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PornTop 10 Best Deepfake Porn Software of 2026
Compare Top 10 Deepfake Porn Software tools with rankings and picks for creators. See how Deepfake Studio, DeepFaceLab, and FaceSwap compare.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Deepfake Studio
Face swap processing workflow with alignment-first steps for improved identity tracking
Built for creators needing fast deepfake video face swaps with minimal production overhead.
DeepFaceLab
Interactive training and conversion pipeline with configurable model, alignment, and inference parameters
Built for users who want maximum training control for local face-swaps.
FaceSwap
Face detection with automated swapping to rapidly create face-to-video results
Built for quick face-to-video swaps for small projects requiring minimal setup.
Related reading
Comparison Table
This comparison table evaluates deepfake porn software tools including Deepfake Studio, DeepFaceLab, FaceSwap, Reface, and Avatarify. It summarizes how each tool handles face swapping and generation, what hardware and workflow requirements apply, and which outputs and controls are available for editing. Readers can use the side-by-side view to match tool capabilities to specific production goals and technical constraints.
| # | Tool | Category | Overall | Features | Ease of Use | Value |
|---|---|---|---|---|---|---|
| 1 | Deepfake Studio Provides a web-based workflow for creating and editing deepfake-style video content. | web editor | 7.4/10 | 7.6/10 | 7.2/10 | 7.3/10 |
| 2 | DeepFaceLab Open-source deepfake software for training and generating face-swaps with configurable training pipelines. | open-source | 7.4/10 | 8.2/10 | 6.6/10 | 7.1/10 |
| 3 | FaceSwap Delivers online face-swap generation tools intended for producing swapped-face video clips. | online generator | 7.2/10 | 7.6/10 | 7.0/10 | 6.9/10 |
| 4 | Reface Uses AI to generate face-swapped video effects from uploaded images and videos. | consumer app | 7.2/10 | 7.2/10 | 7.6/10 | 6.7/10 |
| 5 | Avatarify Enables real-time face animation and video effects using uploaded facial content. | real-time face | 6.6/10 | 6.7/10 | 7.0/10 | 6.2/10 |
| 6 | Vidyo AI Offers AI video editing features for transforming faces and generating altered video outputs. | AI editing | 5.0/10 | 5.1/10 | 6.0/10 | 3.8/10 |
| 7 | DeepSwap Supports AI-driven face swapping by converting an input face into a target video stream. | AI swapping | 6.8/10 | 6.8/10 | 7.0/10 | 6.5/10 |
| 8 | Sensity AI Provides detection-grade media analytics that can be paired with creation workflows for evaluation. | media assessment | 6.4/10 | 6.6/10 | 6.1/10 | 6.5/10 |
| 9 | Kairos Provides face recognition and video analytics services used to assess and target facial regions. | face analytics | 6.2/10 | 6.4/10 | 6.1/10 | 6.0/10 |
| 10 | Clarifai Offers computer vision APIs for face detection and content tagging that support deepfake pipelines. | vision API | 5.8/10 | 6.2/10 | 6.0/10 | 4.9/10 |
Provides a web-based workflow for creating and editing deepfake-style video content.
Open-source deepfake software for training and generating face-swaps with configurable training pipelines.
Delivers online face-swap generation tools intended for producing swapped-face video clips.
Uses AI to generate face-swapped video effects from uploaded images and videos.
Enables real-time face animation and video effects using uploaded facial content.
Offers AI video editing features for transforming faces and generating altered video outputs.
Supports AI-driven face swapping by converting an input face into a target video stream.
Provides detection-grade media analytics that can be paired with creation workflows for evaluation.
Provides face recognition and video analytics services used to assess and target facial regions.
Offers computer vision APIs for face detection and content tagging that support deepfake pipelines.
Deepfake Studio
web editorProvides a web-based workflow for creating and editing deepfake-style video content.
Face swap processing workflow with alignment-first steps for improved identity tracking
Deepfake Studio distinguishes itself by positioning an end-to-end workflow for face swap style deepfake creation aimed at adult content scenarios. Core capabilities focus on generating edited videos and producing usable output clips with a creator-friendly interface. The tool’s value for deepfake porn creation depends heavily on reliable face alignment and output stability across varied source footage. Practical quality is constrained by input video conditions and the technical controls available for refinement.
