---
id: daily-higgsfield-product-photoshoot
name: "higgsfield-product-photoshoot"
url: https://skills.yangsir.net/skill/daily-higgsfield-product-photoshoot
author: higgsfield-ai
domain: multimedia
tags: ["generative-ai", "image-generation", "marketing", "e-commerce"]
install_count: 21800
rating: 4.60 (3 reviews)
github: https://github.com/higgsfield-ai/skills
---

# higgsfield-product-photoshoot

> 自动生成电商产品摄影级图片，无需实体拍摄，支持多种场景和风格

**Stats**: 21,800 installs · 4.6/5 (3 reviews)

## Before / After 对比

### 产品摄影成本

**Before**:

雇佣专业摄影师和搭建摄影棚，购买道具布景，协调模特和产品寄送，拍摄10张产品图需要预算5000元和时间周期2周

**After**:

上传产品图片自动生成多种场景的摄影级图片，支持指定风格和角度，5分钟获得10张商用级产品图，成本仅需几美元

| Metric | Before | After | Change |
|---|---|---|---|
| 单张成本 | 500美元 | 1美元 | -99.8% |

## Readme

# higgsfield-product-photoshoot

# 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:

- If `higgsfield` is not on `$PATH`, install it:

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

```

- 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

- Be concise. Print only image URLs in the final reply.

- Detect language, respond in it. Mode names and CLI flags stay English.

- Ask at most 4 short questions before submitting. Use labeled options, never open-ended.

- Skip questions whose answer is obvious from context (uploaded image, prior turn, brand memory).

- Never write the gpt_image_2 prompt yourself — backend assembles it.

- Polling is silent. Wait until URLs are ready, then deliver.

## Modes

Mode
When user wants…

`product_shot`
Product on neutral / studio / catalog background

`lifestyle_scene`
Product in real-world environment, hands, action, atmosphere

`closeup_product_with_person`
Tight crop with hands / partial face — beauty application, holding, demonstrating

`moodboard_pin`
Vertical 2:3 Pinterest-native aesthetic, moodboard feel

`hero_banner`
Wide-format website / email / campaign header

`social_carousel`
3–10 connected slides for IG / LinkedIn / Facebook

`ad_creative_pack`
Coordinated pack of static ad variants for Meta / TikTok / Pinterest / Google Ads

`virtual_model_tryout`
Product worn or used by an AI-rendered model

`conceptual_product`
Surreal / CGI-style / levitating / splash / sculptural product

`restyle`
Transform 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 → `moodboard_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" → `moodboard_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"

- How many? `[1 / 3 / 5]`

- What style/mood? `[Clean studio / Lifestyle / Conceptual / With a model / Other]`

- Where will you use them? `[Shopify / Instagram / Pinterest / Paid ads / Website hero]`

- 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:

- How many? (if multi-output mode)

- What's the offer / mood / hook?

- Anything in particular to emphasize?

### Type C — text only, no product photo

- Can you upload a product photo? (preferred — much higher fidelity)

- If not, describe the product — category, packaging, color, distinctive features.

- What style? (same options as Type A)

- Where will you use it?

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

→ `restyle`

- What aesthetic? `[Clean girl / Cottagecore / Quiet luxury / Dark academia / Y2K / Other]`

- Seasonal context? `[Christmas / Valentine's / Halloween / Black Friday / None]`

- What to preserve, what to change? (only if ambiguous)

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

→ `virtual_model_tryout`

- Model archetype? (suggest 2–3 based on brand audience)

- Environment? `[Studio clean / Outdoor natural / Street style / Editorial / Home cozy]`

- 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".

- What product or topic?

- Goal? `[Sell on a marketplace / Build awareness / Run paid ads / Update website]`

- 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.

```
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:

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

```

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

```

```
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`.

## Resolution

Use `2k` for every product-photoshoot job.

## 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.

```
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.

Weekly Installs2.4KRepository[higgsfield-ai/skills](https://github.com/higgsfield-ai/skills)GitHub Stars45First Seen1 day agoSecurity Audits[Gen Agent Trust HubPass](/higgsfield-ai/skills/higgsfield-product-photoshoot/security/agent-trust-hub)[SocketPass](/higgsfield-ai/skills/higgsfield-product-photoshoot/security/socket)[SnykFail](/higgsfield-ai/skills/higgsfield-product-photoshoot/security/snyk)

---
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