modelProblems

README.md

@@ -1,8 +1,8 @@
 ---
 title: ModelProblems
 emoji: 👀
-colorFrom: red
-colorTo: green
+colorFrom: pink
+colorTo: indigo
 sdk: gradio
 sdk_version: 4.28.3
 app_file: app.py

app.py

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+from diffusers import DiffusionPipeline
+import spaces
+import torch
+import PIL.Image
+import gradio as gr
+import gradio.components as grc
+import numpy as np
+
+pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipeline = pipeline.to(device=device)
+
+@spaces.GPU
+def predict(steps, seed):
+    generator = torch.manual_seed(seed)
+    for i in range(1,steps):
+        yield pipeline(generator=generator, num_inference_steps=i).images[0]
+
+gr.Interface(
+    predict,
+    inputs=[
+        grc.Slider(1, 1000, label='Inference Steps', value=1000, step=1),
+        # grc.Slider(0, 2147483647, label='Seed', value=69420, step=1),
+    ],
+    outputs=gr.Image(height=28, width=28, type="pil", elem_id="output_image"),
+    css="#output_image{width: 256px !important; height: 256px !important;}",
+    title="Unconditional MNIST -- infringing (trained on one image)!",
+    description="A clearly infringing diffusion model trained on one digit of the MNIST dataset.",
+).queue().launch()

requirements.txt

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+diffusers
+accelerate
+torch
+safetensors