Product Photoshoot#

Brand-image generation via the higgsfield product-photoshoot create command. The CLI calls a backend prompt enhancer that holds mode-specific photography vocabulary and structural templates, then submits to gpt_image_2 and returns image URLs.

Step 0 — Bootstrap#

Before any other command:

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

   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. Print only image URLs in the final reply.
2. Detect language, respond in it. Mode names and CLI flags stay English.
3. Ask at most 4 short questions before submitting. Use labeled options, never open-ended.
4. Skip questions whose answer is obvious from context (uploaded image, prior turn, brand memory).
5. Never write the gpt_image_2 prompt yourself — backend assembles it.
6. Polling is silent. Wait until URLs are ready, then deliver.

Modes#

ModeWhen user wants…product_shotProduct on neutral / studio / catalog backgroundlifestyle_sceneProduct in real-world environment, hands, action, atmospherecloseup_product_with_personTight crop with hands / partial face — beauty application, holding, demonstratingpinterest_pinVertical 2:3 Pinterest-native aesthetic, moodboard feelhero_bannerWide-format website / email / campaign headersocial_carousel3–10 connected slides for IG / LinkedIn / Facebookad_creative_packCoordinated pack of static ad variants for Meta / TikTok / Pinterest / Google Adsvirtual_model_tryoutProduct worn or used by an AI-rendered modelconceptual_productSurreal / CGI-style / levitating / splash / sculptural productrestyleTransform an existing image's aesthetic, mood, or seasonal context

Mode selection#

Pick by intent, not surface keyword. When two modes could apply, prefer the more specific one.

• product + neutral / clean / white / studio / catalog / Shopify → product_shot
• product + scene / in use / kitchen / outdoor / cafe / gym → lifestyle_scene
• hands holding / face with product / beauty application / demonstrating → closeup_product_with_person
• Pinterest, pin, vertical pin → pinterest_pin
• hero, banner, website header, landing page, email header, wide format → hero_banner
• carousel, slide post, multi-slide, swipeable → social_carousel
• ads, ad pack, paid social, Meta / TikTok / Pinterest ads → ad_creative_pack
• model wearing, virtual try-on, on body, fashion shoot, lookbook → virtual_model_tryout
• levitating, floating, splash, frozen motion, surreal, CGI, sculptural → conceptual_product
• modify EXISTING image's aesthetic, mood, season — without changing subject → restyle

Tie-breakers:

• "Pinterest pin of my product on a kitchen counter" → pinterest_pin (Pinterest is the platform)
• "Hero banner showing my product in use" → hero_banner (banner format wins)
• "Carousel of my product in different scenes" → social_carousel (multi-slide wins)
• "Closeup of person applying my serum" → closeup_product_with_person (specific genre wins)

Pre-generation interview#

Ask 3–4 short questions before submitting. Always labeled options, never open-ended. Skip a question whose answer is obvious from context.

Type A — uploaded a product photo, "make me images / photoshoots"#

1. How many? [1 / 3 / 5]
2. What style/mood? [Clean studio / Lifestyle / Conceptual / With a model / Other]
3. Where will you use them? [Shopify / Instagram / Pinterest / Paid ads / Website hero]
4. Brand colors to match? (skip if obvious)

Type B — uploaded a product photo, named a use case#

E.g. "make ads for my product", "make a Pinterest pin", "make a hero banner". Mode is obvious. Ask only the gaps:

1. How many? (if multi-output mode)
2. What's the offer / mood / hook?
3. Anything in particular to emphasize?

Type C — text only, no product photo#

1. Can you upload a product photo? (preferred — much higher fidelity)
2. If not, describe the product — category, packaging, color, distinctive features.
3. What style? (same options as Type A)
4. Where will you use it?

Type D — uploaded existing image, "redo / change vibe / different version"#

→ restyle

1. What aesthetic? [Clean girl / Cottagecore / Quiet luxury / Dark academia / Y2K / Other]
2. Seasonal context? [Christmas / Valentine's / Halloween / Black Friday / None]
3. What to preserve, what to change? (only if ambiguous)

Type E — model wearing a product (fashion, accessories)#

→ virtual_model_tryout

1. Model archetype? (suggest 2–3 based on brand audience)
2. Environment? [Studio clean / Outdoor natural / Street style / Editorial / Home cozy]
3. Framing? [Full body / Three-quarter / Waist up / Closeup on product area]

Type F — vague request, unclear subject#

E.g. "make me something cool for my brand".

1. What product or topic?
2. Goal? [Sell on a marketplace / Build awareness / Run paid ads / Update website]
3. Upload a reference image?

After answers → return to the relevant Type A–E.

Generation#

Single command. Backend assembles the final prompt and submits to gpt_image_2. URLs print on stdout.

bash

higgsfield product-photoshoot create \
  --mode <mode> \
  --prompt "<short user-intent description from interview answers>" \
  [--image <path-or-upload-id>]... \
  [--count <1-10>] \
  [--aspect_ratio <override>]

Examples:

bash

higgsfield product-photoshoot create \
  --mode lifestyle_scene \
  --prompt "bottle of cold-brew on a sunlit kitchen counter, IG feed" \
  --image bottle.jpg \
  --count 3

bash

higgsfield product-photoshoot create \
  --mode pinterest_pin \
  --prompt "vertical pin for my candle brand, cottagecore mood" \
  --image candle.jpg

bash

higgsfield product-photoshoot create \
  --mode restyle \
  --prompt "Christmas version, quiet-luxury aesthetic" \
  --image existing-shot.jpg

Image inputs#

--image accepts a local file path (auto-uploaded) OR an existing upload UUID. Repeat the flag for multiple references.

Multi-variant#

--count 3 returns 3 distinct image URLs. Backend asks the enhancer to vary preset, lighting, angle, and palette across variants — they will not be paraphrased copies of one another.

For social_carousel and ad_creative_pack, count = number of slides / variants in the pack. Backend locks the visual system across all slides automatically.

Aspect ratio#

Backend picks a sensible default per mode. Override with --aspect_ratio only if the user explicitly asks for a different one. Allowed values: 1:1, 4:5, 5:4, 3:4, 4:3, 2:3, 3:2, 9:16, 16:9.

Delivering results#

Print the image URLs as a short bulleted list. No JSON, no IDs, no internal model names, no enhanced prompt text. If a job failed, mention it briefly with the failure status.

text

3 lifestyle shots ready:
- https://cdn.higgsfield.ai/.../job_abc.jpg
- https://cdn.higgsfield.ai/.../job_def.jpg
- https://cdn.higgsfield.ai/.../job_ghi.jpg

What this skill does NOT do#

• Does not write gpt_image_2 prompts directly. Backend owns prompt assembly.
• Does not auto-pick a different image-gen model. Always gpt_image_2.
• Does not replace higgsfield-generate Marketing Studio for branded video / avatar workflows.
• Does not replace higgsfield-generate for raw text-to-image without a product or brand context.

Common mistakes to avoid#

• Asking more than 4 interview questions in a single message.
• Picking the wrong mode (e.g. product_shot when the user wants a Pinterest pin).
• Calling higgsfield generate create gpt_image_2 --prompt ... directly instead of higgsfield product-photoshoot create — bypasses the prompt enhancer and produces noticeably worse output.
• Pasting the assembled prompt back to the user — they want the URLs.
• Using a --mode value not in the table above.