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---
license: mit
base_model: warp-ai/wuerstchen-prior
datasets:
- dongOi071102/meme-pretreatment-dataset-100rows
tags:
- wuerstchen
- text-to-image
- diffusers
- diffusers-training
- lora
inference: true
---
# LoRA Finetuning - dongOi071102/wuerstchen-prior-naruto-lora-2
This pipeline was finetuned from **warp-ai/wuerstchen-prior** on the **dongOi071102/meme-pretreatment-dataset-100rows** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a catton cat with angry face']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"warp-ai/wuerstchen", torch_dtype=float32
)
# load lora weights from folder:
pipeline.prior_pipe.load_lora_weights("dongOi071102/wuerstchen-prior-naruto-lora-2", torch_dtype=float32)
image = pipeline(prompt=prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* LoRA rank: 4
* Epochs: 5
* Learning rate: 0.0002
* Batch size: 8
* Gradient accumulation steps: 1
* Image resolution: 512
* Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/2111818-no/text2image-fine-tune/runs/js2o268h).
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