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Textual inv save log memory (#2184)

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* Quality check and adding tokenizer

* Adapted stable diffusion to mixed precision+finished up style fixes

* Fixed based on patrick's review

* Fixed oom from number of validation images

* Removed unnecessary np.array conversion

---------

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
This commit is contained in:
Isamu Isozaki
2023-02-08 18:37:10 +09:00
committed by GitHub
parent 41db2dbf90
commit c1971a53bc
1 changed files with 8 additions and 2 deletions
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10
examples/textual_inversion/textual_inversion.py
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@@ -781,7 +781,10 @@ def main():
args.pretrained_model_name_or_path,
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
unet=unet,
vae=vae,
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
@@ -791,8 +794,11 @@ def main():
generator = (
None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
)
prompt = args.num_validation_images * [args.validation_prompt]
images = pipeline(prompt, num_inference_steps=25, generator=generator).images
images = []
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
images.append(image)
for tracker in accelerator.trackers:
if tracker.name == "tensorboard":