license: other license_name: qwen-research base_model:

Qwen/Qwen-Image-2.1
pottokao/Qwen-Image-2.1-Text-Encoder-Heretic base_model_relation: quantized pipeline_tag: text-to-image library_name: openvino tags:
openvino
qwen-image
uncensored
abliterated
heretic
int4
text-to-image
intel-arc

Qwen-Image-2.1 Uncensored — OpenVINO INT4

OpenVINO IR build of Qwen-Image-2.1 with an abliterated text encoder, quantized to INT4. Runs text-to-image on Intel Arc iGPUs.

Uncensoring lives in the prompt-embedding path, so only the Qwen3-VL text encoder needed replacing — the transformer, VAE and vision encoder are the stock model.

What's inside

Component	Precision	Size	Source
`text_encoder`	INT4 asym	3.9 GB	abliterated Qwen3-VL
`text_encoder_i2i`	INT4 asym	3.9 GB	same (editing slot)
`transformer`	INT4 asym	3.5 GB	stock Qwen-Image-2.1
`vision_encoder`	INT8	553 MB	stock
`vae_decoder`	INT8	243 MB	stock
`vae_encoder`	INT8	76 MB	stock

Total ~13 GB.

Abliteration verified

Embeddings for "a portrait of a woman, photorealistic" differ decisively from the stock encoder — this is not a renamed copy:

stock   mean 0.59543
heretic mean 0.22803
max abs diff : 1550.99353
mean abs diff: 10.12831
identical    : False

Measured performance

Intel Core Ultra 7 258V, Arc 130V/140V iGPU, 8 cores, 30 GB RAM:

Test	Result
pipeline load	43.5 s
512x512, 10 steps	12.9 s (1.29 s/step)
peak RSS	7.1 GB

Usage

import torch
from optimum.intel import OVDiffusionPipeline

pipe = OVDiffusionPipeline.from_pretrained(
    "blaj/Qwen-Image-2.1-Uncensored-OpenVINO-INT4",
    compile=True, device="GPU",
)
img = pipe(
    "your prompt",
    num_inference_steps=20, height=1024, width=1024,
    generator=torch.Generator().manual_seed(42),
).images[0]
img.save("out.png")

Requires the nightly stack: diffusers and optimum-intel from main, plus OpenVINO 2026.5.0 nightly. See the full conversion write-up: https://devnotes.page/how-to-convert-qwen-image-2-1-to-openvino-ir-and-build-an-uncensored-version

How it was built

Exported the abliterated text encoder to fp16 IR with optimum (57 s, 14.4 GB), taking only .model.language_model.
Compressed that IR to INT4 with NNCF — group_size=-1 is required, 128 and 64 raise InvalidGroupSizeError (37 s, → 3.9 GB).
Copied a pre-converted stock INT4 pipeline and replaced text_encoder/ and text_encoder_i2i/.

Python 3.12 is required for step 1. On Python 3.14 functools.partial is a descriptor, so NORMALIZED_CONFIG_CLASS resolves to a bound method and the export dies with:

TypeError: NormalizedConfig.__init__() got multiple values for argument 'allow_new'

A full-pipeline export of the 33 GB source is OOM-killed (exit 137) on a 30 GB machine — export components separately.

Limitations

Image editing (i2i) loads but exhausts 30 GB during generation; it needs the vision encoder and both text encoders resident at once. Not resolution-bound — 256x256 with 8 steps still reached 28 GB. Text-to-image is unaffected.
The abliterated text encoder is a community artifact and carries its own behaviour changes beyond removing refusals.
Inherits the Qwen-Image-2.1 research licence. Uncensored output is your responsibility.

Component

Precision

Size

Source

text_encoder

INT4 asym

3.9 GB

abliterated Qwen3-VL

text_encoder_i2i

INT4 asym

3.9 GB

same (editing slot)

transformer

INT4 asym

3.5 GB

stock Qwen-Image-2.1

vision_encoder

INT8

553 MB

stock

vae_decoder

INT8

243 MB

stock

vae_encoder

INT8

76 MB

stock

Test

Result

pipeline load

43.5 s

512x512, 10 steps

12.9 s (1.29 s/step)

peak RSS

7.1 GB

Usage

import torch from optimum.intel import OVDiffusionPipeline pipe = OVDiffusionPipeline.from_pretrained( "blaj/Qwen-Image-2.1-Uncensored-OpenVINO-INT4", compile=True, device="GPU", ) img = pipe( "your prompt", num_inference_steps=20, height=1024, width=1024, generator=torch.Generator().manual_seed(42), ).images[0] img.save("out.png")

How it was built

Exported the abliterated text encoder to fp16 IR with optimum (57 s, 14.4 GB), taking only .model.language_model.

Compressed that IR to INT4 with NNCF — group_size=-1 is required, 128 and 64 raise InvalidGroupSizeError (37 s, → 3.9 GB).

Copied a pre-converted stock INT4 pipeline and replaced text_encoder/ and text_encoder_i2i/.

Python 3.12 is required for step 1. On Python 3.14 functools.partial is a descriptor, so NORMALIZED_CONFIG_CLASS resolves to a bound method and the export dies with:

TypeError: NormalizedConfig.__init__() got multiple values for argument 'allow_new'

A full-pipeline export of the 33 GB source is OOM-killed (exit 137) on a 30 GB machine — export components separately.

Limitations

Image editing (i2i) loads but exhausts 30 GB during generation; it needs the vision encoder and both text encoders resident at once. Not resolution-bound — 256x256 with 8 steps still reached 28 GB. Text-to-image is unaffected.

The abliterated text encoder is a community artifact and carries its own behaviour changes beyond removing refusals.

Inherits the Qwen-Image-2.1 research licence. Uncensored output is your responsibility.