--- license: other base_model: black-forest-labs/FLUX.1-dev tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - safe-for-work - lora - template:sd-lora - standard inference: true widget: - text: unconditional (blank prompt) parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_0_0.png - text: >- In the style of a b3nbr4nd painting, A steaming bowl of ramen with chopsticks resting on the edge, against a background of concentric orange and blue circles. The noodles are detailed in a geometric pattern and the steam creates a rhythmic design. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_1_0.png - text: >- In the style of a b3nbr4nd painting, A vintage record player with vinyl spinning, set on a yellow table. The background features an alternating chevron pattern in purple and green. The turntable's mechanical parts are rendered in precise geometric shapes. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_2_0.png - text: >- In the style of a b3nbr4nd painting, A sleeping cat curled up in a modernist chair, with a background of interlocking hexagons in red and blue. The cat's fur is stylized into rhythmic curves, matching the geometric environment. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_3_0.png - text: >- In the style of a b3nbr4nd painting, A classic motorcycle viewed from the side, against a backdrop of radiating diamond patterns in teal and gold. The chrome parts reflect abstract shapes, and the wheels create circular motifs in the composition. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_4_0.png - text: >- In the style of a b3nbr4nd painting, Portrait of a woman with silver hair wearing dotted blue glasses and a white lace collar, against a swirling background of green and yellow patterns. The background features geometric circles and zigzag designs. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_5_0.png - text: >- In the style of a b3nbr4nd painting, A storefront sign for 'Golden Palace Noodles' in both English and Chinese characters, mounted on a tall pole against a geometric cityscape with blue and tan buildings. A small arrow points to available parking. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_6_0.png - text: >- In the style of a b3nbr4nd painting, Dark purple figs sliced in half on a terra cotta plate, revealing their seeded interiors. The background features a repeating pattern of blue and yellow squares, with wavy lines creating a dynamic lower section. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_7_0.png - text: >- In the style of a b3nbr4nd painting, Two young people wearing matching navy shirts and light gray face masks, posed against a warm yellow background. Their curly hair and gentle head tilts create a symmetrical composition. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_8_0.png - text: >- In the style of a b3nbr4nd painting, A hamster wearing tiny glasses and a bowtie sitting at a miniature desk with a tiny laptop, against a background of spiral patterns in teal and orange. Office supplies scaled to hamster-size are arranged neatly on the desk. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_9_0.png - text: >- In the style of a b3nbr4nd painting, A bearded man in a plaid shirt and denim apron carefully sanding a mid-century modern chair, surrounded by woodworking tools. The background features overlapping triangles in rust and navy blue colors, with sawdust creating delicate patterns in the air. parameters: negative_prompt: blurry, cropped, ugly output: url: ./assets/image_10_0.png - text: In the style of a b3nbr4nd painting, wolf of the north output: url: images/example_hue95rjbg.png --- # Ben-Brand-LoRA This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). No validation prompt was used during training. None ## 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: The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 1 - Training steps: 2000 - Learning rate: 0.0001 - Learning rate schedule: polynomial - Warmup steps: 100 - Max grad norm: 0.1 - Effective batch size: 6 - Micro-batch size: 2 - Gradient accumulation steps: 3 - Number of GPUs: 1 - Gradient checkpointing: True - Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible', 'flux_lora_target=all']) - Optimizer: adamw_bf16 - Trainable parameter precision: Pure BF16 - Caption dropout probability: 10.0% - LoRA Rank: 64 - LoRA Alpha: None - LoRA Dropout: 0.1 - LoRA initialisation style: default ## Datasets ### ben-brand-256 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 0.065536 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ben-brand-crop-256 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 0.065536 megapixels - Cropped: True - Crop style: center - Crop aspect: square - Used for regularisation data: No ### ben-brand-512 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ben-brand-crop-512 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: True - Crop style: center - Crop aspect: square - Used for regularisation data: No ### ben-brand-768 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ben-brand-crop-768 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: True - Crop style: center - Crop aspect: square - Used for regularisation data: No ### ben-brand-1024 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 7 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ben-brand-crop-1024 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 1.048576 megapixels - Cropped: True - Crop style: center - Crop aspect: square - Used for regularisation data: No ### ben-brand-1440 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 2.0736 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### ben-brand-crop-1440 - Repeats: 10 - Total number of images: 98 - Total number of aspect buckets: 1 - Resolution: 2.0736 megapixels - Cropped: True - Crop style: center - Crop aspect: square - Used for regularisation data: No ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = 'black-forest-labs/FLUX.1-dev' adapter_id = 'davidrd123/Ben-Brand-LoRA' pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 pipeline.load_lora_weights(adapter_id) prompt = "An astronaut is riding a horse through the jungles of Thailand." ## 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") ```