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fix inference in custom diffusion #5329

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Merged
merged 4 commits into from
Oct 9, 2023
Merged

fix inference in custom diffusion #5329

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b020af1
Update train_custom_diffusion.py
caopulan Oct 7, 2023
d7abbf7
Merge branch 'main' into patch-1
sayakpaul Oct 8, 2023
6cfd46f
make style
caopulan Oct 8, 2023
ce7147d
Empty-Commit
sayakpaul Oct 9, 2023
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88 changes: 45 additions & 43 deletions examples/custom_diffusion/train_custom_diffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,7 +207,7 @@ def __init__(
with open(concept["class_prompt"], "r") as f:
class_prompt = f.read().splitlines()

class_img_path = [(x, y) for (x, y) in zip(class_images_path, class_prompt)]
class_img_path = list(zip(class_images_path, class_prompt))
self.class_images_path.extend(class_img_path[:num_class_images])

random.shuffle(self.instance_images_path)
Expand Down Expand Up @@ -1214,50 +1214,52 @@ def main(args):
if global_step >= args.max_train_steps:
break

if accelerator.is_main_process:
images = []
if accelerator.is_main_process:
images = []

if args.validation_prompt is not None and global_step % args.validation_steps == 0:
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)

# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[0]
for _ in range(args.num_validation_images)
]

for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
if args.validation_prompt is not None and global_step % args.validation_steps == 0:
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
# create pipeline
pipeline = DiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
tokenizer=tokenizer,
revision=args.revision,
torch_dtype=weight_dtype,
)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)

del pipeline
torch.cuda.empty_cache()
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator, eta=1.0).images[
0
]
for _ in range(args.num_validation_images)
]

for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"validation": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)

del pipeline
torch.cuda.empty_cache()

# Save the custom diffusion layers
accelerator.wait_for_everyone()
Expand Down