INT4 ConvRot W4A4 Quantization: Running Large Image Models on Low-VRAM GPUs in ComfyUI

Background: The VRAM Pressure of Image Generation Models

As diffusion models continue to balloon in parameter count, running large image generation models locally demands ever more VRAM. Whether it's the Krea2 series or the Qwen-Image series released by Alibaba's Tongyi Qianwen team, models with billions of parameters put them out of reach for many consumer GPU users.

Diffusion models are the mainstream paradigm in image generation today. Their core principle is to progressively add noise to data (the forward process), then train a neural network to learn step-by-step denoising (the reverse process) in order to generate high-quality images. Since the explosion of models like DALL-E and Stable Diffusion in 2021, parameter counts have rapidly grown from hundreds of millions to billions or even tens of billions. A new generation of Transformer-based diffusion models (DiT, Diffusion Transformer) such as Flux and SD3 has pushed parameter counts even higher.

The architectural root of this parameter explosion lies in diffusion models' migration from convolutional architectures (U-Net) to Transformer architectures (DiT). U-Net performed excellently in early Stable Diffusion thanks to its local receptive fields and skip connections, but its ability to model global context was limited. DiT uses a Vision Transformer as its backbone, capturing long-range dependencies through global self-attention, which dramatically improves generation quality and instruction-following ability—at the cost of parameter counts that grow quadratically with sequence length and layer depth. The Flux.1 series has already reached 12 billion parameters, and newer models continue to climb in extended scenarios like multimodal condition injection and video generation. This makes the "larger parameter count = higher quality" rule hold true in image generation as well, while simultaneously intensifying the deployment burden on consumer hardware.

Behind this scale explosion lies a harsh VRAM math reality: loading a 7-billion-parameter model at FP16 precision requires roughly 14GB of VRAM just for the weights (each parameter takes 2 bytes, 7 billion × 2 = 14GB). During inference, the KV cache, activation buffers, and intermediate computation states consume an additional 30%–50% of VRAM, making it entirely impossible for consumer GPUs (typically 8–12GB VRAM) to cope. This mathematical reality creates a gap between consumer hardware and top-tier models that is difficult to bridge directly, and it is precisely the fundamental driver behind the rise of quantization techniques.

In the Reddit community, one developer shared a complete workflow for converting several mainstream image models to the INT4 ConvRot W4A4 format, adapted for the latest version of ComfyUI. This work offers a viable new option for VRAM-constrained users and represents the current frontier of image generation model quantization.

What Is INT4 ConvRot W4A4

The Core Logic of Quantization

At its essence, model quantization replaces the original high-precision floating-point numbers (such as FP16 or BF16) with lower-precision numerical representations, thereby dramatically reducing VRAM usage and memory bandwidth pressure. Understanding this mechanism first requires a basic grasp of floating-point precision: FP16 takes 2 bytes per value and can represent numbers up to around 65504; INT8 compresses values into 8-bit integers (256 discrete values); INT4 compresses further to 4 bits, offering only 16 discrete values. INT4 achieves 4x storage compression compared to FP16, with a corresponding boost in memory read bandwidth—this is especially critical for memory-bound inference scenarios and is the physical basis for how low-bit quantization can accelerate inference. The higher the compression ratio, the less information each value can carry, and the larger the "quantization error" becomes. This is precisely the central engineering challenge of low-bit quantization.

The quantization process is essentially a lossy compression problem: mapping continuous floating-point values to a finite discrete set inevitably introduces rounding errors. The magnitude of quantization error is determined by the quantization step size (scale) and the zero-point offset, and the choice of these two parameters directly affects the error distribution. Calibration data plays a key role here: by measuring the actual distribution range of activation values across a small set of representative input samples, quantization parameters can be independently optimized for each layer. For image generation models, the choice of calibration data is particularly sensitive—prompts of different styles, resolutions, and content types trigger vastly different activation distributions, and the breadth of the calibration set directly determines the generalization robustness of the quantized model. This is why professional quantization toolkits carefully construct calibration datasets tailored to specific model families rather than reusing generic text corpora.

It's worth noting that the acceleration potential of INT4 quantization cannot be realized on all hardware. NVIDIA's Ada Lovelace architecture (the RTX 40 series) was the first to introduce dedicated INT4 Tensor Cores on consumer GPUs, theoretically enabling up to 4x the matrix computation throughput compared to FP16. The Tensor Cores in the RTX 30 series (Ampere architecture) have limited native support for INT4; INT4 operations typically need to be unpacked and simulated, greatly reducing the actual speedup. The RTX 20 series and earlier architectures gain almost no computational acceleration from INT4 quantization—the VRAM savings still exist, but inference latency may actually increase slightly due to data format conversion overhead. This means that for RTX 30 series users, the main value of the W4A4 approach lies in VRAM savings rather than speed improvements, with actual gains tied directly to hardware generation. This hardware capability's arrival is precisely the key prerequisite for the W4A4 approach to move from theory to large-scale practical use, explaining why this format only began to see widespread community activity around 2024.

