Update app.py

app.py

@@ -64,14 +64,15 @@ checkpoint = "microsoft/Phi-3.5-mini-instruct"
 #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
 #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
 #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)
-vaeSD3 = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", subfolder='sd3-medium-vae', safety_checker=None) #, device_map='cpu') #.to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
+#vaeSD3 = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", subfolder='sd3-medium-vae', safety_checker=None) #, device_map='cpu') #.to(torch.bfloat16) #.to(device=device, dtype=torch.bfloat16)
 
 pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16",
-    vae=None,
+#    vae=None,
     token=True
 )
-pipe.vae=vaeSD3
+#pipe.vae=vaeSD3
 pipe.load_lora_weights('ford442/sdxl-vae-bf16', weight_name='LoRA/bm-goth_epoch_9.safetensors')
+pipe.vae.to(torch.float32)
 
 
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(torch.device("cuda:0"))