DeepCreamPy Alternatives: Every Decensoring Method Compared (2026)

DeepCreamPy was groundbreaking when it launched in 2018. It was the first widely available AI tool purpose-built for removing manga censorship. But it requires manual green masking in Photoshop for every single image, depends on Python 3.6 and TensorFlow 1.x (both long EOL), and has not been meaningfully updated in years. Getting it to run on a modern system is a genuine struggle.

If you are looking for something better, here is every option available in 2026, from zero-setup browser tools to full Stable Diffusion workflows.

How AI Decensoring Actually Works

Every tool on this list uses some form of AI inpainting. The process is:

1. Detection: Identify censored regions (mosaic patterns, black bars, white-out areas)
2. Masking: Create a mask marking what needs to be reconstructed
3. Inpainting: A neural network generates replacement pixels based on surrounding context
4. Blending: The generated region is blended into the original image

One thing to understand upfront: no tool "reveals" hidden content. Mosaic and bars permanently destroy the original pixels. The AI generates new artwork that fits the surrounding context. This is why thin bars produce near-perfect results (very little to generate) while heavy mosaic or white-out produces inconsistent output (the AI is essentially drawing from scratch).

For a detailed breakdown of how different censorship types affect quality, see our doujinshi translation guide.

Method 1: Browser-Based Tools (No Setup)

The fastest path. Everything runs server-side, so you do not need a GPU, Python, or any local setup.

KitsuTL is a browser extension that handles both decensoring and manga translation. Install the extension, navigate to any manga reader site (or drag-drop local images), click an image, and select "Restore." Results appear in 1-2 seconds. It supports batch processing for full volumes and also offers combined "Translate + Restore" mode.

Cost: Pay-per-use, roughly $0.002 per image. A 30-page doujinshi costs about 6 cents.

Best for: Most users who want fast results without any technical setup.

Limitations: Requires an internet connection. You are sending images to a server, which may be a concern for some users.

Method 2: DeepCreamPy + hent-AI (The Classic Free Pipeline)

This is the original approach and still fully free, but it takes real effort to set up.

DeepCreamPy

The original tool by deeppomf. Uses a deep convolutional neural network trained on the Danbooru anime dataset. The most maintained fork is Deepshift/DeepCreamPy.

The workflow:

1. Open a censored image in Photoshop or GIMP
2. Paint over every censored region with exact green (#00FF00) using the pencil tool (not brush, anti-aliasing must be off)
3. Save the masked image into DCP's input folder
4. For mosaic, also place the original unmasked image in a separate folder
5. Run DCP, which fills in the green regions

The painful part is step 2. For a 30-page doujinshi, manually painting green masks on every page takes hours. This is the main reason people look for alternatives.

Requirements: Python 3.6 (specifically), TensorFlow 1.x, Windows 64-bit for pre-built binaries. Can run from source on Mac/Linux. Works on CPU (slow) or GPU.

hent-AI (Automatic Detection)

hent-AI was created to solve DCP's manual masking problem. It uses Mask R-CNN to automatically detect censored regions and generate the green masks that DCP needs.

The combined pipeline:

1. Run hent-AI on a folder of images to auto-detect and mask censored regions
2. Feed the masked images into DeepCreamPy for reconstruction
3. hent-AI also offers ESRGAN as an alternative reconstruction engine for mosaic

What it handles: Bar censorship and mosaic detection work well. It struggles with white bars, partially transparent bars, and mixed censorship types.

The catch: hent-AI has not been updated since 2020. It requires Python 3.5, TensorFlow 1.8, and CUDA 9.0. Getting both hent-AI and DCP running together on a modern system is a dependency challenge. Tools like Tsuki package the pipeline together and make setup somewhat easier.

Best for: Users who want a fully free, local solution and are comfortable with Python dependency management.

