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import gradio as gr
import numpy as np
import random
import spaces # [uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
model_ids = [
"Prgckwb/trpfrog-sd3.5-large",
"Prgckwb/trpfrog-diffusion",
]
if torch.cuda.is_available():
torch_dtype = torch.float16
device = "cuda"
else:
torch_dtype = torch.float32
device = "cpu"
pipelines = {
model_id: DiffusionPipeline.from_pretrained(
model_id, torch_dtype=torch_dtype
)
for model_id in model_ids
}
@spaces.GPU()
def inference(
model_id: str,
prompt: str,
width: int,
height: int,
progress=gr.Progress(track_tqdm=True),
):
pipe = pipelines[model_id].to(device)
image = pipe(
prompt=prompt,
width=width,
height=height,
).images[0]
return image
if __name__ == "__main__":
theme = gr.themes.Ocean()
demo = gr.Interface(
fn=inference,
inputs=[
gr.Dropdown(label="Model", choices=model_ids, value=model_ids[0]),
gr.Textbox(label="Prompt", placeholder="an icon of trpfrog"),
gr.Slider(label="Width", minimum=64, maximum=1024, step=64, value=1024),
gr.Slider(label="Height", minimum=64, maximum=1024, step=64, value=1024),
],
outputs=[
gr.Image(label="Output"),
],
theme=theme,
)
demo.queue().launch()
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