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[fix] fix for prior preservation and mixed precision sampling #11873

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[fix] fix for prior preservation and mixed precision sampling #11873

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8 changes: 7 additions & 1 deletion examples/dreambooth/train_dreambooth_lora_flux_kontext.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1193,6 +1193,7 @@ def main(args):
subfolder="transformer",
revision=args.revision,
variant=args.variant,
torch_dtype=torch_dtype,
)
pipeline = FluxKontextPipeline.from_pretrained(
args.pretrained_model_name_or_path,
Expand All @@ -1215,7 +1216,8 @@ 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()
Expand Down Expand Up @@ -1789,6 +1791,10 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
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:
Expand Down