YiYiXu/yiyi_kandinsky_decoder

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/yiyikandinskydecoder

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']:

[image]

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.