Qwen3-VL 8B Abliterated · INT8 ConvRot & BF16

A complete multimodal Qwen3-VL text encoder for Qwen Image 2.1 Turbo in ComfyUI, reconstructed from AtomicChat's BF16 GGUF release. Choose the 9.35 GB INT8 ConvRot checkpoint for reduced storage, or 17.53 GB BF16 for the original reconstructed precision.

Both include the entire BF16 vision tower and image-to-language mergers needed for image conditioning. No separate mmproj is required. The source modification affects the language encoder; the diffusion transformer and VAE are unchanged.

Choose your checkpoint

The INT8 checkpoint matches the official Comfy-Org encoder's exact file size and tensor precision policy. It is 46.67% smaller than BF16. Each file is a complete encoder; download one for ordinary use.

Quick start in ComfyUI

1. Download your chosen checkpoint into ComfyUI/models/text_encoders/.
2. In CLIPLoader, select the checkpoint and set type to qwen_image.
3. Connect the CLIP output to TextEncodeQwenImage21 in a Qwen Image 2.1 Turbo workflow.
4. Use the matching diffusion model and VAE below. For image editing, also connect your reference image and VAE to TextEncodeQwenImage21, then use its latent output as the sampler's input.

CLI download, from the directory containing your ComfyUI installation:

hf download NidAll/Qwen3VL-8B-Abliterated-INT8-ConvRot \
  qwen3vl_8b_abliterated_int8_convrot.safetensors \
  --local-dir ComfyUI/models/text_encoders

To download BF16 instead, substitute qwen3vl_8b_abliterated_bf16.safetensors.

Required companion models

Download these separately from Comfy-Org/Qwen-Image-2.1:

For the tested Turbo setup, use Euler, CFG 1, eight steps, with these exact sigmas:

1.0, 0.978453, 0.95418, 0.926626, 0.89508,
0.845148, 0.704534, 0.414568, 0.0

Image-editing tests used the same reference image across encoder variants. Use the reference image's dimensions and preserve the reference conditioning produced by the encoder node.

The tested ComfyUI revision is 0df64eb242b7c5759c3e86afd5d1846d923b1033, with comfy-kitchen==0.2.37. An older ComfyUI build may lack Qwen Image 2.1 support or the required inline INT8 ConvRot/embedding loader.

What is included

Language: all 36 transformer blocks, token embeddings, normalization tensors and the language output head. Exactly 73 tensors differ from the original BF16 reference: 36 attention-output projections, 36 MLP down-projections and the token embedding.

Vision: the complete 27-block tower, patch and positional embeddings, visual attention/MLPs, normalization, the main merger and three DeepStack mergers. All 351 final vision tensors remain BF16 and were verified numerically against the BF16 mmproj. The GGUF's two temporal patch-convolution slices were correctly reassembled.

The reconstructed checkpoint contains 750 BF16 tensors. GGUF BF16 bits were extracted directly, without an FP16 intermediate. GGUF FP32 tensors were reused in BF16 only after exact equality with the widened reference was established. All 677 unchanged reference tensors are preserved bit for bit.

INT8 ConvRot policy

Conversion started from the validated BF16 checkpoint, using the ComfyUI Native Quantizer codec and a pinned official tensor-layout template. No activation calibration, learned rounding, rank reduction or double quantization was applied. Every selected matrix passed the gate; there are no BF16 boundary-block exclusions.

The final file has 1,258 stored tensors: 254 INT8 weights, 496 BF16 tensors, 254 FP32 scales and 254 inline quantization metadata tensors. 93.42% of model parameter elements are INT8.

Storage and execution

Stock ComfyUI CLIP encoding can retain INT8 storage while performing floating-point matrix multiplication after inverse rotation. The filename does not imply automatic INT8 GEMM acceleration. The quantized embeddings use native token-row dequantization with inverse ConvRot.

A separate test explicitly enabled native quantized operations and disabled forced floating casts in runtime test objects. It observed 504 CUDA INT8 linear calls per generation, plus native embedding dequantization and INT8 tensor-core GEMM kernels. That experimental compute mode is not automatically enabled by ordinary CLIPLoader use.

Validation results

Reconstruction passed complete tensor coverage, serialized identity, tokenizer and NaN/Inf checks. Source reconstruction errors were zero; all 151,669 token IDs and 151,387 BPE merges matched. Independent comfy-kitchen decoding of all INT8 matrices produced:

16 fresh INT8 generations—eight stock-compute and eight native-INT8-compute runs—were compared against eight unchanged BF16 baseline images. All used 1024-pixel output, seed 42, identical prompts/reference image, diffusion model, VAE and sampling settings. Every image edit exercised the vision tower and three DeepStack mergers; all conditioning, latent and decoded-image tensors were finite.

Matched image similarity results

SSIM measures image similarity, not prompt adherence or semantic correctness.

Manual inspection confirmed readable English/Chinese text, the requested object order and successful teapot-to-watering-can replacement in these examples. The library scene showed placement/detail differences. Watercolor and dusk changes were weak in BF16 and both INT8 modes: those cases passed execution but did not pass semantic transformation acceptance.

These measurements cover eight prompts at one seed. They do not establish universal quality equivalence, robust typography, broad style editing, multi-reference behavior, chat generation quality or refusal reduction. Weight error is not activation error. No portable latency or memory-capacity claim is made.

Sources and attribution

AtomicChat sources: Qwen-Image-2.1-Turbo-Abliterated-Uncensored-BF16.gguf and mmproj-Qwen-Image-2.1-Turbo-Abliterated-Uncensored-BF16.gguf. The original architecture and weights originate from Qwen/Qwen3-VL-8B-Instruct.

Credit to Qwen, AtomicChat, Comfy-Org, the ComfyUI Native Quantizer and comfy-kitchen projects for the original weights, modifications, compatible layout and conversion/runtime implementations. The silveroxides/convert_to_quant project was researched for format compatibility; its learned-rounding converter did not produce these files.

The source's Not For All Audiences classification is retained. “Abliterated” identifies the inherited language-weight modification; no image uncensoring effect or new safety/refusal behavior has been established by this release.

SHA-256 checksums

34153e12be478cac8debc0619c6c1d4f4dd53cd42c333346585e268d5ca7dde3  qwen3vl_8b_abliterated_int8_convrot.safetensors
e9e4e7f0c046ac95749628a50234faf1fd3334a62a7a34273e6c578464095cbc  qwen3vl_8b_abliterated_bf16.safetensors

License and notices

The Qwen3-VL weights and AtomicChat-derived encoder are distributed under Apache-2.0. This distribution reconstructs the 73 modified language tensors in the Comfy-Org checkpoint layout and supplies an INT8 ConvRot derivative. Both include the complete vision tower. The diffusion model and VAE are separate dependencies and are not redistributed here.

ComfyUI and comfy-kitchen retain their own licenses. Conversion used the Apache-2.0 ComfyUI Native Quantizer backend; conversion software is not bundled in this model-only repository.

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