| --- |
| library_name: diffusers |
| pipeline_tag: image-to-image |
| tags: |
| - dit4sr |
| - super-resolution |
| - diffusion-transformer |
| base_model: stabilityai/stable-diffusion-3.5-medium |
| --- |
| # DiT4SR Replication |
|
|
| This repository contains the DiT4SR transformer weights exported from the local `dit4sr-replication` experiment at `checkpoint-150000`. |
|
|
| ## What This Repo Contains |
|
|
| This Hugging Face repo publishes only the `transformer/` checkpoint used by `SD3Transformer2DModel`. It does not include the full Stable Diffusion 3.5 base model, tokenizers, schedulers, or the rest of the DiT4SR inference stack. |
|
|
| ## Files |
|
|
| - `transformer/` contains the publishable model weights and config. |
| - `source_checkpoint.json` records the local source path and checkpoint name used for the upload. |
|
|
| ## Checkpoint Metadata |
|
|
| - Experiment: `dit4sr-replication` |
| - Source checkpoint: `checkpoint-150000` |
| - Published artifact: transformer weights only |
|
|
| ## Loading In DiT4SR |
|
|
| ```python |
| from model_dit4sr.transformer_sd3 import SD3Transformer2DModel |
| |
| model = SD3Transformer2DModel.from_pretrained("NisargUpadhyay/ImageSuperResolution-replication", subfolder="transformer") |
| ``` |
|
|
| You still need the rest of the DiT4SR codebase and the base SD3 assets described in the project README. |
|
|
| ## Related Resources |
|
|
| - Project repo: `NisargUpadhyayIITJ/Deep-Learning-Course-Project` |
| - Training/evaluation dataset repo: `NisargUpadhyay/ImageSuperResolution` |
| - Matching training subset in the dataset repo: `Replication/` |
|
|
