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class PresetCreateResponse: …
preset: Preset
parameters: PresetParametersThe inference parameters
The inference parameters
Full text prompt including the model placeholder. (example: “an illustration of phoenix in a fantasy world, flying over a mountain, 8k, bokeh effect”)
type: Literal["controlnet", "controlnet_img2img", "controlnet_inpaint", 15 more]The type of inference to use. Example: txt2img, img2img, etc.
Selecting the right type will condition the expected parameters.
Note: for inpainting with models that support multiple base models, Scenario determines the best available baseModel for a given modelId.
The type of inference to use. Example: txt2img, img2img, etc.
Selecting the right type will condition the expected parameters.
Note: for inpainting with models that support multiple base models, Scenario determines the best available baseModel for a given modelId.
aspect_ratio: Optional[Literal["16:9", "1:1", "21:9", 8 more]]The aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra.
The aspect ratio is a string formatted as “width:height” (example: “16:9”).
The aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra. The aspect ratio is a string formatted as “width:height” (example: “16:9”).
The base model to use for the inference. Only Flux LoRA models can use this parameter.
Allowed values are available in the model’s attribute: compliantModelIds
concepts: Optional[List[PresetParametersConcept]]
Specifies how long the ControlNet guidance should be applied during the inference process.
Only available for Flux.1-dev based models.
The value represents the percentage of total inference steps where the ControlNet guidance is active. For example:
- 1.0: ControlNet guidance is applied during all inference steps
- 0.5: ControlNet guidance is only applied during the first half of inference steps
Default values:
- 0.5 for Canny modality
- 0.6 for all other modalities
Specifies the starting point of the ControlNet guidance during the inference process.
Only available for Flux.1-dev based models.
The value represents the percentage of total inference steps where the ControlNet guidance starts. For example:
- 0.0: ControlNet guidance starts at the beginning of the inference steps
- 0.5: ControlNet guidance starts at the middle of the inference steps
If set to true, the entire input image will likely change during inpainting. This results in faster inferences, but the output image will be harder to integrate if the input is just a small part of a larger image.
If false, the process uses the given image to detect the modality. If true (default), the process will not try to detect the modality of the given image.
For example:
with pose modality and false value, the process will detect the pose of people in the given image
with depth modality and false value, the process will detect the depth of the given image
with scribble modality and truevalue, the process will use the given image as a scribble
⚠️ For models of the FLUX schnell or dev families, this parameter is ignored. The modality detection is always disabled. ⚠️
Controls how closely the generated image follows the prompt. Higher values result in stronger adherence to the prompt. Default and allowed values depend on the model type:
- For Flux dev models, the default is 3.5 and allowed values are within [0, 10]
- For Flux pro models, the default is 3 and allowed values are within [2, 5]
The height of the generated images, must be a 8 multiple (within [64, 2048], default: 512)
If model.type is flux.1.1-pro-ultra, you can use the aspectRatio parameter instead
If set, generated assets will be hidden and not returned in the list of images of the inference or when listing assets (default: false)
Enable or disable the intermediate images generation (default: false)
IpAdapter scale factor (within [0.0, 1.0], default: 0.9).
IpAdapter scale factors (within [0.0, 1.0], default: 0.9).
ip_adapter_type: Optional[Literal["character", "style"]]The type of IP Adapter model to use. Must be one of [style, character], default to `style“
The type of IP Adapter model to use. Must be one of [style, character], default to `style“
The modality associated with the control image used for the generation: it can either be an object with a combination of maximum
For models of the FLUX schnell or dev families:
- one modality from:
canny,tile,depth,blur,pose,gray,low-quality
Optionally, you can associate a value to these modalities or presets. The value must be within ]0.0, 1.0].
Examples:
cannydepth:0.5,pose:1.0canny:0.5,depth:0.5,lines:0.3landscapecharacter:0.5illusion:1
Note: if you use a value that is not supported by the model family, this will result in an error.
The epoch of the model to use for the inference. Only available for Flux Lora Trained models.
The prompt not to guide the image generation, ignored when guidance < 1 (example: “((ugly face))”) For Flux based model (not Fast-Flux): requires negativePromptStrength > 0 and active only for inference types txt2img / img2img / controlnet.
Only applicable for flux-dev based models for txt2img, img2img, and controlnet inference types.
Controls the influence of the negative prompt. Default 0 means the negative prompt has no effect. Higher values increase negative prompt influence. Must be > 0 if negativePrompt is provided.
The number of denoising steps for each image generation (within [1, 150], default: 30)
The number of images to generate (within [1, 128], default: 4)
Whether to use reference query for self attention’s context Only for “reference” inference type
scheduler: Optional[Literal["DDIMScheduler", "DDPMScheduler", "DEISMultistepScheduler", 12 more]]The scheduler to use to override the default configured for the model. See detailed documentation for more details.
The scheduler to use to override the default configured for the model. See detailed documentation for more details.
Used to reproduce previous results. Default: randomly generated number.