What W4A4 Means

W4A4 is the standard notation for a quantization configuration:

• W4: Weights are quantized to 4-bit
• A4: Activations are also quantized to 4-bit

This is more aggressive than the common W4A16 scheme (4-bit weights, 16-bit activations). W4A16 is currently the most widely deployed quantization scheme in the open-source community, represented by toolkits such as GPTQ, AWQ, and llama.cpp. Its advantage lies in keeping activations at high precision, concentrating quantization error mainly on the weight side, resulting in relatively controllable precision loss.

The aggressiveness of W4A4 lies in simultaneously compressing activations, which brings a dual benefit: further reducing the VRAM footprint of activation caches, and achieving substantial matrix-operation-level acceleration on hardware with native INT4 tensor computation support (such as NVIDIA's Ada Lovelace architecture). However, quantizing activations is far more difficult than quantizing weights—weights are static and can be finely calibrated after offline distribution analysis, whereas activations are dynamic, changing in real time with input, making it impossible to fully predict their distribution range in advance. This poses a greater challenge to maintaining quantization precision and makes perceptible quality loss more likely.

The Key Role of ConvRot Technology

ConvRot (Convolution Rotation / Rotation) is a core technique for addressing the precision issues of low-bit quantization. Rotation-transform quantization methods are among the most important technical breakthroughs in large-model quantization over the past couple of years. Their theoretical foundation comes from the properties of orthogonal matrices in linear algebra: applying an orthogonal rotation to a weight matrix does not change the model's mathematical equivalence (the inverse of an orthogonal matrix equals its transpose), yet it redistributes the statistical characteristics of the values.

Outliers are the core obstacle to low-bit quantization—neural network activations often contain a few extremely large values. Directly quantizing them causes the quantization step size (scale) to be stretched by these extremes, sharply degrading the precision of the majority of values within the normal range. Rotation transforms "stir" these extremes together, making the distribution more concentrated and allowing the limited 4-bit discrete levels to more efficiently cover the true numerical range.

The core mathematical tool of this method is the Hadamard matrix—a class of orthogonal matrices whose elements are only +1 or -1, satisfying H×Hᵀ=nI. This property makes it both satisfy the requirement that orthogonal rotation preserves model equivalence and extremely computationally efficient: the Hadamard transform can be completed with O(n log n) complexity via fast algorithms, approaching the efficiency of FFT and avoiding the O(n²) overhead of explicit matrix multiplication. From a signal-processing intuition, the Hadamard transform spreads the "energy" concentrated in a few dimensions of the original space (i.e., the outliers) evenly across all dimensions—similar to breaking up an extremely uneven spectrum into a flatter distribution—effectively gaining higher effective precision within the same 4-bit budget.

The academic lineage of this direction is clearly traceable: QuaRot (2024, ETH Zurich) was the first to systematically integrate Hadamard rotation into every computational node of the Transformer, verifying near-FP16 precision on the LLaMA series; SpinQuant (2024, Meta) further introduced learnable rotation matrices, optimizing rotation directions through a small amount of calibration data—adding learnable fine-tuning rotations on top of Hadamard rotation, representing the evolution from static mathematical transforms toward data-driven optimization; ConvRot builds on this work with specialized engineering adaptations for image generation model architectures that contain convolutional layers, introducing rotation transforms into the quantization pipeline of convolutional kernel tensor dimensions—this is a key node on the migration path of rotation-transform quantization from LLMs to visual generation models, and an important prerequisite for making INT4 activation quantization truly practical.

