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YiYiXu
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yiyi_kandinsky_decoder

Text-to-Image
Diffusers
Safetensors
KandinskyV22Pipeline
kandinsky
Inference Endpoints
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yiyi_kandinsky_decoder / README.md
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metadata
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-decoder
datasets:
  - lambdalabs/pokemon-blip-captions
prior:
  - kandinsky-community/kandinsky-2-2-prior
tags:
  - kandinsky
  - text-to-image
  - diffusers
inference: true

Finetuning - YiYiXu/yiyi_kandinsky_decoder

This pipeline was finetuned from kandinsky-community/kandinsky-2-2-decoder 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

pipeline = AutoPipelineForText2Image.from_pretrained("YiYiXu/yiyi_kandinsky_decoder", torch_dtype=torch.float16)
prompt = "A robot pokemon, 4k photo"
image = pipeline(prompt).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.