Spestly/OdysseyXL-MK2-Alpha

OdysseyXL-MK2-Alpha

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1. Model Description

OdysseyXL-MK2-Alpha is an early preview release of the OdysseyXL MK2 line, specifically engineered for Landscape & Scene Excellence. It is a custom finetuned realism model designed to generate breathtaking, ultra-realistic environments, cinematic vistas, natural worlds, and complex large-scale scene compositions.

In the OdysseyXL MK2 family, this Alpha release is specialized for:

• Alpha: Exceptional at landscapes, nature, wide shots, environments, scenic realism, and atmospheric depth.
• Beta (Upcoming): Focused on human realism, facial detail, object fidelity, and close-up precision.

Key Features

• Ultra-Realistic Rendering: Enhanced fine details, complex materials, and controlled noise.
• Environmental Mastery: Tuned for sweeping environments, realistic ecosystems, and complex spatial composition.
• Enhanced Lighting Engine: Supports natural light, volumetric effects, HDR scenes, and stylized color grading.
• Cross-Domain Performance: High fidelity across landscapes, wildlife, architecture, and cinematic frames.
• SDXL-Compatible: Finetuned on top of SDXL 1.0 for broad ecosystem support.

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2. Technical Specifications

Parameter	Value
Base Architecture	Stable Diffusion XL 1.0
Model Variant	Custom finetuned realism model (OdysseyXL MK2 Alpha)
Training Strategy	LoRA + full-model hybrid cycles, High-resolution patch-based training, Multi-domain sampling
Target Output Resolution	1024×1024 native
Dataset Composition	Humans, wildlife, urban scenes, nature photography, cinematic stills
Format	.safetensors

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3. Usage

The model is compatible with the Hugging Face diffusers library, as well as popular UIs like ComfyUI and Automatic1111.(Soon)

3.1. Installation via Diffusers

from diffusers import StableDiffusionXLPipeline
import torch

pipe = StableDiffusionXLPipeline.from_pretrained(
    "Spestly/OdysseyXL-MK2-Alpha",
    torch_dtype=torch.float16
).to("cuda")

# Example Usage
prompt = "Ultra-realistic portrait of a tiger in soft morning light, razor-sharp detail, cinematic depth of field"
image = pipe(prompt).images[0]
image.save("tiger.png")

3.2. Installation via UIs (ComfyUI / Automatic1111)

1. Download the model weights (.safetensors file) from the Hugging Face repository.
2. Place the .safetensors file in your models/Stable-Diffusion directory.
3. Reload your user interface.

3.3. Recommended Prompting Style

For optimal realism and to leverage the model's environmental tuning, use concise prompts and include specific lighting and lens cues.

• Lighting Cues: volumetric light, soft shadows, HDR intensities, icy morning light.
• Landscape Cues: time of day, weather, lens focal length (e.g., 24mm wide-angle lens).
• Wildlife Cues: fur detail, lens type, distance (e.g., 200mm telephoto lens).

Example Prompt: "High-fidelity wildlife portrait of a snow leopard, 200mm telephoto lens, crisp fur detail, icy morning light, natural color grading"

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4. Limitations (Alpha Stage)

This is an early alpha release, and users should be aware of the following known limitations:

• Color Shifts: May occasionally appear in extreme HDR (High Dynamic Range) scenarios.
• Anatomical Inconsistencies: Rare poses may exhibit minor anatomical inaccuracies, particularly with humans and wildlife.
• Multi-Character Scenes: Composition and consistency for scenes involving multiple subjects are still under active tuning.
• Close-up Wildlife: Wildlife generated at extreme close-up distances may require careful negative prompting to maintain fidelity.

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6. License and Acknowledgements

License: This model is released under a OMK2-L. Users must review the license file in the repository for full terms and commercial use stipulations.

Acknowledgements: Special thanks to the OdysseyXL research line, contributors, and testers whose efforts were instrumental in shaping the MK2 Alpha release.

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7. Contact and Contributions

For collaboration, testing access, integration inquiries, or to report issues, please open an issue on the repository or contact the model maintainer directly.

Model Maintainer: Aayan Mishra