badigadiii/game-diffusion-1k

Text-to-image finetuning - badigadiii/game-diffusion-blip

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the badigadiii/blip-captions-medium dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['a game screenshot of two characters fighting with a hammer']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("badigadiii/game-diffusion-blip", torch_dtype=torch.float16)
prompt = "a game screenshot of two characters fighting with a hammer"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 1
• Learning rate: 0.0001
• Batch size: 1
• Gradient accumulation steps: 4
• Image resolution: 256
• Mixed-precision: bf16

More information on all the CLI arguments and the environment are available on your wandb run page.

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]