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kandinsky_prior_pokemon

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KandinskyV22PriorPipeline
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metadata
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-prior
datasets:
  - lambdalabs/pokemon-blip-captions
tags:
  - kandinsky
  - text-to-image
  - diffusers
inference: true

Finetuning - YiYiXu/kandinsky_prior_pokemon

This pipeline was finetuned from kandinsky-community/kandinsky-2-2-prior on the lambdalabs/pokemon-blip-captions dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['A robot pokemon, 4k photo']:

val_imgs_grid

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipe_prior = DiffusionPipeline.from_pretrained("YiYiXu/kandinsky_prior_pokemon", torch_dtype=torch.float16)
pipe_t2i = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
prompt = "A robot pokemon, 4k photo"
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: 2
  • Learning rate: 1e-05
  • Batch size: 1
  • Gradient accumulation steps: 1
  • Image resolution: 768
  • Mixed-precision: fp16

More information on all the CLI arguments and the environment are available on your wandb run page.