README.md

metadata

base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - diffusers-training
inference: true

Text-to-image finetuning - haorandai/png_Random_Noise_banana_Gaussian_Noise_epsilon0.1_1samples_with1constraints

This pipeline was finetuned from CompVis/stable-diffusion-v1-4 on the haorandai/png_Random_Noise_banana_Gaussian_Noise_epsilon0.1_1samples_with1constraints 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("haorandai/png_Random_Noise_banana_Gaussian_Noise_epsilon0.1_1samples_with1constraints", torch_dtype=torch.float16)
prompt = "None"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

• Epochs: 200
• Learning rate: 1e-05
• Batch size: 1
• Gradient accumulation steps: 4
• Image resolution: 224
• 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]