higgsfield-canvas

Higgsfield Canvas — Node-Based Production Workspace

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Contents

• 1. When to use Canvas
• 2. Node categories
• 3. Models that run inside Canvas
• 4. Named canvas patterns
• 5. Cost model
• 6. Templates and assets-as-nodes
• 7. Shared Canvas — live collaboration
• 8. Cross-surface context
• 9. Source acknowledgment

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Canvas is a node-based editor where prompts, references, and generations from

any Higgsfield model live on a single infinite board. Instead of running each

generation in isolation, you connect nodes — a prompt feeds an image, an

image feeds a video, a style branches into variations — so a whole scene or

campaign becomes one continuous, re-runnable pipeline.

Canvas is its own top-level surface in the Higgsfield nav bar (alongside

Image, Video, Cinema Studio, Marketing Studio). It is model-agnostic: it

*hosts* the other models rather than being a mode inside any one of them, so

this sub-skill routes model-specific craft back to the relevant sibling

sub-skill.

Why Canvas matters for this skill. Canvas is another place to *deploy the

prompts this skill builds* inside the Higgsfield workflow — instead of taking

a finished prompt straight to the Image/Video generator or Cinema Studio, you

drop it into a Prompt node on the board and wire it into a generator. The

prompt-craft is identical (MCSLA scene prompts, Seedance prompt modes, GPT

Image 2.0 formats — all still apply); Canvas just changes *where* the prompt

is executed and lets one prompt feed a multi-step pipeline. Think of the

Prompt node as the on-canvas home for everything the rest of this skill

teaches.

Translated from Higgsfield's official Canvas guide (higgsfield.ai/canvas-intro)

plus the in-product Canvas screenshots and a canvas-workflow walkthrough.

---

1. When to use Canvas

Reach for Canvas when the work is a *pipeline*, not a single shot:

• A multi-shot scene or episode where one reference set drives several shot

prompts.

• An ad campaign that fans one product/brand reference into many variants.
• A storyboard that flows assets → story beats → prompts → image/video

generation in columns.

• Any repeatable pipeline you want to save as a template and re-run with

swapped inputs.

For a single prompt-and-generate, the standard Image/Video tabs are simpler.

Canvas earns its keep when steps connect.

2. Node categories

A node is one step in the pipeline with typed input sockets (left) and a

typed output socket (right); curved edges carry data from one node's output

into the next node's input. The board exposes these node categories:

• Prompt — text starting points.
• Image generator — produces or edits images.
• Video generator — produces video.
• Voice / audio generator — produces voice or audio.
• LLM assistant — a wired-in language model you can task with prompt

drafting or shaping (e.g. a "cinematographer-prompter" role inside a

storyboard). It is a general LLM node you author, not a fixed named

assistant. (Separate from Higgsie, the in-Canvas chatbot — that's a

conversational helper, not part of the prompt-to-generator pipeline this

sub-skill covers.)

• Style / motion — style-transfer and motion-control steps that branch a

look or movement across downstream nodes.

• Render output — final deliverables.
• References — Upload (drop in your own files) and Assets (pull

from your library).

Connections are type-aware: an image output feeds an image input, a prompt

feeds a generator, and so on. Fan-out (one node feeding many) is free-form;

fan-in is more structured.

3. Models that run inside Canvas

All seven Higgsfield models run as nodes on the same board:

| Model | What it's for | Detail |

|---|---|---|

| Soul 2.0 | photoreal images, fashion/editorial, consistent characters | ../higgsfield-soul/SKILL.md |

| Seedance 2.0 | flagship video, 1080p | ../higgsfield-seedance/SKILL.md |

| Kling 3.0 | cinematic video, multi-shot continuity, native audio | ../higgsfield-motion/SKILL.md |

| Wan 2.7 | high-action / dynamic motion | ../../model-guide.md |

| Veo 3.1 | Google flagship video, native audio | ../../model-guide.md |

| GPT Image 2.0 | 4K image gen, near-perfect text rendering | ../higgsfield-gpt-image-2/SKILL.md |

| Nano Banana Pro | precision image editing and placement | ../../image-models.md |

Mixing models on one board is the point — generate a character in Soul,

edit a product in Nano Banana Pro, animate the result in Seedance, all in one

graph.

4. Named canvas patterns

The in-product canvases ship as recognizable layouts. Use them as starting

shapes:

• Simple Seedance 2.0 — one image + one prompt → one video. The minimal

pipeline.

• Extend Video — a sequential chain where the last frame of one

generation feeds the next, for longer continuous sequences.

• Image Edit — multi-reference compositing into one image generator node.
• StoryBoard With Elements — a three-column flow (Assets → Story →

Prompts) feeding image/video generators, often with an LLM node shaping the

shot prompts.

• Long Video (fan-out) — one reference set fanned into several shot

prompts that each generate independently, for multi-shot assembly.

Any board can be saved and reused (see § 6).

5. Cost model

Building the graph is free. You pay only for the models you actually run:

• Connecting nodes, editing prompts, staging a board, and arranging a

50-node storyboard cost no credits.

• Credits are deducted only when a node generates an image or video, at

the same rate that model charges elsewhere on Higgsfield.

So you can stage an entire campaign pipeline and pay only for the renders you

choose to execute. (Per-node credit badges shown in the UI reflect the

underlying model's rate; this sub-skill does not restate specific credit

numbers — confirm live in-product.)

6. Templates and assets-as-nodes

• Save any canvas as a reusable template — for ad variants, character

sheets, storyboards, or any pipeline you run repeatedly. Duplicate it and

swap the inputs.

• Your assets drop in as nodes — Soul ID characters, uploaded products,

brand references, and any of your previous generations can be added

directly to a board as reference nodes.

7. Shared Canvas — live collaboration

Canvas supports real-time team collaboration over a shared link, the way a

team works in Figma:

• Multiple people on one board at once — teammates can drop nodes, chain

pipelines, and generate simultaneously on the same canvas.

• Automatic versioning — versions save as you work.
• Node-attached comments — comments stay attached to the specific node

they reference, so feedback lands on the exact step it's about.

For project-level collaboration beyond a single board — shared projects,

real-time chat, voice/video calls, and pushing a generation into a team

feed — see ../higgsfield-workspaces/SKILL.md § Higgsfield Collab.

8. Cross-surface context

• Model-specific prompt craft lives in the sibling sub-skills (§ 3 table).
• For programmatic ad-campaign orchestration (research → plan → generate →

publish → report) outside the visual board, see

../higgsfield-content-factory/SKILL.md.

• For the static-image asset prep that often seeds a Canvas pipeline

(product reference sheets), see

../higgsfield-gpt-image-2/reference-sheet-workflow.md.

9. Source acknowledgment

Translated from Higgsfield's official Canvas guide

(higgsfield.ai/canvas-intro) plus in-product Canvas screenshots and a

canvas-workflow walkthrough. Node categories, the seven supported models, the

named patterns, the cost model, and Shared Canvas collaboration are stated

per those sources. This sub-skill deliberately does not assert hard node

or fan-in limits, fixed per-node credit numbers, or plan-gating — none of

those are documented in the source material. The in-Canvas chatbot Higgsie

is a conversational helper, out of scope for a prompting tool and not

documented here.