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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 +7 -5
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src/flux/__pycache__/model.cpython-310.pyc CHANGED
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src/flux/__pycache__/util.cpython-310.pyc CHANGED
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src/flux/modules/__pycache__/autoencoder.cpython-310.pyc CHANGED
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src/flux/modules/__pycache__/conditioner.cpython-310.pyc CHANGED
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src/flux/modules/__pycache__/layers.cpython-310.pyc CHANGED
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src/flux/modules/conditioner.py CHANGED
@@ -1,6 +1,6 @@
1
  from torch import Tensor, nn
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  from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
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- T5Tokenizer, AutoConfig)
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  import os
5
 
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  class HFEmbedder(nn.Module):
@@ -12,9 +12,7 @@ class HFEmbedder(nn.Module):
12
 
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  if self.is_clip:
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  self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
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- config = AutoConfig.from_pretrained(version, max_position_embeddings=77)
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- self.hf_module = CLIPTextModel.from_pretrained(version, config=config, **hf_kwargs)
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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)
@@ -33,8 +31,12 @@ class HFEmbedder(nn.Module):
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  return_tensors="pt",
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  )
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  outputs = self.hf_module(
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- input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
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  attention_mask=None,
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  output_hidden_states=False,
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  )
 
1
  from torch import Tensor, nn
2
  from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
3
+ T5Tokenizer)
4
  import os
5
 
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  class HFEmbedder(nn.Module):
 
12
 
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  if self.is_clip:
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  self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
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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)
 
31
  return_tensors="pt",
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  )
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+ input_ids = batch_encoding["input_ids"]
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+ print(f"input_ids shape: {input_ids.shape}, max_length: {self.max_length}") # Debug
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+ assert input_ids.shape[1] == 77, f"Sequence length {input_ids.shape[1]} exceeds max_length 77"
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+
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  outputs = self.hf_module(
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+ input_ids=input_ids.to(self.hf_module.device),
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  attention_mask=None,
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  output_hidden_states=False,
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  )