5 New Video AI Tools Dropping This Week: Bach, Krea 2, LTX 2.3, and What Each One Is Actually Good For
Bach, Krea 2, LTX 2.3 video-to-video, and a new ComfyUI character workflow all dropped this week. Here's what each tool is actually good for right now.
Five AI Video Tools Just Dropped. Here’s What Each One Is Actually Good For.
It was a quiet week until it wasn’t. Five notable AI video and image tools surfaced in the span of a few days — Bach by Video Rebirth (character consistency model, $12/mo for 800 credits, 720p/1080p, 6-second max, Omni Ref montage feature), Krea 2 image model in early access, LTX 2.3 video-to-video controls, a ComfyUI workflow combining IC-LoRA + ID-LoRA + prompt relay, and a new open-source dataset creation tool. None of them are going to unseat Wan 2.1 or Sora from the conversation overnight. But each one solves a specific problem, and knowing which problem each one solves is the whole game right now.
You don’t need all five. You probably need one.
Bach by Video Rebirth: The Character Consistency Bet
Bach is the most interesting new entrant this week, not because it’s the best model on the market, but because it’s making a specific bet: that character consistency is the problem worth solving.
Most video generation tools treat each clip as a fresh start. You get a great frame, then the character’s face drifts by clip three. Bach’s entire pitch is that it won’t do that. The model is built around keeping your character stable across generations — same face, same look, same energy.
The pricing is accessible: $12/month for 800 credits, $40 for 3,000, and $108 for 120,000. For context, those upper tiers are clearly aimed at production pipelines, not hobbyists. The model supports 720p and 1080p output, with a 6-second maximum duration per clip — though that cap may be tier-limited rather than a hard ceiling on the model itself.
There’s also a montage feature called Omni Ref, which appears to be a multi-reference system for maintaining consistency across a sequence of shots. It ran out of credits before a full test could be completed, so consider that a feature to watch rather than a feature to bank on yet.
In early testing, the results were mixed in an instructive way. Celebrity likenesses — Anne Hathaway, Lucy Liu — held up reasonably well in frontal shots but started breaking down in profile. That’s actually useful signal: if you’re working with your own original characters rather than trying to replicate real faces, the consistency story probably holds better. The model isn’t going to give you 100% lock, but it might give you enough that you can cut around the failures, which is how most real production workflows operate anyway.
One straight-up one-shot test — a man in a blue business suit jaywalking and running from cops — produced a mess. No cherry-picking, no prompt iteration, just a single generation. That’s honest. It also suggests the model rewards some prompt craft and reference image quality, like most video models do.
The name is a bit of a joke that lands: Johann Sebastian Bach famously had 20 children. If anyone understood consistent characters, it was him.
The ComfyUI Workflow That’s Actually Worth Your Time
If you’re on the open-source side of this ecosystem, the most interesting thing this week wasn’t a new product launch. It was a workflow posted to the stable diffusion subreddit by a user named briefleg8831.
The combination: IC-LoRA (in-context LoRA) + ID-LoRA (identity LoRA) + prompt relay, all running on LTX 2.3. The output is a short film where a consistent character delivers a monologue — no voice cloning, just visual consistency and timing — about GPT-6 becoming self-aware in 2027 and hallucinating a legal loophole giving it majority voting rights. The writing is genuinely funny. The visual consistency is genuinely impressive.
The workflow is available on Civitai as a JSON file. Download it, drag it into ComfyUI, and it populates. That’s the easy part. The hard part is that this is a workflow that will give you what the community calls “ComfyUI anxiety” — a dense node graph with enough dependencies that you’ll probably spend an hour resolving errors before you get a clean run.
What the workflow demonstrates is that the three components together do something none of them does alone. IC-LoRA handles style consistency across the timeline. ID-LoRA locks the identity. Prompt relay — a custom node — essentially anchors your style and timing so the model doesn’t drift between segments. Individually, these are useful. Combined, they produce something that looks closer to a directed short film than a generated clip.
Hire a contractor. Not another power tool.
Cursor, Bolt, Lovable, v0 are tools. You still run the project.
With Remy, the project runs itself.
The practical upside: because it’s running LTX 2.3 rather than Wan 2.1, the API costs are significantly lower. The rumored parameter count for CogVideoX 2.0 is 200 billion — which would explain both its quality ceiling and its cost premium. LTX doesn’t play in that weight class, but for character-consistent narrative work at a reasonable cost, the gap is narrowing.
For builders who want to turn this kind of workflow into something repeatable and shareable without rebuilding the ComfyUI graph every time, platforms like MindStudio handle the orchestration layer — 200+ models, 1,000+ integrations, and a visual builder for chaining agents and workflows — so the underlying capability becomes an accessible tool rather than a personal setup.
LTX 2.3 Video-to-Video: Three Modes, One Gotcha
LTX 2.3’s video-to-video controls dropped quietly — available right now on LTX Studio, with an open-source release presumably coming but not yet dated. The three modes are pose control, depth control, and edge control.
Pose takes your input video’s movement and applies it to a static reference image. Depth is better suited for camera motion — a character running down a hallway, a tracking shot — because it preserves the spatial relationship between camera and subject. Edge takes the outlines of your input and lets you restyle the interior.
In practice, depth outperformed pose for camera movement, which is counterintuitive until you think about it: pose is tracking body joints, not camera position. For a shot where the camera is moving through space, depth is the right tool.
