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terminus-xl-refiner

This is a full rank finetune derived from segmind/SSD-1B.

The main validation prompt used during training was:

a cute anime character named toast

Validation settings

  • CFG: 7.5
  • CFG Rescale: 0.7
  • Steps: 30
  • Sampler: ddpm
  • Seed: 420420420
  • Resolution: 1024

Note: The validation settings are not necessarily the same as the training settings.

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

  • Training epochs: 0
  • Training steps: 12800
  • Learning rate: 2e-06
  • Effective batch size: 16
    • Micro-batch size: 4
    • Gradient accumulation steps: 4
    • Number of GPUs: 1
  • Prediction type: v_prediction
  • Rescaled betas zero SNR: True
  • Optimizer: AdamW, stochastic bf16
  • Precision: Pure BF16
  • Xformers: Enabled

Datasets

pixel-art

  • Repeats: 0
  • Total number of images: 1040
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

signs

  • Repeats: 0
  • Total number of images: 368
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

experimental

  • Repeats: 0
  • Total number of images: 3024
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

ethnic

  • Repeats: 0
  • Total number of images: 3072
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

sports

  • Repeats: 0
  • Total number of images: 784
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

architecture

  • Repeats: 0
  • Total number of images: 4336
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

shutterstock

  • Repeats: 0
  • Total number of images: 21072
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

cinemamix-1mp

  • Repeats: 0
  • Total number of images: 9008
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

nsfw-1024

  • Repeats: 0
  • Total number of images: 10800
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

anatomy

  • Repeats: 5
  • Total number of images: 16417
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

yoga

  • Repeats: 0
  • Total number of images: 3600
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

photo-aesthetics

  • Repeats: 0
  • Total number of images: 33136
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

text-1mp

  • Repeats: 5
  • Total number of images: 13170
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

photo-concept-bucket

  • Repeats: 0
  • Total number of images: 567554
  • Total number of aspect buckets: 3
  • Resolution: 1.0 megapixels
  • Cropped: True
  • Crop style: random
  • Crop aspect: random

Inference

import torch
from diffusers import DiffusionPipeline



model_id = "terminus-xl-refiner"
prompt = "a cute anime character named toast"
negative_prompt = "malformed, disgusting, overexposed, washed-out"

pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt='blurry, cropped, ugly',
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1152,
    height=768,
    guidance_scale=7.5,
    guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")
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