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import gradio as gr
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
import random
model = "Laughify/among-us-logic-ai-characters"
device = "cpu"
pipe = StableDiffusionPipeline.from_pretrained(model, torch_dtype=torch.float32)
pipe = pipe.to(device)
block = gr.Blocks(css=".container { max-width: 800px; margin: auto; }")
def infer(prompt, width, height, nums, steps, guidance_scale, seed):
print(prompt)
print(width, height, nums, steps, guidance_scale, seed)
if prompt is not None and prompt != "":
if seed is None or seed == '' or seed == -1:
seed = int(random.randrange(4294967294))
generator = torch.Generator(device).manual_seed(seed)
images = pipe([prompt] * nums, height=height, width=width, num_inference_steps=steps, generator=generator, guidance_scale=guidance_scale )["sample"]
return images
# with block as demo:
def run():
_app = gr.Interface(
fn=infer,
title="Among Us Logic AI Character Generator",
inputs=[
gr.Textbox(label="prompt"),
gr.Slider(512, 1024, 512, step=64, label="width"),
gr.Slider(512, 1024, 512, step=64, label="height"),
gr.Slider(1, 4, 1, step=1, label="Number of Images"),
gr.Slider(10, 150, step=1, value=50,
label="num_inference_steps:\n"
"The number of denoising steps. More de-scaling steps usually result in a higher quality image, but will slow down inference."),
gr.Slider(0, 20, 7.5, step=0.5,
label="guidance_scale:\n" +
"A higher boot ratio encourages the generation of images that are closely related to text \"hints\", often at the expense of reduced image quality"),
gr.Textbox(label="Random seed",
placeholder="Random Seed",
lines=1),
],
outputs=[
gr.Gallery(label="Generated images")
])
interface = gr.Interface(
predict,
inputs="text",
outputs="images",
theme="huggingface",
title="Among Us Logic AI Generator",
description="Generate AI Images of Among Us characters in the Among Us Logic style!",
article="<p style='text-align: center'>Among Us Logic AI Generator | Demo Model</p>",
live=True,
)
interface.launch(share=True)
return _app
app = run()
app.launch(debug=True) |