Pros
- Guided workflow supports importing, processing, and exporting deepfake video outputs
- Face swap pipeline emphasizes alignment steps for more consistent results
- Output-focused editing reduces extra steps compared with general editors
Cons
- Result quality drops on low-light or heavily compressed source videos
- Limited visible controls for fine-grained artifact correction and consistency tuning
- Adult-content use increases risk of detection and misuse consequences
Best For
Creators needing fast deepfake video face swaps with minimal production overhead
More related reading
DeepFaceLab
open-sourceOpen-source deepfake software for training and generating face-swaps with configurable training pipelines.
Interactive training and conversion pipeline with configurable model, alignment, and inference parameters
DeepFaceLab stands out as a highly configurable deepfake training and face-swapping toolkit built for manual control over model, dataset, and preprocessing steps. It supports common workflows like training, generating swapped faces, and iterating with preview outputs across multiple model options. The project emphasizes GPU-accelerated pipeline stages such as face extraction, alignment, training runs, and inference rather than turnkey one-click synthesis. Its capabilities are strongest for users who can tune training inputs and accept technical setup overhead.
Pros
- Strong control over training inputs, model behavior, and conversion settings
- Works with common face-swapping workflows like extract, train, and merge
- GPU-accelerated pipeline stages support iterative experimentation
- Flexible preprocessing and alignment controls for different source material
- Offline training and generation avoid reliance on external services
Cons
- Setup and dependency management are heavy for nontechnical users
- Many tuning parameters require iterative trial and error
- Limited guidance compared to more curated deepfake tools
- Quality depends heavily on dataset balance and alignment accuracy
- Higher risk of failed runs due to GPU and data pipeline mismatches
Best For
Users who want maximum training control for local face-swaps
FaceSwap
online generatorDelivers online face-swap generation tools intended for producing swapped-face video clips.
Face detection with automated swapping to rapidly create face-to-video results
FaceSwap stands out for face-to-video swapping built around a web-based workflow that targets quick creation and iteration. It provides core capabilities like face detection, swapping, and export for generating altered visuals from supplied media. The tool is more focused on visual face replacement than on higher-end controls like consistent subject identity across many scenes. Overall, it suits users who want fast results more than users who need production-grade manipulation tooling.
Pros
- Web-based workflow reduces setup friction for face swapping
- Automated face detection speeds up mask and alignment steps
- Exports generated results in a usable video format quickly
- Works with user-provided source images and videos for fast iteration
Cons
- Limited advanced controls for identity consistency across long sequences
- Quality depends heavily on input alignment and lighting similarity
- Fewer post-processing and compositing tools than dedicated pipelines
- Less suited for large-scale batch production workflows
Best For
Quick face-to-video swaps for small projects requiring minimal setup
Reface
consumer appUses AI to generate face-swapped video effects from uploaded images and videos.
Identity-preserving face swapping from short clips with quick generation cycles
Reface is known for swapping faces in generated videos using short input clips and rapid editing workflows. Core capabilities include face recognition, identity transfer, and video output with model-driven synthesis. The tool is positioned for quick “reface” style results rather than deep, production-grade control over motion, lighting, or scene context. As a deepfake porn software option, it can produce explicit-style imagery only if users supply appropriate source material and prompts.
Pros
- Fast face swapping from short source clips with consistent identity transfer
- Workflow supports high-volume creation with minimal editing steps
- Strong synthesis quality for common angles and clean source footage
- Outputs are easy to share due to simple render and export flow
Cons
- Limited fine control over facial micro-motion and expression timing
- Artifacts increase with low-light scenes, motion blur, or side profiles
- Scene realism can break when backgrounds or lighting differ sharply
- Explicit content generation increases policy and misuse risk
Best For
Quick deepfake face swaps for explicit video mashups needing minimal editing
More related reading
Avatarify
real-time faceEnables real-time face animation and video effects using uploaded facial content.
