> ## Documentation Index > Fetch the complete documentation index at: https://docs.livepeer.org/llms.txt > Use this file to discover all available pages before exploring further. # Text-to-Image ## Overview The `text-to-image` pipeline of the Livepeer AI network allows you to generate **high-quality** images from text descriptions. This pipeline is powered by the latest diffusion models in the HuggingFace [text-to-image](https://huggingface.co/models?pipeline_tag=text-to-image) pipeline.
```mermaid theme={"theme":{"light":"github-light","dark":"dark-plus"}} graph LR A["A cool cat on the beach"] --> B[Gateway] B --> C[Orchestrator] C --> B B --> D[
Image of cool cat
] ```
## Models ### Warm Models The current warm model requested for the `text-to-image` pipeline is: * [SG161222/RealVisXL\_V4.0\_Lightning](https://huggingface.co/SG161222/RealVisXL_V4.0_Lightning): A streamlined version of RealVisXL\_V4.0, designed for faster inference while still aiming for photorealism. Furthermore, several Orchestrators are currently maintaining the following model in a ready state: * [ByteDance/SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning): A high-performance diffusion model developed by ByteDance. For faster responses with different [text-to-image](https://huggingface.co/models?pipeline_tag=text-to-image) diffusion models, ask Orchestrators to load it on their GPU via the `ai-video` channel in [Discord Server](https://discord.gg/livepeer). ### On-Demand Models The following models have been tested and verified for the `text-to-image` pipeline: If a specific model you wish to use is not listed, please submit a [feature request](https://github.com/livepeer/ai-worker/issues/new?assignees=\&labels=enhancement%2Cmodel\&projects=\&template=model_request.yml) on GitHub to get the model verified and added to the list. * [SG161222/Realistic\_Vision\_V6.0\_B1\_noVAE](https://huggingface.co/SG161222/Realistic_Vision_V6.0_B1_noVAE): Latest (experimental) release of the Realistic Vision model specialized in creating photorealistic portraits. * [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): A base model for stable diffusion by Stability AI. * [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5): A stable diffusion model by Runway ML. * [prompthero/openjourney-v4](https://huggingface.co/prompthero/openjourney-v4): A model by Prompthero for open-ended journey generation. * [ByteDance/SDXL-Lightning](https://huggingface.co/ByteDance/SDXL-Lightning): A lightning-fast diffusion model by ByteDance. * [SG161222/RealVisXL\_V4.0](https://huggingface.co/SG161222/RealVisXL_V4.0): A diffusion model that excels in generating high-quality, photorealistic images. * [SG161222/RealVisXL\_V4.0\_Lightning](https://huggingface.co/SG161222/RealVisXL_V4.0_Lightning): A streamlined version of RealVisXL\_V4.0, designed for faster inference while still aiming for photorealism. * [stabilityai/sd-turbo](https://huggingface.co/stabilityai/sd-turbo): A high-performance diffusion model by Stability AI ([limited-commercial use license](https://stability.ai/license)). * [stabilityai/sdxl-turbo](https://huggingface.co/stabilityai/sdxl-turbo): An extended version of sd-turbo with enhanced capabilities ([limited-commercial use license](https://stability.ai/license)). * [stabilityai/stable-diffusion-3-medium-diffusers](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers/): A Multimodal Diffusion Transformer (MMDiT) model with superior image quality, advanced typography, and enhanced prompt comprehension ([limited-commercial use license](https://stability.ai/license)). ## Basic Usage Instructions For a detailed understanding of the `text-to-image` endpoint and to experiment with the API, see the [Livepeer AI API Reference](/ai/api-reference/text-to-image). For examples of effective prompts, visit [PromptHero](https://prompthero.com/). To generate an image with the `text-to-image` pipeline, send a `POST` request to the Gateway's `text-to-image` API endpoint: ```bash theme={"theme":{"light":"github-light","dark":"dark-plus"}} curl -X POST "https:///text-to-image" \ -H "Content-Type: application/json" \ -d '{ "model_id":"ByteDance/SDXL-Lightning", "prompt":"A cool cat on the beach", "width": 1024, "height": 1024 }' ``` In this command: * `` should be replaced with your AI Gateway's IP address. * `model_id` is the diffusion model for image generation. * `prompt` is the text description for the image. For additional optional parameters, refer to the [Livepeer AI API Reference](/ai/api-reference/text-to-image). After execution, the Orchestrator processes the request and returns the response to the Gateway: ```json theme={"theme":{"light":"github-light","dark":"dark-plus"}} { "images": [ { "nsfw": false, "seed": 2562822894, "url": "https:///stream/d0fc1fc6/8fdf5a94.png" } ] } ``` The `url` in the response is the URL of the generated image. Download the image with: ```bash theme={"theme":{"light":"github-light","dark":"dark-plus"}} curl -O "https:///stream/d0fc1fc6/8fdf5a94.png" ``` ## Applying LoRa Models To apply LoRa filters to an image, include the `loras` field in your request: ```bash theme={"theme":{"light":"github-light","dark":"dark-plus"}} curl -X POST "https:///text-to-image" \ -H "Content-Type: application/json" \ -d '{ "model_id":"stabilityai/stable-diffusion-xl-base-1.0", "prompt":"A cool cat on the beach", "width": 1024, "height": 1024, "loras": "{ \"latent-consistency/lcm-lora-sdxl\": 1.0, \"nerijs/pixel-art-xl\": 1.2}" }' ``` You can find a list of available LoRa models for various models on [lora-studio](https://huggingface.co/spaces/enzostvs/lora-studio). ## Orchestrator Configuration To configure your Orchestrator to serve the `text-to-image` pipeline, refer to the [Orchestrator Configuration](/ai/orchestrators/get-started) guide. ### System Requirements The following system requirements are recommended for optimal performance: * [NVIDIA GPU](https://developer.nvidia.com/cuda-gpus) with **at least 24GB** of VRAM. ## Recommended Pipeline Pricing We are planning to simplify the pricing in the future so orchestrators can set one AI price per compute unit and have the system automatically scale based on the model's compute requirements. The pricing for the `text-to-image` pipeline is based on competitor pricing. However, we strongly encourage orchestrators to set their own pricing based on their costs and requirements. Setting a competitive price will help attract more jobs, as Gateways can set their maximum price for a job. The current recommended pricing for this pipeline is `1.9073484e-08 USD` per **output pixel** (`height * width * output images`). ## API Reference Explore the `text-to-image` endpoint and experiment with the API in the Livepeer AI API Reference.