flux-dev corresponds to Black Forest Labs' FLUX.1 [dev], an open-weight image model trained with guidance distillation that excels at turning text descriptions into visual works. On this platform, it supports text generation and existing image editing, making it suitable for concept design, illustration drafts, and visual concept experiments, with the creative workflow organized through prompts, aspect ratios, and quantities.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
api.acedata.cloudflux-devInput parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
The architecture and training method are native features of FLUX.1 [dev], while sizing, editing, and task management are capabilities provided by this platform.
FLUX.1 [dev] emphasizes prompt adherence, making it suitable for describing the subject, environment, composition, and lighting in a single passage. When creating, first define the main subject, then add materials and visual style, comparing results iteratively; prompt phrasing affects the generated image, so avoid piling all requirements into mutually contradictory keywords.
In addition to starting from text, flux-dev can also receive an existing image link and modification instructions through edit mode, taking drafts into the next round of creation. It is suitable for trying new visual directions or adjusting image content, but editing does not equal precise local replacement; elements that need to be preserved should be clearly described, and actual results should be checked one by one.
Square, portrait, and landscape dimensions allow the same theme to be developed for different display placements. Generation tasks can use count to request multiple candidate images, then retrieve works from the result links; asynchronous queries or completion callbacks make it easier to integrate the generation process into background tasks without requiring the frontend to keep waiting on the same connection.
Enter character traits, scene atmosphere, color direction, and composition requirements to generate concept images or illustration drafts for discussion. Suitable for exploring multiple visual directions before formal drawing, then having designers select and refine them; the deliverables are image candidates for review, not automatically completed final design specifications.
Describe the background, materials, lighting, and placement around a product theme to generate scene visual proposals; existing images can also be submitted for editing attempts. Suitable for comparing presentation atmosphere and composition directions; the shape, branding, and details of real products still need verification, and generated images should not be directly regarded as accurate product photographs.
Content applications can turn users' theme descriptions into prompts, select landscape or portrait format, and submit generation tasks, then display image links for users to choose from when complete. Suitable for article illustrations, social content, and event visual drafts; when accurate titles or brand text are needed, typography can be left to subsequent design stages.
If the main task is validating prompts, exploring styles, and creating concept drafts, flux-dev is a suitable starting point. FLUX.1 [dev] and FLUX.1 [pro] are different models, and the official positioning assigns higher output quality to the contemporary pro model; for final-output tasks with stricter detail requirements, try the same creative idea with each rather than treating dev as another name for pro.
When an existing image needs editing attempts, you can use flux-dev's edit mode; if the task focuses on context-aware editing around the original image, Flux Kontext should be evaluated first. Do not equate dev's editing entry point with Kontext's dedicated editing capabilities, and do not apply FLUX.2's new features to this generation of models.
For text-to-image, specify the subject, materials, lighting, and aspect ratio; for modifying an existing image, prepare image_url and clearly state what to change and preserve.
Submit model=flux-dev, action=generate, prompt, and size=1024x1024 to /flux/images; when editing an image, select edit and add the image URL, while count is only used for generation.
Save the returned image_url; for asynchronous tasks, query via task_id or configure callback_url. Compare key elements before and after modification, then move the completed image into subsequent design work.
A seaside lighthouse, a twilight sky, and a small boat in the distance, rendered in a delicate printmaking style, limited to three colors: deep blue, orange, and off-white, in a vertical composition.
First compare the composition and style; if parts of the prompt are omitted, shorten the requirements and try again. Open-weight licensing and image use on this platform are separate matters.
flux-dev is the invocation ID for selecting FLUX.1 [dev] on this platform, and the model is developed by Black Forest Labs. It belongs to the FLUX.1 series; it is not Flux Kontext or FLUX.2, and it does not automatically gain the capabilities of those models merely by sharing an image interface.
Submit model=flux-dev, action=generate, prompt, and size to POST /flux/images, and use Bearer Token authentication. Results are returned as JSON, and images are obtained through image_url in data; set count when multiple candidates are needed.
Yes. Use action=edit and submit image_url, a modification prompt, and size. Editing is suitable for further exploring changes to an existing image, but unspecified areas are not guaranteed to remain completely unchanged; count is not used for editing tasks, and important details should be checked item by item after the results are returned.
For square images, use 1024x1024; for portrait images, use 1024x1792; for landscape images, use 1792x1024. You can also set the canvas through the image aspect ratio. It is recommended to first determine the orientation based on the display location, then describe the subject position and whitespace in the prompt, avoiding changing only the dimensions while overlooking composition.
Yes. Set async=true to first obtain task_id, then query the task result; you can also provide callback_url to receive a completion notification. Applications should distinguish between a submitted task and a completed image, and display the image only after obtaining its link, avoiding treating the task ID as the generated image result.
Choose a development approach or application suited to this model and follow its setup guide.
↗Python SDK Integration Guide
SDK installation, authentication and examples↗TypeScript SDK Integration Guide
SDK installation, authentication and examples
↗
↗This is only a basic call example. See the full docs for more parameters and advanced usage.
View Docs