diffusers safetensors stable-diffusion stable-diffusion-diffusers text-to-image dataset:lambdalabs/pokemon-blip-captions base_model:CompVis/stable-diffusion-v1-4 base_model:finetune:CompVis/stable-diffusion-v1-4 license:creativeml-openrail-m autotrain_compatible endpoints_compatible diffusers:StableDiffusionPipeline region:us
---
base_model: CompVis/stable-diffusion-v1-4
datasets:
- lambdalabs/pokemon-blip-captions
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
    
# Text-to-image finetuning - quocanh34/sd-pokemon-model-test

This pipeline was finetuned from **CompVis/stable-diffusion-v1-4** on the **lambdalabs/pokemon-blip-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['cute bear']: 

![val_imgs_grid](./val_imgs_grid.png)


## Pipeline usage

You can use the pipeline like so:

```python
from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("quocanh34/sd-pokemon-model-test", torch_dtype=torch.float16)
prompt = "cute bear"
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: 512
* Mixed-precision: fp16


More information on all the CLI arguments and the environment are available on your [`wandb` run page]().
