Instructions to use liming518/krea2pulledout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use liming518/krea2pulledout with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("liming518/krea2pulledout") prompt = "Cropped example picture from the creator" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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<Gallery />
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## Model description
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These LoRAs were extracted from fine-tuned checkpoints. I did this primarily for myself because LoRAs are easier to work with: they can be mixed in real time and assigned different weights/strengths. This also saves space on your local computer. They work well at 100–150% strength. Choose a rank according to your taste and your hardware capabilities (the lower the rank, the less memory you need). However, some of my test generations showed that a lower rank can sometimes be preferable: it does not cause artifacts, and the result is closer to the reference image generated using the checkpoint from which the LoRA was extracted.
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A lot of time, electricity, and compute went into this on my small and not particularly powerful Mac Mini M4. But it was an interesting research project.
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## Download model
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[Download](/Omnico/Krea2_turbo_diff_loras/tree/main) them in the Files & versions tab.
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# Lora for Krea 2 Turbo
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excessive saliva version
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<Gallery />
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