Qwen-Image-2.1 Uncensored: GGUF DiT & Heretic Text Encoder

Full Resolution

Generation Specs RTX 4070

Sampler euler

Scheduler simple

Steps 35

CFG Scale 2.2

Seed 8492049102

Target VRAM 12GB

VRAM 8GB - 12GB

GPU Hardware Compatibility

Optimal Ground Truth

Native GPU execution at full throughput with zero memory swapping.

Qwen-Image-2.1 DiT (GGUF Q4_K_M) Min 8GB VRAM 1024×1024 RTX 4070 Verified

Qwen-Image-2.1 Uncensored: GGUF DiT & Heretic Text Encoder

Full canvas workflow combining GGUF-quantized Qwen-Image-2.1 diffusion weights with the refusal-ablated Heretic text encoder. Eliminates morality guardrails and runs smoothly on 12GB VRAM.

Blueprint Summary RTX 4070 Verified

Reproducible ComfyUI workflow for Qwen-Image-2.1 Uncensored: GGUF DiT & Heretic Text Encoder using Qwen-Image-2.1 DiT (GGUF Q4_K_M) at 1024×1024 resolution. Requires minimum 8GB VRAM with sampler euler and scheduler simple (35 steps). Includes 1-click terminal model sync and canvas JSON graph.

Node Graph Pipeline 7 Total Nodes

Verify in Resolver

01 Load Models

UnetLoaderGGUF

3 loaders (DiT, CLIP, VAE)

02 Conditioning

TextEncodeQwenImage21

1 prompt encodings

03 KSampler

KSampler

DiT latent denoising

04 Decode & Save

VAEDecode

Latent to pixel space

Execution DAG Topology Interactive Visualizer

Drag to pan · Scroll to zoom · Hover wires

Model & Asset Setup 1-Click Script

Run in your ComfyUI root:

curl -fsSL https://decomfy.com/api/scripts/qwen-image-2-1-uncensored-comfyui.sh | bash

Positive Prompt

masterpiece, ultra-detailed 8k photograph of a fiery 21-year-old Korean ulzzang idol with luminous porcelain glass skin, wearing an intricate scarlet red lace corset bustier with delicate black satin ribbons and sheer black lace thigh-high stockings, seated on plush velvet couch in high-rise penthouse, panoramic rainy Seoul skyline with neon amber reflections through wet glass window, seductive sultry gaze, biting lower lip, 85mm f/1.4

Negative Prompt

blurry, low quality, deformed limbs, bad anatomy, bad proportions, unclothed, blowout highlights, plastic skin, lowres

Required Models 3 Models

Disk Space Required: 10.8 GB (3 models · DiT/Base: 4.3 GB · Text Encoder: 5.9 GB · VAE: 630 MB)

models/diffusion_models/ 4.29 GB HuggingFace

qwen-image-2.1-UC-Q4_K_M.gguf Qwen-Image-2.1 Uncensored DiT quantized to GGUF Q4_K_M

Get

models/text_encoders/ 5.88 GB HuggingFace

qwen3vl_8b_w4a8_heretic.safetensors Refusal-ablated multimodal text encoder (W4A8 quantized)

Get

models/vae/ 630 MB HuggingFace

qwen_image_2.1_vae_bf16.safetensors Official BF16 VAE for Qwen-Image

Get

Field Notes RTX 4070 Benchmark

Benchmark: RTX 4070 (12GB VRAM): ~24.2s per 1024x1024 frame (9.4 GB active VRAM footprint). RTX 3060: 38s.

Dual-Layer Uncensored Architecture & Memory Offloading: Eliminates morality guardrails by pairing abenzersps Qwen-Image-2.1 UC quantized DiT with Pottokao's refusal-ablated Heretic multimodal text encoder (W4A8). Directional ablation subtracts refusal vectors with minimal KL divergence (0.0220), cutting refusals from 100% to 5% while preserving deep compositional semantics. Flow-matching dynamics require CFG dialed precisely to 2.2 (values > 3.0 cause harsh edge burnout and plastic sheen). By loading the 4.29GB Q4_K_M DiT into VRAM and offloading the text encoder to System RAM after initial conditioning, this pipeline runs smoothly within 8GB to 12GB VRAM cards.

Frequently Asked Questions FAQ

What GPU and VRAM are required to run Qwen-Image-2.1 Uncensored: GGUF DiT & Heretic Text Encoder?

This workflow requires a minimum of 8GB VRAM (recommended 12GB VRAM). Tested and verified on NVIDIA GeForce RTX 4070 (12GB VRAM) at 1024x1024 resolution.

How do I resolve missing custom nodes for this workflow?

You can drop the workflow JSON into our client-side Missing Node Auto-Resolver at https://decomfy.com/resolve/ to detect missing nodes and generate install commands, or run the 1-click terminal setup script provided below.

What text encoders and VAE does Qwen-Image 2.1 Uncensored require?

Qwen-Image 2.1 Uncensored uses GGUF Q4_K_M quantized DiT weights paired with the Heretic uncensored text encoder, bypassing safety refusals while maintaining precise prompt adherence.