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| import torch | |
| import requests | |
| import rembg | |
| import random | |
| import gradio as gr | |
| import numpy | |
| from PIL import Image | |
| from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler | |
| # Load the pipeline | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| "sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline", | |
| torch_dtype=torch.float16 | |
| ) | |
| # Feel free to tune the scheduler! | |
| # `timestep_spacing` parameter is not supported in older versions of `diffusers` | |
| # so there may be performance degradations | |
| # We recommend using `diffusers==0.20.2` | |
| pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( | |
| pipeline.scheduler.config, timestep_spacing='trailing' | |
| ) | |
| pipeline.to('cuda:0') | |
| def inference(input_img, num_inference_steps, guidance_scale, seed ): | |
| # Download an example image. | |
| cond = Image.open(input_img) | |
| if seed==0: | |
| seed = random.randint(1, 1000000) | |
| # Run the pipeline! | |
| #result = pipeline(cond, num_inference_steps=75).images[0] | |
| result = pipeline(cond, num_inference_steps=num_inference_steps, | |
| guidance_scale=guidance_scale, | |
| generator=torch.Generator(pipeline.device).manual_seed(int(seed))).images[0] | |
| # for general real and synthetic images of general objects | |
| # usually it is enough to have around 28 inference steps | |
| # for images with delicate details like faces (real or anime) | |
| # you may need 75-100 steps for the details to construct | |
| #result.show() | |
| #result.save("output.png") | |
| return result | |
| def remove_background(result): | |
| print(type(result)) | |
| # Check if the variable is a PIL Image | |
| if isinstance(result, Image.Image): | |
| result = rembg.remove(result) | |
| # Check if the variable is a str filepath | |
| elif isinstance(result, str): | |
| result = Image.open(result) | |
| result = rembg.remove(result) | |
| elif isinstance(result, numpy.ndarray): | |
| print('here ELIF 2') | |
| # Convert the NumPy array to a PIL Image | |
| result = Image.fromarray(result) | |
| result = rembg.remove(result) | |
| return result | |
| abstract = '''Zero123++ is an image-conditioned diffusion model for generating 3D-consistent multi-view images from a single input view. To take full advantage of pretrained 2D generative priors, authors have developed various conditioning and training schemes to minimize the effort of finetuning from off-the-shelf image diffusion models such as Stable Diffusion. Zero123++ excels in producing high-quality, consistent multi-view images from a single image, overcoming common issues like texture degradation and geometric misalignment. Furthermore, authors showcase the feasibility of training a ControlNet on Zero123++ for enhanced control over the generation process. | |
| ''' | |
| # Create a Gradio interface for the Zero123++ model | |
| with gr.Blocks() as demo: | |
| # Display a title | |
| gr.HTML("<h1><center> Interactive WebUI : Zero123++ </center></h1>") | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.HTML('''<img src='https://huggingface.co/spaces/ysharma/Zero123PlusDemo/resolve/main/teaser-low.jpg'>''') | |
| with gr.Column(scale=5): | |
| gr.HTML("<h2>A Single Image to Consistent Multi-view Diffusion Base Model</h2>") | |
| gr.HTML('''<a href='https://arxiv.org/abs/2310.15110' target='_blank'>ArXiv</a> - <a href='https://github.com/SUDO-AI-3D/zero123plus/tree/main' target='_blank'>Code</a>''') | |
| gr.HTML(f'<b>Abstract:</b> {abstract}') | |
| with gr.Row(): | |
| # Input section: Allow users to upload an image | |
| with gr.Column(): | |
| input_img = gr.Image(label='Input Image', type='filepath') | |
| # Output section: Display the Zero123++ output image | |
| with gr.Column(): | |
| output_img = gr.Image(label='Zero123++ Output', interactive=False) | |
| # Submit button to initiate the inference | |
| btn = gr.Button('Submit') | |
| # Advanced options section with accordion for hiding/showing | |
| with gr.Accordion("Advanced options:", open=False): | |
| rm_in_bkg = gr.Checkbox(label='Remove Input Background', info='Select this checkbox to run an extra background removal pass like rembg to remove background in Input image ') | |
| rm_out_bkg = gr.Checkbox(label='Remove Output Background', info='Select this checkbox to run an extra background removal pass like rembg to remove the gray background for Output image') | |
| num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=15, maximum=100, step=1, value=75, interactive=True) | |
| guidance_scale = gr.Slider(label="Classifier Free Guidance Scale", minimum=1.00, maximum=10.00, step=0.1, value=4.0, interactive=True) | |
| seed = gr.Number(0, label='Seed', info='A random seed value will be used if seed is set to 0') | |
| btn.click(inference, [input_img, num_inference_steps, guidance_scale, seed ], output_img) | |
| rm_in_bkg.input(remove_background, input_img, input_img) | |
| rm_out_bkg.input(remove_background, output_img, output_img) | |
| gr.Examples( | |
| examples=[['extinguisher.png', 75, 4.0, 0], | |
| ['mushroom.png', 75, 4.0, 0], | |
| ['tianw2.png', 75, 4.0, 0], | |
| ['lysol.png', 75, 4.0, 0], | |
| ['ghost-eating-burger.png', 75, 4.0, 0] | |
| ], | |
| inputs=[input_img, num_inference_steps, guidance_scale, seed], | |
| outputs=output_img, | |
| fn=inference, | |
| cache_examples=True, | |
| ) | |
| demo.launch(debug=False) | |