diffusers tensorboard safetensors kandinsky text-to-image diffusers-training dataset:kbharat7/DogChestXrayDatasetNew base_model:kandinsky-community/kandinsky-2-2-decoder license:creativeml-openrail-m endpoints_compatible diffusers:KandinskyV22Pipeline region:us

---
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-decoder
datasets:
- kbharat7/DogChestXrayDatasetNew
prior:
- kandinsky-community/kandinsky-2-2-prior
tags:
- kandinsky
- text-to-image
- diffusers
- diffusers-training
inference: true
---
    
# Finetuning - aditya11997/kandi2-decoder-3.1

This pipeline was finetuned from **kandinsky-community/kandinsky-2-2-decoder** on the **kbharat7/DogChestXrayDatasetNew** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['photo of dogxraysmall']: 

![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("aditya11997/kandi2-decoder-3.1", torch_dtype=torch.float16)
prompt = "photo of dogxraysmall"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```

## Training info

These are the key hyperparameters used during training:

* Epochs: 1
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 768
* Mixed-precision: None

