Browse Models
Launch a dedicated cloud GPU server running Laboratory OS to download and run ControlNet SD 1.5 Shuffle using any compatible app or framework.
Must be used with a compatible app, notebook, or codebase. May run slowly, or not work at all, depending on local system resources, particularly GPU(s) and available VRAM.
Generate images and videos using a powerful low-level workflow graph builder - the fastest, most flexible, and most advanced visual generation UI.
Forge is a platform built on top of Stable Diffusion WebUI to make development easier, optimize resource management, speed up inference, and study experimental features.
Automatic1111's legendary web UI for Stable Diffusion, the most comprehensive and full-featured AI image generation application in existence.
Train your own LoRAs and finetunes for Stable Diffusion and Flux using this popular GUI for the Kohya trainers.
ControlNet SD 1.5 Shuffle is an experimental image generation model in the ControlNet 1.1 family that reorganizes image content through random flow shuffling techniques. Built on the Stable Diffusion 1.5 architecture, it enables style transfer and content recomposition by rearranging input images according to text prompts, facilitating controlled image-to-image transformations without requiring external computer vision modules.
ControlNet SD 1.5 Shuffle is a generative artificial intelligence model belonging to the ControlNet 1.1 family, designed to enhance the controllability and flexibility of Stable Diffusion 1.5 via guided image content shuffling. This model employs an approach for reorganizing image content, thereby enabling transformations in image style and structure under prompt-based guidance. As an experimental addition to the ControlNet 1.1 suite, ControlNet SD 1.5 Shuffle introduces mechanisms for direct manipulation of image composition, facilitating image-to-image tasks while leveraging the generative capabilities of Stable Diffusion.
Diagram illustrating the naming conventions for ControlNet models, clarifying each element of model filenames in the ControlNet 1.1 release.
ControlNet SD 1.5 Shuffle maintains architectural consistency with ControlNet 1.0, utilizing the same core neural network structure. This deliberate architectural continuity is expected to persist through at least version 1.5 of ControlNet models, simplifying integration and compatibility across different model variants. Shuffle is characterized as a "pure ControlNet," indicating that it operates independently of external computer vision modules such as CLIP, relying solely on its internal mechanisms for image analysis and modification.
A distinguishing architectural feature of the Shuffle model is the inclusion of a global average pooling layer between the encoder outputs and the Stable Diffusion U-Net layers. This layer ensures that global image statistics inform the generative process, fostering coherent reorganization during content shuffling. The implementation is managed through a global average pooling configuration entry in the model’s YAML configuration file. In use, ControlNet SD 1.5 Shuffle must be applied to the conditional branch of classifier-free guidance, a technique widely adopted for fine-tuned diffusion model control.
While specific training datasets for ControlNet SD 1.5 Shuffle have not been publicly disclosed, the model is described as having been "trained to reorganize images" using a technique referred to as random flow shuffling. This process focuses on disrupting and rearranging image content, thereby equipping the model to direct Stable Diffusion in reconstructing and recomposing images according to textual prompts.
The broader ControlNet 1.1 family benefited from targeted improvements to training datasets, such as reducing duplication, removing low-quality samples, and refining paired prompts. These interventions contribute to improved model robustness and diversity of outputs, as evidenced across the release suite.
ControlNet SD 1.5 Shuffle is designed to perform image transformation, including content recomposition, restyling, and the introduction of structural variations in output images. One of its abilities is to operate effectively even when the input image is not pre-shuffled, demonstrating an inherent capability for interpreting and reorganizing original image content.
The model’s core use cases include style transfer, where an input image is rearranged or stylized based on a provided prompt, and content recomposition, where the model reconstructs shuffled or original imagery according to textual or multimodal instructions. Its utility is further enhanced when used in tandem with other ControlNet models, supporting multi-conditional workflows for visual manipulation tasks.
Output of ControlNet SD 1.5 Shuffle reorganizing an urban night scene with the prompt 'hong kong' (seed 12345). The model transforms and stylizes the cityscape via content shuffling.
Style change result from ControlNet SD 1.5 Shuffle with the prompt 'iron man' (seed 12345). The model takes the input figure and outputs diverse, Iron Man-inspired armor designs.
Model output for the prompt 'spider man' (seed 12345). The input is transformed into variations of armored Spider-Man-like characters, demonstrating flexible content recomposition.
ControlNet 1.1 comprises 14 models, with ControlNet SD 1.5 Shuffle categorized as one of three experimental variants. It is distributed alongside both established and experimental methodologies, including models for depth inference, edge detection (e.g., Canny, MLSD), pose estimation, semantic segmentation, and other style-relevant transformations. All models in the family share the foundational ControlNet architecture but vary in their control mechanisms and training data optimizations.
Notably, experimental models such as Instruct Pix2Pix and Tile, launched in parallel with Shuffle, explore paradigms for controlled image generation, such as instruction-based image transformation and tile-based high-resolution synthesis, respectively. The broader ControlNet suite is intended to facilitate research and development in guided image generation, testing the boundaries of multimodal conditioning and content-level control.
ControlNet SD 1.5 Shuffle is explicitly marked as experimental within the ControlNet 1.1 release. While early communications described Shuffle as a primary method for image stylization—especially in contrast to CLIP-based approaches—further development signals openness to supporting additional stylization techniques in the future. Therefore, the model's long-term direction and canonical role within the family remain subject to ongoing evaluation and potential revision. There is no documented information regarding licensing within official repository materials; practitioners are advised to consult the distribution platform for up-to-date licensing terms.
ControlNet SD 1.5 Shuffle was introduced as part of the broader ControlNet 1.1 release, which featured expanded training protocols and the initiation of public beta testing within the Automatic1111 (A1111) ecosystem. This version introduced experimental integrations and improvements over prior iterations, notably in dataset curation and conditional guidance mechanics.
For additional information, technical documentation, datasets, and related discussion, consult the following resources: