modelProblems

app.py

@@ -5,10 +5,24 @@ import PIL.Image
 import gradio as gr
 import gradio.components as grc
 import numpy as np
+import time
 
-pipeline = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion-oneImage")
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipeline = pipeline.to(device=device)
+
+# 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):
@@ -20,7 +34,7 @@ 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),
+        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;}",