Sotediffusion V2

🚀 SoteDiffusion V2

An Anime finetune of Würstchen V3 / Stable Cascade, offering high - quality anime image generation capabilities.

🚀 Quick Start

ComfyUI

Use these arguments when starting ComfyUI: --fp16-vae --fp16-unet

Download the Stage C to unet folder: sotediffusion-v2-stage_c.safetensors
Download the Stage C Text Encoder to clip folder: sotediffusion-v2-stage_c_text_encoder.safetensors
Download the Stage B to unet folder: sotediffusion-v2-stage_b.safetensors
Download the Stage A to vae folder: stage_a_ft_hq.safetensors

Download the workflow and load it: comfyui_workflow.json

Stage C sampler: DPMPP 2M or DPMPP 2M SDE with SGM Uniform scheduler
Stage C steps: 28
Stage C CFG: 6.0

Stage B sampler: LCM with Exponential scheduler
Stage B steps: 14
Stage B CFG: 1.0

SD.Next

URL: https://github.com/vladmandic/automatic/

Go to Models -> Huggingface and type Disty0/sotediffusion-v2 into the model name and press download.
Load Disty0/sotediffusion-v2 after the download process is complete.

Prompt:

your prompt goes here
very aesthetic, best quality, newest,

(New lines act the same way as BREAK in SD.Next)

Negative Prompt:

very displeasing, displeasing, worst quality, bad quality, low quality, realistic, monochrome, comic, sketch, oldest, early, artist name, signature, blurry, simple background, upside down,

Parameters:
Sampler: Default

Steps: 28
Refiner Steps: 14

CFG: 5.0 to 6.0
Secondary CFG: 1.0 to 1.5

Resolution: 1280x1280, 1024x1536, 1024x2048, 2048x1152
Anything works as long as it's a multiply of 128.

Diffusers

pip install git+https://github.com/huggingface/diffusers

import torch
import diffusers

device = "cuda"
dtype = torch.float16
model_path = "Disty0/sotediffusion-v2"
pipe = diffusers.AutoPipelineForText2Image.from_pretrained(model_path, torch_dtype=dtype)

# de-dupe
pipe.decoder_pipe.text_encoder = pipe.text_encoder = None # nothing uses this
del pipe.decoder_pipe.text_encoder
del pipe.prior_prior
del pipe.prior_text_encoder
del pipe.prior_tokenizer
del pipe.prior_scheduler
del pipe.prior_feature_extractor
del pipe.prior_image_encoder

pipe = pipe.to(device, dtype=dtype)
pipe.prior_pipe = pipe.prior_pipe.to(device, dtype=dtype)


def encode_prompt(
    prior_pipe,
    device,
    num_images_per_prompt,
    prompt=""
    ):

    if prompt == "":
        text_inputs = prior_pipe.tokenizer(
            prompt,
            padding="max_length",
            max_length=77,
            truncation=False,
            return_tensors="pt",
        )
        input_ids = text_inputs.input_ids
        attention_mask=None
    else:   
        text_inputs = prior_pipe.tokenizer(
            prompt,
            padding="longest",
            truncation=False,
            return_tensors="pt",
        )
        chunk = []
        padding = []
        max_len = 75
        start_token = text_inputs.input_ids[:,0].unsqueeze(0)
        end_token = text_inputs.input_ids[:,-1].unsqueeze(0)
        raw_input_ids = text_inputs.input_ids[:,1:-1]
        prompt_len = len(raw_input_ids[0])
        last_lenght = prompt_len % max_len
        
        for i in range(int((prompt_len - last_lenght) / max_len)):
            chunk.append(torch.cat([start_token, raw_input_ids[:,i*max_len:(i+1)*max_len], end_token], dim=1))
        for i in range(max_len - last_lenght):
            padding.append(text_inputs.input_ids[:,-1])
        
        last_chunk = torch.cat([raw_input_ids[:,prompt_len-last_lenght:], torch.tensor([padding])], dim=1)
        chunk.append(torch.cat([start_token, last_chunk, end_token], dim=1))
        input_ids = torch.cat(chunk, dim=0)
        attention_mask = torch.ones(input_ids.shape, device=device, dtype=torch.int64)
        attention_mask[-1,last_lenght+1:] = 0

    text_encoder_output = prior_pipe.text_encoder(
        input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
    )

    prompt_embeds = text_encoder_output.hidden_states[-1].reshape(1,-1,1280)
    prompt_embeds = prompt_embeds.to(dtype=prior_pipe.text_encoder.dtype, device=device)
    prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)

    prompt_embeds_pooled = text_encoder_output.text_embeds[0].unsqueeze(0).unsqueeze(1)
    prompt_embeds_pooled = prompt_embeds_pooled.to(dtype=prior_pipe.text_encoder.dtype, device=device)
    prompt_embeds_pooled = prompt_embeds_pooled.repeat_interleave(num_images_per_prompt, dim=0)

