Text-to-image finetuning - ouvic215/sd-pokemon-model-200-steps

This pipeline was finetuned from runwayml/stable-diffusion-v1-5 on the lambdalabs/pokemon-blip-captions dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['pokemon yoda']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("ouvic215/sd-pokemon-model-200-steps", torch_dtype=torch.float16)
prompt = "pokemon yoda"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 100
• Learning rate: 1e-05
• Batch size: 16
• Gradient accumulation steps: 4
• Image resolution: 512
• Mixed-precision: fp16