POST
/
v3
/
async
/
inpainting

Inpainting is a conservation process in which damaged, deteriorated, or missing parts of an artwork are filled in to present a complete image.

This is an asynchronous API; only the task_id will be returned. You should use the task_id to request the Task Result API to retrieve the image generation results.

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Request Headers

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Content-Type
string
required

Enum: application/json

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Authorization
string
required

Bearer authentication format, for example: Bearer {{API Key}}.

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Request Body

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extra
object

Optional extra parameters for the request.

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request
object
required
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model_name
string
required

This parameter specifies the name of the model checkpoint. Retrieve the corresponding sd_name value by invoking the Query Model API with filter.types=checkpoint&filter.is_inpainting=true as the query parameter.

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image_base64
string
required

The base64 representation of the original image, with a maximum resolution of 16 megapixels and a maximum file size of 30 Mb.

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mask_image_base64
string
required

The base64 representation of the mask image, with a maximum resolution of 16 megapixels and a maximum file size of 30 Mb. The mask image should have the same resolution as the original image.

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prompt
string
required

Text input required to guide the image generation, divided by , . Range [1, 1024].

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image_num
integer
required

Images numbers generated in one single generation. Range [1, 8].

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steps
integer
required

The number of denoising steps. More steps usually can produce higher quality images, but take more time to generate, Range [1, 100].

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guidance_scale
number(float32)
required

This setting says how close the Stable Diffusion will listen to your prompt, higer guidance forces the model to better follow the prompt, but result in lower quality output. Range [1, 30].

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sampler_name
string
required

This parameter determines the denoising algorithm employed during the sampling phase of Stable Diffusion. Each option represents a distinct method by which the model incrementally generates new images. These algorithms differ significantly in their processing speed, output quality, and the specific characteristics of the images they generate, allowing users to tailor the image generation process to meet precise requirements. Get reference at A brief introduction to Sampler.
Enum: Euler a, Euler, LMS, Heun, DPM2, DPM2 a, DPM++ 2S a, DPM++ 2M, DPM++ SDE, DPM fast, DPM adaptive, LMS Karras, DPM2 Karras, DPM2 a Karras, DPM++ 2S a Karras, DPM++ 2M Karras, DPM++ SDE Karras, DDIM, PLMS, UniPC

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mask_blur
integer

Defines the degree of border blurring for the filled area. A lower value results in a sharper border, maintaining clear delineation between masked and unmasked areas. Conversely, a higher value increases the blur effect, creating a smoother, more blended transition at the borders. This adjustment allows for greater control over the visual integration of the mask with the original image. Range [0, 64].

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negative_prompt
string

Text input that specifies what to exclude from the generated images, divided by , . Range [1, 1024].

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sd_vae
string

VAE (Variational Auto Encoder). The sd_vae can be accessed in the API /v3/models with query parameters type=vae, such as sd_name: customVAE.safetensors. Get reference at A brief introduction to VAE.

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seed
integer

A seed is a number from which Stable Diffusion generates noise, which, makes generation deterministic. Using the same seed and set of parameters will produce identical image each time, minimum -1. Defaults to -1.

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loras
object[]

LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. Currently supports up to 5 LoRAs.

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embeddings
object[]

Textual Inversion is a training method for personalizing models by learning new text embeddings from a few example images, currently supports up to 5 embeddings.

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clip_skip
integer

This parameter indicates the number of layers to stop from the bottom during optimization, so clip_skip on 2 would mean, that in SD1.x model where the CLIP has 12 layers, you would stop at 10th layer, Range [1, 12], get reference at A brief introduction to Clip Skip.

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strength
number(float)¦null

Conceptually, the strength indicates the degree to which the reference image_base64 should be transformed. Must be between 0 and 1. image_base64 will be used as a starting point, with increasing levels of noise added as the strength value increases. The number of denoising steps depends on the amount of noise initially added. When strength is 1, added noise will be maximum and the denoising process will run for the full number of iterations specified in steps. A value of 1, therefore, essentially ignores image_base64.

