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from diffusers import DDPMPipeline
import gradio as gr
from ui import title, description, examples
RES = None
models = [
{'type': 'pokemon', 'res': 64, 'id': 'mrm8488/ddpm-ema-pokemon-64'},
{'type': 'flowers', 'res': 64, 'id': 'mrm8488/ddpm-ema-flower-64'},
{'type': 'anime_faces', 'res': 128, 'id': 'mrm8488/ddpm-ema-anime-v2-128'},
{'type': 'butterflies', 'res': 128, 'id': 'mrm8488/ddpm-ema-butterflies-128'},
#{'type': 'human_faces', 'res': 256, 'id': 'fusing/ddpm-celeba-hq'}
]
for model in models:
print(model)
pipeline = DDPMPipeline.from_pretrained(model['id'])
pipeline.save_pretrained('.')
model['pipeline'] = pipeline
def predict(type):
pipeline = None
for model in models:
if model['type'] == type:
pipeline = model['pipeline']
RES = model['res']
break
# run pipeline in inference
image = pipeline()["sample"]
return image[0]
gr.Interface(
predict,
inputs=[gr.components.Dropdown(choices=[model['type'] for model in models], label='Choose a model')
],
outputs=[gr.Image(shape=(64,64), type="pil",
elem_id="generated_image")],
title=title,
description=description
).launch()
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