sayakpaul HF staff commited on
Commit
3e649c7
1 Parent(s): e0ae9da
Files changed (1) hide show
  1. my_pipeline.py +4 -13
my_pipeline.py CHANGED
@@ -27,12 +27,7 @@ from diffusers.loaders import (
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  from diffusers.models import AutoencoderKL
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  from .scheduler.my_scheduler import MyScheduler
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  from .unet.my_unet_model import MyUNetModel
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- from diffusers.models.attention_processor import (
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- AttnProcessor2_0,
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- LoRAAttnProcessor2_0,
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- LoRAXFormersAttnProcessor,
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- XFormersAttnProcessor,
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- )
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  from diffusers.models.lora import adjust_lora_scale_text_encoder
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  from diffusers.utils import (
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  USE_PEFT_BACKEND,
@@ -136,6 +131,7 @@ class MyPipeline(
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  watermark output images. If not defined, it will default to True if the package is installed, otherwise no
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  watermarker will be used.
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  """
 
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  model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
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  _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
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@@ -572,12 +568,7 @@ class MyPipeline(
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  self.vae.to(dtype=torch.float32)
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  use_torch_2_0_or_xformers = isinstance(
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  self.vae.decoder.mid_block.attentions[0].processor,
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- (
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- AttnProcessor2_0,
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- XFormersAttnProcessor,
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- LoRAXFormersAttnProcessor,
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- LoRAAttnProcessor2_0,
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- ),
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  )
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  # if xformers or torch_2_0 is used attention block does not need
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  # to be in float32 which can save lots of memory
@@ -972,4 +963,4 @@ class MyPipeline(
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  # Offload all models
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  self.maybe_free_model_hooks()
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- return (image,)
 
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  from diffusers.models import AutoencoderKL
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  from .scheduler.my_scheduler import MyScheduler
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  from .unet.my_unet_model import MyUNetModel
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+ from diffusers.models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
 
 
 
 
 
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  from diffusers.models.lora import adjust_lora_scale_text_encoder
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  from diffusers.utils import (
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  USE_PEFT_BACKEND,
 
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  watermark output images. If not defined, it will default to True if the package is installed, otherwise no
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  watermarker will be used.
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  """
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+
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  model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
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  _optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"]
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  self.vae.to(dtype=torch.float32)
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  use_torch_2_0_or_xformers = isinstance(
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  self.vae.decoder.mid_block.attentions[0].processor,
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+ (AttnProcessor2_0, XFormersAttnProcessor),
 
 
 
 
 
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  )
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  # if xformers or torch_2_0 is used attention block does not need
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  # to be in float32 which can save lots of memory
 
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  # Offload all models
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  self.maybe_free_model_hooks()
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+ return (image,)