Avatar-to-video face animation using driving footage and uploaded target likeness
Avatarify centers on generating face-swapped or avatar-style video outputs from provided media, using automated pipelines rather than manual editing. The core workflow typically involves uploading a target image or video and supplying driving footage to animate likeness movement. It emphasizes quick iteration and template-driven generation steps for producing short clips. Output control and quality depend heavily on input footage quality and consistency across frames.
Pros
- Fast generation workflow from uploaded target media
- Template-like steps reduce need for post-production expertise
- Good results when driving and target footage match well
- Supports avatar-style face animation use cases
Cons
- Quality drops when faces or lighting vary across frames
- Limited fine-grained control over artifacts and expression fidelity
- Prone to identity drift in longer clips
- Output style choices can feel constrained by preset pipelines
Best For
Creators prototyping avatar-driven face animation clips with consistent source footage
Vidyo AI
AI editingOffers AI video editing features for transforming faces and generating altered video outputs.
Automated generative video synthesis pipeline driven by input face and source footage
Vidyo AI centers on automating face and video synthesis workflows using generative AI. It focuses on producing deepfake-style results through an AI pipeline that transforms supplied visual inputs into new video outputs. The product emphasizes repeatable generation steps rather than interactive, frame-by-frame editing. It is best understood as a synthesis tool for creating altered likeness video rather than a full post-production suite.
Pros
- Streamlined pipeline for generating altered-video outputs from provided inputs
- Consistent generation workflow reduces the need for complex manual steps
- Quick iteration loop for producing multiple variations of a target scene
Cons
- Limited evidence of advanced controls for identity consistency and artifacts
- Less suited for professional editorial workflows like grading and compositing
- Value drops for teams needing fine-tuned governance and quality auditing
Best For
Creators exploring automated likeness video generation with minimal editing overhead
DeepSwap
AI swappingSupports AI-driven face swapping by converting an input face into a target video stream.
Web-based face-swapping pipeline that converts uploaded media without local model setup
DeepSwap is positioned around face-swap deepfake generation using a web-based workflow. The core capability focuses on swapping faces in supplied media with automated results and minimal setup steps. It emphasizes quick iteration by running conversions from the browser rather than requiring local pipeline configuration. The tool is primarily geared toward creating adult deepfake content, which narrows legitimate use cases and raises substantial ethical and legal risk.
Pros
- Browser-based workflow reduces setup friction for face-swaps
- Fast conversion loop supports quick iteration across multiple inputs
- Streamlined upload and processing flow for basic face replacement
Cons
- Limited creative control versus advanced local deepfake toolchains
- Quality can degrade on complex lighting, motion, and occlusion
- Adult deepfake focus increases misuse risk and enforcement pressure
Best For
Casual creators needing rapid browser-based face swaps for adult edits
More related reading
Sensity AI
media assessmentProvides detection-grade media analytics that can be paired with creation workflows for evaluation.
Structured detection reports that support moderation and risk-oriented downstream actions
Sensity AI is positioned around synthetic media detection and safety workflows rather than straightforward deepfake creation. The tool emphasizes identifying manipulated visuals and supporting downstream risk actions tied to content handling. It also supports structured analysis outputs that can feed moderation and compliance processes. For deepfake porn use cases, it is best evaluated as an ingestion and detection control layer, not as a production pipeline.
Pros
- Focuses on spotting manipulated media with structured detection outputs
- Integrates detection results into review and moderation workflows
- Designed for safety and risk handling around synthetic visuals
Cons
- Not a purpose-built deepfake porn generation workflow
- Deepfake detection performance can vary across formats and compression
- Workflow setup may require more engineering than simple point tools
Best For
Teams needing synthetic porn risk detection inside content review systems
Kairos
face analyticsProvides face recognition and video analytics services used to assess and target facial regions.
Batch workflow orchestration for synthetic video ingest, generation, and export
Kairos focuses on automation around synthetic media creation using a managed workflow that can scale beyond single asset edits. It provides tools for generating and transforming video outputs using model-driven pipelines rather than manual, frame-by-frame editing. The product emphasizes repeatable processing steps such as ingest, processing, and export so teams can standardize renders across projects. For a deepfake porn use case, these workflow primitives can speed up production but they also increase the risk profile for misuse.
