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comment out customer_attention_context
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@ -59,12 +59,15 @@ def get_uc_and_c_and_ec(prompt_string,
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if log_tokens or getattr(Globals, "log_tokenization", False):
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if log_tokens or getattr(Globals, "log_tokenization", False):
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log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
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log_tokenization(positive_prompt, negative_prompt, tokenizer=model.tokenizer)
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with InvokeAIDiffuserComponent.custom_attention_context(model.unet,
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# The below has been commented out as it is an instance method used for cleanly loading LoRA models, but is not currently needed.
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extra_conditioning_info=None,
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# TODO: Reimplement custom_attention for 3.0 support of LoRA.
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step_count=-1):
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c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
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# with InvokeAIDiffuserComponent.custom_attention_context(model.unet,
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uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
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# extra_conditioning_info=None,
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[c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
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# step_count=-1):
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# c, options = compel.build_conditioning_tensor_for_prompt_object(positive_prompt)
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# uc, _ = compel.build_conditioning_tensor_for_prompt_object(negative_prompt)
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# [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
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# now build the "real" ec
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# now build the "real" ec
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ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
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ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(tokens_count_including_eos_bos=tokens_count,
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