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[Flux LoRA] fix for prior preservation and mixed precision sampling, follow up on #11873 (#12264)

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* propagate fixes from https://github.com/huggingface/diffusers/pull/11873/ to flux script

* propagate fixes from https://github.com/huggingface/diffusers/pull/11873/ to flux script

* propagate fixes from https://github.com/huggingface/diffusers/pull/11873/ to flux script

* Apply style fixes

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Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This commit is contained in:
Linoy Tsaban
2025-09-02 11:30:33 +03:00
committed by GitHub
parent 9e4a75b142
commit 006d092751
2 changed files with 11 additions and 4 deletions
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4
examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
View File

@@ -1399,6 +1399,7 @@ def main(args):
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = FluxPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
@@ -1419,7 +1420,8 @@ def main(args):
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
with torch.autocast(device_type=accelerator.device.type, dtype=torch_dtype):
images = pipeline(prompt=example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()

11
examples/dreambooth/train_dreambooth_lora_flux.py
View File

@@ -1131,6 +1131,7 @@ def main(args):
torch_dtype = torch.float16
elif args.prior_generation_precision == "bf16":
torch_dtype = torch.bfloat16
pipeline = FluxPipeline.from_pretrained(
args.pretrained_model_name_or_path,
torch_dtype=torch_dtype,
@@ -1151,7 +1152,8 @@ def main(args):
for example in tqdm(
sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
):
images = pipeline(example["prompt"]).images
with torch.autocast(device_type=accelerator.device.type, dtype=torch_dtype):
images = pipeline(prompt=example["prompt"]).images
for i, image in enumerate(images):
hash_image = insecure_hashlib.sha1(image.tobytes()).hexdigest()
@@ -1159,8 +1161,7 @@ def main(args):
image.save(image_filename)
del pipeline
if torch.cuda.is_available():
torch.cuda.empty_cache()
free_memory()
# Handle the repository creation
if accelerator.is_main_process:
@@ -1728,6 +1729,10 @@ def main(args):
device=accelerator.device,
prompt=args.instance_prompt,
)
else:
prompt_embeds, pooled_prompt_embeds, text_ids = compute_text_embeddings(
prompts, text_encoders, tokenizers
)
# Convert images to latent space
if args.cache_latents: