Local AI Video Generation with ComfyUI: How It Actually Works
How a 128GB local workstation runs ComfyUI with Qwen image and LTX video models to generate unlimited AI content without API fees.
Local AI Video Generation with ComfyUI: How It Actually Works
Running image and video generation models locally through ComfyUI lets you produce unlimited AI-generated visuals without paying per-generation API fees, provided you have enough memory to hold the models. On a workstation with 128GB of unified memory, models like Qwen Image and LTX Video run entirely on local hardware, image renders complete in around 20 seconds, and the whole pipeline can be automated through ComfyUI’s API instead of clicking through a UI for every prompt.
TL;DR
- Unified memory changes the math for local generation because a single pool shared between CPU and GPU (128GB in this case) can hold much bigger models than a discrete GPU with a fixed VRAM ceiling.
- ComfyUI ships preinstalled on AMD’s Ryzen AI Halo developer image along with a default image model (Qwen Image was added manually), so there’s no ROCm driver wrangling before you can start rendering.
- Per-image render times of roughly 19 to 20 seconds were observed for Qwen Image outputs, fast enough that a batch job expected to run overnight finished much sooner.
- API/dev mode in ComfyUI lets you drive the whole pipeline with code, scripting prompt variations, seed changes, and batch runs instead of manually queuing jobs in the interface.
- Control nets extend what’s possible, letting you take a reference pose or edge map (like a still from a dance video) and apply it to a generated character or subject.
- Zero marginal cost per generation is the core appeal: once the hardware is paid for, there’s no metering, no rate limit, and no per-call charge the way there is with hosted video and image APIs.
- Video models like LTX are also supported in the same environment, meaning a single machine can serve as the source-image generator and the video generator in one pipeline.
Why does local generation matter if cloud APIs already do this?
Proprietary image and video generation services from major AI labs produce high-quality output, but they charge per generation, and video generation in particular gets expensive fast. If you’re trying to mass-produce images to hand-pick the best ones, or run dozens of variations of a prompt to find the right pose, style, or composition, those costs add up quickly.
Local generation flips that model. Once you’ve got the hardware and the models downloaded, there’s no per-image or per-second charge. You can render as many variations as your hardware allows, discard the ones that don’t work, and keep the pipeline running in the background without watching a usage meter. That matters most for workflows built around mass production and curation: generating dozens of candidate images and picking a handful worth turning into video, generating many seed variations of the same prompt to find the best one, or running long overnight batch jobs.
What hardware do you actually need?
The limiting factor for local generation isn’t raw compute, it’s memory. Image and video generation models, especially newer ones in the tens of billions of parameters, need to fit into VRAM (or unified memory) to run at reasonable resolutions and speeds. A discrete GPU with a fixed VRAM budget, say 32GB, handles smaller models fine but hits a hard wall with larger ones. Once a model doesn’t fit, the system has to offload layers to system RAM, and speeds drop sharply.
Machines built around unified memory architectures, where the CPU and GPU draw from one shared pool, sidestep that wall. In the case of the Ryzen AI Halo workstation referenced here, the system has 128GB of LPDDR5X memory total, and the user can allocate the majority of it to the GPU for model loading (in one configuration, 75% was set as shared GPU memory). That headroom is what makes it possible to run bigger image and video models, and to run multiple models side by side, such as a language model generating prompts while ComfyUI renders images from them.
How does the ComfyUI pipeline actually work?
ComfyUI is a node-based interface for building image and video generation workflows, and it comes preinstalled on some AI-focused developer images, meaning there’s no separate install of ROCm drivers or dependency chasing before first use.
The basic workflow demonstrated looks like this:
- Load a base model. Qwen Image was downloaded and dropped into the correct ComfyUI folder, one of several models compatible with the setup alongside video-focused models like LTX and other open-weight video generators.
- Turn on developer/API mode. This exposes ComfyUI’s API, which means workflows can be triggered from code rather than manually queued in the browser UI.
