--- license: other base_model: "black-forest-labs/FLUX.1-dev" 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: 'The La Mer Treatment Lotion bottle rests on a polished glass surface with a glowing mist swirling around it. Soft oceanic hues of deep blue and aquamarine radiate in the background, creating an ethereal and luxurious atmosphere.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'A macro shot of the La Mer Treatment Lotion being dispensed. Its silky, translucent texture glistens under soft, diffused light against a minimalist white background with subtle ocean ripple effects.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'The La Mer Treatment Lotion bottle is placed on a smooth coastal rock surrounded by fresh sea kelp and gently flowing water. The sunlight filters through a soft morning haze, evoking natural purity and serenity.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'The La Mer Treatment Lotion bottle is set against a minimalist, abstract backdrop of a glowing world map. Subtle ocean-inspired hues and soft lighting emphasize universal luxury and timeless appeal.' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'A macro shot of the La Mer Treatment Lotion being dispensed. Its silky, translucent texture glistens under soft, diffused light against a minimalist white background with subtle ocean ripple effects' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png --- # simpletuner-lora This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). The main validation prompt used during training was: ``` A macro shot of the La Mer Treatment Lotion being dispensed. Its silky, translucent texture glistens under soft, diffused light against a minimalist white background with subtle ocean ripple effects ``` ## Validation settings - CFG: `3.0` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024x1024` 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: The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 0 - Training steps: 15 - Learning rate: 0.0001 - Max grad norm: 1.0 - Effective batch size: 3 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 3 - Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_value=1.0']) - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: Pure BF16 - Quantised: Yes: int8-quanto - Xformers: Not used - LyCORIS Config: ```json { "algo": "lokr", "multiplier": 1.0, "linear_dim": 10000, "linear_alpha": 1, "factor": 16, "apply_preset": { "target_module": [ "Attention", "FeedForward" ], "module_algo_map": { "Attention": { "factor": 16 }, "FeedForward": { "factor": 8 } } } } ``` ## Datasets ### La_Mer_Treatment_Lotion_FLUX_v01 - Repeats: 1000 - Total number of images: ~36 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square - 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 = 'black-forest-labs/FLUX.1-dev' adapter_repo_id = 'salmonrusty/simpletuner-lora' 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 macro shot of the La Mer Treatment Lotion being dispensed. Its silky, translucent texture glistens under soft, diffused light against a minimalist white background with subtle ocean ripple effects" ## 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(1641421826), width=1024, height=1024, guidance_scale=3.0, ).images[0] image.save("output.png", format="PNG") ```