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--- |
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license: other |
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base_model: black-forest-labs/FLUX.1-dev |
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tags: |
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- flux |
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- flux-diffusers |
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- text-to-image |
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- diffusers |
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- simpletuner |
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- safe-for-work |
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- lora |
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- template:sd-lora |
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- standard |
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inference: true |
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widget: |
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- text: unconditional (blank prompt) |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_0_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A steaming bowl of ramen with |
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chopsticks resting on the edge, against a background of concentric orange |
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and blue circles. The noodles are detailed in a geometric pattern and the |
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steam creates a rhythmic design. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_1_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A vintage record player with vinyl |
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spinning, set on a yellow table. The background features an alternating |
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chevron pattern in purple and green. The turntable's mechanical parts are |
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rendered in precise geometric shapes. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_2_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A sleeping cat curled up in a modernist |
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chair, with a background of interlocking hexagons in red and blue. The cat's |
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fur is stylized into rhythmic curves, matching the geometric environment. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_3_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A classic motorcycle viewed from the |
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side, against a backdrop of radiating diamond patterns in teal and gold. The |
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chrome parts reflect abstract shapes, and the wheels create circular motifs |
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in the composition. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_4_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, Portrait of a woman with silver hair |
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wearing dotted blue glasses and a white lace collar, against a swirling |
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background of green and yellow patterns. The background features geometric |
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circles and zigzag designs. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_5_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A storefront sign for 'Golden Palace |
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Noodles' in both English and Chinese characters, mounted on a tall pole |
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against a geometric cityscape with blue and tan buildings. A small arrow |
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points to available parking. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_6_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, Dark purple figs sliced in half on a |
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terra cotta plate, revealing their seeded interiors. The background features |
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a repeating pattern of blue and yellow squares, with wavy lines creating a |
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dynamic lower section. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_7_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, Two young people wearing matching navy |
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shirts and light gray face masks, posed against a warm yellow background. |
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Their curly hair and gentle head tilts create a symmetrical composition. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_8_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A hamster wearing tiny glasses and a |
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bowtie sitting at a miniature desk with a tiny laptop, against a background |
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of spiral patterns in teal and orange. Office supplies scaled to |
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hamster-size are arranged neatly on the desk. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_9_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A bearded man in a plaid shirt and |
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denim apron carefully sanding a mid-century modern chair, surrounded by |
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woodworking tools. The background features overlapping triangles in rust and |
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navy blue colors, with sawdust creating delicate patterns in the air. |
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parameters: |
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negative_prompt: blurry, cropped, ugly |
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output: |
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url: ./assets/image_10_0.png |
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- text: >- |
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In the style of a b3nbr4nd painting, windows, hanging lights, cube shapes, |
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night sky, interior, glass walls, cityscape view, reflections, indoor |
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lighting, architectural design, ceiling grid, geometric shapes, open space, |
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floor reflection, frame structures, exterior environment, horizon, enclosed |
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glass room, symmetrical arrangement. |
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output: |
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url: images/example_1o217307s.png |
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- text: >- |
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In the style of a b3nbr4nd painting, A light-skinned, blonde-haired |
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character with blue-green eyes is partially hidden behind a dark brown |
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wooden easel, holding a thin paintbrush in their right hand. They are |
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wearing a dark grey long-sleeved shirt and are positioned in a room with a |
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window showing greenery outside. Sunlight appears to be coming through the |
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window, casting a warm light on the scenes |
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output: |
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url: images/example_ucr8z54gk.png |
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- text: >- |
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In the style of a b3nbr4nd painting, snake, black snakes, desert, cacti, |
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cactus, prickly pear cactus, yucca plant, orange eye, mountains, purple |
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mountains, blue sky, green cactus, yellow desert, close-up view, coiled |
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snake, landscape, arid environment, vegetation, plant, side view, foreground |
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snake, background mountains |
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output: |
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url: images/example_dzflrmjnh.png |
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- text: >- |
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In the style of a b3nbr4nd painting, elephant, blue elephant, pink earz, |
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yellow stars on ears, tusks, LV logo on forehead, standing in water, night |
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sky, stars, hills in background, orange sun, grassy hills, river, centered |
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composition, foreground elephant, background hills, detailed ears, yellow |
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tusks |
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output: |
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url: images/example_qcn3jzw0k.png |
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--- |
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# Ben-Brand-LoRA |
