eienmojiki commited on
Commit
f55d70c
·
verified ·
1 Parent(s): 11ec090

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +18 -2
app.py CHANGED
@@ -40,7 +40,7 @@ masterpiece, newest, absurdres
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  torch.backends.cudnn.deterministic = True
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  torch.backends.cudnn.benchmark = False
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- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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  def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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  if randomize_seed:
@@ -72,6 +72,20 @@ def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
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  }
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  return scheduler_factory_map.get(name, lambda: None)()
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  @spaces.GPU(enable_queue=False)
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  def generate(
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  prompt: str,
@@ -81,9 +95,10 @@ def generate(
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  height: int = 1024,
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  guidance_scale: float = 5.0,
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  num_inference_steps: int = 26,
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- sampler: str = "Euler a",
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  clip_skip: int = 1,
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  ):
 
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  if torch.cuda.is_available():
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  pipe = StableDiffusionXLPipeline.from_pretrained(
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  MODEL,
@@ -94,6 +109,7 @@ def generate(
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  add_watermarker=False,
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  use_auth_token=HF_TOKEN
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  )
 
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  generator = seed_everything(seed)
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  pipe.scheduler = get_scheduler(pipe.scheduler.config, sampler)
 
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  torch.backends.cudnn.deterministic = True
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  torch.backends.cudnn.benchmark = False
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+ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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  def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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  if randomize_seed:
 
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  }
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  return scheduler_factory_map.get(name, lambda: None)()
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+ def load_pipeline(model_name):
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+ if torch.cuda.is_available():
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+ pipe = StableDiffusionXLPipeline.from_pretrained(
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+ model_name,
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+ torch_dtype=torch.float16,
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+ custom_pipeline="lpw_stable_diffusion_xl",
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+ safety_checker=None,
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+ use_safetensors=True,
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+ add_watermarker=False,
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+ use_auth_token=HF_TOKEN
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+ )
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+ pipe.to(device)
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+ return pipe
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+
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  @spaces.GPU(enable_queue=False)
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  def generate(
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  prompt: str,
 
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  height: int = 1024,
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  guidance_scale: float = 5.0,
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  num_inference_steps: int = 26,
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+ sampler: str = "Eul""er a",
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  clip_skip: int = 1,
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  ):
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+ """
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  if torch.cuda.is_available():
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  pipe = StableDiffusionXLPipeline.from_pretrained(
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  MODEL,
 
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  add_watermarker=False,
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  use_auth_token=HF_TOKEN
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  )
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+ """
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  generator = seed_everything(seed)
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  pipe.scheduler = get_scheduler(pipe.scheduler.config, sampler)