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42:["$","$L45",null,{"hasPartnerEndpoint":false}] 4b:T1275,# Overview ## Description: FLUX.1 is a collection of generative image AI models creating high quality, realistic images: * FLUX.1-dev generates images from simple text prompts. * FLUX.1-Canny-dev combines the text prompt with an image input processed to canny edges to guide the output image structure. * FLUX.1-Depth-dev combines the text prompt with an image input processed to depth map leveraging LiheYoung/Depth-anything-large-hf model to guide the output image structure. This model is ready for non-commercial use. Contact sales@blackforestlabs.ai for commercial terms. ## Third-Party Community Consideration: This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to: * [black-forest-labs/FLUX.1-dev Model Card](https://huggingface.co/black-forest-labs/FLUX.1-dev) * [black-forest-labs/FLUX.1-Canny-dev Model Card](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) * [black-forest-labs/FLUX.1-Depth-dev Model Card](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) * [LiheYoung/Depth-anything-large-hf Model Card](https://huggingface.co/LiheYoung/depth-anything-large-hf) ### Terms of use GOVERNING TERMS: The trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Contact sales@blackforestlabs.ai for commercial terms to use the Flux.1-dev model. ADDITIONAL INFORMATION: [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md), [NVIDIA Community Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) and [Llama 2 Community Model License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama2/LICENSE). ### Deployment Geography: Global ## Use Case: Creators and professionals can use this model to generate high-quality images from text prompts, simplifying visual communication. ### Release Date: August 1, 2024 ### References * [Flux on Black Forest Labs](https://blackforestlabs.ai/) * [Flux blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) ## Model Architecture: **Architecture Type**: Transformer and Convolutional Neural Network (CNN) **Network Architecture**: Diffusion Transformer LiheYoung/Depth-anything-large-hf leverages the DPT architecture with a DINOv2 backbone. ## Input: **Input Type**: Text, Image (optional) **Input Parameters**: Text: 1D. Image: 2D **Input Format**: Text: String. Image: Red, Green, Blue (RGB) **Other Properties Related to Input**: Steps, Classifier-Free Guidance Scale, Output Image Aspect Ratio, and Seed per the [API Reference Page](https://docs.api.nvidia.com/nim/reference/black-forest-labs-flux_1-dev) ## Output: **Output Type**: Image **Output Parameters**: 2D **Output Format**: Red, Green, Blue (RGB) **Other Properties Related to Output**: 1024x1024, 768x1344, 1344x768, 1344x768, 1344x768, 1344x768, 1216x832 ## Software Integration: **Runtime Engines:** * TensorRT **Supported Hardware Platforms**: * NVIDIA Blackwell
* NVIDIA Hopper
* NVIDIA Lovelace
**Supported Operating Systems**: Linux, Windows Subsystem for Linux
## Model Version(s): * FLUX.1-dev * FLUX.1-Canny-dev * FLUX.1-Depth-dev * LiheYoung/Depth-anything-large-hf # Training, Testing, and Evaluation Datasets: ## Training Dataset: * Data Collection Method by Dataset: Undisclosed * Labeling Method by Dataset: Undisclosed **Properties (Quantity, Dataset Descriptions, Sensor(s)):** Undisclosed ## Testing Dataset: * Data Collection Method by Dataset: Undisclosed * Labeling Method by Dataset: Undisclosed **Properties (Quantity, Dataset Descriptions, Sensor(s)):** Undisclosed ## Evaluation Dataset: * Data Collection Method by Dataset: Undisclosed * Labeling Method by Dataset: Undisclosed **Properties (Quantity, Dataset Descriptions, Sensor(s)):** Undisclosed ## Inference: **Engine**: TensorRT **Test Hardware**: H100 ## Ethical Considerations: NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).4c:T557,invoke_url='https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev' authorization_header="Authorization: Bearer $NVIDIA_API_KEY" accept_header='Accept: application/json' content_type_header='Content-Type: application/json' data='{ "prompt": <%- JSON.stringify(request.prompt).replaceAll('\'', '\'\\\'\'') %>, <% if (request.mode !