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inpainting_full_res
integer

Specifies whether to apply or protect the filled area. When set to 0, the inpainting process considers the entire image, which may result in the mask area failing to present the correct details, but the mask area will look more natural or blend better with the whole image. When set to 1, only the masked area is inpainted, ignoring the unmasked areas, which can produce more detailed and natural results within the mask but may appear strange or incompatible with the original background. Default is 0.
Enum: 0, 1

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inpainting_full_res_padding
integer

This setting controls how many additional pixels can be used as a reference point for only masked mode. You can increase this amount if you are having trouble producing a proper image. This is a numerical value for how much margin to set when Only masked is selected. The downside of increasing this value is that it may decrease the quality of the output. Guidance: https://civitai.com/articles/161/basic-inpainting-guide, Range [0, 256]. Default is 8.

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inpainting_mask_invert
integer

Specifies whether to invert the mask. Set to 1 to invert the mask. Default is 0.
Enum: 0, 1

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initial_noise_multiplier
number(float32)

Noise multiplier for img2img settings. This scaling factor is applied to the random latent tensor for img2img. Lowering the value of this multiplier reduces the amount of noise introduced into the image transformation process, which can help reduce flickering or instability in the output image. Range [0, 1.5]. Default is 0.5.

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Response

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task_id
string

Use the task_id to request the Task Result API to retrieve the generated outputs.

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Example

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I have no mask images. How do I generate mask parameters in the body?

You can use our playground to get the mask base64 information. Please be aware that mask images should have the same resolution as the input images. Guidance can be found here: Click Here

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I already have mask images. How do I convert mask images to base64?

You can use the following code to convert mask images to base64.

import base64
# mask files path
filename_input = "mask_edited.png"

# read mask file
with open(filename_input, "rb") as f:
    base64_pic = base64.b64encode(f.read()).decode("utf-8")

# write mask file
with open("input.txt", "w") as f:
    f.write(base64_pic)

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Start requesting inpainting.

Please set the Content-Type header to application/json in your HTTP request to indicate that you are sending JSON data. Currently, only JSON format is supported.

"model_name":"realisticVisionV40_v40VAE-inpainting_81543.safetensors" in body represent inpainting models, which, can be accessed in API /v3/model with sd_name like %inpainting%.

Request:

curl --location --request POST 'http://api.novita.ai/v3/async/inpainting' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer {{API Key}}' \
--data-raw '{
    "extra": {
        "response_image_type": "jpeg"
    },
    "request": {
        "model_name": "realisticVisionV40_v40VAE-inpainting_81543.safetensors",
        "prompt": "Leonardo DiCaprio",
        "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, BadDream, UnrealisticDream",
        "image_num": 1,
        "steps": 25,
        "seed": -1,
        "clip_skip": 1,
        "guidance_scale": 7.5,
        "sampler_name": "Euler a",
        "mask_blur": 1,
        "inpainting_full_res": 1,
        "inpainting_full_res_padding": 32,
        "inpainting_mask_invert": 0,
        "initial_noise_multiplier": 1,
        "strength": 0.85,
        "image_base64": "{{base64 encoded image}}",
        "mask_image_base64": "{{base64 encoded mask image}}"
    }
}'

Response:

{
    "code": 0,
    "msg": "",
    "data": {
        "task_id": "270f4fba-2cb0-4a56-8b82-xxxx"
    }
}
````"model_name":"realisticVisionV40_v40VAE-inpainting_81543.safetensors"` in body represent inpainting models, which, can be accessed in API /v3/model with `sd_name` like %inpainting%.

`Request:`
```bash
curl --location --request POST 'http://api.novita.ai/v3/async/inpainting' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer {{API Key}}' \
--data-raw '{
    "extra": {
        "response_image_type": "jpeg"
    },
    "request": {
        "model_name": "realisticVisionV40_v40VAE-inpainting_81543.safetensors",
        "prompt": "Leonardo DiCaprio",
        "negative_prompt": "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, BadDream, UnrealisticDream",
        "image_num": 1,
        "steps": 25,
        "seed": -1,
        "clip_skip": 1,
        "guidance_scale": 7.5,
        "sampler_name": "Euler a",
        "mask_blur": 1,
        "inpainting_full_res": 1,
        "inpainting_full_res_padding": 32,
        "inpainting_mask_invert": 0,
        "initial_noise_multiplier": 1,
        "strength": 0.85,
        "image_base64": "{{base64 encoded image}}",
        "mask_image_base64": "{{base64 encoded mask image}}"
    }
}'

Response:

{
    "code": 0,
    "msg": "",
    "data": {
        "task_id": "270f4fba-2cb0-4a56-8b82-xxxx"
    }
}

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