README.md

---
license: mit
base_model: warp-ai/wuerstchen-prior
datasets:
- haorandai/Mammal_Mice_lr0.01_e0.1_20_with20constraints
tags:
- wuerstchen
- text-to-image
- diffusers
- diffusers-training
inference: true
---
    
# Finetuning - haorandai/temp

This pipeline was finetuned from **warp-ai/wuerstchen-prior** on the **haorandai/Mammal_Mice_lr0.01_e0.1_20_with20constraints** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['An image of a mice and a cat']: 

![val_imgs_grid](./val_imgs_grid.png)


## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipe_prior = DiffusionPipeline.from_pretrained("haorandai/temp", torch_dtype=torch.float16)
pipe_t2i = DiffusionPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=torch.float16)
prompt = "An image of a mice and a cat"
(image_embeds,) = pipe_prior(prompt).to_tuple()
image = pipe_t2i(image_embeddings=image_embeds, prompt=prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 40
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16