metadata
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-decoder
datasets:
- kbharat7/DogChestXrayDatasetNew
prior:
- kandinsky-community/kandinsky-2-2-prior
tags:
- kandinsky
- text-to-image
- diffusers
- diffusers-training
inference: true
Finetuning - aditya11997/kandi2-decoder-3.2
This pipeline was finetuned from kandinsky-community/kandinsky-2-2-decoder on the kbharat7/DogChestXrayDatasetNew dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['photo of dogxraysmall']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("aditya11997/kandi2-decoder-3.2", torch_dtype=torch.float16)
prompt = "photo of dogxraysmall"
image = pipeline(prompt).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 43
- Learning rate: 1e-05
- Batch size: 1
- Gradient accumulation steps: 4
- Image resolution: 768
- Mixed-precision: None