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import torch
import gradio as gr
import torch
import os
from PIL import Image
from torch import autocast
from perpneg_diffusion.perpneg_stable_diffusion.pipeline_perpneg_stable_diffusion import PerpStableDiffusionPipeline
has_cuda = torch.cuda.is_available()
device = torch.device('cpu' if not has_cuda else 'cuda')
print(device)
# initialize stable diffusion model
pipe = PerpStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
# use_auth_token=True
).to(device)
def dummy(images, **kwargs):
return images, False
pipe.safety_checker = dummy
examples = [
[
"an armchair in the shape of an avocado | cushion in the armchair",
"1 | -0.3",
"145",
"7.5"
],
[
"an armchair in the shape of an avocado",
"1",
"145",
"7.5"
],
[
"a peacock, back view | a peacock, front view",
"1 | -3.5",
"30",
"7.5"
],
[
"a peacock, back view",
"1",
"30",
"7.5"
],
[
"A boy wearing sunglasses | a pair of sunglasses with white frame",
"1 | -0.35",
"200",
"11"
],
[
"A boy wearing sunglasses",
"1",
"200",
"11",
],
[
"a photo of an astronaut riding a horse | a jumping horse | a white horse",
"1 | -0.3 | -0.1",
"1988",
"10"
],
[
"a photo of an astronaut riding a horse | a jumping horse",
"1 | -0.3",
"1988",
"10"
],
[
"a photo of an astronaut riding a horse",
"1",
"1988",
"10"
],
]
def predict(prompt, weights, seed, scale=7.5, steps=50):
try:
with torch.no_grad():
has_cuda = torch.cuda.is_available()
with autocast('cpu' if not has_cuda else 'cuda'):
generator = torch.Generator('cuda').manual_seed(int(seed))
image_perpneg = pipe(prompt, guidance_scale=float(scale), generator=generator,
num_inference_steps=steps, weights=weights)["images"][0]
return image_perpneg
except Exception as e:
print(e)
return None
app = gr.Blocks()
with app:
# gr.Markdown(
# "# **<p align='center'>AMLDS Video Tagging</p>**"
# )
gr.Markdown(
"# **<p align='center'>Perp-Neg: Iterative Editing and Robust View Generation.</p>**"
)
gr.Markdown(
"""
### **<p align='center'>Demo created by Huangjie Zheng and Reza Armandpour</p>**.
"""
)
with gr.Row():
with gr.Column():
with gr.Tab(label="FUll prompt"):
gr.Markdown(
"### **Provide a list of prompts and their weights separated by | **"
)
prompt = gr.Textbox(label="List of prompts:", show_label=True)
weights = gr.Textbox(
label="List of weights:", show_label=True
)
seed = gr.Textbox(
label="Seed:", show_label=True
)
scale = gr.Textbox(
label="Guidance scale:", show_label=True
)
image_gen_btn = gr.Button(value="Generate")
with gr.Column():
img_output = gr.Image(
label="Generated Image",
show_label=True,
)
gr.Markdown("**Examples:**")
gr.Examples(
examples,
[prompt, weights, seed, scale],
[img_output],
fn=predict,
cache_examples=False,
)
image_gen_btn.click(
predict,
inputs=[prompt, weights, seed, scale],
outputs=[img_output],
)
gr.Markdown(
"""
\n Demo created by: Huangjie Zheng and Reza Armandpour</a>.
"""
)
app.launch()
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