Controls the noise intensity introduced to the input image, where a value of 1.0 completely erases the original image’s details. Available for img2img and inpainting. (within [0.01, 1.0], default: 0.75)
class PresetListResponse: …
presets: List[Preset]
parameters: PresetParametersThe inference parameters
The inference parameters
Full text prompt including the model placeholder. (example: “an illustration of phoenix in a fantasy world, flying over a mountain, 8k, bokeh effect”)
type: Literal["controlnet", "controlnet_img2img", "controlnet_inpaint", 15 more]The type of inference to use. Example: txt2img, img2img, etc.
Selecting the right type will condition the expected parameters.
Note: for inpainting with models that support multiple base models, Scenario determines the best available baseModel for a given modelId.
The type of inference to use. Example: txt2img, img2img, etc.
Selecting the right type will condition the expected parameters.
Note: for inpainting with models that support multiple base models, Scenario determines the best available baseModel for a given modelId.
aspect_ratio: Optional[Literal["16:9", "1:1", "21:9", 8 more]]The aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra.
The aspect ratio is a string formatted as “width:height” (example: “16:9”).
The aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra. The aspect ratio is a string formatted as “width:height” (example: “16:9”).
The base model to use for the inference. Only Flux LoRA models can use this parameter.
Allowed values are available in the model’s attribute: compliantModelIds
concepts: Optional[List[PresetParametersConcept]]
Specifies how long the ControlNet guidance should be applied during the inference process.
Only available for Flux.1-dev based models.
The value represents the percentage of total inference steps where the ControlNet guidance is active. For example:
- 1.0: ControlNet guidance is applied during all inference steps
- 0.5: ControlNet guidance is only applied during the first half of inference steps
Default values:
- 0.5 for Canny modality
- 0.6 for all other modalities
Specifies the starting point of the ControlNet guidance during the inference process.
Only available for Flux.1-dev based models.
The value represents the percentage of total inference steps where the ControlNet guidance starts. For example:
- 0.0: ControlNet guidance starts at the beginning of the inference steps
- 0.5: ControlNet guidance starts at the middle of the inference steps
If set to true, the entire input image will likely change during inpainting. This results in faster inferences, but the output image will be harder to integrate if the input is just a small part of a larger image.
If false, the process uses the given image to detect the modality. If true (default), the process will not try to detect the modality of the given image.
For example:
with pose modality and false value, the process will detect the pose of people in the given image
with depth modality and false value, the process will detect the depth of the given image
with scribble modality and truevalue, the process will use the given image as a scribble
⚠️ For models of the FLUX schnell or dev families, this parameter is ignored. The modality detection is always disabled. ⚠️
Controls how closely the generated image follows the prompt. Higher values result in stronger adherence to the prompt. Default and allowed values depend on the model type:
- For Flux dev models, the default is 3.5 and allowed values are within [0, 10]
- For Flux pro models, the default is 3 and allowed values are within [2, 5]
The height of the generated images, must be a 8 multiple (within [64, 2048], default: 512)
If model.type is flux.1.1-pro-ultra, you can use the aspectRatio parameter instead
If set, generated assets will be hidden and not returned in the list of images of the inference or when listing assets (default: false)
Enable or disable the intermediate images generation (default: false)
IpAdapter scale factor (within [0.0, 1.0], default: 0.9).
IpAdapter scale factors (within [0.0, 1.0], default: 0.9).
ip_adapter_type: Optional[Literal["character", "style"]]The type of IP Adapter model to use. Must be one of [style, character], default to `style“
The type of IP Adapter model to use. Must be one of [style, character], default to `style“
The modality associated with the control image used for the generation: it can either be an object with a combination of maximum
For models of the FLUX schnell or dev families:
- one modality from:
canny,tile,depth,blur,pose,gray,low-quality
Optionally, you can associate a value to these modalities or presets. The value must be within ]0.0, 1.0].
Examples:
cannydepth:0.5,pose:1.0canny:0.5,depth:0.5,lines:0.3landscapecharacter:0.5illusion:1
Note: if you use a value that is not supported by the model family, this will result in an error.
The epoch of the model to use for the inference. Only available for Flux Lora Trained models.
The prompt not to guide the image generation, ignored when guidance < 1 (example: “((ugly face))”) For Flux based model (not Fast-Flux): requires negativePromptStrength > 0 and active only for inference types txt2img / img2img / controlnet.
Only applicable for flux-dev based models for txt2img, img2img, and controlnet inference types.
Controls the influence of the negative prompt. Default 0 means the negative prompt has no effect. Higher values increase negative prompt influence. Must be > 0 if negativePrompt is provided.
The number of denoising steps for each image generation (within [1, 150], default: 30)
The number of images to generate (within [1, 128], default: 4)
Whether to use reference query for self attention’s context Only for “reference” inference type
scheduler: Optional[Literal["DDIMScheduler", "DDPMScheduler", "DEISMultistepScheduler", 12 more]]The scheduler to use to override the default configured for the model. See detailed documentation for more details.
The scheduler to use to override the default configured for the model. See detailed documentation for more details.
Used to reproduce previous results. Default: randomly generated number.
