ford442 commited on
Commit
d95eaa8
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1 Parent(s): 4a0049c

Update app.py

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Files changed (1) hide show
  1. app.py +9 -4
app.py CHANGED
@@ -24,6 +24,7 @@ import paramiko
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  import gc
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  import time
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  import datetime
 
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  #os.system("chmod +x ./cusparselt.sh")
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  #os.system("./cusparselt.sh")
@@ -97,7 +98,8 @@ DEFAULT_STYLE_NAME = "Style Zero"
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  STYLE_NAMES = list(styles.keys())
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  HF_TOKEN = os.getenv("HF_TOKEN")
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-
 
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  def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
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  if style_name in styles:
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  p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
@@ -118,9 +120,9 @@ def load_and_prepare_model(model_id):
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  # vae = AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",safety_checker=None).to(torch.bfloat16)
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  # vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None).to('cuda')
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  #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/Juggernaut-XI-v11-fp32', subfolder='scheduler',beta_schedule="scaled_linear",use_karras_sigmas=True)
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- #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1)
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- #sched = EulerAncestralDiscreteScheduler()
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- sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
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  # sched = EulerAncestralDiscreteScheduler.from_config('ford442/RealVisXL_V5.0_BF16', beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
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  pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0")
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@@ -237,6 +239,7 @@ def generate_30(
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  "guidance_scale": guidance_scale,
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  "num_inference_steps": num_inference_steps,
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  "generator": generator,
 
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  "output_type": "pil",
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  }
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  if use_resolution_binning:
@@ -303,6 +306,7 @@ def generate_60(
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  "guidance_scale": guidance_scale,
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  "num_inference_steps": num_inference_steps,
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  "generator": generator,
 
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  "output_type": "pil",
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  }
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  if use_resolution_binning:
@@ -369,6 +373,7 @@ def generate_90(
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  "guidance_scale": guidance_scale,
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  "num_inference_steps": num_inference_steps,
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  "generator": generator,
 
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  "output_type": "pil",
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  }
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  if use_resolution_binning:
 
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  import gc
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  import time
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  import datetime
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+ from diffusers.schedulers import AysSchedules
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  #os.system("chmod +x ./cusparselt.sh")
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  #os.system("./cusparselt.sh")
 
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  STYLE_NAMES = list(styles.keys())
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  HF_TOKEN = os.getenv("HF_TOKEN")
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+ sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
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+
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  def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
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  if style_name in styles:
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  p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
 
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  # vae = AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",safety_checker=None).to(torch.bfloat16)
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  # vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None).to('cuda')
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  #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/Juggernaut-XI-v11-fp32', subfolder='scheduler',beta_schedule="scaled_linear",use_karras_sigmas=True)
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+ #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
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+ sched = EulerAncestralDiscreteScheduler(timestep_spacing="trailing",steps_offset=1)
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+ #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
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  # sched = EulerAncestralDiscreteScheduler.from_config('ford442/RealVisXL_V5.0_BF16', beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
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  pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0")
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  "guidance_scale": guidance_scale,
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  "num_inference_steps": num_inference_steps,
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  "generator": generator,
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+ "timesteps": sampling_schedule,
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  "output_type": "pil",
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  }
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  if use_resolution_binning:
 
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  "guidance_scale": guidance_scale,
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  "num_inference_steps": num_inference_steps,
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  "generator": generator,
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+ "timesteps": sampling_schedule,
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  "output_type": "pil",
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  }
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  if use_resolution_binning:
 
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  "guidance_scale": guidance_scale,
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  "num_inference_steps": num_inference_steps,
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  "generator": generator,
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+ "timesteps": sampling_schedule,
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  "output_type": "pil",
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  }
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  if use_resolution_binning: