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[Training] make DreamBooth SDXL LoRA training script compatible with torch.compile #6483

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Merged
merged 4 commits into from
Jan 9, 2024
Merged

[Training] make DreamBooth SDXL LoRA training script compatible with torch.compile #6483

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eb9dfc6
make it torch.compile comaptible
sayakpaul Jan 8, 2024
e035a0d
make the text encoder compatible too.
sayakpaul Jan 8, 2024
ddcd43d
style
sayakpaul Jan 8, 2024
cbdb12c
Merge branch 'main' into training/torch-compile
sayakpaul Jan 9, 2024
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16 changes: 10 additions & 6 deletions examples/dreambooth/train_dreambooth_lora_sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -780,13 +780,12 @@ def encode_prompt(text_encoders, tokenizers, prompt, text_input_ids_list=None):
text_input_ids = text_input_ids_list[i]

prompt_embeds = text_encoder(
text_input_ids.to(text_encoder.device),
output_hidden_states=True,
text_input_ids.to(text_encoder.device), output_hidden_states=True, return_dict=False
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Why do we need to add output_hidden_states=True here?

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It was already there. Look again, please.

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True here too:

diffusers/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

Line 415 in aa1797e

prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)

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Ah sorry yeah indeed we need it! Thanks for double-checking

)

# We are only ALWAYS interested in the pooled output of the final text encoder
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
prompt_embeds = prompt_embeds[-1][-2]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
prompt_embeds_list.append(prompt_embeds)
Expand Down Expand Up @@ -1429,7 +1428,8 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
timesteps,
prompt_embeds_input,
added_cond_kwargs=unet_added_conditions,
).sample
return_dict=False,
)[0]
else:
unet_added_conditions = {"time_ids": add_time_ids.repeat(elems_to_repeat_time_ids, 1)}
prompt_embeds, pooled_prompt_embeds = encode_prompt(
Expand All @@ -1443,8 +1443,12 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
)
prompt_embeds_input = prompt_embeds.repeat(elems_to_repeat_text_embeds, 1, 1)
model_pred = unet(
noisy_model_input, timesteps, prompt_embeds_input, added_cond_kwargs=unet_added_conditions
).sample
noisy_model_input,
timesteps,
prompt_embeds_input,
added_cond_kwargs=unet_added_conditions,
return_dict=False,
)[0]

# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
Expand Down