Instructions to use Andyrasika/lora_diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Andyrasika/lora_diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Andyrasika/lora_diffusion", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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pipeline_tag: text-to-image
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---
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The model is created using the following steps:
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- Find the desired model (
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- You can see some conversion scripts in diffusesrs. This time, only the scripts for converting checkpoit and lora are used.
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It depends on the model type of Civitai. If it is a lora model, you need to specify a basic model
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- Using __load_lora function from https://towardsdatascience.com/improving-diffusers-package-for-high-quality-image-generation-a50fff04bdd4
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pipeline_tag: text-to-image
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---
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The model is created using the following steps:
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- Find the desired model (checkpoint or lora) on Civitai
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- You can see some conversion scripts in diffusesrs. This time, only the scripts for converting checkpoit and lora are used.
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It depends on the model type of Civitai. If it is a lora model, you need to specify a basic model
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- Using __load_lora function from https://towardsdatascience.com/improving-diffusers-package-for-high-quality-image-generation-a50fff04bdd4
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