> ## Documentation Index > Fetch the complete documentation index at: https://docs.venice.ai/llms.txt > Use this file to discover all available pages before exploring further. # Image Generation > Generate images from text prompts with Venice's native image API or the OpenAI-compatible images endpoint, with style controls and binary or base64 output. Image generation on Venice is synchronous. Send a prompt to `/image/generate` and receive your image in the same response, either as base64 inside JSON or as raw binary when `return_binary` is `true`. ## Endpoints | Endpoint | Purpose | When to use | | -------------------------- | -------------------------------------- | ----------------------------------------------------- | | `POST /image/generate` | Native Venice image generation API | Use this for full feature support | | `GET /image/styles` | List available style presets | Use this before sending `style_preset` | | `POST /images/generations` | OpenAI-compatible image generation API | Use this when migrating existing OpenAI image clients | ## Step 1: Send a generation request Sizing is model-specific. Some models accept explicit `width` and `height`; some expose `aspect_ratio`; and resolution-tier models expose `aspect_ratio` plus `resolution` values such as `1K`, `2K`, or `4K`. **Pixel-based sizing example:** ```bash theme={"system"} POST https://api.venice.ai/api/v1/image/generate Authorization: Bearer $VENICE_API_KEY Content-Type: application/json { "model": "venice-sd35", "prompt": "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour, warm window lights reflecting on the water", "negative_prompt": "blurry, low quality, distorted anatomy, text, watermark", "width": 1024, "height": 1024, "format": "webp" } ``` **Aspect-ratio sizing example:** ```bash theme={"system"} POST https://api.venice.ai/api/v1/image/generate Authorization: Bearer $VENICE_API_KEY Content-Type: application/json { "model": "qwen-image-2", "prompt": "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour", "aspect_ratio": "16:9", "format": "webp" } ``` **Resolution-tier sizing example:** ```bash theme={"system"} POST https://api.venice.ai/api/v1/image/generate Authorization: Bearer $VENICE_API_KEY Content-Type: application/json { "model": "gpt-image-2", "prompt": "A cinematic wide shot of a gondola passing through a narrow Venice canal at blue hour", "aspect_ratio": "16:9", "resolution": "4K", "format": "png" } ``` The same pattern applies to other resolution-tier models: ```json theme={"system"} { "model": "nano-banana-pro", "prompt": "A serene canal in Venice at sunset", "aspect_ratio": "16:9", "resolution": "2K" } ``` Use [Image Models](/models/image) or the [Models API](/api-reference/endpoint/models/list) to confirm which sizing fields each model accepts. **Response (200):** ```json theme={"system"} { "id": "generate-image-1234567890", "images": [ "UklGRiIAAABXRUJQVlA4IBYAAAAwAQCdASoQABAAPm..." ], "timing": { "inferenceDuration": 1840, "inferencePreprocessingTime": 22, "inferenceQueueTime": 31, "total": 1893 } } ``` The `images` array contains base64-encoded image data. Decode the first item to save or display it. `timing.total` is the full request duration in milliseconds. ## Step 2: Decode and save the image ```python Python theme={"system"} import base64 import os import requests response = requests.post( "https://api.venice.ai/api/v1/image/generate", headers={ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}", "Content-Type": "application/json", }, json={ "model": "venice-sd35", "prompt": "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour, warm window lights reflecting on the water", "width": 1024, "height": 1024, "format": "webp", }, ) data = response.json() image_bytes = base64.b64decode(data["images"][0]) with open("output.webp", "wb") as f: f.write(image_bytes) print(f"Saved image from request {data['id']}") ``` ```javascript Node.js theme={"system"} import fs from "fs"; const response = await fetch("https://api.venice.ai/api/v1/image/generate", { method: "POST", headers: { Authorization: `Bearer ${process.env.VENICE_API_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ model: "venice-sd35", prompt: "A cinematic photo of a gondola passing through a narrow Venice canal at blue hour, warm window lights reflecting on the water", width: 1024, height: 1024, format: "webp", }), }); const data = await response.json(); const imageBuffer = Buffer.from(data.images[0], "base64"); fs.writeFileSync("output.webp", imageBuffer); console.log(`Saved image from request ${data.id}`); ``` ## Step 3: Return binary instead of JSON (optional) If you want the response body to be the image file itself, set `return_binary: true`. This is useful when you want to stream or save the image directly without base64 decoding. ```bash theme={"system"} curl https://api.venice.ai/api/v1/image/generate \ -H "Authorization: Bearer $VENICE_API_KEY" \ -H "Content-Type: application/json" \ -o output.png \ -d '{ "model": "qwen-image-2", "prompt": "Minimalist poster of a moonlit Venetian bridge in deep blue tones", "format": "png", "return_binary": true }' ``` When `return_binary` is `true`, the response body is raw `image/jpeg`, `image/png`, or `image/webp` data based on the `format` you requested. `variants` is only supported when `return_binary` is `false`. *** ## Step 4: List available image styles (optional) If you want to use `style_preset`, first fetch the available styles from `/image/styles`: ```bash theme={"system"} curl https://api.venice.ai/api/v1/image/styles \ -H "Authorization: Bearer $VENICE_API_KEY" ``` **Response (200):** ```json theme={"system"} [ "3D Model", "Analog Film", "Anime", "Cinematic", "Digital Art" ] ``` Then pass one of those values into your generation request: ```json theme={"system"} { "model": "qwen-image-2", "prompt": "A futuristic Venice