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AVC Labs Review: AI Image Upscaling & Face Refinement Tested (2026)

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Tested Hands-OnAI Image UpscalingFace RefinementPreview Before ProcessingLast verified April 2026

Our take

AVC Labs is a clean, consistent upscaling tool with two features that stand out for editorial use — a Face Refinement toggle on by default for portrait inputs, and a Preview step that allows quality review before spending a credit. Output is reliable and artifact-free across all inputs. The main limitations are the absence of model selection, no DPI confirmation, and no TIFF support.

Download video: Demo videoWatch video (streaming): Demo video

In-Depth Review

Our detailed analysis of AVC Labs — features, performance, and real-world testing.

AJ
Athulya Jaikish
AI Demos Team
Verified Review

Feature-by-Feature Breakdown

We tested each feature individually. Click any card to see inputs, outputs, and our observations.

Image Enhancer, Upscaler and Face Refinement Toggle
6/10
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Test Summary
Feature tested: Image Enhancer, Upscaler and Face Refinement Toggle
Result: Passed (6/10)

Feature tested: Image Enhancer, Upscaler and Face Refinement Toggle

Result: Passed (6/10)

Expected behavior: Claims to upscale images at 2x, 3x, or 4x while adding face refinement for a polished appearance. Suitable for personal or professional use.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — Input 1 - Headshot-13.png

Observed output: Output artifact (Image): Output — Screenshot (242).png

Input artifact: Input artifact (Image): Input — Input 1 - Headshot-13.png

Output artifact: Output artifact (Image): Output — Screenshot (242).png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — Input 1 - Headshot-14.png

Observed output: Output artifact (Image): Output — 1776934618308_0_Input 1.jpg

Input artifact: Input artifact (Image): Input — Input 1 - Headshot-14.png

Output artifact: Output artifact (Image): Output — 1776934618308_0_Input 1.jpg

What changed: Image transformed into Image

Why it matters / Conclusion: Reliable upscaling output across all input types. Face Refinement on gives natural portrait results. Single algorithm applied to all inputs — no model choice available. Useful portrait-specific toggle. On by default — appropriate for most editorial portrait inputs. Turn off for product and archival inputs where face processing is irrelevant.

Claims to upscale images at 2x, 3x, or 4x while adding face refinement for a polished appearance. Suitable for personal or professional use.

IMAGE
Input artifact for "Image Enhancer, Upscaler and Face Refinement Toggle" test: Input, Input 1 - Headshot-13.png
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IMAGE
Output artifact for "Image Enhancer, Upscaler and Face Refinement Toggle" test: Output, Screenshot (242).png
IMAGE
Input artifact for "Image Enhancer, Upscaler and Face Refinement Toggle" test: Input, Input 1 - Headshot-14.png
↓
IMAGE
Output artifact for "Image Enhancer, Upscaler and Face Refinement Toggle" test: Output, 1776934618308_0_Input 1.jpg
Bottom Line
Reliable upscaling output across all input types. Face Refinement on gives natural portrait results. Single algorithm applied to all inputs — no model choice available. Useful portrait-specific toggle. On by default — appropriate for most editorial portrait inputs. Turn off for product and archival inputs where face processing is irrelevant.
Preview Step
7/10
▾
Test Summary
Feature tested: Preview Step
Result: Passed (7/10)

Feature tested: Preview Step

Result: Passed (7/10)

Expected behavior: Allows the editor to preview the upscaled result before confirming and spending a credit.

Test case: Text prompt → Image

Input type: Text prompt

Input used: Input artifact (Text prompt): Three different types of input images were tested.

Observed output: Output artifact (Image): Output — Screenshot (240).png

Input artifact: Input artifact (Text prompt): Three different types of input images were tested.

Output artifact: Output artifact (Image): Output — Screenshot (240).png

What changed: Text prompt transformed into Image

Why it matters / Conclusion: Practical quality gate — the only tested tool with a preview-before-payment step. Prevents credit waste on unsatisfactory outputs.

Allows the editor to preview the upscaled result before confirming and spending a credit.

TEXT
Three different types of input images were tested.
↓
IMAGE
Output artifact for "Preview Step" test: Output, Screenshot (240).png
Bottom Line
Practical quality gate — the only tested tool with a preview-before-payment step. Prevents credit waste on unsatisfactory outputs.
Archival Image Upscaling
7/10
▾
Test Summary
Feature tested: Archival Image Upscaling
Result: Passed (7/10)

Feature tested: Archival Image Upscaling

Result: Passed (7/10)

Expected behavior: No dedicated archival model. Same upscaling algorithm used for greyscale degraded inputs.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — Input 2 - Historic_photo-9.png

Observed output: Output artifact (Image): Output — Screenshot (243).png

Input artifact: Input artifact (Image): Input — Input 2 - Historic_photo-9.png

Output artifact: Output artifact (Image): Output — Screenshot (243).png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — Input 2 - Historic_photo-10.png

Observed output: Output artifact (Image): Output — 1776934677642_0_Input 2.jpg

Input artifact: Input artifact (Image): Input — Input 2 - Historic_photo-10.png

Output artifact: Output artifact (Image): Output — 1776934677642_0_Input 2.jpg

What changed: Image transformed into Image

Why it matters / Conclusion: Clean structural recovery without a dedicated archival model. Grain managed without smearing. No hallucinated content. Adequate for web and standard print archival use.

