> ## Documentation Index > Fetch the complete documentation index at: https://docs.together.ai/llms.txt > Use this file to discover all available pages before exploring further. # Text-to-image generation > Generate images from text prompts. Using a coding agent? Install the [together-images](https://github.com/togethercomputer/skills/tree/main/skills/together-images) skill to let your agent write correct image generation code automatically. See [agent skills](/docs/agent-skills) for details. ## Generate an image To query an image model, use the `.images` method and specify the image model: ```python Python theme={null} from together import Together client = Together() # Generate an image from a text prompt response = client.images.generate( prompt="A serene mountain landscape at sunset with a lake reflection", model="black-forest-labs/FLUX.1-schnell", steps=4, ) print(f"Image URL: {response.data[0].url}") ``` ```typescript TypeScript theme={null} import Together from "together-ai"; const together = new Together(); async function main() { const response = await together.images.generate({ prompt: "A serene mountain landscape at sunset with a lake reflection", model: "black-forest-labs/FLUX.1-schnell", steps: 4, }); console.log(response.data[0].url); } main(); ``` ```bash cURL theme={null} curl -X POST "https://api.together.ai/v1/images/generations" \ -H "Authorization: Bearer $TOGETHER_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "black-forest-labs/FLUX.1-schnell", "prompt": "A serene mountain landscape at sunset with a lake reflection", "steps": 4 }' ``` Example response structure and output: ```json theme={null} { "id": "oFuwv7Y-2kFHot-99170ebf9e84e0ce-SJC", "model": "black-forest-labs/FLUX.1-schnell", "data": [ { "index": 0, "url": "https://api.together.ai/v1/images/..." } ] } ``` Generated image of a serene mountain landscape at sunset with a lake reflection. ## Save to disk The recommended pattern for saving a generated image locally is `response_format="base64"`. This avoids a separate download step and works without any special HTTP headers. ```python Python theme={null} import base64 from together import Together client = Together() response = client.images.generate( prompt="A serene mountain landscape at sunset with a lake reflection", model="black-forest-labs/FLUX.1-schnell", steps=4, response_format="base64", ) image_data = base64.b64decode(response.data[0].b64_json) with open("output.png", "wb") as f: f.write(image_data) print(f"Saved {len(image_data)} bytes to output.png") ``` ```typescript TypeScript theme={null} import { writeFileSync } from "fs"; import Together from "together-ai"; const together = new Together(); const response = await together.images.generate({ prompt: "A serene mountain landscape at sunset with a lake reflection", model: "black-forest-labs/FLUX.1-schnell", steps: 4, response_format: "base64", }); const imageData = Buffer.from(response.data[0].b64_json!, "base64"); writeFileSync("output.png", imageData); console.log(`Saved ${imageData.length} bytes to output.png`); ``` If you use the returned `url` instead, download it with a non-empty `User-Agent` header. The CDN rejects requests with a blank User-Agent (HTTP 403): ```python Python theme={null} import urllib.request req = urllib.request.Request( response.data[0].url, headers={"User-Agent": "my-app/1.0"}, ) with urllib.request.urlopen(req) as r: with open("output.png", "wb") as f: f.write(r.read()) ``` ## Supported models For the current list of image-generation models, see the [serverless catalog](/docs/serverless/models) or the [dedicated endpoint model catalog](/docs/dedicated-endpoints/models). The table below shows the most commonly used serverless image models. | Model | API string | Serverless | Notes | | ------------------ | -------------------------------------- | :--------: | ---------------------------------------------- | | FLUX.1 Schnell | `black-forest-labs/FLUX.1-schnell` | ✓ | Fastest; default 4 steps. Good starting point. | | FLUX.1.1 Pro | `black-forest-labs/FLUX.1.1-pro` | ✓ | Higher quality than Schnell. | | FLUX.2 Pro | `black-forest-labs/FLUX.2-pro` | ✓ | High fidelity; supports reference images. | | FLUX.2 Dev | `black-forest-labs/FLUX.2-dev` | ✓ | Supports `guidance`, `steps`, and LoRAs. | | FLUX.2 Flex | `black-forest-labs/FLUX.2-flex` | ✓ | Adjustable steps/guidance; best typography. | | FLUX.1 Kontext Pro | `black-forest-labs/FLUX.1-kontext-pro` | ✓ | Image editing via `image_url`. | | FLUX.2 Max | `black-forest-labs/FLUX.2-max` | ✓ | Highest fidelity FLUX.2 variant. | | FLUX.1 Kontext Max | `black-forest-labs/FLUX.1-kontext-max` | ✓ | Highest-quality image editing. | ## Parameters The image generation endpoint accepts a common set of parameters across models, including `prompt`, `model`, `width`, `height`, `n`, `steps`, `seed`, and `negative_prompt`. For the full parameter reference (including `image_url`, `reference_images`, `frame_images`, and model-specific notes), see [Image generation parameters](/docs/inference/images/parameters). A few quick notes: * `prompt` is required for all models except Kling. * `width` and `height` rely on defaults unless otherwise specified. Available dimensions differ by model. * FLUX Schnell and Kontext (Pro/Max/Dev) models use the `aspect_ratio` parameter to set the output image size, whereas FLUX.1 Pro, FLUX 1.1 Pro, and FLUX.1 Dev use `width` and `height`. ## Generate multiple variations Generate multiple variations of the same prompt and choose between them: ```python Python theme={null} response = client.images.generate( prompt="A cute robot assistant helping in a modern office", model="black-forest-labs/FLUX.1-schnell", n=4, steps=4, ) print(f"Generated {len(response.data)} variations") for i, image in enumerate(response.data): print(f"Variation {i+1}: {image.url}") ``` ```typescript TypeScript theme={null} const response = await together.images.generate({ prompt: "A cute robot assistant helping in a modern office", model: "black-forest-labs/FLUX.1-schnell", n: 4, steps: 4, }); console.log(`Generated ${response.data.length} variations`); response.data.forEach((image, i) => { console.log(`Variation ${i + 1}: ${image.url}`); }); ``` ```bash cURL theme={null} curl -X POST "https://api.together.ai/v1/images/generations" \ -H "Authorization: Bearer $TOGETHER_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "black-forest-labs/FLUX.1-schnell", "prompt": "A cute robot assistant helping in a modern office", "n": 4, "steps": 4 }' ``` Example output: Multiple generated image variations ## Next steps * [Image-to-image generation](/docs/inference/images/reference-images): edit or transform an existing image with `image_url` or `reference_images`. * [Image generation parameters](/docs/inference/images/parameters): full parameter reference, custom dimensions, quality control, base64 responses, safety checker, and troubleshooting.