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comment out customer_attention_context

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This commit is contained in:
Kent Keirsey 2023-05-12 13:59:00 -04:00
parent af060188bd
commit 8f8cd90787
1 changed files with 9 additions and 6 deletions
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15
invokeai/backend/prompting/conditioning.py
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@ -59,12 +59,15 @@ def get_uc_and_c_and_ec(prompt_string,
if log_tokens or getattr(Globals, "log_tokenization", False):
log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
with InvokeAIDiffuserComponent.custom_attention_context(model.unet,
extra_conditioning_info=None,
step_count=-1):
c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
[c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
# The below has been commented out as it is an instance method used for cleanly loading LoRA models, but is not currently needed.
# TODO: Reimplement custom_attention for 3.0 support of LoRA.
# with InvokeAIDiffuserComponent.custom_attention_context(model.unet,
# extra_conditioning_info=None,
# step_count=-1):
# c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
# uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
# now build the "real" ec
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,