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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()
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