Yes—one author reports running Qwen-Image 2.1 locally in ComfyUI on an M5 Pro MacBook Pro with 48 GB of unified memory. In that specific setup, a still-life image took a reported warm median of 143.5 seconds at 1024 × 1024 and 343.5 seconds at 1536 × 1536. Those figures describe one machine, one workflow and one task; they are not a speed guarantee for every 48 GB Mac.
The practical takeaway is that the model can produce compelling images and handle edits, transparency and reference inputs, but a good-looking result may still miss exact placement, lettering or revision requirements. Plan to inspect and retry outputs when those details matter.
What Qwen-Image 2.1 does in ComfyUI
ComfyUI announced native support on September 20, 2026. Its announcement describes Qwen-Image-2.1 as a 7-billion-parameter image-generation and editing model, with native 2048 × 2048 output, RGBA transparency, and support for up to 10 reference images. The announcement also describes one checkpoint for both generation and editing. These are documented capabilities, not promises about speed or fidelity on a particular Mac. ComfyUI’s announcement
The QwenLM repository records the release and ComfyUI support, and includes example text-to-image and editing workflows. Its Diffusers example uses 40 inference steps; that is a different workflow from the 25-step ComfyUI test discussed below, so the results should not be treated as directly comparable. QwenLM’s Qwen-Image repository
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For ComfyUI, the model setup uses separate diffusion-model, text-encoder and VAE files; the guide lists BF16 and INT8 diffusion-model options and says to update ComfyUI if the relevant nodes or templates are missing. File names and compatibility details can change, so check the current guide and repository before setting up a workflow. ComfyUI’s Qwen-Image guide
What the 48 GB Mac test actually measured
Kevin Mullin’s 2026 report describes a single Apple M5 Pro MacBook Pro with 18 CPU cores, 20 GPU cores and 48 GB of unified memory, running macOS 27.0. The report says ComfyUI 0.37.0, PyTorch 2.12.1 and MPS were identified in the startup log. It used INT8 ConvRot image-model and text-encoder files with a BF16 VAE. These configuration details and results are reported by the author; they have not been independently reproduced here. Kevin Mullin’s test report
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Settings and timing
For the reported workflow, the author used 25 sampling steps, Euler, the simple scheduler, CFG 1, batch size one and an empty negative prompt. The report says there was no LoRA, upscaler, prompt enhancement or manual retouching. With those conditions, the author reports these warm medians for a still-life task:
| Output size | Reported warm median | What the figure represents |
|---|---|---|
| 1024 × 1024 | 143.5 seconds | Kevin Mullin’s single-machine still-life test, using the configuration and settings described above. |
| 1536 × 1536 | 343.5 seconds | Kevin Mullin’s single-machine still-life test, using the configuration and settings described above. |
The test report describes 20 local generations across baseline images, transparent assets, edits, posters, higher-resolution images and a synthetic reference. The figures are not broad benchmarks: other prompts, resolutions, model files, settings, memory use and Mac hardware may produce different results. The 1536 × 1536 run was a new composition, not an upscale of the 1024 × 1024 image.
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Why 48 GB is not a complete performance specification
Unified-memory capacity alone does not identify the chip, cooling behavior, software stack, model precision or other memory use. A separate report covers Qwen-Image-2.1 on an M5 Max using different Q8 and Q4 conditions, but that is not a controlled, like-for-like comparison with Mullin’s M5 Pro test. It cannot establish a general speed ranking between those Macs. The separate M5 Max report
To judge a result or compare configurations fairly, keep the hardware, weight precision, ComfyUI version, backend, sampler, scheduler, step count, CFG, batch size and any custom nodes consistent. Also compare the same kind of task and resolution, and define success in practical terms—such as correct placement, readable text, preserved edits or clean transparency—not just whether the image looks appealing.
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Real examples: where the report found misses
The author’s examples show why image quality and task reliability are different measures. These small counts describe that session only; they are not general success rates.
- Object placement: A “mug beside notebook” request passed the placement check on one of four seeds at 1024 × 1024.
- Poster lettering: One of three poster attempts passed the report’s exact-text check; the other attempts included duplicate text.
- Higher-resolution composition: The same-seed 1536 × 1536 run produced a new composition rather than an upscale, and object placement could flip.
- Transparent assets: The author advises inspecting edges and alpha residue. A pleasing background preview by itself does not establish that the exported transparency is clean.
These examples do not show that the model always fails at precise text or placement. They do show that a single attractive sample is weak evidence when a deliverable depends on those constraints.
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How to evaluate it for a real asset workflow
Test the whole job, not just the first generation. Kevin Mullin’s practical advice is: “Test one complete asset workflow. Generate it, make one specific revision, place it in a real layout, and count every attempt.” Kevin Mullin’s test report
- Write down acceptance criteria. Specify the must-have objects and their placement, exact text if required, edit elements that must remain unchanged, or the transparency standard the asset must meet.
- Generate and record each attempt. Keep the prompt, settings, output size and seed with the result. Count retries instead of judging only the best image.
- Make a targeted revision. Check whether the requested change succeeds without damaging the parts that were already correct.
- Inspect the deliverable in context. Place it in the intended layout; for RGBA output, inspect the alpha and edges rather than relying on the preview background.
- Decide based on usable results. Consider time and retries to reach an asset you can actually use, not just the time to generate one image.
As Mullin puts it: “An attractive first image is useful; an asset you can revise and deliver consistently is much more valuable.” Kevin Mullin’s test report
Is it a fit for your 48 GB Mac?
The evidence supports a narrow conclusion: a 48 GB M5 Pro MacBook Pro ran this ComfyUI workflow, and the author reports useful results alongside specific misses and multi-minute warm medians for the still-life task. It does not establish a minimum Mac specification, predict performance on other 48 GB models, or show how much memory headroom a different setup will have.
If your goal is exploration, concept images or assets you can inspect and revise, the documented generation and editing features may be worth testing locally. If a job requires exact lettering, fixed object placement, reliable edit preservation or production-clean transparency, evaluate those criteria across complete attempts before relying on the model. Native 2048 × 2048 support describes an available output size; it does not mean higher-resolution output will be faster or preserve a lower-resolution composition.
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