- Generate a batch of prompts. In one example, a screenshot from a short-form video (cute animals in dance poses) was fed to a language model, which analyzed it and produced a base prompt, then generated multiple prompt variations (different poses like “disco point,” “spin,” “hip hop”) to diversify the outputs.
- Render in batch. A script iterated through the prompt variations and seeds, sending each to ComfyUI’s API and collecting outputs automatically instead of requiring manual clicks per image.
- Apply control nets for pose or edge guidance. ComfyUI supports control nets such as Canny edge detection, which can be used to transfer a pose or composition from a reference image onto a newly generated subject, a common technique behind the “same pose, different subject” style content seen on social platforms.
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Each image render in this setup took approximately 19 to 20 seconds, fast enough that a batch expected to take an entire overnight run instead finished well ahead of schedule.
Is running video generation locally actually practical?
Partially, and it depends on what “practical” means for your use case. The workstation supports open-weight video models like LTX directly inside ComfyUI, so the same machine that generates the source images can also generate video clips from them. That means a full local pipeline is possible: language model writes prompts, image model renders candidate stills, you pick the best ones, and a video model animates them, all without leaving local hardware or paying an API bill.
The tradeoff is speed and quality relative to top-tier proprietary video models. Open-weight video models have improved substantially and can be fine-tuned for specific styles to get noticeably better results, but they generally still trail the best hosted models in fidelity and consistency out of the box. For high-volume experimentation, prototyping, and content pipelines where you’re generating many variants and curating afterward, local generation makes sense. For a single polished hero shot, a hosted model might still win on quality per generation.
What’s the real advantage over just using a hosted API?
Three things stand out. First, cost: there’s no per-call charge once the hardware is in place, which matters enormously if your workflow depends on generating large numbers of variations rather than a single clean output. Second, control: running locally through ComfyUI’s node graph and API means you can script arbitrary prompt and seed variations, chain control nets, and build custom automation that a hosted API might not expose. Third, no rate limits: batch jobs can run unattended overnight or in the background without hitting usage caps.
The tradeoff is setup and hardware cost upfront, plus the ongoing work of managing models, control nets, and fine-tunes yourself rather than relying on a managed service.
Frequently Asked Questions
Do I need a 128GB machine to run ComfyUI locally?
No. ComfyUI runs on much smaller GPUs for lighter models, but larger image and video models benefit from more memory. A 32GB discrete GPU can handle a lot of image generation work, but hits limits with the largest current models, which is why higher unified-memory configurations open up bigger models like larger Qwen image variants without needing aggressive quantization.
What is Qwen Image used for in this pipeline?
Qwen Image is an image generation model that was downloaded and loaded into ComfyUI to render still images from text prompts. In the demonstrated workflow, it was used to mass-produce variations of a prompt (changing seeds and pose descriptions) to generate a batch of candidate images quickly, each taking roughly 19 to 20 seconds.
What does a control net do in image generation?
A control net constrains the output of an image model based on a reference input, such as an edge map (Canny edge detection) or a pose skeleton. This lets you take the composition or pose from a reference image and apply it to a newly generated subject, which is how many “same pose, different character” image and video trends are produced.
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Can I automate ComfyUI instead of using the manual interface?
Yes. ComfyUI has a developer/API mode that, once enabled, exposes an API for triggering workflows from code. This allows you to script batch generations, loop through prompt variations, and integrate ComfyUI into a larger pipeline (for example, having a language model generate prompts that are fed automatically into image or video generation jobs).
Is local video generation as good as proprietary cloud models?
Not quite, in general. Open-weight video models such as LTX have improved significantly and can be fine-tuned for specific styles or use cases, but they typically still lag top proprietary hosted video models in overall output quality and consistency. They’re well suited to high-volume experimentation and iteration where cost matters more than squeezing out the single best possible clip.