== "base") { %>"mode": <%- JSON.stringify(request.mode) %>, "image": "<%- request.image %>",<% } else { %>"mode": <%- JSON.stringify(request.mode) %>,<% } %> "cfg_scale": <%- request.cfg_scale %>, <% switch(request.ratio) { case "16:9": %>"width": 768, "height": 1344,<% break; case "9:16": %>"width": 1344, "height": 768,<% break; case "5:4": %>"width": 896, "height": 1152,<% break; case "4:5": %>"width": 1152, "height": 896,<% break; case "3:2": %>"width": 832, "height": 1216,<% break; case "2:3": %>"width": 1216, "height": 832,<% break; case "1:1": default: %>"width": 1024, "height": 1024,<% break; } %> "seed": <%- request.seed %>, "steps": <%- request.steps %> }' response=$(curl --silent -i -w "\n%{http_code}" --request POST \ --url "$invoke_url" \ --header "$authorization_header" \ --header "$accept_header" \ --header "$content_type_header" \ --data "$data" ) http_code=$(echo "$response" | tail -n 1) echo "$response" | awk '/{/,EOF-1' 4d:T5c9,import fetch from "node-fetch"; const invokeUrl = "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev" const headers = { "Authorization": "Bearer $NVIDIA_API_KEY", "Accept": "application/json", } const payload = { "prompt": <%- JSON.stringify(request.prompt).replaceAll('\'', '\'\\\'\'') %>, <% if (request.mode !== "base") { %>"mode": <%- JSON.stringify(request.mode) %>, "image": "<%- request.image %>",<% } else { %>"mode": <%- JSON.stringify(request.mode) %>,<% } %> "cfg_scale": <%- request.cfg_scale %>, <% switch(request.ratio) { case "16:9": %>"width": 768, "height": 1344,<% break; case "9:16": %>"width": 1344, "height": 768,<% break; case "5:4": %>"width": 896, "height": 1152,<% break; case "4:5": %>"width": 1152, "height": 896,<% break; case "3:2": %>"width": 832, "height": 1216,<% break; case "2:3": %>"width": 1216, "height": 832,<% break; case "1:1": default: %>"width": 1024, "height": 1024,<% break; } %> "seed": <%- request.seed %>, "steps": <%- request.steps %> } let response = await fetch(invokeUrl, { method: "post", body: JSON.stringify(payload), headers: { "Content-Type": "application/json", ...headers } }); if (response.status != 200) { let errBody = await (await response.blob()).text() throw "invocation failed with status " + response.status + " " + errBody } let response_body = await response.json() console.log(JSON.stringify(response_body)) 4e:T4d1,import requests invoke_url = "https://ai.api.nvidia.com/v1/genai/black-forest-labs/flux.1-dev" headers = { "Authorization": "Bearer $NVIDIA_API_KEY", "Accept": "application/json", } payload = { "prompt": <%- JSON.stringify(request.prompt).replaceAll('\'', '\'\\\'\'') %>, <% if (request.mode !== "base") { %>"mode": <%- JSON.stringify(request.mode) %>, "image": "<%- request.image %>",<% } else { %>"mode": <%- JSON.stringify(request.mode) %>,<% } %> "cfg_scale": <%- request.cfg_scale %>, <% switch(request.ratio) { case "16:9": %>"width": 768, "height": 1344,<% break; case "9:16": %>"width": 1344, "height": 768,<% break; case "5:4": %>"width": 896, "height": 1152,<% break; case "4:5": %>"width": 1152, "height": 896,<% break; case "3:2": %>"width": 832, "height": 1216,<% break; case "2:3": %>"width": 1216, "height": 832,<% break; case "1:1": default: %>"width": 1024, "height": 1024,<% break; } %> "seed": <%- request.seed %>, "steps": <%- request.steps %> } response = requests.post(invoke_url, headers=headers, json=payload) response.raise_for_status() response_body = response.json() print(response_body) 4f:Ta70,## Step 1. Get Credentials ::generate-api-key To access FLUX.1-dev model read and accept [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev), [FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev), [FLUX.1-Depth-dev](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) and [FLUX.1-dev-onnx](https://huggingface.co/black-forest-labs/FLUX.1-dev-onnx) License Agreements and Acceptable Use Policy. Create a new [Hugging Face token](https://huggingface.co/settings/tokens) with *Read access to contents of all public gated repos you can access* permission. Export your personal credentials as environment variables: ```bash export NGC_API_KEY= export HF_TOKEN= ``` ## Step 2. Pull and Run the NIM Login to NVIDIA NGC so that you can pull the NIM container: ```bash echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin ``` Pull and run the NIM with the command below. ```bash # Create the cache directory on the host machine. export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" chmod 777 $LOCAL_NIM_CACHE docker run -it --rm --name=nim-server \ --runtime=nvidia --gpus='"device=0"' \ -e