Transcript of Fast Low VRAM Wan 2.2 14B AIO | 5 seconds in 5 minutes | Text-to-Video & Image-to-Video | ComfyUI

Imagine running one of the most advanced AI video models, WAN 2.2, on your old GPU with just 8GB of VRAM, and still getting cinematic quality results in minutes instead of hours. Sounds impossible, right? Well, I've got a method that makes it happen. And in this video, I'll show you exactly how you can do the same. Hi everyone, welcome to the channel. Today I'm super excited to share something that absolutely blew my mind. If you've tried WAN 2.2 14b, you know how resource hungry it is. On a decent setup, generating even a 5 second clip can take anywhere from 30 to 45 minutes. That means you wait forever, only to find out the results aren't what you expected. It is total nightmare. But here's the thing. You can run WAN 2.2 extremely fast, even on older GPUs with low VRAM. In fact, people have reported running it on as little as 8GB of VRAM. I tested it myself on an RTX 3060 with 12GB VRAM, and honestly, the results were incredible. The model nailed camera movements, dynamic lighting, and even subtle human emotions, things that usually push models to their limit. And guess what? It only took 5 minutes to render a 5 second clip. That's insane compared to the usual 30 to 45 minutes. Here are some of the clips I rendered. You can see the quality is superb. The lighting effects are great, the camera movement is smooth, emotions are spot on, and the scenes come together beautifully. Honestly, that's pretty impressive for a low-end GPU with low VRAM, and all in such a short time. This speed boost completely changes the game. Instead of wasting hours waiting, I was able to test over 90 different prompts in just 8 hours. Everything from cinematic camera pans to dramatic lighting effects. The time saved was massive, and the quality stayed top notch. So in this video, I'll walk you through every step to set this up, explain the tweets you need to make it run smoothly on low VRAM GPUs, and share some of the best practices for writing prompts that give you those perfect, cinematic results. Stick around till the end because I'll showcase all the clips and prompts I generated. You'll see just how powerful this setup really is. This all is made possible using the rapid all-in-one checkpoint. It uses FP8 precision to cut memory use while keeping the visuals sharp. No extra samplers, no separate clip or VAE nodes. Just the one checkpoint and you are good to go. Download it from Hugging Face and check back often. They drop new versions every few days with better quality and fresh options. This is available in multiple variants. Base, V2, V3, and so on. Each adds new features and generally improves on the last. I tested it using the V8.1 for text to video and V8 for image to video. Each checkpoint is a bit different. You can try them all and see what fits your workflow best. Just download the version you need and drop it into the models checkpoints folder inside your Comfy UI directory. Links for all models and workflows are down in the description. And while you check those out, don't forget to like and subscribe. It costs you nothing, but helps us bring more content like this your way. Thanks! Alright, first we're going to test the text to video workflow, and after that we'll move on to image to video. So let's get started. Load the T2V workflow. Now, the great thing about WAN 2.2 Rapid AIO is that it's really simple to set up. You don't need to mess around with multiple models. Just use the basic Comfy UI load checkpoint node and from there you can load everything. The VAE, clip, and the model all from the single AIO safe tensors file you downloaded and saved in your checkpoints folder. All versions of this model are designed to run with CFG set to one and just four steps. That's it. The recommended samples for each version are listed on their Hugging Face page, but for this test we're going with SA Solver as the sampler and Beta as the scheduler, just like they suggest. Now for the resolution, since we're running this on a low VRAM setup, I've set the width to 832 and height to 480. The video length is set to 81 frames with a frame rate of 16, which gives us a clean five second clip. Everything else can stay at the default settings. Just select the checkpoint you downloaded, set steps to four, CFG to one, and you're good to go. Alright, everything's set up. Let's put in our first prompt. Here's what we're going with. A cinematic, hyper-realistic shot of a red Ferrari racing through a dark dirt road at night. The scene is lit only by the car's bright headlights and glowing red rear lights and occasional street light. The shot begins with a low angle view of the Ferrari speeding towards the camera with a slow shutter speed causing heavy motion blur on the wheels and background. As it passes, the camera quickly pans left and tracks the car from behind, keeping it centered as it accelerates and kicks up glittering sand flecks into the air. So this is a pretty detailed cinematic prompt. We've got movement, lighting, motion blur, and even camera direction. It's a good stress test because if it can handle this kind of scene tracking motion, dynamic lighting, and environmental effects, then you know it's going to perform well with simpler prompts too. Let's run this and see how it comes out. And the results are in. This one was fast, and the total render time came to about 315 seconds, or roughly 5 minutes and 15 seconds. That's seriously impressive, especially when we're running this on an RTX 3060 with just 12 GB VRAM. For a low VRAM setup, that kind of speed and efficiency is a game changer. And now, here's the actual clip it generated. You can see how well it handled the motion blur, the lighting, and the overall cinematic feel of the scene. The camera started with a clean tracking shot of the Ferrari from the front, then smoothly panned left to follow it from behind, just like we described in the prompt. Prompt adherence was spot on. The visual quality looks impressive too. Even the driver inside the car is clearly visible, which made the whole scene feel much more natural and realistic. For a render that took just over 5 minutes on a low-end GPU, that's pretty incredible. Okay, now let's push things a bit further and test out some extreme lighting effects to see how well it handles them. For this, I'm using a very detailed cinematic prompt. Mixed lighting, contrast, under lighting, short side composition, night time, mixed colors, close up shot, low angle shot, side lighting, cool colors. In a dimly lit room, a man stands silhouetted against a projection screen. The eye level close up shot reveals he is wearing a white tank top with a silver earring blinting in his ear. His gaze is lost in the distance, his expression pensive and thoughtful. As the camera pans to the right, the background dissolves into a blur of intertwining blue and purple light, creating an atmosphere that is both mysterious and captivating. This ethereal glow