Prompt
The image features an older man, a long white beard and mustache, He has a stern expression, giving the impression of a wise and experienced individual. The mans beard and mustache are prominent, adding to his distinguished appearance. The close-up shot of the mans face emphasizes his facial features and the intensity of his gaze.

Prompt
Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd

Prompt
cinematic film still of Kodak Motion Picture Film: (Sharp Detailed Image) An Oscar winning movie for Best Cinematography a woman in a kimono standing on a subway train in Japan Kodak Motion Picture Film Style, shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy

Prompt: ("Proteus" text logo) powerful aura, swirling power, cinematic, masterpiece, award-winning

Proteus v0.6

I'm excited to introduce Proteus v0.6, a complete rebuild of my AI image generation model. This is the first version of the rework, focusing entirely on enhancing photorealism. While it's not aiming to be state-of-the-art, I believe it's a good step forward in producing high-quality images. Please note that this is a preliminary version, and it's not the final, fully-featured checkpoint—more improvements and features will come in future updates.

Overview

Proteus v0.6 is a total rework from the ground up. In previous versions, combining different training methods and learning rates caused the model to become unstable during large-scale training. Learning from those experiences, I've retrained the model using only the photorealism aspects of the Proteus dataset.

For now, I'm calling this new training technique Multi-Perspective Fusion.

Multi-Perspective Fusion

This approach involves:

Training Multiple LoRAs and Full-Parameter Checkpoints: I trained several Low-Rank Adaptation (LoRA) modules and full-parameter checkpoints on the same dataset multiple times to capture different "perspectives" of the data.
Integrating into an Overarching Framework: These varied models are then combined within a larger framework to enhance overall performance.

I'm hoping this method will be interesting to data scientists exploring advanced training techniques.

Key Improvements in v0.6

Total Rebuild: Constructed entirely from scratch to address previous issues.
Enhanced Photorealism: Focused on producing good-quality photorealistic images.
Stable Training Process: Refined training methods to prevent the model from falling apart during large-scale training.
Preliminary Version: This is the first version of the rework; expect more features and improvements in future releases.

Limitations

No Illustrations or Anime: Currently, the model can't generate illustrations or anime-style images because it's only been trained on photorealistic data.
Not State-of-the-Art: While the model performs well, I'm not claiming it's state-of-the-art—just that it's a good starting point.
Work in Progress: This is not the final, fully-featured checkpoint. More updates are planned.

Usage

Recommended Settings

Clip Skip: 1
CFG Scale: 7
Steps: 25 - 50
Sampler: DPM++ 2M SDE
Scheduler: Karras
Resolution: 1024x1024

Use it with 🧨 diffusers

Here's how you can use Proteus v0.6 with the Hugging Face 🧨 diffusers library:

import torch
from diffusers import (
    StableDiffusionXLPipeline, 
    KDPM2AncestralDiscreteScheduler,
    AutoencoderKL
)

# Load VAE component
vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix", 
    torch_dtype=torch.float16
)

# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "dataautogpt3/Proteus-v0.6", 
    vae=vae,
    torch_dtype=torch.float16
)
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')

# Define prompts and generate image
prompt = "a cat wearing sunglasses on the beach"
negative_prompt = ""

image = pipe(
    prompt, 
    negative_prompt=negative_prompt, 
    width=1024,
    height=1024,
    guidance_scale=7,
    num_inference_steps=50,
).images[0]

image.save("generated_image.png")

Future Plans

Following the approach from the first version, I plan to gradually introduce new concepts and visual styles by adding one large training batch at a time. This incremental method aims to expand the model's capabilities while keeping it stable.

Collaborations

If anyone is interested, I'd be open to collaborating on papers about this work. I'm looking for a team to help me publish, but I'm new to this and would appreciate any guidance.

License

License Options:

Given my goal to allow personal use and commercial use up to a certain revenue threshold while requiring larger entities to contact me for a separate agreement, I'm considering the following existing licenses:

Polyform Small Business License 1.0.0

Permits: Use by individuals and entities with annual gross revenues under a specified amount (e.g., $5 million USD).
Requires: Entities exceeding the revenue threshold to obtain a commercial license from me.

For more details, see the Polyform Small Business License.

Acknowledgments

This is a personal project developed solely by me.

Citation

If you use Proteus v0.6 in your work, please cite it as:

[Alexander Rafael Izquierdo], "Proteus v0.6: Multi-Perspective Fusion," 2024.

dataautogpt3
/

Proteus-v0.6