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config.yaml

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+config:
+  name: myanmar-kathein-festival-cartoon
+  process:
+  - datasets:
+    - cache_latents_to_disk: true
+      caption_dropout_rate: 0.05
+      caption_ext: txt
+      folder_path: datasets/2671791f-966a-4502-a163-5e9cf151cc87
+      resolution:
+      - 512
+      - 768
+      - 1024
+      shuffle_tokens: false
+    device: cuda:0
+    model:
+      assistant_lora_path: ostris/FLUX.1-schnell-training-adapter
+      is_flux: true
+      low_vram: true
+      name_or_path: black-forest-labs/FLUX.1-schnell
+      quantize: true
+    network:
+      linear: 16
+      linear_alpha: 16
+      type: lora
+    sample:
+      guidance_scale: 4
+      height: 1024
+      neg: ''
+      prompts:
+      - people are celebrate festival, kathein festival cartoon
+      sample_every: 1000
+      sample_steps: 4
+      sampler: flowmatch
+      seed: 42
+      walk_seed: true
+      width: 1024
+    save:
+      dtype: float16
+      hf_private: true
+      hf_repo_id: shweaung/myanmar-kathein-festival-cartoon
+      max_step_saves_to_keep: 4
+      push_to_hub: true
+      save_every: 10000
+    train:
+      batch_size: 1
+      disable_sampling: false
+      dtype: bf16
+      ema_config:
+        ema_decay: 0.99
+        use_ema: true
+      gradient_accumulation_steps: 1
+      gradient_checkpointing: true
+      lr: 0.0004
+      noise_scheduler: flowmatch
+      optimizer: adamw8bit
+      skip_first_sample: true
+      steps: 1000
+      train_text_encoder: false
+      train_unet: true
+    training_folder: output
+    trigger_word: kathein festival cartoon
+    type: sd_trainer
+job: extension
+meta:
+  name: '[name]'
+  version: '1.0'

metadata.jsonl

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+{"file_name": "images 67.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 66.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 65.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 64.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 63.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 62.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 61.jpeg", "prompt": "[trigger]"}
+{"file_name": "\u1000\u1011\u1014\u1015\u1010 Kahtein Festival large.png_1713719538.png", "prompt": "[trigger]"}
+{"file_name": "images 59.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 60.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 58.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 56.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 55.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 54.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 52.jpeg", "prompt": "[trigger]"}
+{"file_name": "images 51.jpeg", "prompt": "[trigger]"}

requirements.txt

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+git+https://github.com/huggingface/diffusers.git
+lycoris-lora==1.8.3
+flatten_json
+pyyaml
+oyaml
+tensorboard
+kornia
+invisible-watermark
+einops
+toml
+albumentations
+pydantic
+omegaconf
+k-diffusion
+open_clip_torch
+prodigyopt
+controlnet_aux==0.0.7
+python-dotenv
+lpips
+pytorch_fid
+optimum-quanto

script.py

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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 checkout bc693488eb3cf48ded8bc2af845059d80f4cf7d0 && 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)