# Z Image Turbo Controlnet Lora > Generate images from text and edge, depth or pose images using custom LoRA and Z-Image Turbo, Tongyi-MAI's super-fast 6B model. ## Overview - **Endpoint**: `https://fal.run/fal-ai/z-image/turbo/controlnet/lora` - **Model ID**: `fal-ai/z-image/turbo/controlnet/lora` - **Category**: image-to-image - **Kind**: inference **Tags**: turbo, z-image, fast, lora ## Pricing - **Price**: $0.01 per megapixels For more details, see [fal.ai pricing](https://fal.ai/pricing). ## API Information This model can be used via our HTTP API or more conveniently via our client libraries. See the input and output schema below, as well as the usage examples. ### Input Schema The API accepts the following input parameters: - **`prompt`** (`string`, _required_): The prompt to generate an image from. - Examples: "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation." - **`image_size`** (`ImageSize | Enum`, _optional_): The size of the generated image. Default value: `auto` - Default: `"auto"` - One of: ImageSize | Enum - **`num_inference_steps`** (`integer`, _optional_): The number of inference steps to perform. Default value: `8` - Default: `8` - Range: `1` to `8` - **`seed`** (`integer`, _optional_): The same seed and the same prompt given to the same version of the model will output the same image every time. - **`sync_mode`** (`boolean`, _optional_): If `True`, the media will be returned as a data URI and the output data won't be available in the request history. - Default: `false` - **`num_images`** (`integer`, _optional_): The number of images to generate. Default value: `1` - Default: `1` - Range: `1` to `4` - **`enable_safety_checker`** (`boolean`, _optional_): If set to true, the safety checker will be enabled. Disabling it requires account authorization; unauthorized requests are always checked, and images flagged as unsafe are returned as black images. Default value: `true` - Default: `true` - **`output_format`** (`OutputFormatEnum`, _optional_): The format of the generated image. Default value: `"png"` - Default: `"png"` - Options: `"jpeg"`, `"png"`, `"webp"` - **`acceleration`** (`AccelerationEnum`, _optional_): The acceleration level to use. Default value: `"regular"` - Default: `"regular"` - Options: `"none"`, `"regular"`, `"high"` - **`enable_prompt_expansion`** (`boolean`, _optional_): Whether to enable prompt expansion. Note: this will increase the price by 0.0025 credits per request. - Default: `false` - **`image_url`** (`string`, _required_): URL of Image for ControlNet generation. - Examples: "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg" - **`control_scale`** (`float`, _optional_): The scale of the controlnet conditioning. Default value: `0.75` - Default: `0.75` - Range: `0` to `1` - **`control_start`** (`float`, _optional_): The start of the controlnet conditioning. - Default: `0` - Range: `0` to `1` - **`control_end`** (`float`, _optional_): The end of the controlnet conditioning. Default value: `0.8` - Default: `0.8` - Range: `0` to `1` - **`preprocess`** (`Enum`, _optional_): What kind of preprocessing to apply to the image, if any. Default value: `none` - Default: `"none"` - Options: `"none"`, `"canny"`, `"depth"`, `"pose"` - Examples: "none" - **`loras`** (`list`, _optional_): List of LoRA weights to apply (maximum 3). - Default: `[]` - Array of LoRAInput **Required Parameters Example**: ```json { "prompt": "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.", "image_url": "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg" } ``` **Full Example**: ```json { "prompt": "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.", "image_size": "auto", "num_inference_steps": 8, "num_images": 1, "enable_safety_checker": true, "output_format": "png", "acceleration": "regular", "image_url": "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg", "control_scale": 0.75, "control_end": 0.8, "preprocess": "none", "loras": [] } ``` ### Output Schema The API returns the following output format: - **`images`** (`list`, _required_): The generated image files info. - Array of ImageFile - Examples: [{"content_type":"image/png","width":1536,"height":1024,"url":"https://storage.googleapis.com/falserverless/example_outputs/z-image-turbo-controlnet-output.jpg"}] - **`timings`** (`Timings`, _required_): The timings of the generation process. - **`seed`** (`integer`, _required_): Seed of the generated Image. It will be the same value of the one passed in the input or the randomly generated that was used in case none was passed. - **`has_nsfw_concepts`** (`list`, _required_): Whether the generated images contain NSFW concepts. - Array of boolean - **`prompt`** (`string`, _required_): The prompt used for generating the image. **Example Response**: ```json { "images": [ { "content_type": "image/png", "width": 1536, "height": 1024, "url": "https://storage.googleapis.com/falserverless/example_outputs/z-image-turbo-controlnet-output.jpg" } ], "prompt": "" } ``` ## Usage Examples ### cURL ```bash curl --request POST \ --url https://fal.run/fal-ai/z-image/turbo/controlnet/lora \ --header "Authorization: Key $FAL_KEY" \ --header "Content-Type: application/json" \ --data '{ "prompt": "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.", "image_url": "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg" }' ``` ### Python Ensure you have the Python client installed: ```bash pip install fal-client ``` Then use the API client to make requests: ```python import fal_client def on_queue_update(update): if isinstance(update, fal_client.InProgress): for log in update.logs: print(log["message"]) result = fal_client.subscribe( "fal-ai/z-image/turbo/controlnet/lora", arguments={ "prompt": "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.", "image_url": "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg" }, with_logs=True, on_queue_update=on_queue_update, ) print(result) ``` ### JavaScript Ensure you have the JavaScript client installed: ```bash npm install --save @fal-ai/client ``` Then use the API client to make requests: ```javascript import { fal } from "@fal-ai/client"; const result = await fal.subscribe("fal-ai/z-image/turbo/controlnet/lora", { input: { prompt: "A single leopard, its spotted golden coat detailed with black rosettes, cautiously peeks its head through dense green foliage. The leopard’s eyes are alert and focused forward, ears perked, whiskers slightly visible. The bushes consist of thick, leafy shrubs with varying shades of green, some leaves partially obscuring the leopard’s muzzle and forehead. Soft natural daylight filters through the canopy above, casting dappled shadows across the animal’s fur and surrounding leaves. The composition is a medium close-up, centered on the leopard’s head emerging from the undergrowth, with shallow depth of field blurring the background vegetation.", image_url: "https://storage.googleapis.com/falserverless/example_inputs/z-image-turbo-controlnet-input.jpg" }, logs: true, onQueueUpdate: (update) => { if (update.status === "IN_PROGRESS") { update.logs.map((log) => log.message).forEach(console.log); } }, }); console.log(result.data); console.log(result.requestId); ``` ## Additional Resources ### Documentation - [Model Playground](https://fal.ai/models/fal-ai/z-image/turbo/controlnet/lora) - [API Documentation](https://fal.ai/models/fal-ai/z-image/turbo/controlnet/lora/api) - [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/z-image/turbo/controlnet/lora) ### fal.ai Platform - [Platform Documentation](https://fal.ai/docs/documentation) - [Python Client](https://fal.ai/docs/api-reference/client-libraries/python) - [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript) ### Other agent-readable surfaces This file covers one model. To find anything else: - [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs - [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page - [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined - Any other model: `https://fal.ai/models//llms.txt`