metadata
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-prior
datasets:
- kbharat7/DogChestXrayDatasetNew
tags:
- kandinsky
- text-to-image
- diffusers
- diffusers-training
inference: true
Finetuning - aditya11997/test_prior
This pipeline was finetuned from kandinsky-community/kandinsky-2-2-prior on the kbharat7/DogChestXrayDatasetNew dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['dogxraysmall']:
Pipeline usage
You can use the pipeline like so:
from diffusers import DiffusionPipeline
import torch
pipe_prior = DiffusionPipeline.from_pretrained("aditya11997/test_prior", torch_dtype=torch.float16)
pipe_t2i = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
prompt = "dogxraysmall"
image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple()
image = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0]
image.save("my_image.png")
Training info
These are the key hyperparameters used during training:
- Epochs: 1
- Learning rate: 1e-05
- Batch size: 1
- Gradient accumulation steps: 4
- Image resolution: 768
- Mixed-precision: None
More information on all the CLI arguments and the environment are available on your wandb run page.