flux-fp8
Maintainer: Kijai
480
📈
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The flux-fp8 model is a float8 quantized version of the FLUX.1-dev and FLUX.1-schnell models developed by Black Forest Labs. These are 12 billion parameter rectified flow transformers capable of generating images from text descriptions. The FLUX.1-dev model is optimized for open research and innovation, while the FLUX.1-schnell model is focused on competitive performance. The flux-fp8 model aims to provide the same capabilities as these larger models, but with reduced memory and computational requirements through 8-bit floating point quantization.
Model inputs and outputs
The flux-fp8 model takes text descriptions as input and generates high-quality, photorealistic images as output. The model was trained using advanced techniques like latent adversarial diffusion distillation, which allows for fast image generation in just 1-4 steps.
Inputs
- Text descriptions to guide the image generation process
Outputs
- Photorealistic images generated from the input text descriptions
Capabilities
The flux-fp8 model is capable of generating a wide variety of images, from landscapes and cityscapes to portraits and abstract art. It can capture fine details and complex compositions, and has shown strong performance in prompt following compared to other open-source alternatives.
What can I use it for?
The flux-fp8 model can be used for a variety of creative and commercial applications, such as concept art, product visualization, and illustration. Developers and artists can incorporate the model into their workflows using the reference implementation and sampling code provided in the Black Forest Labs GitHub repository. The model is also available through API endpoints from bfl.ml, replicate.com, and fal.ai, making it accessible to a wide range of users.
Things to try
Experiment with different prompting styles and techniques to see how the flux-fp8 model responds. Try using more specific or detailed descriptions, or combining the model with other tools like ComfyUI for a node-based workflow. The quantized nature of the flux-fp8 model may also lead to interesting visual effects or artifacts that you can explore and incorporate into your creative projects.
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
🎯
LivePortrait_safetensors
51
The LivePortrait_safetensors model is an AI model that can be used for image-to-image tasks. Similar models include furryrock-model-safetensors, ControlNet-modules-safetensors, DynamiCrafter_pruned, and sakasadori. These models share some common capabilities when it comes to image generation and manipulation. Model inputs and outputs The LivePortrait_safetensors model takes image data as input and generates new or modified images as output. The specific input and output formats are not provided in the description. Inputs Image data Outputs Generated or modified image data Capabilities The LivePortrait_safetensors model is capable of performing image-to-image transformations. This could include tasks such as style transfer, image inpainting, or image segmentation. The model's exact capabilities are not detailed in the provided information. What can I use it for? The LivePortrait_safetensors model could be used for a variety of image-related applications, such as photo editing, digital art creation, or even as part of a larger computer vision pipeline. By leveraging the model's ability to generate and manipulate images, users may be able to create unique visual content or automate certain image processing tasks. However, the specific use cases for this model are not outlined in the available information. Things to try With the LivePortrait_safetensors model, you could experiment with different input images and explore how the model transforms or generates new visuals. You might try using the model to enhance existing photos, create stylized artwork, or even generate entirely new images based on your creative ideas. The model's flexibility and capabilities could enable a wide range of interesting applications, though the specific limitations and best practices for using this model are not provided.
Updated Invalid Date
🚀
flux1-schnell
107
The flux1-schnell model is a text-to-text AI model developed by Comfy-Org. It has weights in FP8, which allows it to run much faster and use less memory in the ComfyUI platform. This model is similar to other flux1 models like flux1-dev and the [FLUX.1 [schnell]](https://aimodels.fyi/models/huggingFace/flux1-schnell-black-forest-labs) model from Black Forest Labs. Model inputs and outputs The flux1-schnell model takes text prompts as input and generates corresponding text outputs. This allows users to generate human-readable text based on provided descriptions. Inputs Text prompts that describe the desired output Outputs Generated text that matches the input prompts Capabilities The flux1-schnell model can generate high-quality text outputs that closely match the provided input prompts. It is optimized for speed and efficiency, making it well-suited for applications that require fast text generation. What can I use it for? The flux1-schnell model could be used for a variety of text generation tasks, such as creating product descriptions, writing short stories, or generating captions for images. Its efficient design also makes it a good choice for local development and personal use cases within the ComfyUI platform. Things to try One interesting thing to try with the flux1-schnell model is experimenting with different prompting styles to see how it affects the generated text outputs. Subtle variations in the prompts can lead to significantly different results, so it's worth exploring the model's capabilities across a range of input formats.
Updated Invalid Date
🔮
FLUX.1-schnell
2.0K
FLUX.1 [schnell] is a cutting-edge text-to-image generation model developed by the team at black-forest-labs. With a 12 billion parameter architecture, the model can generate high-quality images from text descriptions, matching the performance of closed-source alternatives. The model was trained using latent adversarial diffusion distillation, allowing it to produce impressive results in just 1 to 4 steps. Model inputs and outputs FLUX.1 [schnell] takes text descriptions as input and generates corresponding images as output. The model can handle a wide range of prompts, from simple object descriptions to more complex scenes and concepts. Inputs Text descriptions of the desired image Outputs High-quality images matching the input text prompts Capabilities FLUX.1 [schnell] demonstrates impressive text-to-image generation capabilities, with the ability to capture intricate details and maintain faithful representation of the provided prompts. The model's performance is on par with leading closed-source alternatives, making it a compelling option for developers and creators looking to leverage state-of-the-art image generation technology. What can I use it for? FLUX.1 [schnell] can be a valuable tool for a variety of applications, such as: Rapid prototyping and visualization for designers, artists, and product developers Generating custom images for marketing, advertising, and content creation Powering creative AI-driven applications and experiences Enabling novel use cases in areas like entertainment, education, and research Things to try Explore the limits of FLUX.1 [schnell]'s capabilities by experimenting with a diverse range of text prompts, from simple object descriptions to more complex scenes and concepts. Additionally, try combining FLUX.1 [schnell] with other AI models or tools to develop unique and innovative applications.
Updated Invalid Date
📶
SSD-1B-anime
51
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 Invalid Date