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Disty0
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sote-diffusion-cascade-decoder_alpha0

Text-to-Image
Diffusers
Safetensors
StableCascadeDecoderPipeline
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sote-diffusion-cascade-decoder_alpha0
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---
pipeline_tag: text-to-image
license: other
license_name: stable-cascade-nc-community
license_link: LICENSE
---

# SoteDiffusion Cascade

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

## Code Example

```shell
pip install diffusers
```

```python
import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline

prompt = "newest, 1girl, solo, cat ears, looking at viewer, blush, light smile,"
negative_prompt = "very displeasing, worst quality, monochrome, sketch, fat, child,"

prior = StableCascadePriorPipeline.from_pretrained("Disty0/sote-diffusion-cascade_alpha0", torch_dtype=torch.float16)
decoder = StableCascadeDecoderPipeline.from_pretrained("Disty0/sote-diffusion-cascade-decoder_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=7.0,
    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=1.5
    output_type="pil",
    num_inference_steps=10
).images[0]
decoder_output.save("cascade.png")
```

## Dataset

Used the same dataset as Disty0/sote-diffusion-cascade-decoder_pre-alpha0.  
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: 1
grad_accum_steps: 1
updates: 98000
backup_every: 2048
save_every: 1024
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/sotediffusion-sc_3b-stage_b.safetensors
```


## Limitations and Bias

### Bias

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

### Limitations
- Far shot eyes are still bad thanks to the heavy latent compression.