| --- |
| license: apache-2.0 |
| library_name: transformers |
| pipeline_tag: text-to-image |
| tags: |
| - image |
| - text-to-image |
| - duper |
| - dit |
| - rectified-flow |
| --- |
| |
| # Dupe-R-IMG |
|
|
| The **Supra2-IMG** denoiser, the **flan-t5-base** encoder and the **sd-vae-ft-mse** VAE |
| packed into one `model.safetensors` |
|
|
| | section | params | source | license | |
| |---|---|---|---| |
| | `dit.*` | 104,193,168 | [SupraLabs/Supra2-IMG](https://huggingface.co/SupraLabs/Supra2-IMG) | apache-2.0 | |
| | `text_encoder.*` | 109,628,544 | [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) | apache-2.0 | |
| | `vae.*` | 83,653,863 | [stabilityai/sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) | mit | |
| | **total** | **297,475,575** | | | |
|
|
| ## What this is |
|
|
| A **repackaging, not a weight blend**. The DiT was trained against exactly these two |
| encoders, so averaging anything would break it. Every tensor is copied unmodified and |
| re-namespaced: |
|
|
| - `dit.*` — SupraDiT denoiser, 14 blocks, d_model 576, patch 2 |
| - `text_encoder.*` — T5 encoder stack, `shared.weight` token embedding included |
| - `vae.*` — full AutoencoderKL |
|
|
| Not bundled: the T5 `decoder.*` stack (113M) and `lm_head.weight` (24.7M). Text-to-image |
| never runs the seq2seq half. |
|
|
| ## Files |
|
|
| | file | size | what | |
| |---|---|---| |
| | `model.safetensors` | ~567 MB | all 297,475,575 params, prefixed `dit.` / `text_encoder.` / `vae.` | |
| | `supra2img.himg` | supra2img converted to .himg ~417 MB | `mmap` `PROT_READ`, zero-copy load | |
| | `model_final_ema.pt` | ~416 MB | untouched upstream checkpoint (bf16) | |
| | `config.json` | | geometry, per-section param counts, component provenance | |
| | `vae_config.json` | | native `AutoencoderKL` config (separate name on purpose) | |
| | `tokenizer.json`, `spiece.model`, … | | flan-t5 tokenizer | |
|
|
| Prefer `.himg` when memory is tight: so a 417 MB model |
| costs no resident RAM. `model.safetensors` is the portable single-file form. |
|
|
| ## Precision |
|
|
| Stored `float16`, **compute `float32`** (upcast on load). Storage-only: a build comparing |
| the C core against a PyTorch reference from this same file still agrees at 1e-6, since |
| both read identical bytes. Only parity against the original upstream checkpoint moves |
| (~1e-3), which is not visible in a pixel. The `.himg` is exact float32 if you need bit |
| parity. |
|
|
| ## Specs |
|
|
| | | | |
| |---|---| |
| | resolution | 256x256 | |
| | latent | 4 x 32 x 32 | |
| | context | 128 tokens (flan-t5-base) | |
| | sampler | rectified flow, Euler (Heun supported) | |
| | default steps / cfg | 50 / 3.0 | |
| | latent scale | 0.18215 | |
|
|
| ## Usage |
|
|
| ```python |
| import json |
| from huggingface_hub import hf_hub_download |
| from safetensors import safe_open |
| from diffusers import AutoencoderKL |
| |
| repo = "ShimbaLLM/Dupe-R-IMG" |
| dit_sd, te_sd, vae_sd = {}, {}, {} |
| with safe_open(hf_hub_download(repo, "model.safetensors"), framework="pt") as f: |
| for k in f.keys(): |
| (dit_sd if k.startswith("dit.") else |
| te_sd if k.startswith("text_encoder.") else |
| vae_sd)[k.split('.', 1)[1]] = f.get_tensor(k).float() |
| |
| vae = AutoencoderKL.from_config( |
| json.load(open(hf_hub_download(repo, "vae_config.json")))) |
| vae.load_state_dict(vae_sd) |
| ``` |
|
|
| The T5 encoder stack and the SupraDiT module definitions are in `config.json` under |
| `components`, and in the notebook that produced this release. |
|
|
| ## Licenses |
|
|
| Bundled components keep their own licenses and carry independent provenance in |
| `config.json`: DiT apache-2.0 (SupraLabs), T5 encoder apache-2.0 (Google), VAE mit |
| (Stability AI). All three permit redistribution, individually and combined. |
|
|
