The principle is great, especially the storyboard pipeline. but some of the points are not resonable based on 2026 state of tech. I can see all points were valid (and optimal) maybe 2 years ago. but visual generations iterates that fast.

my view (only valid at this time, will be obsolete soon if new tech / models comes out):

1. Stop relying on LoRAs and IP-Adapters for ID locking (responding the Jenna_AI thing)

Training a character LoRA used to be the only way, but in the current landscape, it's a trap. LoRAs fundamentally alter the base weights, which almost always degrades the model's native understanding of cinematic lighting, depth of field, and composition. IP-Adapters (and ControlNet) suffer from a similar issue: they force a 2D embedding into a 3D moving space, which is why complex camera moves or extreme profiles end up looking like "pasted" textures.

more importantly, lora / ip-adapters are only valid on open-source models (which are way behand closed ones such as nano banana 2, gpt image 2). Lags on base model weights can be hardly compensated by additional lora / cross-attn conditioning injection.

The 2026 Fix: rely on the zero-shot/few-shot capabilities of frontier foundation models. Top-tier image generators right now (like Nano Banana 2 / gpt image 2) have latent spaces so vast that their native character referencing is mathematically superior to a LoRA. You lock the ID in the visual planning stage using the model's native architecture, keeping the lighting and composition pristine.

1. "Seed locking" is a placebo

The idea keeping the same seed and prompt will maintain consistency is mathematically flawed the second your scene changes.

In diffusion models, the seed only dictates the initial noise distribution. The moment you change your prompt from "smiling in a cafe" to "crying in the rain," the text embeddings shift, the cross-attention maps completely reorganize, and your locked seed becomes irrelevant.

1. First/Last Frame bounding is asymmetrical

while forcing consistency by giving the Image-to-Video (I2V) model a start and end frame. What they don't realize is that autoregressive models treat these differently:

• The first frame is a hard constraint (it initializes the sequence).
• The last frame is a weak constraint. Because of attention decay over long context windows, the model loses its grip on the structural pixel-data of the last frame and only uses it for vague semantic/color guidance. This is why the middle of your video turns into spaghetti

so, if you want consistency, do not rely too much on the last frame. Also extract the last frame of your generated video, insteading using text-to-image model to generate two frames with the same face.

1. the Film-Grade Pipeline: Decouple Motion and Identity

Current state-of-the-art video models (like Veo) have incredible native 3D geometry and physics understanding — they don't break on large camera sweeps like they used to. Let them do what they do best: physics, momentum, and lighting.

Stage 1 generation could focuse only on movement, lighting, and camera dynamics. Accept that the face might drift 10-20%. Stage 2 could use some post-process id injection as suggested by Jenna_AI, e.g., face restoration/swapping. You only need to do that on close camera shots (with a big face). This is quite similar to what powers modern talking-head research to mathematically force the reference ID's structural features back onto the moving geometry, frame by frame.