Pros
- Pipeline automation supports consistent synthetic media processing across batches
- Model-driven workflow reduces manual editing steps for repeatable outputs
- Managed exports streamline integration into downstream review or publishing steps
Cons
- Workflow complexity increases setup time versus simple one-click creators
- Capabilities focus on generation workflows rather than detailed post-production control
- Deepfake porn use is high-risk and many safeguards are usually insufficient
Best For
Teams running repeatable synthetic video workflows needing production automation
Clarifai
vision APIOffers computer vision APIs for face detection and content tagging that support deepfake pipelines.
Custom model training via Clarifai model development and evaluation pipelines
Clarifai focuses on deep learning inference for images and video, with developer-first model pipelines that support face-related and generative-adjacent workflows. It provides APIs for custom model training, multimodal classification, and content moderation-style detection features that can be used to flag or analyze manipulated media. Its strongest fit is building automated computer vision services around human faces and visual similarity rather than producing deepfake porn content itself. The platform’s workflow depth comes from configurable training and evaluation, but it does not replace end-to-end deepfake creation tools specialized for pornographic outputs.
Pros
- Programmable vision APIs for face-centric detection and similarity tasks
- Custom model training workflow supports domain-specific media analysis
- Multimodal processing helps connect frames, regions, and metadata
Cons
- Best use is detection and analytics, not deepfake generation
- Implementation requires engineering effort and dataset curation
- Lacks turnkey deepfake-specific tooling for pornographic media workflows
Best For
Teams building automated detection and visual analysis around manipulated video
How to Choose the Right Deepfake Porn Software
This buyer's guide helps match Deepfake Studio, DeepFaceLab, FaceSwap, Reface, Avatarify, Vidyo AI, DeepSwap, Sensity AI, Kairos, and Clarifai to the right real-world deepfake workflow needs. It covers end-to-end generation pipelines, local training control, browser-based face swapping, and team-grade detection and analytics options. The guide also calls out common failure points like low-light sensitivity, identity drift in longer clips, and heavy setup overhead.
What Is Deepfake Porn Software?
Deepfake porn software uses AI face-swap and identity-transfer workflows to generate altered video outputs from provided images and video inputs. These tools solve problems like turning face inputs into swapped face video clips, speeding up iteration from short source footage, and standardizing repeatable synthetic video processing. Deepfake Studio provides a web-based end-to-end face swap workflow with alignment-first steps for more consistent identity tracking. DeepFaceLab provides a locally run training and conversion pipeline with configurable model, dataset preprocessing, and inference parameters for users who want maximum control.
Key Features to Look For
The right features determine whether outputs stay stable across scenes, whether identities remain consistent, and whether workflows stay usable under time pressure.
Alignment-first face-swap pipeline for identity tracking
Deepfake Studio emphasizes alignment-first steps inside its face swap processing workflow to improve identity tracking. FaceSwap also relies on automated face detection for rapid swapping, but it provides fewer controls for maintaining identity across longer sequences.
Configurable local training and conversion controls
DeepFaceLab offers interactive training and conversion with configurable model, alignment, and inference parameters. This tool fits workflows where dataset balance and preprocessing choices directly shape output quality, and it runs offline to avoid reliance on external services.
Short-clip identity transfer for fast generation cycles
Reface focuses on identity-preserving face swapping from short clips with quick generation cycles and easy render and export flow. Avatarify uses driving footage plus uploaded target likeness to animate faces quickly, but quality depends strongly on consistent driving and target footage match.
Web-based face swapping with minimal local setup
DeepSwap runs a browser-based workflow that converts uploaded media without local model setup, which reduces friction for casual creators. FaceSwap also uses a web-based workflow with face detection and swapping to export usable video clips quickly.
Template-like generative synthesis workflows
Vidyo AI provides an automated generative video synthesis pipeline driven by input face and source footage, which supports repeatable generation steps. Avatarify similarly uses template-like steps for avatar-driven face animation, and outputs degrade when faces or lighting vary across frames.
Detection and analytics layer for synthetic media risk handling
Sensity AI provides structured detection reports designed for moderation and synthetic porn risk actions rather than deepfake creation. Clarifai provides computer vision APIs and custom model training for face-related similarity and moderation-style detection, making it a fit for teams that need automated analysis around human faces.