Controls the noise intensity introduced to the input image, where a value of 1.0 completely erases the original image’s details. Available for img2img and inpainting. (within [0.01, 1.0], default: 0.75)
class PresetUpdateResponse: …
preset: Preset
parameters: PresetParametersThe inference parameters
The inference parameters
Full text prompt including the model placeholder. (example: “an illustration of phoenix in a fantasy world, flying over a mountain, 8k, bokeh effect”)
type: Literal["controlnet", "controlnet_img2img", "controlnet_inpaint", 15 more]The type of inference to use. Example: txt2img, img2img, etc.
Selecting the right type will condition the expected parameters.
Note: for inpainting with models that support multiple base models, Scenario determines the best available baseModel for a given modelId.
The type of inference to use. Example: txt2img, img2img, etc.
Selecting the right type will condition the expected parameters.
Note: for inpainting with models that support multiple base models, Scenario determines the best available baseModel for a given modelId.
aspect_ratio: Optional[Literal["16:9", "1:1", "21:9", 8 more]]The aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra.
The aspect ratio is a string formatted as “width:height” (example: “16:9”).
The aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra. The aspect ratio is a string formatted as “width:height” (example: “16:9”).
The base model to use for the inference. Only Flux LoRA models can use this parameter.
Allowed values are available in the model’s attribute: compliantModelIds
concepts: Optional[List[PresetParametersConcept]]
Specifies how long the ControlNet guidance should be applied during the inference process.
Only available for Flux.1-dev based models.
The value represents the percentage of total inference steps where the ControlNet guidance is active. For example:
- 1.0: ControlNet guidance is applied during all inference steps
- 0.5: ControlNet guidance is only applied during the first half of inference steps
Default values:
- 0.5 for Canny modality
- 0.6 for all other modalities
Specifies the starting point of the ControlNet guidance during the inference process.
Only available for Flux.1-dev based models.
The value represents the percentage of total inference steps where the ControlNet guidance starts. For example:
- 0.0: ControlNet guidance starts at the beginning of the inference steps
- 0.5: ControlNet guidance starts at the middle of the inference steps
If set to true, the entire input image will likely change during inpainting. This results in faster inferences, but the output image will be harder to integrate if the input is just a small part of a larger image.
If false, the process uses the given image to detect the modality. If true (default), the process will not try to detect the modality of the given image.
For example:
with pose modality and false value, the process will detect the pose of people in the given image
with depth modality and false value, the process will detect the depth of the given image
with scribble modality and truevalue, the process will use the given image as a scribble
⚠️ For models of the FLUX schnell or dev families, this parameter is ignored. The modality detection is always disabled. ⚠️
Controls how closely the generated image follows the prompt. Higher values result in stronger adherence to the prompt. Default and allowed values depend on the model type:
- For Flux dev models, the default is 3.5 and allowed values are within [0, 10]
- For Flux pro models, the default is 3 and allowed values are within [2, 5]
The height of the generated images, must be a 8 multiple (within [64, 2048], default: 512)
If model.type is flux.1.1-pro-ultra, you can use the aspectRatio parameter instead
If set, generated assets will be hidden and not returned in the list of images of the inference or when listing assets (default: false)
Enable or disable the intermediate images generation (default: false)
IpAdapter scale factor (within [0.0, 1.0], default: 0.9).
IpAdapter scale factors (within [0.0, 1.0], default: 0.9).
ip_adapter_type: Optional[Literal["character", "style"]]The type of IP Adapter model to use. Must be one of [style, character], default to `style“
The type of IP Adapter model to use. Must be one of [style, character], default to `style“
The modality associated with the control image used for the generation: it can either be an object with a combination of maximum
For models of the FLUX schnell or dev families:
- one modality from:
canny,tile,depth,blur,pose,gray,low-quality
Optionally, you can associate a value to these modalities or presets. The value must be within ]0.0, 1.0].
Examples:
cannydepth:0.5,pose:1.0canny:0.5,depth:0.5,lines:0.3landscapecharacter:0.5illusion:1
Note: if you use a value that is not supported by the model family, this will result in an error.
The epoch of the model to use for the inference. Only available for Flux Lora Trained models.
The prompt not to guide the image generation, ignored when guidance < 1 (example: “((ugly face))”) For Flux based model (not Fast-Flux): requires negativePromptStrength > 0 and active only for inference types txt2img / img2img / controlnet.
Only applicable for flux-dev based models for txt2img, img2img, and controlnet inference types.
Controls the influence of the negative prompt. Default 0 means the negative prompt has no effect. Higher values increase negative prompt influence. Must be > 0 if negativePrompt is provided.
The number of denoising steps for each image generation (within [1, 150], default: 30)
The number of images to generate (within [1, 128], default: 4)
Whether to use reference query for self attention’s context Only for “reference” inference type
scheduler: Optional[Literal["DDIMScheduler", "DDPMScheduler", "DEISMultistepScheduler", 12 more]]The scheduler to use to override the default configured for the model. See detailed documentation for more details.
The scheduler to use to override the default configured for the model. See detailed documentation for more details.
Used to reproduce previous results. Default: randomly generated number.
Controls the noise intensity introduced to the input image, where a value of 1.0 completely erases the original image’s details. Available for img2img and inpainting. (within [0.01, 1.0], default: 0.75)