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  ---
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  tags:
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  - text-to-image
 
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  - lora
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  - diffusers
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- - template:diffusion-lora
 
 
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  widget:
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- - output:
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- url: images/image.png
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- text: add bioluminescence efficiency
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- base_model: ''
 
 
 
 
 
 
 
 
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  instance_prompt: bioluminescence efficiency
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  license: other
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  license_name: flux-1-dev-non-commercial-license
@@ -17,15 +28,40 @@ license_link: >-
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  ---
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  # FLUX.1-Kontext-dev-LoRA-Bioluminescence-Style
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- <Gallery />
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  ## Trigger words
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  You should use `bioluminescence efficiency` to trigger the image generation.
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- ## Download model
 
 
 
 
 
 
 
 
 
 
 
 
 
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- [Download](/Shakker-Labs/FLUX.1-Kontext-dev-LoRA-Bioluminescence-Style/tree/main) them in the Files & versions tab.
 
 
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  ---
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  tags:
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  - text-to-image
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+ - stable-diffusion
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  - lora
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  - diffusers
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+ - image-generation
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+ - flux
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+ - safetensors
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  widget:
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+ - text: >-
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+ change to illustration style
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+ output:
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+ url: images/example1.png
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+ - text: >-
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+ The edges of the character's body emit a dazzling light effect.
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+ output:
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+ url: images/example2.png
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+ - text: change to illustration style
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+ output:
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+ url: images/example3.png
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+ base_model: black-forest-labs/FLUX.1-Kontext-dev
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  instance_prompt: bioluminescence efficiency
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  license: other
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  license_name: flux-1-dev-non-commercial-license
 
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  ---
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  # FLUX.1-Kontext-dev-LoRA-Bioluminescence-Style
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+ This is a dazzling light effect style style LoRA trained on FLUX.1-Kontext-dev by [zooey](https://huggingface.co/zooeyy).
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+
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+ ## Showcases
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+ <Gallery />
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  ## Trigger words
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  You should use `bioluminescence efficiency` to trigger the image generation.
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+ ## Inference
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+
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+ ```python
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+ import torch
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+ from diffusers import FluxKontextPipeline
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+ from diffusers.utils import load_image
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+
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+ pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16)
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+ pipe.load_lora_weights("Shakker-Labs/FLUX.1-Kontext-dev-LoRA-Bioluminescence-Style", weight_name="FLUX-kontext-lora-bioluminescence-style.safetensors")
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+ pipe.fuse_lora(lora_scale=1.0)
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+ pipe.to("cuda")
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+
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+ input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
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+ prompt = "bioluminescence efficiency"
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+ image = pipe(
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+ image=input_image,
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+ prompt=prompt,
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+ num_inference_steps=24,
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+ guidance_scale=2.5,
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+ ).images[0]
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+ image.save(f"example.png")
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+ ```
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+ ## Acknowledgements
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+ This model is trained by our copyrighted users [zooey](https://huggingface.co/zooeyy). We release this model under permissions. The model follows [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).