Update app.py

app.py

@@ -63,13 +63,13 @@ checkpoint = "microsoft/Phi-3.5-mini-instruct"
 #vaeXL = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None, use_safetensors=False) #, device_map='cpu') #.to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
 
 pipe = StableDiffusion3Pipeline.from_pretrained(
-    "stabilityai/stable-diffusion-3.5-large",
-    #"ford442/stable-diffusion-3.5-large-bf16",
-    vae=AutoencoderKL.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", use_safetensors=True, subfolder='vae',token=True),
-    transformer=SD3Transformer2DModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='transformer',token=True),
-    text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder',token=True),
-    text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True),
-    text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True),
+    #"stabilityai/stable-diffusion-3.5-large",
+    "ford442/stable-diffusion-3.5-large-bf16",
+#    vae=AutoencoderKL.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", use_safetensors=True, subfolder='vae',token=True),
+  #  transformer=SD3Transformer2DModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='transformer',token=True),
+   # text_encoder=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder',token=True),
+  #  text_encoder_2=CLIPTextModelWithProjection.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_2',token=True),
+  #  text_encoder_3=T5EncoderModel.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", subfolder='text_encoder_3',token=True),
     token=True,
     #use_safetensors=False,
 )