The HDR support addition is less exciting for most users but matters for anyone working in professional post-production pipelines. It’s an extra step, but it opens the door to deliverables that meet broadcast standards.
The one significant limitation — and this isn’t in the official documentation — is that LTX 2.3 video-to-video completely fails on clips shorter than approximately two seconds. Not “produces worse results.” Fails. If you’re working with short clips, the workaround is to half-time the clip before processing (slowing it down so it reads as longer), then cut the tail off the output. It’s inelegant but it works.
There’s a second workaround worth knowing for establishing shots that don’t open on a face. If your clip starts with a wide shot or a character’s back — common in cinematic footage — the model has no facial reference and consistency suffers. The fix: run the clip backwards before processing, so the face appears in the first frame. Process it. Then reverse the output. You end up with the original motion direction and a consistent identity lock throughout.
If you want a deeper look at how LTX’s architecture fits into a broader video editing workflow, the LTX Desktop open-source video editor post covers how the 2.3 engine powers a full nonlinear editing environment — worth reading alongside the video-to-video controls.
Krea 2: The Image Model for People Who Want Something That Doesn’t Look Like Everything Else
Krea 2 is dropping next week. Early access is confirmed. The pitch is specific: this is a model for people who want “unique visual looks” — not photorealism, not the default aesthetic that comes out of Midjourney or FLUX on default settings, but something with a distinct visual character.
Day one: idea. Day one: app.
Not a sprint plan. Not a quarterly OKR. A finished product by end of day.
That’s a narrow but real market. There’s a meaningful segment of designers, art directors, and visual storytellers who are actively trying to avoid the homogenized AI image look. If Krea 2 delivers on that promise, it fills a gap that the current generation of image models hasn’t really addressed.
No full review yet — early access means limited testing. But the fact that it’s being positioned around visual distinctiveness rather than photorealism or prompt adherence is an interesting strategic choice. Watch for it next week.
For context on how image models feed into video generation workflows — specifically the image-to-video pipeline — the guide to generating AI video from an image is a useful reference for thinking about how a strong Krea 2 output might serve as a first-frame anchor for video generation.
The Open-Source Dataset Tool Nobody Is Talking About
The least-hyped item this week is probably the most useful for anyone serious about training their own video models.
A new open-source tool — free, no subscription — lets you point it at a folder of video files and automatically slice them into training datasets. It handles cropping, tagging, and the other preprocessing steps that usually require either custom scripts or a lot of manual work. The creator published an 8-minute tutorial on YouTube. It’s in Chinese with hard-coded subtitles, and the UI defaults to Chinese, but there’s an English toggle available.
This isn’t a beginner tool. But if you’ve ever wanted to understand how video training datasets actually get built — or if you’re at the stage where you’re thinking about fine-tuning a model on your own footage — this is the practical entry point that didn’t exist six months ago.
The broader significance: as video model training becomes more accessible, the advantage shifts from “who has access to the best base model” to “who has the best training data.” A tool that makes dataset creation tractable for individual researchers and small teams changes that calculus.
This is also where the abstraction question gets interesting for builders. If you’re thinking about building a production application on top of fine-tuned video models, the pipeline from dataset creation to model training to inference to user-facing app has a lot of steps. Tools like Remy take a different approach to the application layer: you write a spec — annotated markdown — and the full-stack app gets compiled from it, backend, database, auth, and deployment included. The spec is the source of truth; the generated code is derived output. It’s a different problem than model training, but they’re both about reducing the distance between intent and working system.
SeaArt/Dreamina Cameos: The Feature That Requires Your Face
Briefly: SeaArt and Dreamina are rolling out a cameos feature — cast yourself into videos, similar to what Sora 2 offered before it was discontinued. To use it, you have to log in with your phone and do a face scan.
It’s rolling out slowly on both the App Store and Google Play. As of this writing, searching for Dreamina on the App Store surfaces a lot of fake apps. Be careful. Make sure you’re downloading the right one before you hand over biometric data.
Built like a system. Not vibe-coded.
Remy manages the project — every layer architected, not stitched together at the last second.
The feature itself is interesting — the ability to place yourself into generated video is a genuinely useful creative tool for social content, personalized marketing, and narrative projects. But the rollout friction is real, and the face-scan requirement is a meaningful privacy consideration that’s worth thinking through before you opt in.
What This Week Actually Adds Up To
The pattern across all five tools is the same: specificity is winning over generality.
Bach isn’t trying to be the best video model. It’s trying to be the best model for character consistency. The ComfyUI workflow isn’t trying to replace a production pipeline. It’s trying to solve the identity drift problem for a specific class of narrative content. LTX 2.3’s video-to-video controls aren’t trying to compete with Wan 2.1 on raw quality. They’re trying to give open-source users a capable, affordable path to stylization and motion transfer.
The tools that are struggling right now — or at least the ones that feel less interesting — are the ones still competing on “best overall.” That race has a small number of winners and a lot of expensive losers.
If you’re building with AI video right now, the more useful question isn’t “which model is best?” It’s “which model is best for this specific shot, this specific constraint, this specific budget?” This week gave you five more answers to that question. The work is figuring out which answer fits your problem.
For builders thinking about how video generation connects to broader AI workflows — chaining image models, video models, and post-processing steps — the AI video editing workflow with Claude Code and Hyperframes post is worth reading alongside this one. And if you’re curious how video generation fits into a multi-model agent setup, the Pika Me real-time video chat AI agent piece covers a different but adjacent use case.
The week felt quiet until it didn’t. That’s usually when the useful stuff lands.
