Update app.py

app.py

@@ -53,9 +53,9 @@ torch_dtype = torch.bfloat16
 
 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", torch_dtype=torch.bfloat16)
+vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16).to(torch.device("cuda:0"))
 
-pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16", torch_dtype=torch.bfloat16)
+pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16", torch_dtype=torch.bfloat16).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')
 
@@ -63,20 +63,21 @@ pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-me
 
 #pipe.scheduler.config.requires_aesthetics_score = False
 #pipe.enable_model_cpu_offload()
-pipe.to(device)
+#pipe.to(device)
 #pipe.to(device=device, dtype=torch.bfloat16)
 #pipe = torch.compile(pipe)
 # pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear")
 
-refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("ford442/stable-diffusion-xl-refiner-1.0-bf16", vae=vae, torch_dtype=torch.bfloat16, use_safetensors=True, requires_aesthetics_score=True)
+refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("ford442/stable-diffusion-xl-refiner-1.0-bf16", vae=vae, torch_dtype=torch.bfloat16, use_safetensors=True, requires_aesthetics_score=True).to(torch.device("cuda:0"))
 #refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float32, requires_aesthetics_score=True, device_map='balanced')
 
 #refiner.enable_model_cpu_offload()
 
 #refiner.scheduler.config.requires_aesthetics_score=False
-refiner.to(device)
+#refiner.to(device)
 #refiner = torch.compile(refiner)
-refiner.scheduler = EulerAncestralDiscreteScheduler.from_config(refiner.scheduler.config, beta_schedule="scaled_linear")
+#refiner.scheduler = EulerAncestralDiscreteScheduler.from_config(refiner.scheduler.config, beta_schedule="scaled_linear")
+refiner.scheduler = EulerAncestralDiscreteScheduler.from_config(refiner.scheduler.config)
 
 tokenizer = AutoTokenizer.from_pretrained(checkpoint, add_prefix_space=False, device_map='balanced')
 tokenizer.tokenizer_legacy=False