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import os
from huggingface_hub import snapshot_download, delete_repo, metadata_update
import uuid
import json
import yaml
import subprocess
HF_TOKEN = os.environ.get("HF_TOKEN")
HF_DATASET = os.environ.get("DATA_PATH")
def download_dataset(hf_dataset_path: str):
random_id = str(uuid.uuid4())
snapshot_download(
repo_id=hf_dataset_path,
token=HF_TOKEN,
local_dir=f"/tmp/{random_id}",
repo_type="dataset",
)
return f"/tmp/{random_id}"
def process_dataset(dataset_dir: str):
# dataset dir consists of images, config.yaml and a metadata.jsonl (optional) with fields: file_name, prompt
# generate .txt files with the same name as the images with the prompt as the content
# remove metadata.jsonl
# return the path to the processed dataset
# check if config.yaml exists
if not os.path.exists(os.path.join(dataset_dir, "config.yaml")):
raise ValueError("config.yaml does not exist")
# check if metadata.jsonl exists
if os.path.exists(os.path.join(dataset_dir, "metadata.jsonl")):
metadata = []
with open(os.path.join(dataset_dir, "metadata.jsonl"), "r") as f:
for line in f:
if len(line.strip()) > 0:
metadata.append(json.loads(line))
for item in metadata:
txt_path = os.path.join(dataset_dir, item["file_name"])
txt_path = txt_path.rsplit(".", 1)[0] + ".txt"
with open(txt_path, "w") as f:
f.write(item["prompt"])
# remove metadata.jsonl
os.remove(os.path.join(dataset_dir, "metadata.jsonl"))
with open(os.path.join(dataset_dir, "config.yaml"), "r") as f:
config = yaml.safe_load(f)
# update config with new dataset
config["config"]["process"][0]["datasets"][0]["folder_path"] = dataset_dir
with open(os.path.join(dataset_dir, "config.yaml"), "w") as f:
yaml.dump(config, f)
return dataset_dir
def run_training(hf_dataset_path: str):
dataset_dir = download_dataset(hf_dataset_path)
dataset_dir = process_dataset(dataset_dir)
# run training
commands = "git clone https://github.com/ostris/ai-toolkit.git ai-toolkit && cd ai-toolkit && git submodule update --init --recursive"
subprocess.run(commands, shell=True)
commands = f"python run.py {os.path.join(dataset_dir, 'config.yaml')}"
process = subprocess.Popen(commands, shell=True, cwd="ai-toolkit", env=os.environ)
return process, dataset_dir
if __name__ == "__main__":
process, dataset_dir = run_training(HF_DATASET)
process.wait() # Wait for the training process to finish
with open(os.path.join(dataset_dir, "config.yaml"), "r") as f:
config = yaml.safe_load(f)
repo_id = config["config"]["process"][0]["save"]["hf_repo_id"]
metadata = {
"tags": [
"autotrain",
"spacerunner",
"text-to-image",
"flux",
"lora",
"diffusers",
"template:sd-lora",
]
}
metadata_update(repo_id, metadata, token=HF_TOKEN, repo_type="model", overwrite=True)
delete_repo(HF_DATASET, token=HF_TOKEN, repo_type="dataset", missing_ok=True)
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