model_nametextrequired
URL or HuggingFace ID of the base model to generate the image.
unet_nametext
URL or HuggingFace ID of the custom U-Net model to use for the image generation.
varianttext
The variant of the model to use for huggingface models, e.g. 'fp16'.
prompttextarearequired
The prompt to use for generating the image. Be as descriptive as possible for best results.
negative_prompttextareadefault: ""
The negative prompt to use.Use it to address details that you don't want
in the image. This could be colors, objects, scenery and even the small details
(e.g. moustache, blurry, low resolution).
prompt_weightingbooleandefault: false
If set to true, the prompt weighting syntax will be used.
Additionally, this will lift the 77 token limit by averaging embeddings.
image_urlfile
URL of image to use for image to image/inpainting.
mask_urlfile
URL of black-and-white image to use as mask during inpainting.
noise_strengthnumberdefault: 0.5
The amount of noise to add to noise image for image. Only used if the image_url is provided. 1.0 is complete noise and values near 0 are minimal noise.
lorasjsondefault: []
The LoRAs to use for the image generation. You can use any number of LoRAs
and they will be merged together to generate the final image.
embeddingsjsondefault: []
The embeddings to use for the image generation. Only a single embedding is supported at the moment.
The embeddings will be used to map the tokens in the prompt to the embedding weights.
controlnetsjsondefault: []
The control nets to use for the image generation. You can use any number of control nets
and they will be applied to the image at the specified timesteps.
controlnet_guess_modebooleandefault: false
If set to true, the controlnet will be applied to only the conditional predictions.
ip_adapterjsondefault: []
IP adapters for image generation. A non-empty list requires image_encoder_path.
image_encoder_pathtext
Hugging Face ID or HTTPS URL of the image encoder; required when ip_adapter is non-empty. Choose an encoder compatible with the IP-Adapter weights. For h94/IP-Adapter SDXL weights, use h94/IP-Adapter with image_encoder_subfolder='sdxl_models/image_encoder'; for SD1.5 weights, use image_encoder_subfolder='models/image_encoder'.
image_encoder_subfoldertext
Subfolder containing the image encoder weights in image_encoder_path.
image_encoder_weight_nametextdefault: "pytorch_model.bin"
The weight name of the image encoder model to use for the image generation.
ic_light_model_urlfile
The URL of the IC Light model to use for the image generation.
ic_light_model_background_image_urlfile
The URL of the IC Light model background image to use for the image generation.
Make sure to use a background compatible with the model.
ic_light_image_urlfile
The URL of the IC Light model image to use for the image generation.
seedinteger
The same seed and the same prompt given to the same version of Stable Diffusion
will output the same image every time.
num_inference_stepsintegerdefault: 30
Increasing the amount of steps tells Stable Diffusion that it should take more steps
to generate your final result which can increase the amount of detail in your image.
guidance_scalenumberdefault: 7.5
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.
clip_skipintegerdefault: 0
Skips part of the image generation process, leading to slightly different results.
This means the image renders faster, too.
schedulerselect
Scheduler / sampler to use for the image denoising process.
Options: DPM++ 2M, DPM++ 2M Karras, DPM++ 2M SDE, DPM++ 2M SDE Karras, Euler, Euler A, Euler (trailing timesteps), LCM, LCM (trailing timesteps), DDIM, TCD
timestepsjsondefault: {"array":[],"method":"default"}
Optionally override the timesteps to use for the denoising process. Only works with schedulers which support the `timesteps` argument in their `set_timesteps` method.
Defaults to not overriding, in which case the scheduler automatically sets the timesteps based on the `num_inference_steps` parameter.
If set to a custom timestep schedule, the `num_inference_steps` parameter will be ignored. Cannot be set if `sigmas` is set.
sigmasjsondefault: {"array":[],"method":"default"}
Optionally override the sigmas to use for the denoising process. Only works with schedulers which support the `sigmas` argument in their `set_sigmas` method.
Defaults to not overriding, in which case the scheduler automatically sets the sigmas based on the `num_inference_steps` parameter.
If set to a custom sigma schedule, the `num_inference_steps` parameter will be ignored. Cannot be set if `timesteps` is set.
prediction_typeselectdefault: "epsilon"
The type of prediction to use for the image generation.
The `epsilon` is the default.
Options: v_prediction, epsilon
rescale_betas_snr_zerobooleandefault: false
Whether to set the rescale_betas_snr_zero option or not for the sampler
image_formatselectdefault: "png"
The format of the generated image.
Options: jpeg, png
num_imagesintegerdefault: 1
Number of images to generate in one request. Note that the higher the batch size,
the longer it will take to generate the images.
enable_safety_checkerbooleandefault: false
If set to true, the safety checker will be enabled.
tile_widthintegerdefault: 4096
The size of the tiles to be used for the image generation.
tile_heightintegerdefault: 4096
The size of the tiles to be used for the image generation.
tile_stride_widthintegerdefault: 2048
The stride of the tiles to be used for the image generation.
tile_stride_heightintegerdefault: 2048
The stride of the tiles to be used for the image generation.
etanumberdefault: 0
The eta value to be used for the image generation.
debug_latentsbooleandefault: false
If set to true, the latents will be saved for debugging.
debug_per_pass_latentsbooleandefault: false
If set to true, the latents will be saved for debugging per pass.
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