Instructions to use scrapware/highPixelar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use scrapware/highPixelar with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lykon/AnyLoRA", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("scrapware/highPixelar") prompt = " masterpiece, best quality, 1girl, solo, pixelar" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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artifacts, signature, watermark, username, blurry
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output:
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url: images/highPixelarDOS_v1AF.preview.png
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base_model: Lykon/AnyLoRA
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instance_prompt: null
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license: openrail
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artifacts, signature, watermark, username, blurry
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output:
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url: images/highPixelarDOS_v1AF.preview.png
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- text: ' masterpiece, best quality, 1girl, solo, pixelar'
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parameters:
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negative_prompt: >-
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lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
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fewer digits, cropped, worst quality, low quality, normal quality, jpeg
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artifacts, signature, watermark, username, blurry
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output:
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url: images/highPixelarMomokoS_v1AF.preview.png
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- text: ' masterpiece, best quality, 1girl, solo, pixelar'
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parameters:
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negative_prompt: >-
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lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit,
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fewer digits, cropped, worst quality, low quality, normal quality, jpeg
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artifacts, signature, watermark, username, blurry
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output:
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url: images/highPixelarLM_v1AF.preview.png
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base_model: Lykon/AnyLoRA
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instance_prompt: null
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license: openrail
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