metadata

license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
datasets:
  - None
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
inference: true

Text-to-image finetuning - Aminrabi/diff1000

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the None dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['[necklace in flowers shape]']:

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained("Aminrabi/diff1000", torch_dtype=torch.float16)
prompt = "[necklace in flowers shape]"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

Epochs: 77
Learning rate: 1e-05
Batch size: 1
Gradient accumulation steps: 4
Image resolution: 512
Mixed-precision: fp16