How to Choose the Right Deepfake Porn Software
Matching workflow requirements to tool behavior is the fastest path to reliable outputs.
Pick the workflow style: end-to-end, local training, or browser generation
Deepfake Studio suits end-to-end creation needs because it provides an import, process, and export workflow built around face swap processing. DeepFaceLab suits advanced local control because it supports extract, train, and merge-style workflows with configurable preprocessing, alignment, and inference. DeepSwap and FaceSwap suit minimal setup because both use browser-based face swapping with automated face detection and quick conversion loops.
Match output goals to identity consistency across sequences
Deepfake Studio targets more consistent identity tracking using alignment-first steps, which matters when face alignment drives output stability. FaceSwap generates quickly but offers limited advanced controls for identity consistency across long sequences. Avatarify can show identity drift in longer clips, so it fits short, consistent driving and target footage matches.
Decide how much fine-grained control is required for artifacts and motion issues
DeepFaceLab provides many tunable parameters across the pipeline, which helps when artifacts or alignment gaps require iterative trial and error. Deepfake Studio is more output-focused and reduces extra steps, but it has limited visible controls for fine-grained artifact correction and consistency tuning. Reface prioritizes fast cycles from short clips, so artifact increases with low-light scenes, motion blur, or side profiles.
Evaluate input constraints like low-light, compression, and occlusion
Deepfake Studio quality drops on low-light or heavily compressed source videos, so it benefits from cleaner source material. FaceSwap quality depends heavily on input alignment and lighting similarity, which means mismatched lighting can break visual realism. DeepSwap quality can degrade on complex lighting, motion, and occlusion, so it needs predictable footage conditions.
If detection matters, add analytics tools instead of forcing them into creation
Sensity AI focuses on detection-grade media analytics with structured detection outputs for review and moderation workflows. Clarifai and Kairos support face region analysis and synthetic media analytics, which helps teams automate ingest evaluation and risk workflows. These tools are not end-to-end porn generation replacements, so creation should come from tools like Deepfake Studio, DeepFaceLab, Reface, or Vidyo AI.
Who Needs Deepfake Porn Software?
Different teams and creators need different degrees of control, automation, and output consistency.
Creators needing fast face-swap video outputs with minimal production overhead
Deepfake Studio fits this segment because it provides a creator-friendly, web-based workflow with alignment-first steps and export-focused editing. Reface also fits because it supports quick identity transfer from short clips with an easy render and export flow.
Users who want maximum local control over model training and preprocessing
DeepFaceLab fits because it is built as an interactive training and conversion pipeline with configurable model, alignment, and inference parameters. This tool matches users who accept heavy setup and iterative tuning to improve dataset-driven quality.
Casual creators who prefer browser-based conversion with low setup
DeepSwap and FaceSwap fit this segment because both use web-based workflows that convert uploaded media with automated face detection. These tools trade off advanced identity consistency controls for faster iteration and simpler execution.
Teams that need synthetic media safety controls, detection reports, or face analytics around content workflows
Sensity AI fits teams because it outputs structured detection reports designed to support moderation and risk-oriented downstream actions. Clarifai fits developer teams because it provides face detection and tagging APIs plus custom model training for face-centric similarity and detection-style analysis.
Common Mistakes to Avoid
The most common failures come from mismatching tool strengths to footage quality, control needs, and workflow scope.
Using alignment-sensitive tools on low-light or heavily compressed footage
Deepfake Studio quality drops on low-light or heavily compressed source videos, so clean source footage is required for stable face swaps. Reface also shows increased artifacts in low-light scenes and with motion blur or side profiles, so footage quality must match tool strengths.
Expecting perfect identity consistency across long sequences from quick tools
FaceSwap provides fewer advanced controls for identity consistency across long sequences, which can reduce reliability over extended video. Avatarify can experience identity drift in longer clips, so it fits shorter, consistent driving scenarios.
