license: other license_name: qwen-research license_link: LICENSE base_model:

• Qwen/Qwen-Image-2.1 base_model_relation: adapter pipeline_tag: text-to-image library_name: diffusers tags:
• lora
• qwen-image
• qwen-image-2.1
• image-generation
• pose-generation
• image-editing
• safetensors
• diffusers
• uncensored

ChatR Labs Uncensored Omni LoRA v0.1 — for Qwen-Image-2.1

Try the Playground Download LoRA

One LoRA for text-to-image generation and image editing. Built with Qwen.

ChatR Labs Uncensored Omni LoRA v0.1 is an experimental rank-128 adapter fine-tuned from Qwen/Qwen-Image-2.1. Create images from text or transform existing images with natural-language instructions, using one adapter for both workflows.

The download contains LoRA weights only. Load it on top of the original Qwen-Image-2.1 base model; the transformer, text encoder, processor, and VAE are downloaded separately from the base repository.

Try it online

Open the Playground →

No account is required. Choose Text to Image or Image Editing, enter a prompt, and generate. The editing interface accepts one to three reference images. Requests share a queue.

Download

File integrity information is in SHA256SUMS. This release contains a BF16 adapter, without merged base weights or GGUF/FP8 variants.

Recommended settings

These are the settings used to evaluate this adapter. In the tested Diffusers pipeline, an omitted or empty negative prompt disables true CFG even when true_cfg_scale is greater than 1. Use a single space if you do not need a specific negative prompt.

The adapter is trained for Qwen-Image-2.1. Earlier Qwen-Image, Qwen-Image-2512, and Qwen-Image-Edit-2511 have different architectures and are not compatible bases. This is a regular LoRA; it does not provide few-step distillation.

Usage with Diffusers

1. Install

Install a CUDA-compatible PyTorch build for your system, then install the dependencies in requirements.txt:

pip install -r requirements.txt

The requirements pin the Diffusers revision used by the Playground, including QwenImage21Pipeline, together with its tested Transformers, PEFT, and Accelerate versions. The deployment uses PyTorch 2.11.0 with CUDA 12.8.

2. Load the base model and adapter

import torch
from diffusers import QwenImage21Pipeline

pipe = QwenImage21Pipeline.from_pretrained(
    "Qwen/Qwen-Image-2.1",
    torch_dtype=torch.bfloat16,
)
pipe.load_lora_weights(
    "chatrlabs/Qwen-Image-2.1-Uncensored-Omni-LoRA-v0.1",
    weight_name="chatrlabs-omni-lora-v0.1-bf16.safetensors",
    adapter_name="omni",
)
pipe.set_adapters("omni", adapter_weights=1.0)

# CPU offload reduces GPU residency; it needs sufficient system RAM.
pipe.enable_model_cpu_offload()
# Keep VAE tiling off: tiled decoding adds visible vertical colour streaks.
# With sufficient VRAM, replace CPU offload with pipe.to("cuda").

3. Text to image

image = pipe(
    prompt="A blue ceramic mug on a wooden table by a window, soft morning light, product photography",
    negative_prompt=" ",
    width=1024,
    height=1024,
    num_inference_steps=25,
    true_cfg_scale=4.0,
    use_kv_cache=True,
    generator=torch.Generator("cuda").manual_seed(42),
).images[0]
image.save("generated.png")

4. Image editing

Use the same loaded pipeline and pass reference images alongside the instruction:

from PIL import Image

reference = Image.open("generated.png").convert("RGBA")
image = pipe(
    image=[reference],
    prompt="Change the mug to green, keep everything else unchanged",
    negative_prompt=" ",
    width=1024,
    height=1024,
    output_resolution=1024,
    num_inference_steps=25,
    true_cfg_scale=4.0,
    use_kv_cache=True,
    generator=torch.Generator("cuda").manual_seed(45),
).images[0]
image.save("edited.png")

For multiple references, pass a list of PIL images. The Playground has been exercised with one and three references. More references increase memory requirements.

A command-line version of these examples is available in inference.py:

python inference.py --prompt "A blue ceramic mug on a wooden table" --output generated.png
python inference.py --image generated.png --prompt "Change the mug to green" --output edited.png

Memory and performance

The small adapter download does not reflect the full base model's memory requirements. Use CPU offload or a suitable multi-GPU setup when the complete BF16 pipeline does not fit on one GPU.

Our Playground uses two RTX 4090 24 GB GPUs with the BF16 pipeline split across them. Both GPUs serve one request together; simultaneous requests are queued. Observed server-side times after model loading, at 25 steps and CFG 4.0:

These are individual deployment checks, not comparative quality benchmarks. The three-reference check used the same source image in all three slots. Timing depends on the prompt, input images, hardware, and execution path; the CPU-offload example above does not reproduce this custom GPU split.

Checkpoint details

The training dataset is not included in this release. Checkpoint statistics above come from the embedded training metadata. We are not publishing an independent quality or content-coverage benchmark with v0.1.

About “uncensored”

The adapter and example inference code do not add a prompt filter or output safety checker. The name describes the intended release, rather than a guarantee that every requested concept will be rendered. Results depend on the base model, adapter, prompt, and the application running them.

Source and license

• Released by: ChatR Labs
• Base model: Qwen/Qwen-Image-2.1
• Base revision used in the Playground: 790c92633540aa0cb11d9abf19eb46d861714758
• Inference: Hugging Face Diffusers, revision 80c7ed262aeffbeb43ef13ae04baeb9b84515a69
• Modification: ChatR Labs fine-tuned a rank-128 adapter and exported the step-32,000 checkpoint to BF16 Diffusers/PEFT format. This repository distributes the adapter separately from the base model.
• License: Qwen Research License Agreement, with attribution in Notice. Use is limited to non-commercial research and evaluation unless separately licensed by the upstream rights holder.
• Checksums: SHA256SUMS

Built with Qwen. This is a community fine-tune by ChatR Labs, independent of the upstream Qwen team.

Infrastructure Support

GPU compute used for the development and evaluation of Omni v0.1 was provided by Chatr AI.

We thank the Chatr AI team for supporting this research release.