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kadsinky-web-decoder-3.1

Text-to-Image
Diffusers
Safetensors
KandinskyV22Pipeline
kandinsky
diffusers-training
Inference Endpoints
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kadsinky-web-decoder-3.1 / README.md
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metadata
license: creativeml-openrail-m
base_model: kandinsky-community/kandinsky-2-2-decoder
datasets:
  - ChoudharyTAlhaArain/web-kadi-2.0
prior:
  - kandinsky-community/kandinsky-2-2-prior
tags:
  - kandinsky
  - text-to-image
  - diffusers
  - diffusers-training
inference: true

Finetuning - ChoudharyTAlhaArain/kadsinky-web-decoder-3.1

This pipeline was finetuned from kandinsky-community/kandinsky-2-2-decoder on the ChoudharyTAlhaArain/web-kadi-2.0 dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['update web ui/ux']:

val_imgs_grid

Pipeline usage

You can use the pipeline like so:

from diffusers import DiffusionPipeline
import torch

pipeline = AutoPipelineForText2Image.from_pretrained("ChoudharyTAlhaArain/kadsinky-web-decoder-3.1", torch_dtype=torch.float16)
prompt = "update web ui/ux"
image = pipeline(prompt).images[0]
image.save("my_image.png")

Training info

These are the key hyperparameters used during training:

  • Epochs: 116
  • Learning rate: 1e-05
  • Batch size: 1
  • Gradient accumulation steps: 4
  • Image resolution: 512
  • Mixed-precision: None

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