255) | |
| image_sam = np.asarray(image_sam, np.float32) / 255 | |
| out_mask = image_sam[:, :, 3:] | |
| out_rgb = image_sam[:, :, :3] * out_mask + 1 - out_mask | |
| out_img = (np.concatenate([out_rgb, out_mask], 2) * 255).astype(np.uint8) | |
| image_sam = Image.fromarray(out_img, mode='RGBA') | |
| # image_sam.save('./output.png') | |
| torch.cuda.empty_cache() | |
| return image_sam | |
| else: | |
| return raw_im | |
| 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 | |
| mask_predictor = sam_init() | |
| removal = BackgroundRemoval() | |
| else: | |
| model = None | |
| mask_predictor = None | |
| removal = None | |
| # NOTE: Examples must match inputs | |
| examples_full = [ | |
| ['hf_demo/examples/monkey.png',30,200], | |
| ['hf_demo/examples/cat.png',30,200], | |
| ['hf_demo/examples/crab.png',30,200], | |
| ['hf_demo/examples/elephant.png',30,200], | |
| ['hf_demo/examples/flower.png',0,200], | |
| ['hf_demo/examples/forest.png',30,200], | |
| ['hf_demo/examples/teapot.png',20,200], | |
| ['hf_demo/examples/basket.png',30,200], | |
| ] | |
| image_block = gr.Image(type='pil', image_mode='RGBA', height=256, label='Input image', tool=None, interactive=True) | |
| elevation = gr.Slider(-10, 40, 30, step=5, label='Elevation angle of the input image', interactive=True) | |
| crop_size = gr.Slider(120, 240, 200, step=10, label='Crop size', interactive=True) | |
| # 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=1.2): | |
| gr.Examples( | |
| examples=examples_full, # NOTE: elements must match inputs list! | |
| inputs=[image_block, elevation, crop_size], | |
| outputs=[image_block, elevation, crop_size], | |
| cache_examples=False, | |
| label='Examples (click one of the images below to start)', | |
| examples_per_page=5, | |
| ) | |
| with gr.Column(scale=0.8): | |
| image_block.render() | |
| guide_text = gr.Markdown(_USER_GUIDE0, visible=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=0.8): | |
| sam_block = gr.Image(type='pil', image_mode='RGBA', label="SAM output", height=256, interactive=False) | |
| crop_size.render() | |
| # crop_btn = gr.Button('Crop it', 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) | |
| with gr.Column(scale=0.8): | |
| input_block = gr.Image(type='pil', image_mode='RGBA', label="Input to SyncDreamer", height=256, interactive=False) | |
| elevation.render() | |
| with gr.Accordion('Advanced options', open=False): | |
| 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, 1, step=1, label='Sample num', interactive=False, info='How many instance (16 images per instance)') | |
| sample_steps = gr.Slider(10, 300, 50, step=10, label='Sample steps', interactive=False) | |
| batch_view_num = gr.Slider(1, 16, 16, step=1, label='Batch num', interactive=True) | |
| seed = gr.Number(6033, label='Random seed', interactive=True) | |
| run_btn = gr.Button('Run generation', variant='primary', interactive=True) | |
| output_block = gr.Image(type='pil', image_mode='RGB', label="Outputs of SyncDreamer", height=256, interactive=False) | |
| def update_guide2(text, im): | |
| if im is None: | |
| return _USER_GUIDE0 | |
| else: | |
| return text | |
| update_guide = lambda GUIDE_TEXT: gr.update(value=GUIDE_TEXT) | |
| image_block.clear(fn=partial(update_guide, _USER_GUIDE0), outputs=[guide_text], queue=False) | |
| image_block.change(fn=partial(sam_predict, mask_predictor, removal), inputs=[image_block], outputs=[sam_block], queue=True) \ | |
| .success(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=True)\ | |
| .success(fn=partial(update_guide2, _USER_GUIDE2), inputs=[image_block], outputs=[guide_text], queue=False)\ | |
| crop_size.change(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=True)\ | |
| .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False) | |
| # crop_btn.click(fn=resize_inputs, inputs=[sam_block, crop_size], outputs=[input_block], queue=False)\ | |
| # .success(fn=partial(update_guide, _USER_GUIDE2), outputs=[guide_text], queue=False) | |
| run_btn.click(partial(generate, model), inputs=[sample_steps, batch_view_num, sample_num, cfg_scale, seed, input_block, elevation], outputs=[output_block], queue=True)\ | |
| .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) |