license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
base_model: runwayml/stable-diffusion-v1-5
inference: true
LoRA text2image fine-tuning - animanatwork/illustrations-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the animanatwork/text_to_image_dataset dataset.
Below, we can find some images from the dataset:
![](https://cdn-uploads.huggingface.co/production/uploads/66297c313291276a14318d23/fHCi3t9AlK5AasMt_K0nh.png)
![](https://cdn-uploads.huggingface.co/production/uploads/66297c313291276a14318d23/fYdTOG8QKtUHKvDOBw40r.png)
![](https://cdn-uploads.huggingface.co/production/uploads/66297c313291276a14318d23/IXx2U6cM0SH4CFGw1qmjE.png)
The images below are generated from the model using the prompt: "a stylized illustration of a woman sitting in a comfortable chair, reading a book. She is wearing a hat, and her expression appears focused and calm. A black cat is also depicted, sitting beside her and looking at the book, suggesting a shared moment of quiet and companionship. The woman is dressed in a casual outfit with yellow shoes, and the overall color scheme is simple, using black, white, and yellow. The setting seems cozy and peaceful, ideal for reading."
![](/animanatwork/illustrations-lora/resolve/main/image_0.png)
![](/animanatwork/illustrations-lora/resolve/main/image_1.png)
![](/animanatwork/illustrations-lora/resolve/main/image_2.png)
![](/animanatwork/illustrations-lora/resolve/main/image_3.png)
Intended uses & limitations
Do NOT use in production. This model was purely created for research purposes.
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
- The model was trained on the "animanatwork/text_to_image_dataset" dataset using 10_000 training step (default is 15_000) and took several hours to train. For more details see Colab notebook.
- The dataset's tokens were generated using chatGPT vision. During training, I noticed CLIP can only use 77 tokens for a given image. Since most of our image descriptions contained more tokens, we'll have to create a new dataset that doesn't exceed the maximum.
[TODO: describe the data used to train the model]