casts a kaleidoscope of colors onto his face, sharply defining the contours of his features. This is a really challenging prompt because it's asking the model to juggle multiple types of lighting. Under lighting, side lighting, mixed colors, and still deliver a clean cinematic shot with emotion and atmosphere. If it pulls this off, that will show just how powerful WAN 2.2 Rapid AIO really is, even on lower VRAM setups. Let's run it and see how it comes out. And wow, the results here are absolutely stunning. The model nailed the lighting effects beautifully. You can clearly see the mix of blue and purple tones dissolving into the background, giving the whole scene that mysterious dreamlike atmosphere we wanted. The under lighting and side lighting came through really well, sharply defining the contours of his face and making the silver earring pop against the dark setting. The projection screen silhouette gave the shot a really cinematic depth, and the camera pan to the right felt smooth and natural. What impressed me most is how the model handled the emotional detail. The pensive expression on the man's face is clear and believable, adding a sense of realism that matched perfectly with the lighting and mood. Overall, for such a complex and lighting heavy prompt, the output was outstanding. This is exactly the kind of result that shows why this is such a game changer for low VRAM workflows. Alright, let's run one more test. This time we'll see how it handles a social setup with multiple elements in the frame. Here's the detailed prompt we'll be using. Warm colors, practical lighting, soft lighting, low contrast lighting, edge lighting, night time, eye level shot, center composition, medium lens, shallow depth of field, clean shot. In an upscale fine dining restaurant with candle-lit tables, white linen, crystal stemware, and floor-to-ceiling windows revealing soft Boca City lights, a well-dressed man in a tailored dark suit sits across from a stylish woman in a black evening dress with subtle jewelry, both smiling with an intimate, relaxed mood. The woman slowly leans forward across the table, gently placing her left hand on the man's wrist, and softly kisses him. The man leans slightly in to meet her, eyes half closed, affectionate and calm. The camera performs a slow dolly in with a slight right-to-left slide to create parallax through wine glasses and candle flare, maintaining a fixed horizon and tripod-stable frame. A gentle rack focus transitions from foreground glassware and candle to their faces as the kiss lands, then holds focus. Style is cinematic realism, elegant, romantic, and upscale. This prompt is packed with detail. It's asking for warm, romantic lighting, restaurant atmosphere, multiple foreground objects, camera motion, rack focus, and subtle emotional expressions all in one scene. If it can pull this off smoothly, it'll prove it's not just good at technical effects, but also at capturing human connection and mood. Let's run it and see how it turns out. And once again, the results here came out beautifully. The restaurant atmosphere really came through. The warm candlelight, soft glow, and reflections off the glassware gave the scene an elegant and upscale look, exactly as we described in the prompt. The camera movement was smooth, with the slow dolly in and parallax effect through the wine glasses and candle flares, adding a nice cinematic touch. Although the rack focus was very less defined than what I expected, but it was good overall. What really impressed me was how well it captured the intimacy and mood of the moment. The man and woman felt natural and expressive. The subtle smile, the gentle hand placement, and the kiss itself all felt believable and romantic. Overall, this test showed that it isn't just great at action or dramatic lighting, but it can also handle social, emotional, and atmospheric scenes with the same level of cinematic realism. That's a big win for anyone looking to use it for storytelling or more narrative-driven video work. Alright, now let's move on to the image-to-video test. For the image-to-video workflow, we'll also need a clip vision encoder. Don't worry, I've left the download link for it in the description below so you can grab it easily. And while you're down there, don't forget to like and subscribe, it costs you nothing. But it really helps us keep bringing you more content, tutorials, and test runs like this one. We will be using the defaults again, resolution set to 832x480, 81 frames at 16 frames per second in CFG as 1 in 4 steps, SA solver as sampler and beta scheduler. For this, I'll be using an image of a red Ferrari speeding along the freeway cutting through the desert at high noon. This base image was generated using Flux Krea Dev, and it already looks great as a still shot. Now, to really bring it to life, we'll add some camera motion and lighting instructions with this prompt. Camera shows the red Ferrari from the front, zooming on a freeway running through a desert. The camera quickly pans to the left and tracks the car from behind with motion blur. It's a sunny day with a clear blue sky and the bright sunlight casts sharp shadows across the desert landscape. This is a good test because it combines fast camera movement, dynamic motion blur, and strong lighting in a wide open outdoor environment. If it can smoothly animate this from a single still image, it'll really showcase the power of its image to video workflow. Let's run it and see what kind of results we get. This too was fast, and the total render time came to about 315 seconds, or roughly 5 minutes and 15 seconds. The result turned out really well. The camera motion was smooth, starting with a clean front view of the Ferrari as it zoomed down the freeway before quickly panning left to track it from behind. The motion blur on the wheels and the desert background added a great sense of speed and realism. The lighting looked spot on too. The bright sunlight gave the car crisp highlights and cast sharp shadows across the desert road, exactly matching the noontime setup we wanted. The clear blue sky and the warm tones of the desert really helped sell the atmosphere of the scene. Overall, the Ferrari animation felt natural and cinematic, which is impressive considering it was generated from a single still image. This shows just how powerful WAN 2.2 Rapid AIO's image to video workflow can be for turning static images into dynamic, realistic clips quickly. Before we wrap up, here are some of the sample clips I rendered using the detailed prompts we tested today. If you'd like to try them out yourself, I've added download links for all the prompts in the description below. And if you haven't already, please take a moment to like and subscribe. It really helps us out and lets us keep bringing you more content like this. That's it for today's video. Thank you for watching and see you next time. 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 🎵 Thanks for watching and I'll see you soon in the next one.