A multi-style character set should be tested like a data transformation: derive an identity hash from the source, change one style dimension per run, and score every output against the same invariant fields.
The contact sheet above uses the app's stored first-party input and four results. No new generation was submitted for this tutorial.
Define the identity hash
This is not biometric identification. It is a local review record made from visible, non-sensitive traits in a photo you are authorized to transform.
identity_hash = {
"face_shape": "oval",
"brow": "straight, slight right arch",
"eye_spacing": "medium",
"nose": "narrow bridge, rounded tip",
"expression": "neutral, closed mouth",
"hair": "chin-length green-and-purple bob",
"anchors": ["black logo shirt", "arms bent toward camera"]
}
Keep the record descriptive, not identifying. Do not attach a real name, account or private metadata.
Use the dedicated Face To Many Kontext app
Open the current Face To Many Kontext workspace. The live page requires an image, currently offers Style Random, Persona None, Number of Images 1, Match Input Image and PNG. Preserve outfit and Preserve background are off by default; Seed is available under Advanced Settings. Public and Generate are visible.
Upload a clear source you own or have permission to use. Change Style from Random to one deliberate target so the experiment has a known independent variable. Keep Persona at None for the identity baseline. Generate one image first.
Build a one-variable run matrix
runs = [
{"style": "comic", "persona": "None", "preserve_outfit": False},
{"style": "clay", "persona": "None", "preserve_outfit": False},
{"style": "pixel", "persona": "None", "preserve_outfit": False},
]
Use the same source, crop and review hash. If the interface lets you record a seed, store it with the run. A seed helps controlled comparison but is not an identity guarantee.
If clothing is essential to recognition, enable Preserve outfit for a separate matrix instead of mixing the setting halfway through. Do the same for Preserve background.
Score the output
def score(candidate, reference):
keys = ["face_shape", "brow", "eye_spacing", "nose", "expression", "hair"]
matches = sum(candidate.get(k) == reference[k] for k in keys)
anchor_hits = sum(a in candidate.get("anchors", []) for a in reference["anchors"])
return matches + anchor_hits
PASS_THRESHOLD = 7 # out of 8 fields in this example
Human inspection supplies the candidate values. The code makes the release rule explicit; it does not pretend to automate face recognition.
Reject style leakage
Comic style may enlarge eyes, clay style may round the nose, and pixel style may erase eyebrow asymmetry. These are style effects, but they still fail the identity hash when the invariant changes. Reduce style intensity or add an explicit lock sentence:
Apply only the selected character rendering style. Preserve the exact face shape, eyebrow asymmetry, eye spacing, rounded nose tip, neutral closed-mouth expression, chin-length green-and-purple bob, black logo shirt and bent-arm pose.
Credits, visibility and ethics
Check the current signed-in estimate after completing the required input and before Generate; the empty page is not a reliable fixed-price record. Keep Public only with the subject's consent. Never use this process for impersonation, deceptive profiles or datasets assembled without permission.
Finish with a contact sheet
Label each accepted image with its style, settings and date. Display the source beside the set so reviewers can see both transformation and continuity. Do not claim that the outputs are a trained reusable character model.
When the hash and pass threshold are written, start the matrix in the exact Face To Many Kontext app.