NGC_API_KEY=$NGC_API_KEY \ -e HF_TOKEN=$HF_TOKEN \ -p 8000:8000 \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache/" \ nvcr.io/nim/black-forest-labs/flux.1-dev:latest ``` You can specify the desired variant of FLUX by adding ``-e NIM_MODEL_VARIANT=``. Available variants are `base`, `canny`, `depth` and their combinations, such as `base+depth`. When you run the preceding command, the container downloads the model, initializes a NIM inference pipeline, and performs a pipeline warm up. A pipeline warm up typically requires up to three minutes. The warm up is complete when the container logs show ``Pipeline warmup: start/done``. ## Step 3. Test the NIM ```bash invoke_url="http://localhost:8000/v1/infer" output_image_path="result.jpg" response=$(curl -X POST $invoke_url \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -d '{ "prompt": "A simple coffee shop interior", "mode": "base", "seed": 0, "steps": 50 }') response_body=$(echo "$response" | awk '/{/,EOF-1') echo $response_body | jq .artifacts[0].base64 | tr -d '"' | base64 --decode > $output_image_path ``` For more details on getting started with this NIM including configuring using parameters, visit the [Visual GenAI NIM docs](https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html?model=flux.1-dev).50:Te12,## Requirements * NVIDIA GeForce RTX 4080 or above (see [supported GPUs](https://docs.nvidia.com/nim/visual-genai/latest/support-matrix.html)) * Install the latest [NVIDIA GPU Driver](https://www.nvidia.com/en-us/drivers/) on Windows (Version 570+) ## Step 1. Ensure virtualization is enabled in the system BIOS In Windows, open the Task Manager. Select the Performance tab and click on CPU. Check if Virtualization is enabled. If it is disabled, see [here](https://support.microsoft.com/en-us/windows/enable-virtualization-on-windows-c5578302-6e43-4b4b-a449-8ced115f58e1) to enable. ## Step 2. Open the Windows Subsystem for Linux 2 WSL2 Distro [Install](https://assets.ngc.nvidia.com/products/api-catalog/rtx/NIMSetup.exe) the WSL2 Distro. Once installed, open the ``NVIDIA-Workbench`` WSL2 distro using the following command in the Windows terminal. ``` wsl -d NVIDIA-Workbench ``` ## Step 3. Export API Key ::generate-api-key

To access FLUX.1-dev model read and accept [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev), [FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev), [FLUX.1-Depth-dev](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) and [FLUX.1-dev-onnx](https://huggingface.co/black-forest-labs/FLUX.1-dev-onnx) License Agreements and Acceptable Use Policy. Create a new [Hugging Face token](https://huggingface.co/settings/tokens) with *Read access to contents of all public gated repos you can access* permission. Export your personal credentials as environment variables: ```bash export NGC_API_KEY= export HF_TOKEN= ``` ## Step 4. Login to NVIDIA NGC Login to NVIDIA NGC so that you can pull the NIM container: ```bash echo "$NGC_API_KEY" | podman login nvcr.io --username '$oauthtoken' --password-stdin ``` ## Step 5. Pull and Run NVIDIA NIM Pull and run the NIM with the command below. ```bash # Create the cache directory on the host machine. export LOCAL_NIM_CACHE=~/.cache/nim mkdir -p "$LOCAL_NIM_CACHE" chmod 777 $LOCAL_NIM_CACHE podman run -it --rm --name=nim-server \ --device nvidia.com/gpu=all \ -e NGC_API_KEY=$NGC_API_KEY \ -e HF_TOKEN=$HF_TOKEN \ -p 8000:8000 \ -v "$LOCAL_NIM_CACHE:/opt/nim/.cache/" \ nvcr.io/nim/black-forest-labs/flux.1-dev:latest ``` You can specify the desired variant of FLUX by adding ``-e NIM_MODEL_VARIANT=``. Available variants are `base`, `canny`, `depth` and their combinations, such as `base+depth`. When you run the preceding command, the container downloads the model, initializes a NIM inference pipeline, and performs a pipeline warm up. A pipeline warm up typically requires up to three minutes. The warm up is complete when the container logs show ``Pipeline warmup: start/done``. ## Step 6. Test the NIM ```bash invoke_url="http://localhost:8000/v1/infer" output_image_path="result.jpg" response=$(curl -X POST $invoke_url \ -H "Accept: application/json" \ -H "Content-Type: application/json" \ -d '{ "prompt": "A simple coffee shop interior", "mode": "base", "seed": 0, "steps": 50 }') response_body=$(echo "$response" | awk '/{/,EOF-1') echo $response_body | jq .artifacts[0].base64 | tr -d '"' | base64 --decode > $output_image_path ``` For more details on getting started with this NIM including configuring using parameters, visit the [Visual GenAI NIM docs](https://docs.nvidia.com/nim/visual-genai/latest/getting-started.html?model=flux.1-dev). 