--- license: creativeml-openrail-m base_model: "black-forest-labs/FLUX.1-dev" tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - simpletuner - lora - template:sd-lora inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: '''' output: url: ./assets/image_0_0.png - text: 'anime style digital art of a girl with blue-green hair and green eyes wearing a one piece swimsuit' parameters: negative_prompt: '''' output: url: ./assets/image_1_0.png --- # lora-anime-test-02 This is a LoRA 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: ``` anime style digital art of a girl with blue-green hair and green eyes wearing a one piece swimsuit ``` ## Validation settings - CFG: `3.5` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `42` - Resolution: `1024` 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: 142 - Training steps: 5000 - Learning rate: 0.0001 - Effective batch size: 1 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 1 - Prediction type: flow-matching - Rescaled betas zero SNR: False - Optimizer: adamw_bf16 - Precision: bf16 - Quantised: Yes: int8-quanto - Xformers: Not used - LoRA Rank: 4 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### anime-test-01 - Repeats: 0 - Total number of images: 35 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'Disra/lora-anime-test-02' pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.load_lora_weights(adapter_id) prompt = "anime style digital art of a girl with blue-green hair and green eyes wearing a one piece swimsuit" pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') 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=3.5, ).images[0] image.save("output.png", format="PNG") ```