Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
diffusers-training
lora
Instructions to use sageofai/sageofai-lora-kvasir-trained-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use sageofai/sageofai-lora-kvasir-trained-improved 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("sageofai/sageofai-lora-kvasir-trained-improved") 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 sageofai/sageofai-lora-kvasir-trained-improved: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/sageofai/sageofai-lora-kvasir-trained-improved/resolve/main/README.md
- Command line
-
hf download hf://sageofai/sageofai-lora-kvasir-trained-improved/README.md
-
curl -L -o README.md https://huggingface.co/sageofai/sageofai-lora-kvasir-trained-improved/resolve/main/README.md
1.11 kB
metadata
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
LoRA text2image fine-tuning - sageofai/sageofai-lora-kvasir-trained-improved
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the waitwhoami/vqa_caption.dataset-test dataset. You can find some example images in the following.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training details
[TODO: describe the data used to train the model]



