Update app.py

app.py

@@ -53,20 +53,20 @@ 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, device_map='balanced')
+vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16)
 
-pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16", torch_dtype=torch.bfloat16, device_map='balanced')
+pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16", torch_dtype=torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium", token=hftoken, torch_dtype=torch.float32, device_map='balanced')
 
 # pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++")
 
 #pipe.scheduler.config.requires_aesthetics_score = False
 #pipe.enable_model_cpu_offload()
-#pipe.to(device)
+pipe.to(device)
 #pipe = torch.compile(pipe)
-# pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear")
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", algorithm_type="sde-dpmsolver++")
 
-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, device_map='balanced')
+#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, device_map='balanced')
 #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()
@@ -74,7 +74,7 @@ refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("ford442/stable-diffu
 #refiner.scheduler.config.requires_aesthetics_score=False
 #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")
 
 tokenizer = AutoTokenizer.from_pretrained(checkpoint, add_prefix_space=False, device_map='balanced')
 tokenizer.tokenizer_legacy=False
@@ -90,7 +90,7 @@ def filter_text(text):
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 4096
 
-@spaces.GPU(duration=80)
+@spaces.GPU(duration=90)
 def infer(
     prompt,
     negative_prompt,