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Stable Diffusion XL 1.0

DataCrunch Inference Stable Diffusion XL 1.0 documentation
The inference service provides Stable Diffusion XL 1.0 endpoint.

Endpoint features

Examples

Simple base SDXL (no refiner)

To disable refiner set is_ensemble=false and refiner=false.
curl -X POST https://<ENDPOINT_URL>/generate \
-H "Content-Type: application/json" \
-H <AUTH_HEADERS> \
-d \
'{
"prompt": "A cat with a hat",
"height": 512,
"width": 512,
"num_inference_steps": 50,
"num_images_per_prompt": 1,
"seed": 42,
"refiner": false,
"is_ensemble": false
}'

Ensemble of Expert Denoisers

First denoise for num_inference_steps * (1 - refiner_ratio) steps using the base model, and then continue for num_inference_steps * refiner_ratio steps using the refiner, more details here.
To run in the Ensemble of Experts mode set is_ensemble=true and refiner=false.
curl -X POST https://<ENDPOINT_URL>/generate \
-H "Content-Type: application/json" \
-H <AUTH_HEADERS> \
-d \
'{
"prompt": "A cat with a hat",
"height": 512,
"width": 512,
"num_inference_steps": 50,
"num_images_per_prompt": 1,
"seed": 42,
"refiner": false,
"is_ensemble": true,
"refiner_ratio": 0.2
}'

Refine the denoised base image

To run a two-step pipeline, where first the image is fully denoised using the base model, and then refiner is applied as an image-to-image pipeline to the output of the base model set is_ensemble=false and refiner=true. Number of steps for the base model, and the refiner are controlled separately using the num_inference_steps and num_inference_steps_refiner parameters. Also separate guidance_scale and guidance_scale_refiner values are used.
curl -X POST https://<ENDPOINT_URL>/generate \
-H "Content-Type: application/json" \
-H <AUTH_HEADERS> \
-d \
'{
"prompt": "A cat with a hat",
"height": 512,
"width": 512,
"num_inference_steps": 40,
"num_inference_steps_refiner": 10,
"guidance_scale": 4,
"guidance_scale_refiner": 4,
"num_images_per_prompt": 1,
"seed": 42,
"refiner": true,
"is_ensemble": false
}'

Parameters

  • prompt (str, required): Prompt text.
  • height (int, optional): Height of the output image. Setting aspect_ratio overrides this value. Defaults to 1024.
  • width (int, optional): Width of the output image. Setting aspect_ratio overrides this value. Defaults to 1024.
  • num_inference_steps (int, optional): Number of inference (denoising) steps. Defaults to 50.
  • guidance_scale (float, optional): Scaling factor for guidance. Specifies how much to follow the text prompt. Defaults to 4.0.
  • num_images_per_prompt (int, optional): Number of images to generate per prompt. Defaults to 1.
  • seed (int, optional): Seed for random number generator. Defaults to 42.
  • negative_prompt (str, optional): Negative prompt text.
  • seed_image (str, optional): Base64-encoded seed image string.
  • strength (float, optional, Range: [0.05, 1.0]): How much noise is added to the seed_image before generation. Defaults to 0.2.
  • scheduler (str, optional): Scheduler to use. Supported schedulers: DDIM, K_EULER, EulerA, DPMSolverMultistep, KarrasDPM, PNDM, HeunDiscrete. Defaults to DDIM.
  • timestep_spacing: (str, optional): specifies the timestep spacing for the scheduler. Supported values: linspace, trailing, leading. Defaults to linspace.
  • guidance_scale_refiner (float, optional): Scaling factor for refiner guidance (corresponds to guidance_scale). Defaults to 1.0.
  • refiner (bool, optional): Whether to use the refiner model. Defaults to false.
  • num_inference_steps_refiner (int, optional): Number of inference steps for refiner, applied when is_ensemble=false. Defaults to 50.
  • style_selected (str, optional): Apply the specified to the provided prompt, see supported styles.
  • is_ensemble (bool, optional): Whether to use the Ensemble of Expert Denoisers pipeline. Defaults to false.
  • refiner_ratio (float, optional): Requires is_ensemble=true. The fraction of the num_inference_steps steps to run the refiner for. For example, if num_inference_steps=40, and refiner_ratio=0.1 then the base model will run for 40 * (1-0.1) = 36 steps, and the refiner for 40 * 0.1 = 4 steps. Values over 0.2 start to produce unnatural-looking images. Defaults to 0.2.
  • aspect_ratio (str, optional): Aspect ratio of the output image. Setting this value overrides the width and height values.
  • lora_id (str, optional): Finetuned LoRA ID to load (LoRA file must exist on DataCrunch platform).
  • lora_name (str, optional): Public LoRA to be loaded. Currently only supported lora_name="offset" (corresponding to: "sd_xl_offset_example-lora_1.0.safetensors").
  • safety_filter (bool, optional): Whether to use NSFW filter. Defaults to true.
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Last modified 2mo ago