There is no established winner. The sources available here do not provide a controlled comparison of quantized and full-precision diffusion models generating textures on the same mobile device and runtime. Quantization may reduce model storage or inference cost, but whether it preserves texture quality or improves speed depends on the model, denoising process, and mobile backend. Treat any claim that it is faster or equally good for this specific task as unproven until tested on the target phone.
How do quantized and full-precision diffusion models compare for mobile texture synthesis?
Full precision represents model values at their original, higher numerical precision; quantization represents them with lower precision. Lower-precision weights may reduce model storage and can reduce inference cost, but those potential benefits do not guarantee a faster end-to-end texture workflow. Quality and speed depend on the quantization method, the model and runtime, the device, and the complete denoising process.
Texture synthesis also asks more of an output than a generic image-generation benchmark does. A texture may look plausible in one crop yet show a seam when tiled, repeated motifs, color drift, altered directional patterns, or inconsistencies between patches. The available quantization, mobile-generation, and texture-synthesis studies examine separate pieces of this problem; they do not measure quantized against full-precision texture synthesis under matched mobile conditions.
What quantization research says about quality
Post-training quantization is not simply a matter of reducing every model value and assuming the same output. In Q-Diffusion: Quantizing Diffusion Models (arXiv, posted February 8, 2023), the authors identify two challenges: activation distributions change across denoising timesteps, and U-Net shortcut layers can have bimodal activation distributions. Their method uses timestep-aware calibration and split shortcut quantization to address them.
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In the paper’s stated experiments, Q-Diffusion reports converting full-precision unconditional diffusion models to 4-bit with a maximum FID change of 2.34. The authors compare that result with a change greater than 100 for traditional post-training quantization in the stated comparison. They also report W4A8 results with FID increases of 0.39–1.88 across their experiments. These are results for the paper’s models, method, and benchmarks—not measurements of mobile performance or texture-specific quality. FID alone does not establish whether a generated tile has seams, color drift, or consistent structure.
Quantization errors can also accumulate as a model denoises over multiple steps. Liu et al.’s ICML 2026 paper, Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion Models, studies that propagation and reports a 1.2 PSNR improvement over SVDQuant on SDXL W4A4, with less than 0.5% additional time overhead for its compensation strategy. That result indicates that the denoising trajectory and error-compensation method can affect measured image quality; it does not establish a result for mobile texture synthesis.
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What mobile diffusion timings do—and do not—show
Published mobile timings are specific to their model, sampling approach, devices, and software stack. They are not a direct comparison of precision modes.
| Report | Reported setup and result | What it establishes |
|---|---|---|
| Google Research, “MobileDiffusion: Rapid text-to-image generation on-device” (January 31, 2024) | Google describes a 520-million-parameter latent diffusion model with a one-step DiffusionGAN sampling strategy. The authors say they tested it on premium iOS and Android devices and that it can generate one 512×512 image in about half a second. | A result for MobileDiffusion and the authors’ tested premium devices. The approach combines a mobile-oriented architecture, one-step sampling, and decoder optimizations; its timing cannot be attributed to quantization alone. |
| Choi et al., “Squeezing Large-Scale Diffusion Models for Mobile” (2023 workshop paper) | The paper’s search-visible description reports latency under seven seconds for one 512×512 image on a Samsung Galaxy S23 in an optimized Stable Diffusion deployment. | A historical result for that deployment and handset. The paper combines optimization techniques and does not isolate quantization as the sole cause. |
The two timings above are not a controlled comparison: the model, sampling, runtime, device context, and workload differ. Neither tells you how much quantization alone changes texture-generation latency on a phone you plan to use.
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What makes texture quality different
Seams and continuity
NVIDIA Research’s SIGGRAPH 2024 Diffusion Texture Painting adapts a pretrained diffusion model for patch inpainting and successive strokes on a 2D canvas or UV-mapped 3D mesh. Its project page notes that ordinary conditional inpainting can drift from the starting texture after several patches. That makes continuity across patches a practical quality measure, not just a matter of whether each patch looks convincing on its own.
Repeat structure, color, and direction
Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis (arXiv, posted May 13, 2024) fine-tunes a diffusion model on one reference texture and uses patch-based score aggregation to create arbitrarily large outputs. Its authors discuss repetition, color drift, sharpness, and directional statistics as texture concerns. These dimensions may reveal a degradation that a generic image score does not.
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In the paper’s human preference study, Infinite Texture was selected as best 45% of the time across the reported comparison; NSTS received 22%, Image Quilting 15%, STTO 13%, and PSGAN 5%. Those percentages compare texture methods, not quantized and full-precision versions of one model. The authors also report that random crops improved runtime by a factor of 10 over fixed crops with comparable image quality in their described setup. That is a crop-strategy result, not a mobile or quantization result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to test both precision modes fairly on a phone
For a decision that applies to your texture workflow, compare the two precision modes on the target device rather than combining results from unrelated papers. Change precision while holding the rest of the workload constant:
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- Use the same model and texture adaptation. Keep the checkpoint and any fine-tuning or texture-specific adaptation unchanged.
- Match the generation settings. Use the same prompt or reference image, sampler, number of denoising steps, guidance settings, output or tile dimensions, and patch sequence. Do not attribute a speed gain to precision if you also change the sampler or reduce the steps.
- Keep the deployment environment fixed. Use the same runtime and backend on the same phone, operating-system version, and device configuration. Record thermal state and run conditions; report whether timings are single runs or sustained results.
- Measure the whole task. Record end-to-end latency and peak memory. If you can measure them reliably, include energy use and sustained performance, rather than only a model-kernel time.
- Inspect texture-specific outcomes. Check tile edges at the intended repeat boundary, repeated motifs, color drift from the reference, sharpness, directional statistics, and consistency across patches or views. Use the same set of seeds or inputs for both modes and inspect outputs side by side.
- Report generic image metrics only as supplements. FID or PSNR can help describe certain differences, but neither replaces checks for seams, repetition, and cross-patch consistency.
This comparison separates precision from other acceleration choices such as architectural changes, pruning, distillation, or fewer sampling steps. Those changes may be useful, but changing them at the same time makes it difficult to tell what caused a difference.
How to decide
- Choose quantization only after measuring the trade-off you need. Check whether the actual deployment reduces storage, latency, memory use, or energy on your target phone, and whether the resulting textures still meet your quality requirements.
- Keep full precision when texture defects are unacceptable and no validated quantized path is available. This is a risk-management choice, not evidence that full precision is always faster or produces better textures.
- Do not use a general image-generation timing as a texture benchmark. A one-step text-to-image system and a multi-patch texture workflow do not establish the same workload or quality outcome.
The evidence supports quantization as a real diffusion-model optimization area and shows that mobile diffusion and diffusion-based texture synthesis are both possible research targets. It does not show whether quantized or full-precision diffusion is better for mobile texture synthesis. That answer requires a matched test of the same texture task, model, device, backend, and sampling configuration.
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