43:[["$","div",".3",{"className":"relative","children":[["$","div",null,{"className":"relative flex min-h-[194px] w-full flex-col overflow-hidden p-md sm:p-ml bg-manitoulinLightBlack","children":[["$","$L46",null,{"alt":"","className":"object-cover object-center !left-1/2 z-10 !w-1/2 brightness-20 lg:brightness-75","fill":true,"priority":true,"src":"https://assets.ngc.nvidia.com/products/api-catalog/images/flux_1-dev.jpg","style":{"maskImage":"linear-gradient(to left, rgba(0,0,0,1), rgba(0,0,0,0))"}}],["$","div",null,{"className":"z-30 mx-auto flex w-full max-w-screen-xl min-w-0 flex-1 flex-col items-start justify-between gap-md lg:flex-row lg:gap-ml","children":["$","section",null,{"aria-describedby":"resource-description","aria-labelledby":"resource-name","className":"flex w-full min-w-0 flex-col lg:flex-1","children":["$","div",null,{"className":"flex flex-col gap-1.5 whitespace-pre-wrap","children":[["$","$L47",null,{"className":"w-fit text-ml text-n300 lowercase hover:text-n050","href":"/black-forest-labs","children":"Black Forest Labs"}],["$","div",null,{"className":"flex flex-col items-start gap-3 lg:flex-row lg:items-center","children":[["$","h1",null,{"className":"max-w-full text-lg leading-heading font-medium break-words text-primary","data-testid":"resource-name","id":"resource-name","children":"FLUX.1-dev"}],[["$","span","AVAILABLE",{"className":"nv-badge nv-badge--kind-solid h-[18px] py-0","data-testid":"nv-badge","ref":"$undefined","children":["$undefined","Downloadable"]}]],"$undefined"]}],["$","p",null,{"className":"max-w-[85ch] text-ms text-primary","id":"resource-description","children":"FLUX.1 is a state-of-the-art suite of image generation models"}],["$","div",null,{"className":"flex w-full flex-col items-start gap-md lg:flex-row lg:items-end lg:justify-between","children":[["$","div",null,{"className":"flex w-full min-w-0 flex-wrap gap-sm lg:flex-1","children":["$","$L48",null,{"artifactType":"ENDPOINT","density":"standard","labels":["Run-on-RTX","nimType:endpoint:nim_type_run_anywhere","usecase:endpoint:usecase_image_gen","usecase:endpoint:usecase_text_to_image"],"searchRoutePath":"/models"}]}],["$","div",null,{"className":"z-30 flex w-full flex-row justify-start gap-ms sm:justify-end lg:w-auto lg:shrink-0","children":["$undefined",["$","$L49",null,{"cta":{"text":"Apply to Self-Host","url":"https://www.nvidia.com/en-us/ai/nim-notifyme/","nim_available_override_url":"https://catalog.ngc.nvidia.com/orgs/nim/teams/black-forest-labs/containers/flux.1-dev"},"isNIMAccessRestricted":"$undefined","isNIMAvailable":true}]]}]]}]]}]}]}],["$","div",null,{"className":"pointer-events-none absolute right-0 bottom-0 left-0 z-20 h-30 bg-gradient-to-t to-transparent from-manitoulinLightBlack via-manitoulinLightBlack/70"}]]}],["$","div",null,{"className":"px-4 mx-auto flex max-w-screen-xl flex-col pt-0 lg:px-0","data-body-background":"light-black","children":["$","section",null,{"className":"flex w-full flex-col justify-between rounded-4 border-manitoulinBorderColor bg-manitoulinDarkBlack sm:rounded-6 lg:rounded-8 p-ms lg:p-lg","children":["$","$L4a",null,{"artifact":{"artifactType":"ENDPOINT","name":"flux_1-dev","displayName":"FLUX.1-dev","publisher":"black-forest-labs","shortDescription":"FLUX.1 is a state-of-the-art suite of image generation models","logo":"https://assets.ngc.nvidia.com/products/api-catalog/images/flux_1-dev.jpg","labels":"$43:0:props:children:0:props:children:1:props:children:props:children:props:children:3:props:children:0:props:children:props:labels","attributes":[{"key":"AVAILABLE","value":"true"},{"key":"PREVIEW","value":"false"},{"key":"last_month_api_invocation_count","value":"302540"},{"key":"last_month_download_count","value":"0"}],"updatedDate":"2025-06-13T13:43:58.635Z","canGuestDownload":true,"createdDate":"2025-04-09T22:58:24.102Z","description":"$4b","isPublic":true,"isReadOnly":true,"orgName":"qc69jvmznzxy"},"endpointName":"flux_1-dev","endpointSpec":{"openAPISpec":{"openapi":"3.1.0","info":{"contact":{"name":"NVIDIA Support","url":"https://help.nvidia.com/"},"description":"The NVIDIA NIM REST API. 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