sdxl-lcm
Maintainer: lucataco
376
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| Model Link | View on Replicate |
| API Spec | View on Replicate |
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| Paper Link | View on Arxiv |
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Model overview
sdxl-lcm is a variant of the Stability AI's SDXL model that uses a Latent Consistency Model (LCM) to distill the original model into a version that requires fewer steps (4 to 8 instead of the original 25 to 50) for faster inference. This model was developed by lucataco, who has also created similar models like PixArt-Alpha LCM, Latent Consistency Model, SDXL Inpainting, dreamshaper-xl-lightning, and SDXL using DeepCache.
Model inputs and outputs
sdxl-lcm is a text-to-image diffusion model that takes a prompt as input and generates an image as output. The model also supports additional parameters like image size, number of outputs, guidance scale, and more.
Inputs
- Prompt: The text prompt that describes the desired image.
- Negative Prompt: The text prompt that describes what the model should avoid generating.
- Image: An optional input image for img2img or inpainting mode.
- Mask: An optional input mask for inpainting mode, where black areas will be preserved and white areas will be inpainted.
- Seed: An optional random seed to control the output.
Outputs
- Image(s): One or more generated images based on the input prompt.
Capabilities
sdxl-lcm is capable of generating high-quality, photorealistic images from text prompts. The model has been trained on a large dataset of images and text, allowing it to understand and generate a wide variety of visual concepts. The LCM-based optimization makes the model significantly faster than the original SDXL, while maintaining similar quality.
What can I use it for?
You can use sdxl-lcm for a variety of text-to-image generation tasks, such as creating illustrations, concept art, product visualizations, and more. The model's versatility and speed make it a useful tool for creative professionals, hobbyists, and businesses alike. Additionally, the model's ability to generate diverse and high-quality images can be leveraged for applications like game development, virtual reality, and marketing.
Things to try
With sdxl-lcm, you can experiment with different prompts to see the range of images the model can generate. Try combining the text prompt with specific artistic styles, subjects, or emotions to see how the model interprets and visualizes the concept. You can also explore the model's performance on more complex or abstract prompts, and compare the results to other text-to-image models like the ones developed by lucataco.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
lcm-ssd-1b
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lcm-ssd-1b is a Latent Consistency Model (LCM) distilled version created by the maintainer lucataco. This model reduces the number of inference steps needed to only 2 - 8 steps, in contrast to the original LCM model which required 25 to 50 steps. Other similar models created by lucataco include sdxl-lcm, dreamshaper7-img2img-lcm, pixart-lcm-xl-2, and realvisxl2-lcm. Model inputs and outputs The lcm-ssd-1b model takes in a text prompt as input and generates corresponding images. The input prompt can describe a wide variety of scenes, objects, or concepts. The model outputs a set of images based on the input prompt, with options to control the number of outputs, guidance scale, and number of inference steps. Inputs Prompt**: A text description of the desired image to generate Negative Prompt**: An optional text description of elements to exclude from the generated image Num Outputs**: The number of images to generate (between 1 and 4) Guidance Scale**: A factor to scale the image by (between 0 and 10) Num Inference Steps**: The number of inference steps to use (between 1 and 10) Seed**: An optional random seed value Outputs A set of generated images based on the input prompt Capabilities The lcm-ssd-1b model can generate a wide variety of images based on text prompts, from realistic scenes to abstract concepts. By reducing the number of inference steps, the model is able to generate images more efficiently, making it a useful tool for tasks that require faster image generation. What can I use it for? The lcm-ssd-1b model can be used for a variety of applications, such as creating concept art, generating product mockups, or even producing illustrations for articles or blog posts. The ability to control the number of outputs and other parameters can be particularly useful for tasks that require generating multiple variations of an image. Things to try One interesting thing to try with the lcm-ssd-1b model is experimenting with different prompts and negative prompts to see how the generated images change. You can also try adjusting the guidance scale and number of inference steps to see how these parameters affect the output. Additionally, you could explore using the model in combination with other tools or techniques, such as image editing software or other AI models, to create more complex or customized outputs.
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lcm-sdxl
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lcm-sdxl is a Latent Consistency Model (LCM) derived from the Stable Diffusion XL (SDXL) model. LCM is a novel approach that distills the original SDXL model, reducing the number of inference steps required from 25-50 down to just 4-8. This significantly improves the speed and efficiency of the image generation process, as demonstrated in the Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference research paper. The model was developed by Simian Luo, Suraj Patil, and Daniel Gu. Model inputs and outputs The lcm-sdxl model accepts various inputs for text-to-image generation, including a prompt, negative prompt, number of outputs, number of inference steps, and a random seed. The output is an array of image URLs representing the generated images. Inputs Prompt**: The text prompt describing the desired image Negative Prompt**: Text to exclude from the generated image Num Outputs**: The number of images to generate Num Inference Steps**: The number of inference steps to use (2-8 steps recommended) Seed**: A random seed value for reproducibility Outputs Output**: An array of image URLs representing the generated images Capabilities The lcm-sdxl model is capable of generating high-quality images from text prompts, with a significant improvement in speed compared to the original SDXL model. The model can be used for a variety of text-to-image tasks, including creating portraits, landscapes, and abstract art. What can I use it for? The lcm-sdxl model can be used for a wide range of applications, such as: Generating images for social media posts, blog articles, or marketing materials Creating custom artwork or illustrations for personal or commercial use Prototyping and visualizing ideas and concepts Enhancing existing images through prompts and fine-tuning The improved speed and efficiency of the lcm-sdxl model make it a valuable tool for businesses, artists, and creators who need to generate high-quality images quickly and cost-effectively. Things to try Some interesting things to try with the lcm-sdxl model include: Experimenting with different prompt styles and techniques to achieve unique and creative results Combining the model with other AI tools, such as ControlNet, to create more advanced image manipulation capabilities Exploring the model's ability to generate images in different styles, such as photo-realistic, abstract, or cartoonish Comparing the performance and output quality of lcm-sdxl to other text-to-image models, such as the original Stable Diffusion or SDXL models. By pushing the boundaries of what's possible with lcm-sdxl, you can unlock new creative possibilities and discover innovative applications for this powerful AI model.
