Furusu
Models by this creator
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ControlNet
91
ControlNet is a neural network structure developed by Lvmin Zhang and Maneesh Agrawala that can be used to control large pretrained diffusion models like Stable Diffusion. The model allows for additional input conditions, such as edge maps, segmentation maps, and keypoints, to be incorporated into the text-to-image generation process. This can enrich the control and capabilities of the diffusion model. The maintainer, furusu, has provided several ControlNet checkpoint models that were trained on the Waifu Diffusion 1.5 beta2 base model. These include models for edge detection, depth estimation, pose estimation, and more. The models were trained on datasets ranging from 11,000 to 60,000 1-girl images, with training epochs from 2 to 5 and batch sizes of 8 to 16. Model inputs and outputs Inputs Control Image**: An image that provides additional conditional information to guide the text-to-image generation process. This can be an edge map, depth map, pose keypoints, etc. Outputs Generated Image**: The final output image that is generated using both the text prompt and the control image. Capabilities The ControlNet models can enhance the capabilities of the base Stable Diffusion model by allowing more precise control over the generated images. For example, the edge detection model can be used to generate images with specific edge structures, while the pose estimation model can be used to create images with particular human poses. What can I use it for? The ControlNet models can be particularly useful for tasks that require more fine-grained control over the generated images, such as character design, product visualization, and architectural rendering. By incorporating additional input conditions, users can generate images that more closely match their specific requirements. Additionally, the ability to control the diffusion process can also be leveraged for creative experimentation, allowing users to explore novel image generation possibilities. Things to try One interesting aspect of the ControlNet models is the ability to combine multiple input conditions. For example, you could use both the edge detection and pose estimation models to generate images with specific edge structures and human poses. This can lead to more complex and nuanced outputs. Another thing to try is using the ControlNet models with different base diffusion models, such as the more recent Stable Diffusion 2.1. While the models were trained on Waifu Diffusion 1.5, they may still provide useful additional control when used with other diffusion models.
Updated 5/28/2024
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SSD-1B-anime
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SSD-1B-anime is a high-quality text-to-image diffusion model developed by furusu, a maintainer on Hugging Face. It is an upgraded version of the SSD-1B and NekorayXL models, with additional fine-tuning on a high-quality anime dataset to enhance the model's ability to generate detailed and aesthetically pleasing anime-style images. The model has been trained using a combination of the SSD-1B, NekorayXL, and sdxl-1.0 models as a foundation, along with specialized training techniques such as Latent Consistency Modeling (LCM) and Low-Rank Adaptation (LoRA) to further refine the model's understanding and generation of anime-style art. Model inputs and outputs Inputs Text prompts**: The model accepts text prompts that describe the desired anime-style image, using Danbooru-style tagging for optimal results. Example prompts include "1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck". Outputs High-quality anime-style images**: The model generates detailed and aesthetically pleasing anime-style images that closely match the provided text prompts. The generated images can be in a variety of aspect ratios and resolutions, including 1024x1024, 1216x832, and 832x1216. Capabilities The SSD-1B-anime model excels at generating high-quality anime-style images from text prompts. The model has been finely tuned to capture the diverse and distinct styles of anime art, offering improved image quality and aesthetics compared to its predecessor models. The model's capabilities are particularly impressive when using Danbooru-style tagging in the prompts, as it has been trained to understand and interpret a wide range of descriptive tags. This allows users to generate images that closely match their desired style and composition. What can I use it for? The SSD-1B-anime model can be a valuable tool for a variety of applications, including: Art and Design**: The model can be used by artists and designers to create unique and high-quality anime-style artwork, serving as a source of inspiration and a means to enhance creative processes. Entertainment and Media**: The model's ability to generate detailed anime images makes it ideal for use in animation, graphic novels, and other media production, offering a new avenue for storytelling. Education**: In educational contexts, the SSD-1B-anime model can be used to develop engaging visual content, assisting in teaching concepts related to art, technology, and media. Personal Use**: Anime enthusiasts can use the SSD-1B-anime model to bring their imaginative concepts to life, creating personalized artwork based on their favorite genres and styles. Things to try When using the SSD-1B-anime model, it's important to experiment with different prompt styles and techniques to get the best results. Some things to try include: Incorporating quality and rating modifiers (e.g., "masterpiece, best quality") to guide the model towards generating high-aesthetic images. Using negative prompts (e.g., "lowres, bad anatomy, bad hands") to further refine the generated outputs. Exploring the various aspect ratios and resolutions supported by the model to find the perfect fit for your project. Combining the SSD-1B-anime model with complementary LoRA adapters, such as the SSD-1B-anime-cfgdistill and lcm-ssd1b-anime, to further customize the aesthetic of your generated images.
Updated 5/28/2024