Image Critic

Score generated images on six modes (character consistency, realism, prompt adherence, anatomy, aesthetic, style match) plus an all aggregator, with two output handles for self-correction loops.

Overview

The Image Critic node uses a vision-language model to evaluate an image against a chosen criterion and emit a score (0-1) plus 1-3 imperative-sentence feedback. The node has two output handles, approved and rejected, which fire mutually exclusively based on whether the score meets the configured threshold. This makes it the building block for QC gates and automated self-correction loops (e.g., wire rejected → Modify Image.prompt to retry until the image passes).

Configuration

Field	Type	Default	Description
Mode	select	realism	One of: character-consistency, realism, prompt-adherence, anatomy, aesthetic, style-match, all
Threshold	number 0-1	0.7	approved = score >= threshold. Below the threshold, the rejected handle fires instead.
Prompt	text	""	Required for prompt-adherence and all modes. Can be wired via the prompt input edge – the edge wins when present.
LLM model	select	Claude Sonnet 4.6	Any vision-capable model. Pricing varies by tier (1 / 1 / 2 credits for economy / standard / premium).

Inputs & Outputs

Inputs (all 3 handles always render; usage depends on mode):

• image – the image to evaluate. Connect from Generate Image, Modify Image, Image to Image, Upload Image, or Edit Image.
• reference – required for character-consistency, style-match, and all modes. The reference image to compare against.
• prompt – required for prompt-adherence and all modes. The target prompt the image was meant to render.

Outputs:

• approved – fires when score >= threshold. Emits the feedback string.
• rejected – fires when score < threshold. Emits the feedback string.

Modes

Mode	What it scores	Requires
character-consistency	Whether two images depict the same person (face geometry, hair, defining features).	image + reference
realism	Photorealism – calls out plastic skin, broken anatomy, impossible lighting.	image
prompt-adherence	Whether the image renders what the prompt asked for.	image + prompt
anatomy	AI-generation failure modes: hands, eyes, extra limbs, broken faces.	image
aesthetic	Composition, lighting, framing, color – cinematography-style grade.	image
style-match	Palette/mood/treatment adherence to a style reference.	image + reference
all	Runs every applicable check given the inputs. Score = min across checks.	image (plus optional reference/prompt)

Supported Providers

Any vision-capable LLM configured in the editor. The default is Claude Sonnet 4.6. Pricing tiers:

LLM tier	Credits per call
Economy (Gemini Flash, Claude Haiku)	1
Standard (Claude Sonnet 4.6, GPT-5.2) – default	1
Premium (Claude Opus 4.6, GPT-5.4, Gemini Pro)	2

The all mode does NOT multiply the cost – every dimension is scored in a single VLM call.

Self-correction loop pattern

Wire Image Critic.rejected -> Modify Image.prompt. The feedback field is deliberately shaped as imperative sentences (“Reshape the left hand…”) that drop directly into a modify-image prompt. Wrap the pair in a Loop node for bounded retries.

Loop / batch behavior

When Image Critic runs inside a Loop node, the handle dispatch fires only at the post-loop boundary when a downstream consumer resolves its inputs. Per-iteration data is accumulated in __listResults regardless of which handle the downstream is connected to. To selectively process per-iteration verdicts, put the conditional logic outside the loop.

Best Practices

• For self-correction loops, set threshold near 0.7 and rely on feedback to drive the modify-image call.
• For batch QC, use all mode to score every dimension in one VLM call (no per-mode cost multiplier).
• Don’t expect the feedback to be localized; it is LLM-emitted in English regardless of user locale.
• Wire the same prompt source you fed into the upstream generation node when using prompt-adherence – mismatched prompts will produce misleading scores.
• Keep reference connected directly to the source-of-truth image (Upload Image or Create Character output) rather than re-running it through generation, to avoid drift.

Common Use Cases

• QC gate after generate-image with auto-retry through modify-image.
• Batch consistency check across a character’s generated images.
• Style-match scoring against a brand reference.
• Anatomy guard between AI generation and downstream lip-sync or motion-transfer nodes.
• Aesthetic ranking when generating multiple candidate variants and picking the best.

Tips

• The prompt field can be typed directly OR wired via the prompt input edge. Edge wins when present.
• Changing the mode after a successful run clears the previous score (stale-result guard); changing only the threshold reuses the existing score with a client-side recompute.
• Connecting only the approved (or only the rejected) handle is valid – the other handle simply has no downstream effect on that iteration.
• The feedback output is the same string on both handles; the only difference is which handle fires.