Browse Models
The simplest way to self-host New Reality XL. Launch a dedicated cloud GPU server running Laboratory OS to download and serve the model using any compatible app or framework.
Download model weights for local inference. Must be used with a compatible app, notebook, or codebase. May run slowly, or not work at all, depending on your system resources, particularly GPU(s) and available VRAM.
New Reality XL is a Stable Diffusion XL-based model optimized for photographic image generation. It's part of a broader model family with implementations across different SD architectures. The model focuses on producing photographs from text descriptions, leveraging SDXL's expanded architecture.
New Reality XL represents a significant entry in the field of photorealistic image generation, offering capabilities as a comprehensive all-in-one Stable Diffusion model. As detailed in the model documentation, it specializes in producing high-quality photographic outputs while maintaining versatility across various use cases.
New Reality XL exists within a broader ecosystem of related models, each built on different versions of Stable Diffusion technology. The family includes variants utilizing:
This diverse range of architectural bases allows the New Reality family to cater to different use cases and performance requirements. Each variant leverages its underlying technology to offer specific advantages, though the exact differences in capabilities between variants are not explicitly documented.
The model positions itself as a cutting-edge solution for photographic image generation. While specific architectural details are not publicly disclosed, its integration with various Stable Diffusion versions suggests a sophisticated approach to image synthesis. The model demonstrates particular strength in:
The sample outputs, as shown in the accompanying images, demonstrate the model's ability to handle complex scenarios including human subjects, lighting conditions, and detailed clothing textures with impressive fidelity.
While the model shows promise in photorealistic image generation, several aspects remain undocumented, including:
This lack of technical documentation suggests that users may need to experiment with different parameters and settings to achieve optimal results for their specific use cases.
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