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modified: src/flux/modules/conditioner.py

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  1. src/flux/__pycache__/__init__.cpython-310.pyc +0 -0
  2. src/flux/__pycache__/_version.cpython-310.pyc +0 -0
  3. src/flux/__pycache__/math.cpython-310.pyc +0 -0
  4. src/flux/__pycache__/model.cpython-310.pyc +0 -0
  5. src/flux/__pycache__/sampling.cpython-310.pyc +0 -0
  6. src/flux/__pycache__/util.cpython-310.pyc +0 -0
  7. src/flux/modules/__pycache__/autoencoder.cpython-310.pyc +0 -0
  8. src/flux/modules/__pycache__/conditioner.cpython-310.pyc +0 -0
  9. src/flux/modules/__pycache__/layers.cpython-310.pyc +0 -0
  10. src/flux/modules/conditioner.py +0 -1
  11. src/flux/util.py +1 -1
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src/flux/modules/conditioner.py CHANGED
@@ -15,7 +15,6 @@ class HFEmbedder(nn.Module):
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  self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
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  else:
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  self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length)
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- #self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained("black-forest-labs/FLUX.1-dev/tokenizer_2", max_length=max_length)
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  self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs)
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  self.hf_module = self.hf_module.eval().requires_grad_(False)
 
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  self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
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  else:
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  self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length)
 
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  self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs)
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  self.hf_module = self.hf_module.eval().requires_grad_(False)
src/flux/util.py CHANGED
@@ -131,7 +131,7 @@ def load_flow_model(name: str, device: str | torch.device = "cuda", hf_download:
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  def load_t5(device: str | torch.device = "cuda", max_length: int = 512) -> HFEmbedder:
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  # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
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- return HFEmbedder("google/t5-v1_1-xxl", max_length=77, is_clip=False, torch_dtype=torch.bfloat16).to(device)
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  #return HFEmbedder("/homedata/HuggingFace/black-forest-labs/FLUX.1-dev/text_encoder_2", max_length=max_length, is_clip=False, torch_dtype=torch.bfloat16).to(device)
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  def load_t5(device: str | torch.device = "cuda", max_length: int = 512) -> HFEmbedder:
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  # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
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+ return HFEmbedder("google/t5-v1_1-xxl", max_length=max_length, is_clip=False, torch_dtype=torch.bfloat16).to(device)
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  #return HFEmbedder("/homedata/HuggingFace/black-forest-labs/FLUX.1-dev/text_encoder_2", max_length=max_length, is_clip=False, torch_dtype=torch.bfloat16).to(device)
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