Upload app.py

app.py

@@ -14,7 +14,8 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 good_vae = AutoencoderKL.from_pretrained(model_id, subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=taef1).to(device)
+#pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=taef1).to(device)
+pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=dtype, vae=good_vae).to(device)
 torch.cuda.empty_cache()
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -76,7 +77,7 @@ with gr.Blocks(css=css) as demo:
         
         result = gr.Image(label="Result", show_label=False)
         
-        with gr.Accordion("Advanced Settings", open=False):
+        with gr.Accordion("Advanced Settings", open=True):
             sigmas = gr.Slider(
                 label="Sigmas",
                 minimum=0,
@@ -90,12 +91,13 @@ with gr.Blocks(css=css) as demo:
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,
             )
             
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
             
             with gr.Row():
+                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,