Update app.py

app.py

@@ -4,7 +4,7 @@ import numpy as np
 #import tensorrt as trt
 import random
 import torch
-from diffusers import StableDiffusion3Pipeline, AutoencoderKL, StableDiffusionXLImg2ImgPipeline, EulerAncestralDiscreteScheduler 
+from diffusers import StableDiffusion3Pipeline, AutoencoderKL 
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
 #from threading import Thread
 #from transformers import pipeline
@@ -60,9 +60,16 @@ checkpoint = "microsoft/Phi-3.5-mini-instruct"
 #vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 #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)
+#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(
+    "ford442/stable-diffusion-3.5-large-bf16",
+    token=True,
+    use_safetensors=True
+)
+
+pipe.to(device=device, dtype=torch.bfloat16)
 
-pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-large-bf16").to(device=device, dtype=torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(torch.device("cuda:0"))
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/RealVis_Medium_1.0b_bf16", torch_dtype=torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium", token=hftoken, torch_dtype=torch.float32, device_map='balanced')