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Modularize train_text_to_image_lora SD inferencing during and after training in example #8283

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merged 6 commits into from
May 29, 2024
Merged

Modularize train_text_to_image_lora SD inferencing during and after training in example #8283

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eacae10
Modularized the train_lora file
satani99 May 27, 2024
b25aa27
Modularized the train_lora file
satani99 May 27, 2024
b56ce83
Modularized the train_lora file
satani99 May 27, 2024
086ac9d
Modularized the train_lora file
satani99 May 28, 2024
a859ae5
Modularized the train_lora file
satani99 May 28, 2024
d1fd581
Merge branch 'main' into main
sayakpaul May 28, 2024
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146 changes: 61 additions & 85 deletions examples/text_to_image/train_text_to_image_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,9 @@
from diffusers.utils.torch_utils import is_compiled_module


if is_wandb_available():
import wandb

# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.28.0.dev0")

Expand Down Expand Up @@ -99,6 +102,48 @@ def save_model_card(
model_card.save(os.path.join(repo_folder, "README.md"))


def log_validation(
pipeline,
args,
accelerator,
epoch,
is_final_validation=False,
):
logger.info(
f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
f" {args.validation_prompt}."
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
if torch.backends.mps.is_available():
autocast_ctx = nullcontext()
else:
autocast_ctx = torch.autocast(accelerator.device.type)

with autocast_ctx:
for _ in range(args.num_validation_images):
images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])

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


def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
Expand Down Expand Up @@ -414,11 +459,6 @@ def main():
if torch.backends.mps.is_available():
accelerator.native_amp = False

if args.report_to == "wandb":
if not is_wandb_available():
raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
import wandb

# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
Expand Down Expand Up @@ -864,10 +904,6 @@ def collate_fn(examples):

if accelerator.is_main_process:
if args.validation_prompt is not None and epoch % args.validation_epochs == 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,
Expand All @@ -876,38 +912,7 @@ def collate_fn(examples):
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)
pipeline.set_progress_bar_config(disable=True)

# run inference
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
if torch.backends.mps.is_available():
autocast_ctx = nullcontext()
else:
autocast_ctx = torch.autocast(accelerator.device.type)

with autocast_ctx:
for _ in range(args.num_validation_images):
images.append(
pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
)

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)
]
}
)
images = log_validation(pipeline, args, accelerator, epoch)

del pipeline
torch.cuda.empty_cache()
Expand All @@ -925,21 +930,6 @@ def collate_fn(examples):
safe_serialization=True,
)

if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
dataset_name=args.dataset_name,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)

# Final inference
# Load previous pipeline
if args.validation_prompt is not None:
Expand All @@ -949,41 +939,27 @@ def collate_fn(examples):
variant=args.variant,
torch_dtype=weight_dtype,
)
pipeline = pipeline.to(accelerator.device)

# load attention processors
pipeline.load_lora_weights(args.output_dir)

# run inference
generator = torch.Generator(device=accelerator.device)
if args.seed is not None:
generator = generator.manual_seed(args.seed)
images = []
if torch.backends.mps.is_available():
autocast_ctx = nullcontext()
else:
autocast_ctx = torch.autocast(accelerator.device.type)

with autocast_ctx:
for _ in range(args.num_validation_images):
images.append(
pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0]
)
images = log_validation(pipeline, args, accelerator, epoch, is_final_validation=True)

for tracker in accelerator.trackers:
if len(images) != 0:
if tracker.name == "tensorboard":
np_images = np.stack([np.asarray(img) for img in images])
tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
if tracker.name == "wandb":
tracker.log(
{
"test": [
wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
for i, image in enumerate(images)
]
}
)
if args.push_to_hub:
save_model_card(
repo_id,
images=images,
base_model=args.pretrained_model_name_or_path,
dataset_name=args.dataset_name,
repo_folder=args.output_dir,
)
upload_folder(
repo_id=repo_id,
folder_path=args.output_dir,
commit_message="End of training",
ignore_patterns=["step_*", "epoch_*"],
)

accelerator.end_training()

Expand Down
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