Compare Models

!ls projects/${PROJECT_NAME}/references/*.{jpg,jpeg,png,webp} 2>/dev/null && echo "HAS_REFERENCE=true" || echo "HAS_REFERENCE=false"

!ls projects/${PROJECT_NAME}/style-config.yaml 2>/dev/null && echo "HAS_STYLE_CONFIG=true" || echo "HAS_STYLE_CONFIG=false"

!ls projects/${PROJECT_NAME}/notes.md 2>/dev/null && echo "HAS_NOTES=true" || echo "HAS_NOTES=false"

Decision Logic

HAS_RENDERS=false OR RENDER_COUNT < 2?
└── STOP. Need at least 2 renders to compare. Run /render with multiple providers first.

HAS_DEPTH=true AND HAS_REFERENCE=true?
└── Include "Layout Preservation" criterion in comparison.
    Auto-run /validate-layout for each render and include SSIM scores in the table.

HAS_DEPTH=false AND HAS_REFERENCE=true?
└── Layout comparison available but depth map missing.
    Suggest running /preprocess-room first for quantitative layout scoring.
    OR skip layout criterion if user doesn't care about layout fidelity.

HAS_STYLE_CONFIG=true?
└── Read style-config.yaml — use configured colors, materials, style as ground truth for "Style Accuracy" scoring.
    Don't judge style accuracy by memory — check against the config.

HAS_NOTES=true?
└── Read notes.md — check for previous comparison results and feedback trends.
    Append new comparison results to notes.md.

No reference photo (text-to-image only)?
└── Skip "Layout Preservation" criterion entirely — not applicable.

Data-driven comparison > subjective comparison. Use project config as ground truth wherever possible.

Input

• Multiple rendered images from /render (or image paths provided by user)
• The original prompt used
• The target design style

Comparison Criteria

Evaluate each provider's output on:

1. Style Accuracy

• Does it match the requested interior design style?
• Are style-specific materials, colors, and furniture correct?
• Rate: Excellent / Good / Partial / Poor

2. Photorealism

• Does it look like a real photograph?
• Texture quality and material rendering
• Lighting naturalness
• Rate: Photorealistic / Near-photo / Illustrated / Artificial

3. Composition

• Camera angle and framing
• Spatial depth and perspective accuracy
• Room proportions and scale of furniture
• Rate: Professional / Adequate / Awkward

4. Detail Quality

• Fine details: fabric patterns, wood grain, reflections
• Consistency: no floating objects, impossible geometry, weird artifacts
• Text/watermark issues
• Rate: High detail / Medium / Low / Artifacts present

5. Color Fidelity

• Does the color palette match the design brief?
• Color harmony and balance
• Lighting effect on colors (warm/cool cast)

6. Practical Value

• Could this image be used in a client presentation?
• Would it work as a design reference?
• Does it communicate the design intent clearly?

7. Layout Preservation (when comparing against reference photo)

• Does the generated image maintain the original room's spatial layout?
• Depth SSIM score (from /validate-layout): quantitative layout fidelity metric
• Wall positions, window placements, furniture depth ordering preserved?
• Rate: Identical / Good / Fair / Distorted

Output Format

## Model Comparison: [Style] [Room]

| Criteria         | Gemini | OpenAI | Stability | Grok |
|-----------------|--------|--------|-----------|------|
| Style Accuracy  |   ★★★★ |  ★★★★  |   ★★★★    | ★★★  |
| Photorealism    |   ...  |  ...   |   ...     | ...  |
| Composition     |   ...  |  ...   |   ...     | ...  |
| Detail Quality  |   ...  |  ...   |   ...     | ...  |
| Color Fidelity  |   ...  |  ...   |   ...     | ...  |
| Layout Fidelity |   ...  |  ...   |   ...     | ...  |

### Best For:
- **Client presentation**: [provider] — [reason]
- **Design exploration**: [provider] — [reason]
- **Quick iteration**: [provider] — [reason]
- **Budget-conscious**: [provider] — [reason]

### Recommendations:
- [Specific refinement suggestions per provider]

Suggest /refine for the most promising output to iterate further.

Session Relevant Skills

• /refine — after identifying the best provider output, use /refine to iterate on that specific prompt+provider combination. Pass the original prompt and specific feedback from the comparison.
• /render — if the comparison reveals all outputs are poor, consider re-rendering with adjusted prompts. Go back to /generate-prompt if the prompts themselves need rework.
• /generate-prompt — if comparison shows systematic prompt issues (wrong style keywords, bad composition across all providers), the prompts need regeneration, not refinement.
• /style-guide — reference when evaluating style accuracy. Use the style's specific keywords/materials/colors as the ground truth for scoring.
• /edit-design — if one render is close but needs specific changes (swap a piece of furniture, adjust lighting), /edit-design can target those changes rather than re-rendering from scratch.
• /validate-layout — provides quantitative SSIM scores for layout comparison. Run on each provider's output to get objective layout fidelity metrics.
• /preprocess-room — the source of truth for the original room's depth map used in layout comparison.

Gotchas

• Compare apples to apples: Ensure all images used the same (or equivalent) prompt. Different prompts produce different styles — that's a prompt issue, not a provider issue.
• Don't compare more than 4-5 providers at once: The comparison table becomes unwieldy. Focus on 2-3 most promising providers.
• Provider strengths are style-dependent: Stability excels at textured/rustic styles, DALL-E at clean modern, Midjourney at atmospheric/dramatic. A provider ranking for Scandinavian may be reversed for Baroque.
• Practical value matters most: A slightly less photorealistic image that clearly communicates the design intent is more useful than a photorealistic image with wrong style elements.
• Layout comparison only applies when there's a reference photo: For text-only generations (no reference room), skip the layout criterion entirely.
• Different providers have different layout control capabilities: Stability AI structure control and Gemini image editing can preserve layout; DALL-E and Midjourney cannot. Factor this into comparisons.