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This is a standard PEFT LoRA derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev). |
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No validation prompt was used during training. |
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None |
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## Validation settings |
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- CFG: `3.0` |
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- CFG Rescale: `0.0` |
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- Steps: `20` |
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- Sampler: `FlowMatchEulerDiscreteScheduler` |
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- Seed: `42` |
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- Resolution: `1024x1024` |
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- Skip-layer guidance: |
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Note: The validation settings are not necessarily the same as the [training settings](#training-settings). |
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You can find some example images in the following gallery: |
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<Gallery /> |
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The text encoder **was not** trained. |
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You may reuse the base model text encoder for inference. |
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## Training settings |
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- Training epochs: 1 |
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- Training steps: 3000 |
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- Learning rate: 0.0001 |
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- Learning rate schedule: polynomial |
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- Warmup steps: 100 |
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- Max grad norm: 0.1 |
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- Effective batch size: 6 |
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- Micro-batch size: 2 |
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- Gradient accumulation steps: 3 |
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- Number of GPUs: 1 |
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- Gradient checkpointing: True |
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- 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']) |
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- Optimizer: adamw_bf16 |
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- Trainable parameter precision: Pure BF16 |
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- Caption dropout probability: 10.0% |
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- LoRA Rank: 64 |
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- LoRA Alpha: None |
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- LoRA Dropout: 0.1 |
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- LoRA initialisation style: default |
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## Datasets |
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### ben-brand-256 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 6 |
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- Resolution: 0.065536 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### ben-brand-crop-256 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 1 |
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- Resolution: 0.065536 megapixels |
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- Cropped: True |
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- Crop style: center |
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- Crop aspect: square |
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- Used for regularisation data: No |
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### ben-brand-512 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 4 |
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- Resolution: 0.262144 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### ben-brand-crop-512 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 1 |
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- Resolution: 0.262144 megapixels |
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- Cropped: True |
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- Crop style: center |
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- Crop aspect: square |
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- Used for regularisation data: No |
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### ben-brand-768 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 3 |
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- Resolution: 0.589824 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### ben-brand-crop-768 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 1 |
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- Resolution: 0.589824 megapixels |
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- Cropped: True |
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- Crop style: center |
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- Crop aspect: square |
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- Used for regularisation data: No |
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### ben-brand-1024 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 2 |
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- Resolution: 1.048576 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### ben-brand-crop-1024 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 1 |
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- Resolution: 1.048576 megapixels |
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- Cropped: True |
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- Crop style: center |
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- Crop aspect: square |
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- Used for regularisation data: No |
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### ben-brand-1440 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 8 |
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- Resolution: 2.0736 megapixels |
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- Cropped: False |
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- Crop style: None |
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- Crop aspect: None |
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- Used for regularisation data: No |
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### ben-brand-crop-1440 |
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- Repeats: 10 |
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- Total number of images: 98 |
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- Total number of aspect buckets: 1 |
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- Resolution: 2.0736 megapixels |
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- Cropped: True |
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- Crop style: center |
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- Crop aspect: square |
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- Used for regularisation data: No |
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## Inference |
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```python |
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import torch |
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from diffusers import DiffusionPipeline |
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model_id = 'black-forest-labs/FLUX.1-dev' |
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adapter_id = 'davidrd123/Ben-Brand-LoRA' |
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pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 |
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pipeline.load_lora_weights(adapter_id) |
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prompt = "An astronaut is riding a horse through the jungles of Thailand." |
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## Optional: quantise the model to save on vram. |
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## Note: The model was quantised during training, and so it is recommended to do the same during inference time. |
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from optimum.quanto import quantize, freeze, qint8 |
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quantize(pipeline.transformer, weights=qint8) |
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freeze(pipeline.transformer) |
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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 |
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image = pipeline( |
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prompt=prompt, |
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num_inference_steps=20, |
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generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42), |
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width=1024, |
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height=1024, |
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guidance_scale=3.0, |
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).images[0] |
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image.save("output.png", format="PNG") |
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``` |
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