The workflow canvas
Prompts, references and models are boxes joined by lines on a shared canvas, so you can see how a picture was made, change one step and run it again.
Zenflow's canvas is node-based. Prompts, references, generators, editors, verifiers and approvals are nodes; the edges show exactly what feeds what. You can see how an asset was made, change one step, and run it again.
@rex
same dog · new seasonFound @rex · Cultural 2025 in brand memory, approved 19 Oct 2025, pose carried. Generated with Nano Banana 2:
Verified against the brand kit: identity 0.97 · collar #C8471C · palette ΔE 1.2.
Sent for review: Art director ✓ · Creative director ✓ · Client in review.
09:42Asking is free. Attach a model when you want to make something.
Fork a workflow to try three directions from the same references. Run one workflow over a whole list of inputs. Nest a workflow inside another as a sub-canvas so a complex pipeline reads like one step.
Art director → creative director → brand → client, as stages on the canvas, with notes captured at each one. Nothing ships without the chain, and every decision in the chain becomes memory.
Build the pipeline once, then share a simplified interface. Teammates add the inputs, a brief, a product image, a campaign name, and get on-brand results without touching the full canvas.
Cursors, presence, comments and live changes. The canvas is a room, not a file.
Every template is a canvas you can run as-is or rebuild. Each one is wired to the brand memory, so the first run already uses your Context Variables, rules and approved references.
@headphonescut out
input
brand font behind the subjectEvery image model treats every part of a picture as negotiable, including the parts that aren't: the spelling of your wordmark, the face of your contracted ambassador, the shape of your product. When a render is ninety percent right, most tools make you roll again. Zenflow repairs the region that failed and leaves every other pixel untouched.
One asset, all the way through: the wordmark render is detected, checked against the brand kit, restored from logo.svg and re-checked, and the loop runs on its own.
wordmarkgarbledZenflow locates the defect, checks it against your brand kit without touching a pixel, restores it from the brand's real asset, the actual vector logo, the ambassador's identity references, the true product shots, and re-runs the same checks on the result. A repair that doesn't strictly beat the original is discarded. The system can even reject its own refinement when the refinement makes legibility worse.
A wordmark is rigid and belongs to the brand. A face is deformable and has to be modelled. A product has to be reconstructed from reference. Zenflow uses a different repair path for each, because collapsing all three into one inpainting model is how teams ship the pasted look.
Identity is measured, not eyeballed. The identity meter scores how close the repaired face is to the ambassador's references and shows you the number. Wordmarks are checked character by character. Colours are checked against the palette. Every score is stored with the asset, so "identity retained: 0.94" is a fact in the lineage, not a claim in a deck.
Image repair ships as a product. Video repair runs as an experimental feature in pilots: the same memory, the same references, the same lineage, while frame-exact identity across a full spot is still maturing in the underlying models.
Import the brand book, the logos and the last two or three campaigns' approved finals. Zenflow embeds them and registers the obvious Context Variables: mascot, ambassador, logo, hero product.
Refer to what you already have in plain language, or tag it with @. Watch the referential phrases resolve as you type.
Check the candidate card, its date, model and approval. Use it, and it becomes a node on the canvas. Click the context icon on the node to see exactly what was retrieved and what is passed to the model.
Generate four, keep two. The meters score identity, pose, wordmark and palette before you look.
Anything the meters flag goes through the repair pass, from your real assets. Keep only what beats the original.
Every yes and no, with its note, goes into the brand memory. Next run starts further ahead.
This year’s set with last year’s approved mascot, in the approved pose, in a new setting.
@headphonesThe brand's over-ear headphones. The product is never altered and the blue stays exact.
#1@headphones#2start frameIs this the one you meant?
Retrieved context never goes to a model without a person seeing it.
product kept · #1
kept
kept
before
after
Use one model for the hero and another for the variations. Your references, rules and memory come along to each.
A Brand Model is a base model plus your memory plus your rules, saved as one thing: "Northwind · product hero · FLUX.2" or "Northwind · mascot · Nano Banana 2". Create as many as you need per brand, per format, per client. Run them like any other model. When a better base model arrives, upgrade the Brand Model without losing anything it learned.
Enterprise plans connect your own model endpoints and private deployments. They sit in the same registry, behind the same memory, with the same verification.
Run the same brief through several models side by side and let the meters score them. Zenflow tracks which models your brand approves most often for each kind of job, and recommends accordingly.
Models get renamed, repriced and withdrawn without notice. Because Zenflow's memory sits above the models, a workflow built on one model runs on the next.
Model registry highlights this month: Muse Image, Nano Banana 2 and Pro, FLUX.2, Seedream 5, GPT Image 2, Qwen Image 3, Ideogram 4, Kling 3.0, Seedance 2.5, Veo 3.1, Wan 3.0, LTX 2.3, MiniMax H3, and more. The list changes monthly; the memory doesn't.
Add your brand book, your logos and last year's approved campaign. Then ask for the dog from last year's cultural campaign.
