AMT Interpolation

Frames

Output FPS

Recursive Interpolation Passes

AMTFrameInterpolationInput

Video URL

AMTInterpolationInput

Video

AMTInterpolationOutput

Content Type

File Data

The name of the file. It will be auto-generated if not provided.

File Name

File Size

The URL where the file can be downloaded from.

File

Frame

Detail

HTTPValidationError

Location

Message

Error Type

ValidationError

FastAPI

bilateral reference framework (BiRefNet) for high-resolution dichotomous image segmentation (DIS)

Birefnet Background Removal

Height

Width

Image

URL of the image to remove background from

Image Url


            Model to use for background removal.
            The 'General Use (Light)' model is the original model used in the BiRefNet repository.
            The 'General Use (Heavy)' model is a slower but more accurate model.
            The 'Portrait' model is a model trained specifically for portrait images.
            The 'General Use (Light)' model is recommended for most use cases.

            The corresponding models are as follows:
            - 'General Use (Light)': BiRefNet-DIS_ep580.pth
            - 'General Use (Heavy)': BiRefNet-massive-epoch_240.pth
            - 'Portrait': BiRefNet-portrait-TR_P3M_10k-epoch_120.pth
        

Model

The resolution to operate on. The higher the resolution, the more accurate the output will be for high res input images.

Operating Resolution

Output Format

Whether to output the mask used to remove the background

Output Mask

Whether to refine the foreground using the estimated mask

Refine Foreground

Input


            Model to use for background removal.
            The 'General Use (Light)' model is the original model used in the BiRefNet repository.
            The 'General Use (Light)' model is the original model used in the BiRefNet repository but trained with 2K images.
            The 'General Use (Heavy)' model is a slower but more accurate model.
            The 'Matting' model is a model trained specifically for matting images.
            The 'Portrait' model is a model trained specifically for portrait images.
            The 'General Use (Light)' model is recommended for most use cases.

            The corresponding models are as follows:
            - 'General Use (Light)': BiRefNet-DIS_ep580.pth
            - 'General Use (Heavy)': BiRefNet-massive-epoch_240.pth
            - 'Portrait': BiRefNet-portrait-TR_P3M_10k-epoch_120.pth
        

InputV2

Mask Image

Output

Generate short video clips from your images using SVD v1.1

Stable Video Diffusion


            The conditoning augmentation determines the amount of noise that will be
            added to the conditioning frame. The higher the number, the more noise
            there will be, and the less the video will look like the initial image.
            Increase it for more motion.
        

Cond Aug


            Enabling [DeepCache](https://github.com/horseee/DeepCache) will make the execution
            faster, but might sometimes degrade overall quality. The higher the setting, the
            faster the execution will be, but the more quality might be lost.
        

Deep Cache


            The FPS of the generated video. The higher the number, the faster the video will
            play. Total video length is 25 frames.
        

The URL of the image to use as a starting point for the generation.


            The motion bucket id determines the motion of the generated video. The
            higher the number, the more motion there will be.
        

Motion Bucket Id


            The same seed and the same prompt given to the same version of Stable Diffusion
            will output the same image every time.
        

Seed


            The number of steps to run the model for. The higher the number the better
            the quality and longer it will take to generate.
        

Steps

FastSVDImageInput


            Seed of the generated Image. It will be the same value of the one passed in the
            input or the randomly generated that was used in case none was passed.

        

FastSVDOutput

The negative prompt to use as a starting point for the generation.

Negative Prompt

The prompt to use as a starting point for the generation.

Prompt

Video Size

FastSVDTextInput

ImageSize

Illusion Diffusion

Control Guidance End

Control Guidance Start

Controlnet Conditioning Scale


            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.
        

Guidance Scale


            The size of the generated image. You can choose between some presets or
            custom height and width that **must be multiples of 8**.
        

Image Size


            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).
        


            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.
        

Number of inference steps

The prompt to use for generating the image. Be as descriptive as possible for best results.

Scheduler / sampler to use for the image denoising process.

Scheduler


            Seed of the generated Image. It will be the same value of the one passed in the
            input or the randomly generated that was used in case none was passed.
        

IllusionDiffusionInput

IllusionDiffusionOutput

join-audio-video

Audio Url

Video Url

JoinAudioVideoInput

JoinAudioVideoOutput

musicgen

Audio prompt that is used for continued generation.

The duration of the music to generate in seconds.

Duration

Description of the music you want to generate.

MusicGenInput

MusicGenOutput

Stable Diffusion XL

URL or the path to the embedding weights.

Path

The list of tokens to use for the embedding.

Tokens

Embedding

Embeddings

If set to true, the safety checker will be enabled.

Enable Safety Checker

If set to true, the prompt will be expanded with additional prompts.

Expand Prompt

Format

Guidance scale (CFG)

Loras


            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).
        

Num Images

The number of inference steps to perform.

Num Inference Steps


            An id bound to a request, can be used with response to identify the request
            itself.
        

Request Id

The version of the safety checker to use. v1 is the default CompVis safety checker. v2 uses a custom ViT model.

Safety Checker Version

determines how much the generated image resembles the initial image

Strength


            If set to true, the function will wait for the image to be generated and uploaded
            before returning the response. This will increase the latency of the function but
            it allows you to get the image directly in the response without going through the CDN.
        

Sync Mode

ImageToImageInput

The URL of the mask to use for inpainting.

Mask Url

InpaintingInput

If set to true, the embedding will be forced to be used.

Force

URL or the path to the LoRA weights. Or HF model name.


            The scale of the LoRA weight. This is used to scale the LoRA weight
            before merging it with the base model.
        

Scale

LoraWeight

Whether the generated images contain NSFW concepts.

Has Nsfw Concepts

Images

The prompt used for generating the image.

Timings

TextToImageInput

The duration of the music to generate in seconds. Default value: `8`

duration

The model to use for music generation. Default value: `"facebook/musicgen-stereo-medium"`

model

ModelEnum

Output frames per second Default value: `8`

Search

Input

Result

Logs