chatrlabs/Qwen-Image-2.1-Uncensored-Omni-LoRA-v0.1

---

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

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	Format	Size	Use
chatrlabs-omni-lora-v0.1-bf16.safetensors	BF16, Diffusers/PEFT keys	671 MB	Text-to-image and image editing

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

Recommended settings

Setting	Starting value
Base model	Qwen/Qwen-Image-2.1
LoRA strength	1.0
Sampling steps	25
True CFG scale	4.0
Negative prompt	A single space: " "
Output resolution	Approximately 1 megapixel; e.g. 1024 × 1024
KV cache	Enabled

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

See the full description on the original page