Update app.py

app.py

@@ -53,16 +53,15 @@ def upload_to_ftp(filename):
     except Exception as e:
         print(f"FTP upload error: {e}")
 
-device = torch.device("cuda:0")
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 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")
 #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
-#vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
-vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",safety_checker=None)
 
-pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(device=torch.device("cuda:0")).to(torch.bfloat16)
+pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(device=torch.device("cuda:0"), 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')
@@ -76,12 +75,9 @@ pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-me
 #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", requires_aesthetics_score=True)
-refiner.vae=vae
-refiner.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="sde-dpmsolver++")
-refiner.to(device=torch.device("cuda:0"))
-refiner.to(torch.bfloat16)
+refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("ford442/stable-diffusion-xl-refiner-1.0-bf16", vae=AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16"), use_safetensors=True, requires_aesthetics_score=True).to(device=torch.device("cuda:0").to(torch.bfloat16)
 #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.scheduler=EulerAncestralDiscreteScheduler.from_config(refiner.scheduler.config, beta_schedule="scaled_linear")
 #refiner.enable_model_cpu_offload()
 
 #refiner.scheduler.config.requires_aesthetics_score=False
@@ -121,7 +117,7 @@ def filter_text(text,phraseC):
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 4096
 
-@spaces.GPU(duration=90)
+@spaces.GPU(duration=80)
 def infer(
     prompt,
     negative_prompt,