Flux Control LoRA Depth
FLUX Control LoRA Depth is a high-performance endpoint that uses a control image to transfer structure to the generated image, using a depth map.
Features
FLUX Control LoRA Depth uses depth maps to guide image generation, allowing for realistic perspective, spatial relationships, and 3D-aware composition. The model is optimized for scenarios where depth cues significantly influence the final output.LoRA stands for Low-Rank Adaptation, a technique for efficiently fine-tuning pre-trained models to generate videos with specified effects from reference images.
Key Features
- Depth-Guided Generation: Leverages depth maps to inform the structural layout of the generated images, ensuring realistic spatial relationships.
- Spatial Coherence: Maintains consistent camera perspective and depth cues throughout the image generation process.
- Layered Composition: Produces images with a sense of depth and layering, enhancing the three-dimensional feel of the visuals.
- LoRA Integration: Incorporates Low-Rank Adaptation (LoRA) for efficient and flexible control over the generation process.
ComfyUI
FLUX Control LoRA Depth is also available on ComfyUI, providing local inference capabilities through a node-based workflow. This ensures flexible and efficient video generation on your system, catering to various creative workflows.
Limitations
- Input Quality Dependency:The accuracy of the generated images is heavily reliant on the quality and precision of the input depth maps.
- Computational Resources: High-resolution image generation with depth guidance may require substantial computational power.
- Learning Curve: Effective utilization may necessitate familiarity with depth map generation and image-to-image transformation workflows.
Out-of-Scope Use
The model and its derivatives may not be used in any way that violates applicable national, federal, state, local, or international law or regulation, including but not limited to:
- Exploiting, harming, or attempting to exploit or harm minors, including solicitation, creation, acquisition, or dissemination of child exploitative content.
- Generating or disseminating verifiably false information with the intent to harm others.
- Creating or distributing personal identifiable information that could be used to harm an individual.
- Harassing, abusing, threatening, stalking, or bullying individuals or groups.
- Producing non-consensual nudity or illegal pornographic content.
- Making fully automated decisions that adversely affect an individual’s legal rights or create binding obligations.
- Facilitating large-scale disinformation campaigns.
Accelerated Inference
Our accelerated inference approach leverages advanced optimization technology from WavespeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy. For further details, please refer to the blog post.
Authentication
For authentication details, please refer to the Authentication Guide.
API Endpoints
Submit Task & Query Result
# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v2/wavespeed-ai/flux-control-lora-depth" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
"prompt": "A dreamy anime girl in a glowing room, soft ambient light, pastel colors, cinematic composition, Studio Ghibli style",
"control_image": "https://d2g64w682n9w0w.cloudfront.net/media/images/1745416716025416255_1oAHDzwr.jpg",
"control_scale": 1,
"seed": 0,
"num_images": 1,
"size": "864*1636",
"num_inference_steps": 28,
"guidance_scale": 3.5,
"enable_safety_checker": true
}'
# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v2/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"
Parameters
Task Submission Parameters
Request Parameters
Parameter | Type | Required | Default | Range | Description |
---|---|---|---|---|---|
prompt | string | Yes | A dreamy anime girl in a glowing room, soft ambient light, pastel colors, cinematic composition, Studio Ghibli style | - | |
control_image | string | No | https://d2g64w682n9w0w.cloudfront.net/media/images/1745416716025416255_1oAHDzwr.jpg | - | The image to use for control lora. This is used to control the style of the generated image. |
control_scale | number | No | 1 | 0.00 ~ 2.00 | The scale of the control image. |
seed | integer | No | - | -1 ~ 9999999999 | The same seed and the same prompt given to the same version of the model will output the same image every time. |
num_images | integer | No | 1 | 1 ~ 4 | The number of images to generate |
size | string | No | 864*1636 | 512 ~ 1536 per dimension | The size of the generated image. |
num_inference_steps | integer | No | 28 | 1 ~ 50 | The number of inference steps to perform. |
guidance_scale | number | No | 3.5 | 1.0 ~ 30.0 | The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt when looking for a related image to show you |
enable_safety_checker | boolean | No | true | - | If set to true, the safety checker will be enabled. |
Response Parameters
Parameter | Type | Description |
---|---|---|
code | integer | HTTP status code (e.g., 200 for success) |
message | string | Status message (e.g., “success”) |
data.id | string | Unique identifier for the prediction, Task Id |
data.model | string | Model ID used for the prediction |
data.outputs | array | Array of URLs to the generated content (empty when status is not completed ) |
data.urls | object | Object containing related API endpoints |
data.urls.get | string | URL to retrieve the prediction result |
data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
data.status | string | Status of the task: created , processing , completed , or failed |
data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
data.error | string | Error message (empty if no error occurred) |
data.timings | object | Object containing timing details |
data.timings.inference | integer | Inference time in milliseconds |
Result Query Parameters
Result Request Parameters
Parameter | Type | Required | Default | Description |
---|---|---|---|---|
id | string | Yes | - | Task ID |
Result Response Parameters
Parameter | Type | Description |
---|---|---|
code | integer | HTTP status code (e.g., 200 for success) |
message | string | Status message (e.g., “success”) |
data | object | The prediction data object containing all details |
data.id | string | Unique identifier for the prediction |
data.model | string | Model ID used for the prediction |
data.outputs | array | Array of URLs to the generated content (empty when status is not completed ) |
data.urls | object | Object containing related API endpoints |
data.urls.get | string | URL to retrieve the prediction result |
data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
data.status | string | Status of the task: created , processing , completed , or failed |
data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
data.error | string | Error message (empty if no error occurred) |
data.timings | object | Object containing timing details |
data.timings.inference | integer | Inference time in milliseconds |