README.md

---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
inference: true
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
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# LoRA text2image fine-tuning - AlexeyGHT/fine_tuning_gen
These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the AlexeyGHT/Iris_gen dataset. You can find some example images in the following. 

![img_0](./image_0.png)
![img_1](./image_1.png)
![img_2](./image_2.png)
![img_3](./image_3.png)



## Intended uses & limitations

#### How to use

```python
# 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]

!accelerate launch --mixed_precision="no" /content/diffusers/examples/text_to_image/train_text_to_image_lora.py \
  --pretrained_model_name_or_path=$MODEL_NAME \
  --dataset_name=$DATASET_NAME --caption_column="text" \
  --resolution=512 --random_flip \
  --train_batch_size=6 \
  --num_train_epochs=50 --checkpointing_steps=450 \
  --learning_rate=1e-04 --lr_scheduler="constant" --lr_warmup_steps=0 \
  --seed=42 \
  --output_dir="fine_tuning_gen" \
  --validation_prompt "Iris of the eye, web pattern, blue and light blue, beautiful complex pattern" \
  --report_to="wandb" \
  --push_to_hub