skyline at sunrise", "style_preset": "Cinematic" } ``` Use the styles endpoint when you want exact preset names instead of guessing them. *** ## Request Parameters | Parameter | Type | Required | Default | Description | | ------------------- | ------- | ----------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------- | | `model` | string | Yes | - | Model ID to use for generation | | `prompt` | string | Yes | - | What to generate | | `negative_prompt` | string | No | - | What to avoid in the image | | `width` | integer | No | `1024` | Output width in pixels for pixel-based models such as `venice-sd35` and `qwen-image` | | `height` | integer | No | `1024` | Output height in pixels for pixel-based models such as `venice-sd35` and `qwen-image` | | `format` | string | No | `webp` | Output format: `jpeg`, `png`, or `webp` | | `variants` | integer | No | `1` | Number of images to generate (`1`-`4`), only when `return_binary` is `false` | | `return_binary` | boolean | No | `false` | Return raw image bytes instead of base64 JSON | | `safe_mode` | boolean | No | `true` | Blur adult content when enabled | | `seed` | integer | No | random | Reuse the same seed for more consistent iterations | | `cfg_scale` | number | No | model-dependent | Higher values push the model to follow the prompt more closely | | `style_preset` | string | No | - | Apply a preset style from [Image Styles](/api-reference/endpoint/image/styles) | | `aspect_ratio` | string | Conditional | - | Used by models that support ratio-based sizing, such as `qwen-image-2`, `gpt-image-2`, `nano-banana-2`, and `nano-banana-pro` | | `resolution` | string | Conditional | - | Used by models that support resolution tiers such as `1K`, `2K`, or `4K` | | `enable_web_search` | boolean | Conditional | `false` | Allows supported models to use current web information; adds extra cost | Validation is model-specific. Check [Image Models](/models/image) and the [Models API](/api-reference/endpoint/models/list) before relying on a parameter across multiple models. *** ## Model-specific options ### High-resolution generation Some image models support `aspect_ratio` without a selectable `resolution` tier. For example, `qwen-image-2` accepts aspect ratio and maps it to model-specific output dimensions: ```json theme={"system"} { "model": "qwen-image-2", "prompt": "Editorial product photo of a luxury watch on black marble, dramatic studio lighting", "aspect_ratio": "16:9" } ``` Other image models support `aspect_ratio` plus a `resolution` tier. For example, `gpt-image-2`, `nano-banana-2`, and `nano-banana-pro` support `1K`, `2K`, and `4K`: ```json theme={"system"} { "model": "gpt-image-2", "prompt": "Editorial product photo of a luxury watch on black marble, dramatic studio lighting", "aspect_ratio": "16:9", "resolution": "4K" } ``` ```json theme={"system"} { "model": "nano-banana-2", "prompt": "Editorial product photo of a luxury watch on black marble, dramatic studio lighting", "aspect_ratio": "16:9", "resolution": "2K" } ``` Use [Image Models](/models/image) to see which models support higher resolutions and how they are priced. ### Style presets If the selected model supports it, `style_preset` lets you steer the output without rewriting your whole prompt. You can fetch valid preset names from [Image Styles](/api-reference/endpoint/image/styles): ```json theme={"system"} { "model": "qwen-image-2", "prompt": "A futuristic Venice skyline at sunrise", "style_preset": "3D Model" } ``` See [Image Styles](/api-reference/endpoint/image/styles) for the current style list. *** ## OpenAI-compatible endpoint If you're already using OpenAI image SDKs or existing DALL-E integrations, Venice also supports `POST /images/generations`. It offers a simpler request format, but fewer features than the native Venice endpoint. **Request:** ```json theme={"system"} { "model": "qwen-image-2", "prompt": "A clean isometric illustration of an AI control room", "size": "1024x1024", "response_format": "b64_json" } ``` Use the OpenAI-compatible route for faster migrations. Use `/image/generate` when you need Venice-specific options such as `cfg_scale`, `style_preset`, `variants`, or binary responses. *** ## Prompting tips 1. Start with the subject, then add medium, lighting, composition, and mood. 2. Put must-avoid details in `negative_prompt` instead of overloading the main prompt. 3. Reuse `seed` when iterating so you can compare prompt changes without fully changing the composition. 4. Keep sizing model-aware. Some models use `width`/`height`, some use `aspect_ratio`, and resolution-tier models use `aspect_ratio` plus `resolution`. 5. Use `variants` during exploration, then switch back to a single output once you've locked in the direction. *** ## Errors | Status | Meaning | Action | | ------ | ------------------------------------------------------------ | ----------------------------------------------------------------------- | | `400` | Invalid request parameters | Check field names, types, and model-specific constraints | | `401` | Authentication failed or model requires a higher access tier | Check your API key and model access | | `402` | Insufficient balance | Add credits at [venice.ai/settings/api](https://venice.ai/settings/api) | | `415` | Invalid content type | Send JSON with `Content-Type: application/json` | | `429` | Rate limit exceeded or model overloaded | Retry with backoff; check `Retry-After` header | | `500` | Inference processing failed | Retry the request | | `503` | Model at capacity | Retry after a short delay | When Safe Venice is enabled, inspect response headers such as `x-venice-is-blurred` and `x-venice-is-content-violation` if you need to detect moderation outcomes programmatically. *** ## Available Models See [Image Models](/models/image) for the current model list, pricing, and feature support.