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")

bghira
/

terminus-xl-refiner

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