metadata

license: creativeml-openrail-m
base_model: ptx0/terminus-xl-velocity-v2
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - full
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_0_0.png
  - text: two young girls in a classroom setting appearing surprised or concerned
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png

smoldit-base-test

This is a full rank finetune derived from ptx0/terminus-xl-velocity-v2.

The main validation prompt used during training was:

two young girls in a classroom setting appearing surprised or concerned

Validation settings

CFG: 4.0
CFG Rescale: 0.7
Steps: 30
Sampler: ddpm
Seed: 420420420
Resolution: 256

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

You can find some example images in the following gallery:

Prompt
unconditional (blank prompt)

Negative Prompt
blurry, cropped, ugly

Prompt
two young girls in a classroom setting appearing surprised or concerned

Negative Prompt
blurry, cropped, ugly

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

Training settings

Training epochs: 360
Training steps: 167000
Learning rate: 1e-05
Effective batch size: 16
- Micro-batch size: 16
- Gradient accumulation steps: 1
- Number of GPUs: 1
Prediction type: v_prediction
Rescaled betas zero SNR: True
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Xformers: Enabled

Datasets

cinemamix-1mp

Repeats: 0
Total number of images: 7408
Total number of aspect buckets: 1
Resolution: 256 px
Cropped: True
Crop style: center
Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'smoldit-base-test'
pipeline = DiffusionPipeline.from_pretrained(model_id)

prompt = "two young girls in a classroom setting appearing surprised or concerned"
negative_prompt = "blurry, cropped, ugly"

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=4.0,
    guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")