Model reference · open weights

IllustriousV01

Image uYouUs · community Text→image 1 build Licence not stated 1k dl/mo

IllustriousV01 is an open-weight image model from uYouUs. IllustriousV01 (FP16) weighs 6.9 GB; the smallest configuration that runs it is RTX 3060 12 GB.

What it is

Released byuYouUs
TypeImage models
TaskText→image
Parameters (lead)2.6B
Runs withdiffusers
Released2025-02-03
Popularity1k downloads / month
Weights6.9 GB (IllustriousV01 (FP16), file size)
LicenceLicence not stated

What it runs on

Memory and cards for IllustriousV01 (FP16)

Weights 6.9 GB (file size) · its biggest part 5.1 GB · working memory for one 1024×1024 image about 5.0 GB · overhead about 537 MB.

CardOne 1024×1024 imageCounted
memory
RTX 3060 12 GBtight (encoders offloaded)11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

Estimates, not measurements: the weights are the build's file size; one 1024×1024 image needs about 5 GB of working memory (larger images more); "encoders offloaded" means only the biggest part is on the card at once — diffusers' model offload, or ComfyUI unloading the text encoder. diffusers can also place a pipeline's parts on separate cards (device_map) — not estimated here. Counted memory is 92 % of what CUDA reports for the card.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. Runs as it is with diffusers — on the machine, in Python.

# on your rented machine: pip install diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("uYouUs/IllustriousV01", torch_dtype=torch.bfloat16).to("cuda")
image = pipe(prompt="a red bicycle on a cobbled street").images[0]
image.save("/workspace/out.png")
Renting a GPU — connect, tunnels, Python
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