ReplicateText to imageComing soon

black-forest-labs/flux-dev-lora

Name: black-forest-labs/flux-dev-lora
Brand: Replicate

black-forest-labs/flux-dev-lora is a text to image tool from Replicate that runs in the background and returns images. Coming soon: listed with its price, not yet open to runs.

This tool is listed but not yet available to run.

Priced by usage

Pricing

$0.0416 per image. Billed on num_outputs, default 1.

Limits

Runs: In the background
Returns: image
Longest queue wait: 3600s
Run deadline: 1800s
Largest inline input: 1024 KB
Largest input file: 500 MB
Largest output: 4768 MB
Outputs per run: 16

Input

Field	Kind	Default	Description
`prompt` required	text	—	Prompt for generated image
`seed`	number (whole)	—	Random seed. Set for reproducible generation
`image`	URL or file	—	Input image for image to image mode. The aspect ratio of your output will match this image
`go_fast`	true or false	true	Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16. Note that outputs will not be deterministic when this is enabled, even if you set a seed.
`guidance`	number 0 to 10	3	Guidance for generated image
`extra_lora`	text	—	Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'
`lora_scale`	number -1 to 3	1	Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
`megapixels`	JSON	1	Approximate number of megapixels for generated image
`num_outputs`	number (whole) 1 to 4	1	Number of outputs to generate
`aspect_ratio`	JSON	1:1	Aspect ratio for the generated image
`hf_api_token`	text	—	HuggingFace API token. If you're using a hf lora that needs authentication, you'll need to provide an API token.
`lora_weights`	text	—	Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>[/<lora-weights-file.safetensors>], CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet, including signed URLs. For example, 'fofr/flux-pixar-cars'. Civit AI and HuggingFace LoRAs may require an API token to access, which you can provide in the `civitai_api_token` and `hf_api_token` inputs respectively.
`output_format`	JSON	webp	Format of the output images
`output_quality`	number (whole) 0 to 100	80	Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs
`prompt_strength`	number 0 to 1	0.8	Prompt strength when using img2img. 1.0 corresponds to full destruction of information in image
`extra_lora_scale`	number -1 to 3	1	Determines how strongly the extra LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.
`civitai_api_token`	text	—	Civitai API token. If you're using a civitai lora that needs authentication, you'll need to provide an API token.
`num_inference_steps`	number (whole) 1 to 50	28	Number of denoising steps. Recommended range is 28-50, and lower number of steps produce lower quality outputs, faster.

Same request in code

curl https://api.spiral.ai/v1/runs \
  -H "Authorization: Bearer $SPIRAL_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: $(uuidgen)" \
  -d '{
    "provider": "replicate",
    "model": "black-forest-labs/flux-dev-lora",
    "input": {
      "prompt": "<prompt>"
    }
  }'