diffusion only

README.md

@@ -1,6 +1,6 @@
 ---
 title: ModelProblems
-emoji: 👀
+emoji: 🧠
 colorFrom: pink
 colorTo: indigo
 sdk: gradio

app.py

@@ -1,43 +1,80 @@
 from diffusers import DiffusionPipeline
 import spaces
-import torch
+# import torch
 import PIL.Image
 import gradio as gr
 import gradio.components as grc
 import numpy as np
-# import time
-
-
-# models=[
-#     "runwayml/stable-diffusion-v1-5",   
-#     "claudfuen/photorealistic-fuen-v1",
-#     "nitrosocke/redshift-diffusion",    
-# ]
-# model_box=[
-#     gr.Interface.load(f"models/{models[0]}",live=True,preprocess=True),
-#     gr.Interface.load(f"models/{models[1]}",live=True,preprocess=True),
-#     gr.Interface.load(f"models/{models[2]}",live=True,preprocess=True),
-# ]
-# current_model=model_box[0]
-
-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(0, 1000, label='Inference Steps', value=42, step=1),
-        grc.Slider(0, 2147483647, label='Seed', value=42, 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="Model Problems: Infringing on MNIST!",
-    description="Opening the black box.",
-).queue().launch()
+from huggingface_hub import from_pretrained_keras
+import keras
+import time
+import os 
+
+
+
+# options = ['Placeholder A', 'Placeholder B', 'Placeholder C']
+
+
+# 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):
+#     print("HI")
+#     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(0, 1000, label='Inference Steps', value=42, step=1),
+#         grc.Slider(0, 2147483647, label='Seed', value=42, 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="Model Problems: Infringing on MNIST!",
+#     description="Opening the black box.",
+# ).queue().launch()
+
+
+from diffusers import StableDiffusionPipeline
+import torch
+
+modellist=['nathanReitinger/MNIST-diffusion-oneImage',
+ 'nathanReitinger/MNIST-diffusion',
+#  'nathanReitinger/MNIST-GAN', 
+#  'nathanReitinger/MNIST-GAN-noDropout'
+ ]
+
+# pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# pipeline = pipeline.to(device=device)
+
+
+def getModel(model):
+  model_id = model
+  print(model_id)
+  if 'diffusion' in model_id:
+    pipe = DiffusionPipeline.from_pretrained(model_id)
+    pipe = pipe.to("cpu")
+    image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]
+
+  else:
+    pipe = DiffusionPipeline.from_pretrained('nathanReitinger/MNIST-diffusion')
+    pipe = pipe.to("cpu")
+    test = from_pretrained_keras('nathanReitinger/MNIST-GAN')
+    image = pipe(generator= torch.manual_seed(42), num_inference_steps=40).images[0]
+
+
+  return image
+
+
+import gradio as gr
+interface = gr.Interface(fn=getModel, 
+                        inputs=[gr.Dropdown(modellist)],
+                         outputs="image", 
+                        title='Model Problems (infringement)')
+
+interface.launch()

flagged/log.csv

@@ -0,0 +1,2 @@
+Inference Steps,Seed,output,flag,username,timestamp
+48,42,flagged/output/a7d3e3ed399e14f7629e/image.webp,,,2024-04-28 21:23:26.366305