Choosing a creation tool when detection and moderation outputs are the real requirement
Sensity AI is purpose-built for structured detection reports and moderation-oriented downstream actions, so it should not be treated as a replacement for deepfake generation. Clarifai and Kairos also provide analytics primitives, so teams needing synthetic media safety should integrate detection rather than forcing porn-generation workflows.
Underestimating setup and tuning complexity for local training pipelines
DeepFaceLab requires heavy setup and dependency management, and it depends on dataset balance and alignment accuracy for output success. It also has many tuning parameters that require iterative trial and error, so it is not a fit for users who need immediate turnkey results.
How We Selected and Ranked These Tools
We evaluated every tool on three sub-dimensions with weights of features at 0.40, ease of use at 0.30, and value at 0.30. The overall rating is computed as overall = 0.40 × features + 0.30 × ease of use + 0.30 × value. Deepfake Studio separated from lower-ranked tools by delivering stronger features and practical workflow usability together through an alignment-first face swap processing workflow and an export-focused guided pipeline. Tools like DeepFaceLab separated in features for users who want configurable training and conversion control, but lower ease of use pulled the overall result down for nontechnical setups.
Frequently Asked Questions About Deepfake Porn Software
Which tools are best for local, fully controllable face-swap training rather than automated synthesis?
DeepFaceLab supports manual control over model selection, dataset preparation, and preprocessing so training and inference behavior can be tuned end to end. Deepfake Studio focuses on an alignment-first face swap workflow for faster edits, which limits the depth of training control compared with DeepFaceLab.
What option is most suitable for quick browser-based face swaps with minimal setup?
DeepSwap uses a web-based workflow that converts uploaded media in the browser with minimal local configuration. FaceSwap also runs as a web workflow for quick detection, swapping, and export, but DeepSwap is more explicitly oriented toward rapid adult-style outputs.
Which software produces the most reliable face alignment and identity tracking across varied source footage?
Deepfake Studio emphasizes alignment-first processing to improve identity tracking stability across different inputs. DeepFaceLab can achieve strong results too, but its quality depends on dataset consistency and careful preprocessing, not just a guided interface.
How do FaceSwap and Deepfake Studio differ for face-to-video quality and iteration speed?
FaceSwap targets quick iteration by running automated face detection and swapping and then exporting altered visuals. Deepfake Studio is built around a face-swap workflow with alignment steps that often improves output stability when source footage is noisy or inconsistent.
Which tools are oriented toward face animation from driving footage rather than direct face swapping?
Avatarify centers on driving likeness movement using a target upload plus driving footage to generate avatar-style outputs. Vidyo AI is also more pipeline-driven than frame-by-frame editing, but it emphasizes generative synthesis from supplied inputs instead of classic driving-footage animation.
What should be expected from automated synthesis tools like Vidyo AI and Kairos compared with manual pipelines?
Vidyo AI produces altered likeness video through an automated generative pipeline with repeatable steps and less granular post-production control. Kairos provides batch workflow orchestration for ingest, processing, and export, which improves standardization for teams but still relies on upstream input quality and model pipeline behavior.
Which platform is best for building detection and safety workflows instead of generating deepfake porn content?
Sensity AI focuses on synthetic media detection and risk-oriented downstream actions, making it a fit for ingestion and review controls. Clarifai supports developer-first inference and moderation-style detection features, while Kairos and Vidyo AI generate outputs rather than specialized detection reports.
What are common technical failure points when generating swapped video and how do the tools address them?
FaceSwap can struggle with consistent subject identity because it prioritizes fast swapping and export over deep control, especially when face detection fluctuates. DeepFaceLab and Deepfake Studio mitigate instability by emphasizing preprocessing and alignment steps, but output quality still depends on source video clarity and frame-to-frame face visibility.
How can teams integrate creator workflows with compliance-oriented review using multiple tools?
A practical workflow uses DeepSwap or Deepfake Studio to generate swapped clips, then routes the results into Sensity AI for synthetic media detection and structured risk outputs. Clarifai can add developer-built moderation pipelines that classify and analyze manipulated visuals, while Kairos can orchestrate batch processing to standardize the review and export steps at scale.
Conclusion
After evaluating 10 porn, Deepfake Studio stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Referenced in the comparison table and product reviews above.
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