Proteus

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Proteus

Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs.

In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations

Version Detail

SDXL 1.0
ProteusSigma An experimental SDXL-based image generation model exploring higher sigma max approaches and v_prediction methodology. Built around discoveries from Novel AI's groundbreaking research into noise schedule engineering, this model represents an independent implementation focused on testing these concepts in new ways. Overview ProteusSigma implements concepts from Novel AI's research paper "Improving Image Quality and Stability in Text-to-Image Generation through Noise Schedule Engineering and Adaptive Noise Control". While trained on approximately 80,000 photorealistic images, the model has demonstrated remarkable generalization into various artistic styles, suggesting interesting implications for transfer learning in diffusion models. Prompt Format The model uses a specific prompt structure where tags must come after the main prompt (except for anime style): [prompt], stylize (250), chaos (500), 6 For anime generations: anime style, [prompt], stylize (250), chaos (500), 6 Control Parameters Quality Tag: Always add 6 as the last element Similar to PonyDiffusion's approach but with my own implementation Critical for maintaining output quality Stylize: Range 1-10000 Usage: stylize (250) Controls the strength of artistic interpretation Chaos: Range 1-10000 Usage: chaos (500) Dual-purpose parameter In negative prompt: Used as (chaos:0) for stability Negative Prompting The model implements NAI's powerful (tag:0) format for quality control. This system provides fine-grained control over unwanted elements: (bad:0), (low:0), (chaos:0) Each negative tag works to suppress specific attributes, allowing for precise control over the generation process. Users are encouraged to experiment with different combinations based on their specific needs. Example Prompt: a detailed photograph of a mountain landscape, stylize (2000), chaos (1000), 6 Negative: (bad:0), (low:0)

Project Permissions

Model reprinted from : https://civitai.com/models/267242?modelVersionId=666604

Reprinted models are for communication and learning purposes only, not for commercial use. Original authors can contact us to transfer the models through our Discord channel --- #claim-models.

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