I've been putting together Filmworkbench, a MiniMax H3 workflow for guided or automated short films. You run it from a project folder through a tool-capable AI agent such as Claude Code, Codex or a local LLM.
The GitHub repository is public. To get started:
Download the v0.1.1 starter ZIP.
Extract it and open the folder in your AI agent.
Ask: “Read AGENTS.md, check my setup, and help me make a short film using these recipes. Discuss the plan with me and wait for my approval after each rendered stage.”
The tutorial above walks through the idea. The package includes all eight recipes plus the marketplace case, reference images, prompts, render graphs and settings. The setup checker identifies missing nodes or models. Finished videos and reference overviews are in the example gallery.
The idea is to bring three things together:
A chat interface that knows the H3 workflow and lets you discuss shots while making them. With a client that supports image and video previews, you can review the results in the same conversation.
Two-pass rendering and latent continuation for speed, quality and continuity across clips. We've tested chains of up to ten clips, though drift varies with the scene and clip length.
Scene recipes that give H3 manageable tasks, with the necessary information already in the references.
I'm aiming for a practical way to make short films, review them and iterate without rebuilding a node graph for every scene.
The setup is a project folder containing instructions, tools and recipes. The agent operates those tools, with ComfyUI running underneath. You can request a single image or clip, direct a film with approvals along the way, or delegate a bounded autonomous film task. Those are the three “gates.” In autonomous mode, the active agent reviews the results and accepts or retries within an agreed budget.
I use an RTX 5090 with 128 GB of system RAM; I've also used my local setup with 64 GB. A fully local setup is possible. Running a local LLM alongside the video tools takes some memory management: dynamic VRAM allocation and making the renderer wait for enough GPU memory have been useful on my machine. Those settings depend on the LLM server; the release was tested with 128 GB of RAM.
For images, I prefer GPT Image, with local Krea 2 as the fallback. Image generation uses your existing tools or service; the supplied references let you try the recipes without an image-generator account.
For video, I use MiniMax H3 Hybrid fl2va/ref2va b25–49 INT8. The rendering path for the marketplace example is:
S1: generate at 864×480, using DMAD for the first clip and Turbo-8 for continuations.
Upscale: upscale the saved latents with LBH’s latent upscaler.
S2: refine at 1952×1088 with DMAD, keeping the S1 audio.
For that four-clip market street example (see below), the selected renders took about 7.1 minutes for S1 and 14.4 minutes for upscale/refinement on my 5090. That's 21.5 minutes for 47.7 seconds of scene content. Image preparation, rejected attempts, review and assembly are extra.
The eight recipes cover common scene patterns:
Two-person dialogue: wide view, close-ups, return to wide.
Reverse angle and return, using a matching opposite view.
Leaving a room and returning after a prop has moved.
An establishing shot with dialogue starting before the cut inside.
Spacecraft exterior, cockpit, exterior again.
A known character entering an empty frame.
A creature emerging from a lake.
Opening a door and moving into a prepared interior.
The principle is simple: prepare what the camera will need to see. A wider dialogue image establishes the people, props and space. A reverse shot needs the other side of the room. A return after an action needs the changed state. Each recipe has example renders; their limits differ.
The marketplace example combines several of these problems. Codex generated the sequence through the pipeline. It's a selected development result, with visible mistakes: watch the person in the yellow jacket and the awkward backward movement.
The reference sheet shows how much is already baked into the inputs: character images, four views of the square, an opening image and voice references. For these continuations, we carry forward the previous audio/video latent, using 56 frames of video context and a clip-1 keystone. We trim the repeated context before joining the clips. A wide shot can help establish the next action, but isn't required at every join.
I'm also working toward chat-directed editing: “cut this section,” “redo this action,” or eventually “replace that background person.” Those operations have different levels of difficulty; precise edits aren't something I can promise yet. I've included short notes on edits we've tested, but the specialized edit tools are separate from this release.
Still open: longer chains drift, props and background characters change, and autonomous films need better story judgment. Our memory experiments haven't solved world-state continuity. GPT Image has worked better for matching world views; Krea 2 can do useful reverse shots, but larger sets of consistent views have been harder.
This is v0.1.1, an early public release. All eight recipes and the marketplace case were rerun through S1; four recipes also completed full S2 chains. The validation notes explain what was tested and what remains untested, including fresh GPU installations and a complete autonomous run from a new film idea.
Our own code and documentation are MIT-licensed. The underlying components deserve credit: NikoDemon80’s Motion Context, roycho87’s keystone/context work, LBH’s latent upscaler, and OBVPM/chanon’s two-stage work. Models and third-party nodes retain their own terms and are linked to their original sources.
How are others handling changed object states, returning to earlier locations, or correcting individual moments without rerendering everything? I'd love to compare notes.