The Lineup of Converted Models

The developer has so far completed conversions covering two mainstream series:

Krea2 Series

• Krea2 Turbo: An accelerated version aimed at fast inference
• Krea2 Raw: A full version that retains stronger raw generation capabilities

Qwen-Image Series

• Qwen-Image 2512: Tongyi Qianwen's base image generation model
• Qwen-Image-Edit 2511: A version focused on image editing tasks
• FireRed-Image-Edit 1.1: An editing model variant based on the Qwen-Image ecosystem

This lineup covers both the fundamental text-to-image capability and the more precision-sensitive image editing scenarios. Image editing models face far stricter challenges in quantization than pure text-to-image models: editing models typically need to simultaneously process the encoded features of the original image, the semantic representation of text editing instructions, and noise prediction during the diffusion process. When these three signal streams are fused at the cross-attention layers, the precision of the activations directly determines whether the "editing signal" can precisely cover the target region without spilling beyond its boundaries. The rounding errors introduced by quantization have a cumulative effect in this scenario: tiny deviations at each diffusion time step are gradually amplified through denoising iterations, manifesting in the final image as discontinuous edges between the edited region and the original, abrupt tonal shifts, or semantic drift. Image editing tasks require the model to precisely execute local modifications while preserving the original image's semantic content and structural details, placing a stronger dependence on the precision of intermediate-layer features—pushing editing-class models to INT4 quantization is itself a real-world stress test of the upper limit of this approach's precision retention. The successful quantization of models like FireRed-Image-Edit, to some extent, validates the engineering feasibility of the ConvRot approach in the most demanding application scenarios.

Practical Value for ComfyUI Users

Dramatically Lowered VRAM Threshold

ComfyUI is currently the most popular node-based image generation workflow tool, open-sourced by developer comfyanonymous in 2023. Unlike interface-based tools such as WebUI, ComfyUI abstracts each step—model loading, sampler configuration, image post-processing—into independent "nodes." Users build workflows by connecting and combining nodes, offering extremely high flexibility. Its Custom Nodes ecosystem is highly active, and its backend directly calls the PyTorch inference engine, making it naturally suited for integrating experimental quantized inference backends.

From an architectural perspective, ComfyUI internally manages VRAM scheduling uniformly through the model_management module, supporting the dynamic loading of model weights in different quantization formats onto designated devices. The new version's native support for INT4 quantization means that the quantized inference logic has been incorporated into the core code path rather than relying on external plugins—this marks a key milestone in quantization technology being elevated from an "experimental feature" to a "first-class citizen" of the toolchain, and it is the institutional foundation for why many technical enthusiasts in this ecosystem are willing to be early adopters of cutting-edge quantization approaches.

The emergence of INT4 quantized models means that models that once required 16GB or even 24GB of VRAM to run smoothly can now potentially complete inference on GPUs with 8GB or even less VRAM. This has direct practical value for popularizing local image generation and lowering deployment barriers.

Factors to Weigh Before Use

While aggressive quantization schemes like W4A4 offer significant VRAM savings, the following points still require rational evaluation:

• Generation quality: 4-bit activation quantization may bring perceptible quality degradation in areas such as detail reproduction and color transitions. While ConvRot technology can compensate for part of the loss, it cannot eliminate it entirely
• Inference speed: The actual acceleration effect of low-bit quantization depends on whether the hardware has corresponding INT4 computation units—not all GPUs achieve noticeable speedups. The RTX 40 series has better hardware support for INT4 than the previous generation, while the RTX 30 series mainly benefits from VRAM savings rather than speed gains, and the overall ecosystem is still maturing
• Version compatibility: This approach explicitly requires the latest version of ComfyUI and relies on newer quantized inference support, so users on older versions need to upgrade first

Technical Trend: Quantization Rapidly Penetrating Image Models

Although this community practice originated from an individual developer, it reflects a clear direction in the image generation field: quantization technology is rapidly migrating from large language models (LLMs) to diffusion models. This migration is not a simple methodological copy-paste, but a process requiring systematic engineering adaptation to architectural differences.

LLM quantization mainly deals with the weights and activations of linear attention layers and FFN layers; diffusion models additionally introduce unique structures such as convolutional layers, cross-frame temporal attention, and time-step embeddings. Each structure has distinct activation distribution characteristics, requiring differentiated rotation-transform strategies. Furthermore, diffusion model inference is essentially an iterative denoising process—quantization errors continuously accumulate across dozens or even hundreds of time steps, and their error propagation mechanism is fundamentally different from an LLM's single-pass forward inference, imposing additional requirements on the temporal stability of rotation-transform strategies. This is a structural challenge that must be specifically addressed when migrating LLM quantization methods to image generation models.

After methods like QuaRot and SpinQuant matured in the LLM domain, researchers and engineers are systematically introducing them into image generation models. ConvRot is precisely a representative attempt on this technical migration path, making specialized engineering adaptations for the architectural characteristics of image models—not an isolated individual effort.

As open-source toolchains continue to improve support for INT4 and even lower bit widths, running top-tier image generation models on consumer hardware is no longer out of reach. For ComfyUI users focused on local deployment, these practical developments are worth continued tracking and hands-on testing.

Key Takeaways

Key Takeaways

Key Takeaways