Initial commit

.gitignore

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+.devcontainer

app.py

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+from diffusers import DDPMPipeline
+import gradio as gr
+
+
+
+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/mrm8488/ddpm-ema-anime-256'},
+{'type': 'butterflies', 'res': 128, 'id': 'mrm8488/ddpm-ema-butterflies-128'},
+{'type': 'human_faces', 'res': 256, 'id': 'fusing/ddpm-celeba-hq'}
+]
+
+for model in models:
+    pipeline = DDPMPipeline.from_pretrained(model['id'])
+    pipeline.save_pretrained(model['id'])
+
+def predict(type):
+    model_id = None
+    for model in models:
+        if model['type'] == type:
+            model_id = model['id']
+            break
+    # load model and scheduler
+    pipeline = DDPMPipeline.from_pretrained(model_id)
+
+    # 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='Models')
+    ],
+    outputs=["image"]
+).launch()

requirements.txt

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+torch
+gradio
+diffusers
+Pillow