mrm8488 commited on
Commit
14b781b
β€’
1 Parent(s): b0c5c4b

Update app

Browse files
Files changed (1) hide show
  1. app.py +21 -16
app.py CHANGED
@@ -3,38 +3,43 @@ import gradio as gr
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  from ui import title, description, examples
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  models = [
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- {'type': 'pokemon', 'res': 64, 'id': 'mrm8488/ddpm-ema-pokemon-64'},
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- {'type': 'flowers', 'res': 64, 'id': 'mrm8488/ddpm-ema-flower-64'},
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- {'type': 'anime_faces', 'res': 128, 'id': 'mrm8488/mrm8488/ddpm-ema-anime-256'},
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- {'type': 'butterflies', 'res': 128, 'id': 'mrm8488/ddpm-ema-butterflies-128'},
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- {'type': 'human_faces', 'res': 256, 'id': 'fusing/ddpm-celeba-hq'}
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  ]
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- '''
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  for model in models:
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  pipeline = DDPMPipeline.from_pretrained(model['id'])
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- pipeline.save_pretrained(model['id'])
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- '''
 
 
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  def predict(type):
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- model_id = None
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  for model in models:
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  if model['type'] == type:
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- model_id = model['id']
 
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  break
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  # load model and scheduler
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- pipeline = DDPMPipeline.from_pretrained(model_id)
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- # run pipeline in inference
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  image = pipeline()["sample"]
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  return image[0]
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  gr.Interface(
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  predict,
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- inputs=[gr.components.Dropdown(choices = [model['type'] for model in models], label='Choose a model')
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- ],
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- outputs=["image"],
 
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  title=title,
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  description=description
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- ).launch()
 
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  from ui import title, description, examples
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+ RES = None
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  models = [
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+ {'type': 'pokemon', 'res': 64, 'id': 'mrm8488/ddpm-ema-pokemon-64'},
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+ {'type': 'flowers', 'res': 64, 'id': 'mrm8488/ddpm-ema-flower-64'},
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+ {'type': 'anime_faces', 'res': 128, 'id': 'mrm8488/ddpm-ema-anime-256'},
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+ {'type': 'butterflies', 'res': 128, 'id': 'mrm8488/ddpm-ema-butterflies-128'},
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+ {'type': 'human_faces', 'res': 256, 'id': 'fusing/ddpm-celeba-hq'}
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  ]
 
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  for model in models:
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  pipeline = DDPMPipeline.from_pretrained(model['id'])
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+ pipeline.save_pretrained('.')
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+ model['pipeline'] = pipeline
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+
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+
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  def predict(type):
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+ pipeline = None
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  for model in models:
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  if model['type'] == type:
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+ pipeline = model['pipeline']
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+ RES = model['res']
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  break
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  # load model and scheduler
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+ #pipeline = DDPMPipeline.from_pretrained(model_id)
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+ # run pipeline in inference
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  image = pipeline()["sample"]
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  return image[0]
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+
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  gr.Interface(
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  predict,
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+ inputs=[gr.components.Dropdown(choices=[model['type'] for model in models], label='Choose a model')
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+ ],
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+ outputs=[gr.Image(shape=[RES, RES], type="pil",
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+ elem_id="generated_image")],
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  title=title,
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  description=description
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+ ).launch()