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StableDiffusion35Large

GenerativeModel
DashAI.back.models.hugging_face.StableDiffusion35Large

Stable Diffusion 3.5 Large checkpoint (gated).

Downloads its checkpoint into the component's own download folder. This is a gated Hugging Face repo; downloading requires prior authentication (an HF token in the environment).

Parameters​

huggingface_key : string, default=
Hugging Face read-access token required to download these gated models. To obtain one: accept the model license on huggingface.co/stabilityai, then go to Settings → Access Tokens and generate a token with 'Read' scope.
negative_prompt
num_inference_steps : integer, default=15
Number of denoising steps to run. More steps refine the image but increase generation time. Typical range: 20-40 for standard models; use only 4-8 steps with 'large-turbo'. Values above 50 rarely improve output for SD3/SD3.5.
guidance_scale : number, default=3.5
Classifier-Free Guidance (CFG) scale. Controls how strictly the image follows the text prompt. SD3.5 works well at 3.5-4.5. The 'large-turbo' variant is designed for guidance_scale=1 (no CFG). Higher values enforce the prompt but may introduce oversaturation or artifacts.
device : string, default=CPU
Hardware device for inference. Select a GPU option for hardware acceleration, which is strongly recommended for diffusion models. Select 'CPU' on systems without a compatible GPU, but expect significantly longer generation times.
seed : integer, default=-1
Random seed for reproducible generation. A fixed positive integer will always produce the same image for identical settings. Use a negative value (e.g. -1) for a random seed on each run.
width : integer, default=512
Width of the output image in pixels. Must be a multiple of 8. SD3/SD3.5 models are natively trained at 1024x1024 px; using that resolution yields the best quality.
height : integer, default=512
Height of the output image in pixels. Must be a multiple of 8. SD3/SD3.5 models are natively trained at 1024x1024 px; using that resolution yields the best quality.
num_images_per_prompt : integer, default=1
How many images to generate from a single prompt in one batch. Increasing this value is more efficient than running multiple sessions, but requires proportionally more GPU memory.

Methods​

component_dir(cls) -> pathlib.Path

Defined on DownloadableMixin

Return this component's own storage directory.

Returns

pathlib.Path
<COMPONENT_PATH>/<ClassName>.

delete(cls) -> None

Defined on DownloadableMixin

Remove the component's downloaded artifacts.

download(cls, report: Optional[Callable[[Optional[float], Optional[str]], NoneType]] = None) -> None

Defined on HFDownloadableMixin

Download all repos listed in hf_repos() into component_dir().

Parameters

report : ProgressReporter, optional
Callback invoked before each repo download with report(None, "Downloading <repo_id>"). None means no progress reporting.

generate(self, input: str) -> List[Any]

Defined on StableDiffusion3GenerationModel

Generate output from a generative model.

Parameters

input : str
Input data to be generated

Returns

List[Any]
Generated output images in a list

get_metadata(cls) -> Dict[str, Any]

Defined on BaseGenerativeModel

Get metadata values for the current generative model.

Returns

Dict[str, Any]
Dictionary indicating whether the model requires a download before use and the expected download size in bytes.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

hf_repos(cls)

Defined on HFPretrainedDownloadMixin

Derive the single repo entry from MODEL_NAME.

Returns

list of tuple of (str, str)
[(MODEL_NAME, "model")] or an empty list when unset.

is_downloaded(cls) -> bool

Defined on HFDownloadableMixin

Return whether all repo directories exist and are non-empty.

Returns

bool
True when every repo listed in hf_repos() has a non-empty local directory; False otherwise (including when the list is empty).

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.

Compatible with​