Text-to-Image
Diffusers
flux
flux-diffusers
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
lycoris
| license: other | |
| base_model: "terminusresearch/FluxBooru-v0.3" | |
| tags: | |
| - flux | |
| - flux-diffusers | |
| - text-to-image | |
| - diffusers | |
| - simpletuner | |
| - not-for-all-audiences | |
| - lora | |
| - template:sd-lora | |
| - lycoris | |
| inference: true | |
| widget: | |
| - text: 'unconditional (blank prompt)' | |
| parameters: | |
| negative_prompt: 'blurry, cropped, ugly' | |
| output: | |
| url: ./assets/image_0_0.png | |
| - text: 'a figure standing on a rocky terrain, holding a long object, possibly a spear or staff, raised high above their head. The figure is clad in what appears to be heavy, textured clothing or armor. The background features a light, cloudy sky, with the landscape suggesting a barren, mountainous region. The figure''s stance suggests a moment of triumph or challenge.' | |
| parameters: | |
| negative_prompt: 'blurry, cropped, ugly' | |
| output: | |
| url: ./assets/image_1_0.png | |
| # lora-training | |
| This is a LyCORIS adapter derived from [terminusresearch/FluxBooru-v0.3](https://huggingface.co/terminusresearch/FluxBooru-v0.3). | |
| The main validation prompt used during training was: | |
| ``` | |
| a figure standing on a rocky terrain, holding a long object, possibly a spear or staff, raised high above their head. The figure is clad in what appears to be heavy, textured clothing or armor. The background features a light, cloudy sky, with the landscape suggesting a barren, mountainous region. The figure's stance suggests a moment of triumph or challenge. | |
| ``` | |
| ## Validation settings | |
| - CFG: `3.0` | |
| - CFG Rescale: `0.0` | |
| - Steps: `20` | |
| - Sampler: `FlowMatchEulerDiscreteScheduler` | |
| - Seed: `42` | |
| - Resolution: `1024x1024` | |
| - Skip-layer guidance: | |
| Note: The validation settings are not necessarily the same as the [training settings](#training-settings). | |
| You can find some example images in the following gallery: | |
| <Gallery /> | |
| The text encoder **was not** trained. | |
| You may reuse the base model text encoder for inference. | |
| ## Training settings | |
| - Training epochs: 15 | |
| - Training steps: 1000 | |
| - Learning rate: 0.001 | |
| - Learning rate schedule: polynomial | |
| - Warmup steps: 100 | |
| - Max grad norm: 0.01 | |
| - Effective batch size: 2 | |
| - Micro-batch size: 2 | |
| - Gradient accumulation steps: 1 | |
| - Number of GPUs: 1 | |
| - Gradient checkpointing: True | |
| - Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=3.5', 'flow_matching_loss=compatible']) | |
| - Optimizer: adamw_bf16 | |
| - Trainable parameter precision: Pure BF16 | |
| - Caption dropout probability: 0.0% | |
| ### LyCORIS Config: | |
| ```json | |
| { | |
| "algo": "lokr", | |
| "bypass_mode": true, | |
| "multiplier": 1.0, | |
| "full_matrix": true, | |
| "linear_dim": 10000, | |
| "linear_alpha": 1, | |
| "factor": 12, | |
| "apply_preset": { | |
| "target_module": [ | |
| "Attention", | |
| "FeedForward" | |
| ], | |
| "module_algo_map": { | |
| "Attention": { | |
| "factor": 12 | |
| }, | |
| "FeedForward": { | |
| "factor": 6 | |
| } | |
| } | |
| } | |
| } | |
| ``` | |
| ## Datasets | |
| ### dy_banzhangcaogao_ST-1024 | |
| - Repeats: 0 | |
| - Total number of images: 43 | |
| - Total number of aspect buckets: 1 | |
| - Resolution: 1.048576 megapixels | |
| - Cropped: False | |
| - Crop style: None | |
| - Crop aspect: None | |
| - Used for regularisation data: No | |
| ### dy_banzhangcaogao_ST-768 | |
| - Repeats: 0 | |
| - Total number of images: 43 | |
| - Total number of aspect buckets: 1 | |
| - Resolution: 0.589824 megapixels | |
| - Cropped: False | |
| - Crop style: None | |
| - Crop aspect: None | |
| - Used for regularisation data: No | |
| ### dy_banzhangcaogao_ST-512 | |
| - Repeats: 0 | |
| - Total number of images: 43 | |
| - Total number of aspect buckets: 1 | |
| - Resolution: 0.262144 megapixels | |
| - Cropped: False | |
| - Crop style: None | |
| - Crop aspect: None | |
| - Used for regularisation data: No | |
| ## Inference | |
| ```python | |
| import torch | |
| from diffusers import DiffusionPipeline | |
| from lycoris import create_lycoris_from_weights | |
| def download_adapter(repo_id: str): | |
| import os | |
| from huggingface_hub import hf_hub_download | |
| adapter_filename = "pytorch_lora_weights.safetensors" | |
| cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models')) | |
| cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_") | |
| path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path) | |
| path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename) | |
| os.makedirs(path_to_adapter, exist_ok=True) | |
| hf_hub_download( | |
| repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter | |
| ) | |
| return path_to_adapter_file | |
| model_id = 'terminusresearch/FluxBooru-v0.3' | |
| adapter_repo_id = 'uxoah/lora-training' | |
| adapter_filename = 'pytorch_lora_weights.safetensors' | |
| adapter_file_path = download_adapter(repo_id=adapter_repo_id) | |
| pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 | |
| lora_scale = 1.0 | |
| wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer) | |
| wrapper.merge_to() | |
| prompt = "a figure standing on a rocky terrain, holding a long object, possibly a spear or staff, raised high above their head. The figure is clad in what appears to be heavy, textured clothing or armor. The background features a light, cloudy sky, with the landscape suggesting a barren, mountainous region. The figure's stance suggests a moment of triumph or challenge." | |
| ## Optional: quantise the model to save on vram. | |
| ## Note: The model was quantised during training, and so it is recommended to do the same during inference time. | |
| from optimum.quanto import quantize, freeze, qint8 | |
| quantize(pipeline.transformer, weights=qint8) | |
| freeze(pipeline.transformer) | |
| pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level | |
| image = pipeline( | |
| prompt=prompt, | |
| num_inference_steps=20, | |
| generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42), | |
| width=1024, | |
| height=1024, | |
| guidance_scale=3.0, | |
| ).images[0] | |
| image.save("output.png", format="PNG") | |
| ``` | |
| ## Exponential Moving Average (EMA) | |
| SimpleTuner generates a safetensors variant of the EMA weights and a pt file. | |
| The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning. | |
| The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights. | |