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realvisxl2-lcm
292
realvisxl2-lcm is an implementation of the SG161222/RealVisXL_V2.0 model, created by lucataco, that uses a Latent Consistency Model (LCM) to require fewer steps (4 to 8) compared to the original 40 to 50 steps. This model builds on the realvisxl-v2.0 and sdxl-lcm models, also created by lucataco, which use LCM to speed up inference. Model inputs and outputs realvisxl2-lcm takes a prompt as input, along with optional parameters like image, seed, and guidance scale. It outputs one or more images based on the input. The model inputs and outputs are: Inputs Prompt**: The text prompt that describes the desired image. Image**: An optional input image for img2img or inpaint mode. Mask**: An optional input mask for inpaint mode, where black areas will be preserved and white areas will be inpainted. Seed**: An optional random seed value. Scheduler**: The scheduler to use, default is LCM. Num Outputs**: The number of images to generate, up to 4. Guidance Scale**: The scale for classifier-free guidance. Num Inference Steps**: The number of denoising steps. Prompt Strength**: The strength of the prompt when using img2img or inpaint. Disable Safety Checker**: Whether to disable the safety checker for generated images. Outputs One or more generated images, in the form of URIs. Capabilities realvisxl2-lcm is a photorealistic image generation model that can create high-quality images of people, objects, and scenes. It can handle a wide range of prompts, from specific details like "25 y.o latino man" to more abstract concepts like "cinematic shot". The model's use of LCM allows for faster inference compared to the original RealVisXL_V2.0 model. What can I use it for? realvisxl2-lcm can be used for a variety of creative and commercial applications, such as: Generating realistic portraits and headshots for use in social media, marketing materials, or creative projects. Creating cinematic or dramatic images for use in film, photography, or other visual media. Producing high-quality product images or visualizations for e-commerce or marketing purposes. Experimenting with different visual styles and compositions by generating a variety of images from the same prompt. You can also explore other models created by lucataco, such as the sdxl-lcm and dreamshaper7-img2img-lcm models, which may have different capabilities or use cases. Things to try One interesting thing to try with realvisxl2-lcm is experimenting with the prompt strength and guidance scale parameters. Adjusting these values can result in images with different levels of detail, realism, and stylization. You can also try combining realvisxl2-lcm with other models or techniques, such as inpainting or image-to-image translation, to create unique and compelling visual effects.
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dreamshaper7-img2img-lcm
27
dreamshaper7-img2img-lcm is an AI model developed by lucataco that builds upon the Lykon/dreamshaper-7 model by incorporating Latent Consistency Model (LCM) LoRA for faster inference. This model is designed for image-to-image tasks, allowing users to generate new images based on an input image and a textual prompt. It is similar to other Stable Diffusion-based models like sdxl-lcm, dreamshaper-xl-turbo, dreamshaper-xl-lightning, latent-consistency-model, and pixart-lcm-xl-2, all developed by the same maintainer. Model inputs and outputs dreamshaper7-img2img-lcm takes a textual prompt and an input image as inputs, and generates a new image based on the prompt and the provided image. The model allows for various parameters to be adjusted, such as the seed, strength, guidance scale, and number of inference steps. Inputs Prompt**: The text description of the desired output image, e.g., "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k". Image**: The input image that will be used as the starting point for the image generation. Seed**: The random seed used for generating the output image. Leave blank to randomize the seed. Strength**: The strength of the prompt, where 1.0 corresponds to full destruction of information in the input image. Guidance Scale**: The scale for classifier-free guidance, which affects the balance between the input image and the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Output Image**: The generated image, based on the input prompt and image. Capabilities dreamshaper7-img2img-lcm is capable of generating high-quality, detailed images based on a textual description and an input image. The model can produce a wide range of visual styles, from realistic to fantastical, and can handle a variety of subjects, including landscapes, objects, and figures. The addition of the LCM LoRA component allows for faster inference, making the model more practical for real-world applications. What can I use it for? dreamshaper7-img2img-lcm can be used for a variety of creative and practical applications, such as: Generating concept art or illustrations for creative projects Producing custom images for marketing and advertising Enhancing or modifying existing images based on a specific vision or idea Experimenting with different visual styles and artistic expressions Things to try Some interesting things to try with dreamshaper7-img2img-lcm include: Combining different visual styles or elements in the prompt to see how the model blends them Exploring the model's ability to generate images based on specific historical or cultural references Using the model to create surreal or fantastical scenes that push the boundaries of what is visually possible Experimenting with the various input parameters to fine-tune the output and achieve desired results
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