Text-to-image finetuning - Shreyas21032003/sd_ffhq_shorter_captions_model_demo

This pipeline was finetuned from CompVis/stable-diffusion-v1-2 on the irodkin/ffhq_with_llava_shorter_captions dataset. Below are some example images generated with the finetuned pipeline using the following prompts: None:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Shreyas21032003/sd_ffhq_shorter_captions_model_demo", torch_dtype=torch.float16)
# Check if validation_prompts is None and assign a default value
if args.validation_prompts is None:
    args.validation_prompts = ["A person sitting in a park under a tree"]
"
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

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]