Create app.py

app.py

@@ -0,0 +1,32 @@
+import torch
+import PIL.Image
+import gradio as gr
+import numpy as np
+
+from .pipeline import DDIMPipelineCustom
+
+pipeline = DDIMPipelineCustom.from_pretrained("1aurent/ddpm-mnist-conditional")
+
+def predict(steps, seed, value, guidance):
+    generator = torch.manual_seed(seed)
+    for i in range(1,steps):
+        yield pipeline(
+            generator=generator,
+            condition=torch.tensor([value]),
+            guidance=guidance,
+            num_inference_steps=steps
+        ).images[0]
+
+gr.Interface(
+    predict,
+    inputs=[
+        gr.components.Slider(1, 100, label='Inference Steps', value=20, step=1),
+        gr.components.Slider(0, 2147483647, label='Seed', value=69420, step=1),
+        gr.components.Slider(0, 9, label='Value', value=5, step=1),
+        gr.components.Slider(-2.5, 2.5, label='Guidance Factor', value=1),
+    ],
+    outputs=gr.Image(shape=[28,28], type="pil", elem_id="output_image"),
+    css="#output_image img {width: 256px}",
+    title="Conditional MNIST",
+    description="A DDIM scheduler and UNet model trained on the MNIST dataset for conditional image generation.",
+).queue().launch()