Update train_text_to_image_lora.py (#2464)
* Update train_text_to_image_lora.py * Update train_text_to_image_lora.py
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@@ -448,19 +448,6 @@ def main():
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vae.to(accelerator.device, dtype=weight_dtype)
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text_encoder.to(accelerator.device, dtype=weight_dtype)
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if args.enable_xformers_memory_efficient_attention:
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if is_xformers_available():
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import xformers
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xformers_version = version.parse(xformers.__version__)
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if xformers_version == version.parse("0.0.16"):
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logger.warn(
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"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
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)
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unet.enable_xformers_memory_efficient_attention()
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else:
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raise ValueError("xformers is not available. Make sure it is installed correctly")
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# now we will add new LoRA weights to the attention layers
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# It's important to realize here how many attention weights will be added and of which sizes
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# The sizes of the attention layers consist only of two different variables:
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@@ -492,6 +479,20 @@ def main():
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)
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unet.set_attn_processor(lora_attn_procs)
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if args.enable_xformers_memory_efficient_attention:
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if is_xformers_available():
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import xformers
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xformers_version = version.parse(xformers.__version__)
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if xformers_version == version.parse("0.0.16"):
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logger.warn(
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"xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
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)
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unet.enable_xformers_memory_efficient_attention()
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else:
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raise ValueError("xformers is not available. Make sure it is installed correctly")
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lora_layers = AttnProcsLayers(unet.attn_processors)
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# Enable TF32 for faster training on Ampere GPUs,
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