Instructions to use kraina/map_diffusion_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kraina/map_diffusion_lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("kraina/map_diffusion_lora") 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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Download README.md from kraina/map_diffusion_lora: direct link, hf CLI and curl.
- Browser
- Download file 540 Bytes
-
https://huggingface.co/kraina/map_diffusion_lora/resolve/3f6bf115b6c1cc3ae7b3dff39a142850362323a3/README.md
- Command line
-
hf download hf://kraina/map_diffusion_lora@3f6bf115b6c1cc3ae7b3dff39a142850362323a3/README.md
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curl -L -o README.md https://huggingface.co/kraina/map_diffusion_lora/resolve/3f6bf115b6c1cc3ae7b3dff39a142850362323a3/README.md
540 Bytes
metadata
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA text2image fine-tuning - mprzymus/map_diffusion_lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the mprzymus/text2tile_large dataset. You can find some example images in the following.



