graph LR
evaluate["evaluate"]
prepare_contexts["prepare_contexts"]
get_img_feature["get_img_feature"]
decode["decode"]
unpreprocess["unpreprocess"]
watermarking["watermarking"]
evaluate -- "orchestrates" --> prepare_contexts
prepare_contexts -- "calls" --> get_img_feature
evaluate -- "orchestrates" --> decode
evaluate -- "orchestrates" --> unpreprocess
evaluate -- "orchestrates" --> watermarking
The Input/Output Data Pipeline subsystem is primarily encapsulated within the functionalities defined in sample_multi_v0.py and sample_multi_v1.py. These files contain the core methods responsible for preparing input data, orchestrating the generation process, and post-processing the generated outputs.
Acts as the primary control point for the entire input/output data pipeline. It orchestrates the sequence of operations, initiating input preparation, implicitly integrating with the core diffusion model, and then coordinating various post-processing steps for the generated output. This aligns with the "Sampling/Inference Engine" and "Pipeline Architecture" patterns, serving as the entry point for a complete generation run.
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Prepares raw input data (e.g., text prompts, image features) for consumption by the diffusion model. This involves fetching, processing, or transforming features to the required format. It's a key part of the "Data Handling & Preprocessing" component.
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A specialized sub-component of input preparation, specifically focused on extracting or retrieving features from image inputs. This ensures visual data is correctly formatted for the multi-modal diffusion process.
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Handles the transformation of the diffusion model's latent output into a human-interpretable or usable format (e.g., converting latent representations back into images or text). This directly corresponds to the "Modality Decoders" expected component.
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Performs necessary post-processing on the generated output, often reversing initial preprocessing steps to ensure the output is in its final, desired format (e.g., denormalization, color space conversion).
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Applies watermarks or other final metadata/embellishments to the generated output, serving as a final step before delivery.
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