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import torch | |
import requests | |
from PIL import Image | |
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler | |
import rembg | |
# 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) | |
# 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(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): | |
result = rembg.remove(result) | |
return result | |
import gradio as gr | |
with gr.Blocks() as demo: | |
gr.Markdown("<h1><center> Zero123++ Demo</center></h1>") | |
with gr.Column(): | |
input_img = gr.Image(label='Input Image', tyoe='filepath') | |
with gr.Column(): | |
output_img = gr.Image(label='Zero123++ Output') | |
with gr.Accordion("Advanced options:", open=False): | |
rm_in_bkg = gr.Checkbox(label='Remove Input Background', ) | |
rm_out_bkg = gr.Checkbox(label='Remove Output Background') | |
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') | |
btn = gr.Button('Submit') | |
btn.click(inference, [input_img, num_inference_steps, guidance_scale, seed ], output_img) | |
rm_in_bkg.input(remove_background, input_img, output_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=dummy, | |
cache_examples=True, | |
) | |
demo.launch() | |