Image Style Imitation

sn-image-imitate

Image style imitation scene skill (tier 1), relying on the sn-image-recognize, sn-text-optimize, and sn-image-generate tools provided by sn-image-base (tier 0).

Features:

• Extracts high-fidelity long caption from a reference image
• Rewrites caption according to user requested content change while preserving style and layout
• Enforces layout-lock constraints during caption rewrite
• Performs post-generation layout consistency review and bounded retries
• Returns structured process artifacts for debugging and reproducibility

Non-goals

• Pure neural style transfer without content change (use dedicated style-transfer tools instead)
• Local editing / inpainting of specific regions within the reference image
• Processing video or animation input (only single static images are supported)
• Batch generation from multiple reference images in one invocation
• Guaranteeing pixel-level fidelity to the reference; the skill targets layout and style consistency, not exact reproduction

Input Specification

• reference_image (string, required): local path or URL of the style reference image
• target_content (string, required): new content user wants in the generated image
• output_mode (string, default friendly): output mode, friendly or verbose
• aspect_ratio (string, default 16:9): output aspect ratio for generation
• image_size (string, default 2k): output image size preset
• max_attempts (int, default 3): maximum generation attempts for meeting layout consistency
• layout_threshold (float, default 0.75): minimum layout similarity score to accept result

Environment Variable

Dependency installation and API key configuration are for sn-image-base skill.

The minimum environment variables to configure sn-image-base skill running with SenseNova Token Plan:

SN_BASE_URL="https://token.sensenova.cn/v1"
SN_API_KEY="your-api-key"

Fallback priority is dedicated variable > domain shared variable > global variable. Text calls use SN_TEXT_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY; vision calls use SN_VISION_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY; image generation uses SN_IMAGE_GEN_API_KEY -> SN_API_KEY.

Please refer to the Python dependencies and API keys section in sn-image-generate_en.md for more configurations.

API Configuration

All API calls in this skill are executed through the sn_agent_runner.py of the sn-image-base skill, please refer to the sn-image-base skill (README.md) for more details.

• VLM call: sn-image-recognize (Step 1 & 3)
• LLM call: sn-text-optimize (Step 2)
• Image generation call: sn-image-generate (Step 3)

When encountering MissingApiKeyError or needing explicit model control: pass model and auth params explicitly via CLI arguments. See $SN_IMAGE_BASE/references/api_spec.md.

$SN_IMAGE_BASE path explanation: $SN_IMAGE_BASE is the installation directory of the sn-image-base skill (SKILL.md exists). The agent can locate this path by skill name sn-image-base.

Architecture: Main Agent + Worker Agent

This skill uses a two-tier agent architecture:

• Main Agent: receives user request, normalizes parameters, sends preflight, invokes Worker Agent, and sends final text/image to user
• Worker Agent: executes fixed 3-step pipeline and returns structured JSON

Responsibility Boundaries:

• Worker Agent does not send any user-visible message directly
• Main Agent sends all user-facing responses
• Worker Agent last message must be and only be the JSON string defined in Return Contract
• Worker Agent executes VLM/LLM/image calls directly; no nested subagent for these low-level calls

Workflow

Main Agent Workflow

1. Extract reference_image, target_content, output_mode (default friendly), aspect_ratio (default 16:9), image_size (default 2k), max_attempts (default 3), and layout_threshold (default 0.75)
2. Validate required inputs:
   • reference_image is provided and resolvable
   • target_content is non-empty
3. Send preflight message: "Using sn-image-imitate skill to generate a style-consistent image, please wait..."
4. Start Worker Agent with full normalized parameters and working directory
5. On Worker result:
   • status=ok: send final summary and generated image
   • status=error: report the actual error

Worker Agent Workflow

Worker Agent receives reference_image, target_content, output_mode, aspect_ratio, image_size, max_attempts, layout_threshold, and the working directory of this skill ($SKILL_DIR).

Error Handling Strategy:

All sn_agent_runner.py calls share the same error handling rules:

• If the subprocess exits with non-zero code, crashes, or times out: do not fallback, return status=error with the actual error message from stderr or the system error string
• If the subprocess returns invalid JSON or the JSON lacks an expected result field: return status=error, do not silently continue with empty or default values
• If the VLM review call fails during Step 3, treat the attempt as incomplete: do not record a score, and either retry the review once or skip to the next attempt depending on remaining budget

Step 0 — Initialization

1. Generate task_id with format YYYYMMDD_HHMMSS
2. Create temp directory: /tmp/openclaw/sn-image-imitate/<task_id>/ as TEMP_DIR
3. Resolve and normalize REFERENCE_IMAGE
4. Persist user request:

echo "$TARGET_CONTENT" > "$TEMP_DIR/target-content.txt"

Step 1 — Image Annotation (long caption + layout blueprint)

Use prompts/image_annotate.md as system prompt and call sn-image-recognize on reference image.

python "$SN_IMAGE_BASE/scripts/sn_agent_runner.py" sn-image-recognize \
  --system-prompt-path "$SKILL_DIR/prompts/image_annotate.md" \
  --user-prompt "Please annotate this reference image and follow the required output format." \
  --images "$REFERENCE_IMAGE" \
  --output-format json

Parse JSON result, then parse three blocks:

• SHORT_CAPTION: ...
• LONG_CAPTION: ...
• LAYOUT_BLUEPRINT_JSON: { ... }

If parsing fails, LONG_CAPTION is empty, or LAYOUT_BLUEPRINT_JSON is invalid JSON, return status=error.

Persist outputs:

echo "$SHORT_CAPTION" > "$TEMP_DIR/reference-short-caption.txt"
echo "$LONG_CAPTION" > "$TEMP_DIR/reference-long-caption.txt"
echo "$LAYOUT_BLUEPRINT_JSON" > "$TEMP_DIR/layout-blueprint.json"