Disty0/sote-diffusion-cascade-decoder_pre-alpha0

SoteDiffusion Cascade

Anime finetune of Stable Cascade Decoder.
No commercial use thanks to StabilityAI.

Code Example

pip install diffusers

import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline

prompt = "(extremely aesthetic, best quality, newest), 1girl, solo, cat ears, looking at viewer, blush, light smile, upper body,"
negative_prompt = "very displeasing, worst quality, monochrome, sketch, blurry, fat, child,"

prior = StableCascadePriorPipeline.from_pretrained("Disty0/sote-diffusion-cascade_pre-alpha0", torch_dtype=torch.float16)
decoder = StableCascadeDecoderPipeline.from_pretrained("Disty0/sote-diffusion-cascade-decoder_pre-alpha0", torch_dtype=torch.float16)

prior.enable_model_cpu_offload()
prior_output = prior(
    prompt=prompt,
    height=1024,
    width=1024,
    negative_prompt=negative_prompt,
    guidance_scale=6.0,
    num_images_per_prompt=1,
    num_inference_steps=40
)

decoder.enable_model_cpu_offload()
decoder_output = decoder(
    image_embeddings=prior_output.image_embeddings,
    prompt=prompt,
    negative_prompt=negative_prompt,
    guidance_scale=2.0,
    output_type="pil",
    num_inference_steps=10
).images[0]
decoder_output.save("cascade.png")

Dataset

Used the same dataset as SoteDiffusion-Cascade_pre-alpha0.
Selected images from newest dataset that got more than 0.98 score by both aesthetic and quality taggers.
Trained with 98K~ images.

Training:

GPU used for training: 1x AMD RX 7900 XTX 24GB

Software used: https://github.com/2kpr/StableCascade

Config:

experiment_id: sotediffusion-sc-b_3b
model_version: 3B
dtype: bfloat16
use_fsdp: False

batch_size: 64
grad_accum_steps: 64
updates: 3000
backup_every: 128
save_every: 32
warmup_updates: 100

lr: 4.0e-6
optimizer_type: Adafactor
adaptive_loss_weight: True
stochastic_rounding: True

image_size: 768
multi_aspect_ratio: [1/1, 1/2, 1/3, 2/3, 3/4, 1/5, 2/5, 3/5, 4/5, 1/6, 5/6, 9/16]
shift: 4

checkpoint_path: /mnt/DataSSD/AI/SoteDiffusion/StableCascade/
output_path: /mnt/DataSSD/AI/SoteDiffusion/StableCascade/
webdataset_path: file:/mnt/DataSSD/AI/anime_image_dataset/best/newest_best-{0000..0001}.tar

effnet_checkpoint_path: /mnt/DataSSD/AI/models/sd-cascade/effnet_encoder.safetensors
stage_a_checkpoint_path: /mnt/DataSSD/AI/models/sd-cascade/stage_a.safetensors
generator_checkpoint_path: /mnt/DataSSD/AI/SoteDiffusion/StableCascade/stage_b-generator-049152.safetensors

Limitations and Bias

Bias

• This model is intended for anime illustrations.
  Realistic capabilites are not tested at all.

Limitations

• Far shot eyes are bad thanks to the heavy latent compression.