Higgsfield Generate

Submit jobs to any Higgsfield model. Wraps the higgsfield CLI. Covers generic image/video gen, Marketing Studio (branded ads, avatars, products, hooks, settings), and, secondarily, Virality Predictor video scoring.

Step 0 — Bootstrap

Before any other command:

1. If higgsfield is not on $PATH, install it:

   curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh
   

2. If higgsfield account status fails with Session expired / Not authenticated, ask the user to run higgsfield auth login (interactive) and wait for confirmation.

UX Rules

1. Be concise. No raw IDs, no JSON dumps in chat. Print the media URL for generated assets, or the text summary for Virality Predictor.
2. No internal jargon. Don't narrate "calling higgsfield cost", "polling job".
3. Detect the user's language from the first message and reply in it. Technical args (--aspect_ratio 16:9) stay English.
4. Don't batch-ask. Pick a sane default model and ask one thing at a time only if genuinely missing.
5. Don't pre-estimate cost or optimize for cheaper models unless the user asks. Prefer the quality default first.
6. Pass --wait to generate create so the command blocks until done and prints the result URL itself. Avoid the two-step create → wait pattern.

Discovery guardrail

When looking for a Higgsfield feature/model, do not rely only on semantic search or CLI --help. First run an unfiltered model list, then inspect likely job_set_type names. If the user says a model exists but search returns no results, trust that signal and verify with the full model list before answering.

Virality Predictor is exposed as:

• Customer-facing name: Virality Predictor
• Technical job_set_type: brain_activity
• Category/output: text report. This is video-in/text-out analysis, not a text/chat generation model.
• Input: uploaded video
• Purpose: finished-video hook, attention, retention, and virality analysis

If the user says "analyze this video", "score this ad", "evaluate the hook", or similar, route to brain_activity even though it appears under text/analysis models. Classify by task intent and required input, not by output category alone.

Workflow — generic generation

1. Pick a model. Start with the core defaults unless the brief clearly needs a specialist:

   • GPT Image 2 → default image model for high-fidelity general generation, graphic design, UI, banners, typography, and on-image text.
   • Seedance 2.0 → default video model for serious motion, cinematic clips, multi-shot work, image-to-video, and 4–15s production-quality output. 12s is valid.
   • Nano Banana 2/Pro → default for character, cartoon, stylized, and reference-driven image work; use Pro for harder briefs.
   • Marketing Studio → default for ads, UGC, product demos, unboxing, TV spots, presenter videos, and brand/product workflows.

   Image:

   • Brand product visual (Pinterest pin, lifestyle, hero banner, ad pack, virtual try-on) → use higgsfield-product-photoshoot instead. NOT this skill.
   • Generated product concept / packaging / can / bottle with brand name or label text → GPT Image 2.
   • Branded ad image with avatar + product (Marketing Studio shape) → Marketing Studio Image (see Marketing Studio below)
   • Aesthetic UGC / fashion editorial / lifestyle character → Soul 2.0
   • Cinematic still frame → Soul Cinema
   • Highly characterful creative persona (text-only, distinctive) → Soul Cast
   • Locations / environments / no-people scenes → Soul Location (best in class)
   • Vector illustrations OR face edit + complex scene swap → Seedream 4.5
   • Soul Character (reference id from higgsfield-soul-id) → Soul 2.0 for stills, Soul Cinema for cinematic
   • Character or cartoon-style work → Nano Banana 2; step up to Nano Banana Pro on hard cases
   • Fast and cheap iteration → Z Image
   • Default for everything else → GPT Image 2. Graphic design, UI, banners, typography, and high-fidelity general generation.

   Video:

   • All advertising / commercial / branded ad video → Marketing Studio (see Marketing Studio below)
   • Default all-purpose serious video (multi-shot, consistent identity, motion-heavy, image-to-video, 4–15s requests) → Seedance 2.0. SOTA. Do not downgrade to Seedance 1.5 just because its duration enum is easier to read; validate Seedance 2.0 first.
   • Single-plane scene without strong dynamics, cheaper than Seedance 2.0 → Kling 3.0
   • Cheap clean shot without cuts, only when the user asks for cheaper/budget output → Seedance 1.5 Pro
   • Cinema-grade highest fidelity → Cinema Studio Video 3.0
   • Cheap with strong physics, no audio needed → Minimax Hailuo
   • Fast batch / volume → Veo 3.1 Lite

   Video analysis:

   • Rate a finished video's hook, virality potential, attention, retention, or distraction risk → Virality Predictor (brain_activity). This is a video analysis model that returns a text score/report, not a generated media asset.

   For the actual --model ID to pass to higgsfield generate create, run higgsfield model list --json | jq to map display names to IDs. See references/model-catalog.md for the full table.

2. Pass media inputs straight to flags. Media flags accept a local file path or a UUID. CLI auto-uploads paths and auto-detects job vs upload for UUIDs. No need to pre-upload. Each model declares accepted roles (image, start_image, end_image, video, audio) — see references/media-inputs.md.

3. Validate quickly. If unsure of params, run higgsfield model get <jst> --json once and pass only what's needed. Validate the preferred model before falling back to an older one. Use schema defaults otherwise. The server returns adjustments for non-fatal coercions (e.g. aspect_ratio=99:99 → closest match) and a structured error for invalid declared-param values.

4. Submit and wait in one shot. higgsfield generate create <jst> [--prompt "..."] [media flags] [param flags] --wait. Blocks until terminal status and prints the result on stdout. Tunables: --wait-timeout 20m (default 10m), --wait-interval 5s (default 3s). Virality Predictor does not need a prompt; pass --video.

5. Deliver. For generated media, send the URL plus a one-line summary (model, duration if video). For Virality Predictor, deliver the scores, business interpretation, and the Open report link. Do not surface .glb, .bin, or region-table internals in normal chat output.

To inspect or rerun later, higgsfield generate list --json and higgsfield generate get <id> --json work for retrospection. higgsfield generate wait <id> is still available if you ever need to rejoin a job started without --wait.

Media flags

Each flag accepts either a local file path (auto-uploaded) or a UUID (upload id from higgsfield upload create, or a previous job id). Each model declares its own role set via MEDIA_ROLES. See references/media-inputs.md for the full table.

Common params

Flags pass through to model schema. Use higgsfield model get <jst> to discover.

higgsfield generate create gpt_image_2 --prompt "neon city at dusk" --aspect_ratio 16:9 --resolution 2k --wait
higgsfield generate create nano_banana_2 --prompt "anime character concept, expressive