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These are LoRA adaption weights for the FLUX.1 [dev] model (```black-forest-labs/FLUX.1-dev```). The base model is, and you must first get access to it before loading this LoRA adapter.
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This LoRA adapter has rank=64 and alpha=64, trained for
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## Trigger keywords
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The model was fine-tuned with a set of ~1,600 images of biological materials, structures, shapes and other images of nature, using the keyword
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You should use \<bioinspired\> to trigger these features during image generation.
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
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Image generation - Example #2:
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```python
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
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```bibtext
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@article{BioinspiredFluxBuehler2024,
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These are LoRA adaption weights for the FLUX.1 [dev] model (```black-forest-labs/FLUX.1-dev```). The base model is, and you must first get access to it before loading this LoRA adapter.
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This LoRA adapter has rank=64 and alpha=64, trained for 16,000 steps. Earlier checkpoints are available in this repository as well (you can load these via the ```adapter``` parameter, see example below).
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## Trigger keywords
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The model was fine-tuned with a set of ~1,600 images of biological materials, structures, shapes and other images of nature, using the keyword \<bioinspired\>.
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You should use \<bioinspired\> to trigger these features during image generation.
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
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Image generation - Example #2:
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```python
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
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Image generation - Example #3:
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```python
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prompt = "An architectural design in the style of <bioinspired>. The structure itself features key design elements as in <bioinspired>."
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num_samples =1
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num_rows =1
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n_steps=50
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guidance_scale=5.
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all_images = []
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for _ in range(num_rows):
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image = pipeline(prompt,num_inference_steps=n_steps,num_images_per_prompt=num_samples,
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guidance_scale=guidance_scale,).images
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all_images.extend(image)
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grid = image_grid(all_images, num_rows, num_samples, save_individual_files=True, )
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grid
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```
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
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```bibtext
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@article{BioinspiredFluxBuehler2024,
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