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	apache-2.0
text_encoder.*	109,628,544	google/flan-t5-base	apache-2.0
vae.*	83,653,863	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

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.