    return prompt_embeds, prompt_embeds_pooled


prompt = "1girl, solo, looking at viewer, open mouth, blue eyes, medium breasts, blonde hair, gloves, dress, bow, hair between eyes, bare shoulders, upper body, hair bow, indoors, elbow gloves, hand on own chest, bridal gauntlets, candlestand, smile, rim lighting, from side, castle interior, looking side,"
quality_prompt = "very aesthetic, best quality, newest"
negative_prompt = "very displeasing, displeasing, worst quality, bad quality, low quality, realistic, monochrome, comic, sketch, oldest, early, artist name, signature, blurry, simple background, upside down,"
num_images_per_prompt=1

# Encode prompts and quality prompts eperately, long prompt support and don't use attention masks for empty prompts:
# pipe, device, num_images_per_prompt, prompt
empty_prompt_embeds, _ = encode_prompt(pipe.prior_pipe, device, num_images_per_prompt, prompt="")

prompt_embeds, prompt_embeds_pooled = encode_prompt(pipe.prior_pipe, device, num_images_per_prompt, prompt=prompt)
quality_prompt_embeds, _ = encode_prompt(pipe.prior_pipe, device, num_images_per_prompt, prompt=quality_prompt)
prompt_embeds = torch.cat([prompt_embeds, quality_prompt_embeds], dim=1)

negative_prompt_embeds, negative_prompt_embeds_pooled = encode_prompt(pipe.prior_pipe, device, num_images_per_prompt, prompt=negative_prompt)

while prompt_embeds.shape[1] < negative_prompt_embeds.shape[1]:
    prompt_embeds = torch.cat([prompt_embeds, empty_prompt_embeds], dim=1)

while negative_prompt_embeds.shape[1] < prompt_embeds.shape[1]:
    negative_prompt_embeds = torch.cat([negative_prompt_embeds, empty_prompt_embeds], dim=1)

output = pipe(
    width=1024,
    height=1536,
    decoder_guidance_scale=1.0,
    prior_guidance_scale=5.0,
    prior_num_inference_steps=28,
    num_inference_steps=14,
    output_type="pil",
    prompt=prompt + " " + quality_prompt,
    negative_prompt=negative_prompt,
    prompt_embeds=prompt_embeds,
    prompt_embeds_pooled=prompt_embeds_pooled,
    negative_prompt_embeds=negative_prompt_embeds,
    negative_prompt_embeds_pooled=negative_prompt_embeds_pooled,
    num_images_per_prompt=num_images_per_prompt,
).images[0]

display(output)

✨ Features

• An anime finetune of Würstchen V3 / Stable Cascade.
• Trained on 12M text & image pairs with WD tags and natural - language captions.
• Trained with Full FP32 and MAE Loss.

📦 Installation

pip install git+https://github.com/huggingface/diffusers

📚 Documentation

Release Notes

• This release is sponsored by fal.ai/grants
• Trained on 12M text & image paris including WD tags and natural language captions for a single epoch on 8xH100 80GB SXM5 GPUs.
• Trained with Full FP32 and MAE Loss.

Training

Stage C

Base model: Disty0/sotediffusion-wuerstchen3
GPU used: 7x Nvidia H100 80GB SXM5

Stage B

Base model: Disty0/sotediffusion-wuerstchen3-decoder
GPU used: 1x Nvidia H100 80GB SXM5

WD Tags

Date

Aesthetic Tags

Model used: shadowlilac/aesthetic-shadow-v2

Quality Tags

Model used: https://huggingface.co/hakurei/waifu-diffusion-v1-4/blob/main/models/aes-B32-v0.pth

Rating Tags

Custom Tags

Limitations and Bias

Bias

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

Limitations

• Can fall back to realistic.
  Add "realistic" tag to the negatives when this happens.
• Far shot eyes and hands can be bad.
• Still has a lot more room for more training.

🔧 Technical Details

The model is an anime finetune of Würstchen V3 / Stable Cascade. It uses specific training parameters and techniques as described in the training section. The encoding of prompts and the generation process are also detailed in the Diffusers section.

📄 License

SoteDiffusion models falls under Fair AI Public License 1.0-SD license, which is compatible with Stable Diffusion models’ license. Key points:

1. Modification Sharing: If you modify SoteDiffusion models, you must share both your changes and the original license.
2. Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too.
3. Distribution Terms: Any distribution must be under this license or another with similar rules.
4. Compliance: Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values.

Notes: Anything not covered by Fair AI license is inherited from Stability AI Non-Commercial license which is named as LICENSE_INHERIT.