No dedicated archival model. Same upscaling algorithm used for greyscale degraded inputs.

IMAGE
Input artifact for "Archival Image Upscaling" test: Input, Input 2 - Historic_photo-9.png
↓
IMAGE
Output artifact for "Archival Image Upscaling" test: Output, Screenshot (243).png
IMAGE
Input artifact for "Archival Image Upscaling" test: Input, Input 2 - Historic_photo-10.png
↓
IMAGE
Output artifact for "Archival Image Upscaling" test: Output, 1776934677642_0_Input 2.jpg
Bottom Line
Clean structural recovery without a dedicated archival model. Grain managed without smearing. No hallucinated content. Adequate for web and standard print archival use.
Product Image Upscaling
7/10
▾
Test Summary
Feature tested: Product Image Upscaling
Result: Passed (7/10)

Feature tested: Product Image Upscaling

Result: Passed (7/10)

Expected behavior: Claims to upscale product photography with improved edge sharpness.

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — Input 3 - Product_image-5.png

Observed output: Output artifact (Image): Output — Screenshot (244).png

Input artifact: Input artifact (Image): Input — Input 3 - Product_image-5.png

Output artifact: Output artifact (Image): Output — Screenshot (244).png

What changed: Image transformed into Image

Test case: Image → Image

Input type: Image

Input used: Input artifact (Image): Input — Input 3 - Product_image-6.png

Observed output: Output artifact (Image): Output — 1776934717877_0_Input 3.jpg

Input artifact: Input artifact (Image): Input — Input 3 - Product_image-6.png

Output artifact: Output artifact (Image): Output — 1776934717877_0_Input 3.jpg

What changed: Image transformed into Image

Why it matters / Conclusion: Label text legible at 2x. Clean white background preserved. 4x gives more comfortable margin above the print target.

Claims to upscale product photography with improved edge sharpness.

IMAGE
Input artifact for "Product Image Upscaling" test: Input, Input 3 - Product_image-5.png
↓
IMAGE
Output artifact for "Product Image Upscaling" test: Output, Screenshot (244).png
IMAGE
Input artifact for "Product Image Upscaling" test: Input, Input 3 - Product_image-6.png
↓
IMAGE
Output artifact for "Product Image Upscaling" test: Output, 1776934717877_0_Input 3.jpg
Bottom Line
Label text legible at 2x. Clean white background preserved. 4x gives more comfortable margin above the print target.

Use Case Track Record

Enhance and Upscale Images from Low-Resolution Inputs

Pricing & Access

TESTED
Free
$ 0
20 free credits available of free trial
Photo Enhancer & Video Enhancer AI Bundle
$49.95 /month
Fix the blurry image and video to make them clearer, Upscale photo and video by 200%, 300%, and 400%, AI Face Enhancement tool for video and photo, Dedicated noise remover to denoise video and photo, AI colorize black&white image and video.

* Pricing as of April 2026. We re-check quarterly

Is This Right For You?

A side-by-side guide based on our hands-on testing.

✓ Use This If
●You want a clean, simple one-click workflow with no model selection required
●Portrait inputs are common — Face Refinement toggle adds value with no extra steps
●You want to preview output quality before spending a credit
●You need JPG or PNG output for web and standard print publishing
●You want access to a broader editing platform including Denoiser and Colorizer
✕ Skip This If
●You need DPI explicitly confirmed before download
●You need TIFF input or output support
●You need model selection for subject-specific upscaling
●You work primarily with archival content and need a dedicated restoration model
●You process high volumes and need more than 20 free credits
image-generatortext-to-imagetextEditors
No. DPI was not displayed during testing. The interface shows output image size in pixels — editors must manually calculate print size at 300 DPI after download.
Face Refinement applies face-specific enhancement to portrait regions during upscaling. It is on by default. In testing, it gave marginally more natural skin and hair detail compared to upscaling without it. It has no visible effect on product or archival inputs.
Yes. AVC Labs offers a Preview button that shows the upscaled result before the final Enhance step. In testing, the preview accurately matched the final output quality across all three inputs.
No model selection was available during testing. A single enhancement algorithm is applied to all input types. This simplifies the workflow but removes the subject-specific control available in tools like Topaz Gigapixel AI or Upscayl.
TIFF was not listed as a supported format during testing. JPG and PNG were confirmed as both input and output formats. Editors working with TIFF source files will need to convert before uploading.

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