TongyiZImageTurbo
Tongyi Z-Image Turbo fast checkpoint.
Downloads its checkpoint into the component's own download folder.
Parameters
- negative_prompt, default=
None - num_inference_steps : integer, default=
20 - Number of denoising steps. Tongyi Z-Image achieves high quality results with 20-30 steps. More steps refine detail at the cost of generation time.
- guidance_scale : number, default=
5.0 - Classifier-Free Guidance (CFG) scale. Controls how strictly the image follows the text prompt. Values 4-7 work well for Tongyi Z-Image.
- device : string, default=
CPU - Hardware device for inference. GPU is strongly recommended for this 6B parameter model. CPU inference is possible but very slow.
- gpu_memory_mode : string, default=
auto - How much of the model is kept on the GPU. 'auto' measures free VRAM when the model loads and picks the fastest option that fits. 'full' keeps everything on the GPU (fastest, most VRAM). 'model' moves one component (text encoder, denoiser, VAE) to the GPU at a time. 'sequential' streams the weights layer by layer: slowest, but runs large models on small GPUs. Ignored on CPU.
- seed : integer, default=
-1 - Random seed for reproducible generation. A fixed positive integer always produces the same image. Use -1 for a random seed.
- width : integer, default=
1024 - Width of the output image in pixels. Must be a multiple of 8. Tongyi Z-Image natively targets 1024x1024 px.
- height : integer, default=
1024 - Height of the output image in pixels. Must be a multiple of 8. Tongyi Z-Image natively targets 1024x1024 px.
- num_images_per_prompt : integer, default=
1 - How many images to generate from a single prompt in one batch. Requires proportionally more GPU memory per additional image.
Methods
component_dir(cls) -> pathlib.Path
DownloadableMixinReturn this component's own storage directory.
Returns
- pathlib.Path
<COMPONENT_PATH>/<ClassName>.
delete(cls) -> None
DownloadableMixinRemove the component's downloaded artifacts.
download(cls, report: Optional[Callable[[Optional[float], Optional[str]], NoneType]] = None) -> None
HFDownloadableMixinDownload 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>").Nonemeans no progress reporting.
generate(self, input: str) -> List[Any]
TongyiZImageGenerationModelGenerate images from a text prompt.
Parameters
- input : str
- Text prompt to generate an image from.
Returns
- List[Any]
- Generated output images in a list.
get_credential(self, name: str)
ConfigObjectResolve a registered credential component by name.
Parameters
- name : str
- Credential component class name (e.g. "HuggingFaceCredential").
Returns
- BaseCredential
- An instance of the requested credential component.
get_metadata(cls) -> Dict[str, Any]
BaseGenerativeModelGet 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
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
hf_repos(cls)
HFPretrainedDownloadMixinDerive 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
HFDownloadableMixinReturn whether all repo directories exist and are non-empty.
Returns
- bool
Truewhen every repo listed inhf_repos()has a non-empty local directory;Falseotherwise (including when the list is empty).
validate_and_transform(self, raw_data: dict) -> dict
ConfigObjectIt 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.