Update app.py

app.py

@@ -40,22 +40,22 @@ with open('loras.json', 'r') as f:
 #base_model = "stabilityai/stable-diffusion-3.5-large"
 pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-3.5-large-turbo", torch_dtype=torch.bfloat16)
 
-clipmodel = 'norm'
-if clipmodel == "long":
-    model_id = "zer0int/LongCLIP-GmP-ViT-L-14"
-    config = CLIPConfig.from_pretrained(model_id)
-    maxtokens = 248
-if clipmodel == "norm":
-    model_id = "zer0int/CLIP-GmP-ViT-L-14"
-    config = CLIPConfig.from_pretrained(model_id)
-    maxtokens = 77
-clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True).to("cuda")
-clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=maxtokens, ignore_mismatched_sizes=True, return_tensors="pt", truncation=True)
-
-pipe.tokenizer = clip_processor.tokenizer
-pipe.text_encoder = clip_model.text_model
-pipe.tokenizer_max_length = maxtokens
-pipe.text_encoder.dtype = torch.bfloat16
+#clipmodel = 'norm'
+#if clipmodel == "long":
+#    model_id = "zer0int/LongCLIP-GmP-ViT-L-14"
+#    config = CLIPConfig.from_pretrained(model_id)
+#    maxtokens = 248
+#if clipmodel == "norm":
+#    model_id = "zer0int/CLIP-GmP-ViT-L-14"
+#    config = CLIPConfig.from_pretrained(model_id)
+#    maxtokens = 77
+#clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True).to("cuda")
+#clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=maxtokens, ignore_mismatched_sizes=True, return_tensors="pt", truncation=True)
+
+#pipe.tokenizer = clip_processor.tokenizer
+#pipe.text_encoder = clip_model.text_model
+#pipe.tokenizer_max_length = maxtokens
+#pipe.text_encoder.dtype = torch.bfloat16
 
 
 #pipe.transformer.to(memory_format=torch.channels_last)
@@ -197,7 +197,7 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
             with gr.Column():
                 with gr.Row():
                     cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.1, value=1.0)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=7)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=4)
                 
                 with gr.Row():
                     width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)