---
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-decoder
datasets:
- ChoudharyTAlhaArain/web-kadi-2.0
prior:
- kandinsky-community/kandinsky-2-2-prior
tags:
- kandinsky
- text-to-image
- diffusers
- diffusers-training
inference: true
---
    
# Finetuning - ChoudharyTAlhaArain/kadsinky-web-decoder-3.1

This pipeline was finetuned from **kandinsky-community/kandinsky-2-2-decoder** on the **ChoudharyTAlhaArain/web-kadi-2.0** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['update web ui/ux']: 

![val_imgs_grid](./val_imgs_grid.png)


## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("ChoudharyTAlhaArain/kadsinky-web-decoder-3.1", torch_dtype=torch.float16)
prompt = "update web ui/ux"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 116
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: None


More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/tanveer-talha-github/text2image-fine-tune/runs/u24l8tl8).