Instructions to use peteromallet/Flux-Kontext-InScene with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peteromallet/Flux-Kontext-InScene with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("peteromallet/Flux-Kontext-InScene") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
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license: apache-2.0
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# LoRA: InScene
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The `InScene` LoRA was trained on 394 image pairs. This dataset was created by extracting and enriching frames from the WebVid dataset.
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You can find the public dataset used for training here:
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[https://huggingface.co/datasets/peteromallet/InScene-Dataset](https://huggingface.co/datasets/peteromallet/InScene-Dataset)
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license: apache-2.0
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datasets:
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- peteromallet/InScene-Dataset
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base_model:
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- black-forest-labs/FLUX.1-Kontext-dev
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tags:
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- image
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- editing
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# LoRA: InScene
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The `InScene` LoRA was trained on 394 image pairs. This dataset was created by extracting and enriching frames from the WebVid dataset.
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You can find the public dataset used for training here:
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[https://huggingface.co/datasets/peteromallet/InScene-Dataset](https://huggingface.co/datasets/peteromallet/InScene-Dataset)
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