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kandi2-decoder-3.2

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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']:

val_imgs_grid

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