Method 3: Stable Diffusion Inpainting (Best Quality, Most Effort)

This is the modern approach and produces the highest quality results when done well. There is no single "decensoring tool" built on Stable Diffusion. Instead, you use SD's general-purpose inpainting capabilities with anime-focused models.

The basic workflow in ComfyUI or A1111:

1. Load a censored image into img2img inpainting mode
2. Manually mask the censored region (painting over it in the UI)
3. Select an anime-focused model (Anything V5, or similar models from CivitAI)
4. Set denoising strength around 0.5-0.7 (too high loses coherence with the original art)
5. Write a prompt to guide generation
6. Generate multiple candidates and pick the best result

Specialized resources:

• Mosaic Clarity Slider LoRA is a LoRA specifically designed for mosaic removal. Use negative weights (-2 to -1.5) in inpaint mode.
• sd-webui-manga-inpainting is an A1111 extension with manga-specific inpainting that integrates ControlNet.
• FLUX.1 Fill provides next-generation inpainting quality but requires 12GB+ VRAM minimum.

Why it produces the best results: You can match the exact art style using the right model and LoRA combination, control the output with prompts, and generate multiple attempts until one looks right.

Why most people do not use it: Fully manual, requires significant technical knowledge (model selection, prompt engineering, parameter tuning), needs a capable GPU (6-8GB VRAM minimum for SD 1.5, 12GB+ for SDXL/FLUX), and takes minutes per image instead of seconds. Processing a full doujinshi this way could take hours.

Best for: People who want the absolute best quality on specific images and have SD experience.

Method 4: Other Tools

Er0manga Suite

A three-part pipeline: Er0mangaSeg for automatic censorship detection, Er0mangaInpaint for reconstruction using a manga-tuned LaMa model, and a Docker-based web demo that chains them together.

The standout feature is that the inpainting model was fine-tuned specifically on manga, which means it handles black-and-white pages with screentone patterns notably better than generic inpainting models. Most other tools struggle with B&W manga.

Limitation: The detection model currently only recognizes dark-colored bar censorship, not mosaic or white-out.

DeepMosaicsPlus

DeepMosaicsPlus is an actively maintained fork of DeepMosaics. It does automatic mosaic detection and removal for both images and video.

The key differentiator: it supports AMD GPUs via DirectML, not just NVIDIA CUDA. It also works on CPU with automatic fallback. Has both a GUI and command line interface.

Best for: Video decensoring and AMD GPU users.

IOPaint (formerly Lama Cleaner)

IOPaint is a general-purpose inpainting tool with a clean web UI. Install with pip, open in your browser, paint over what you want removed, and it fills it in.

Not purpose-built for decensoring, but it supports multiple inpainting models (LaMa, SD-based, BrushNet) and works on CPU. Supports batch processing via CLI.

Best for: Users who want a simple, general-purpose inpainting tool without dealing with ComfyUI/A1111 complexity.

Full Comparison

Hardware: What Do You Actually Need?

No GPU at all: Browser-based tools (KitsuTL) run everything server-side. IOPaint with the LaMa model also works fine on CPU. DeepCreamPy runs on CPU but is slow.

Entry-level GPU (4-6GB VRAM): Enough for hent-AI, DeepMosaicsPlus, and SD 1.5 inpainting.

Mid-range GPU (8-12GB VRAM): Comfortable for SDXL inpainting. This is the sweet spot for local SD work.

High-end GPU (16GB+ VRAM): Required for FLUX inpainting and running multiple models simultaneously.

No local GPU but want free options: Google Colab's free tier gives you a K80 GPU (enough for DCP and SD 1.5). Kaggle offers 30 hours/week of free P100 time. For more power, RunPod offers RTX 4090s starting around $0.34/hour.

Frequently Asked Questions

What is the easiest DeepCreamPy alternative?

A browser-based tool like KitsuTL. No installation, no Python, no GPU. Install the browser extension and click a button. The tradeoff is that it costs money (about $0.002 per image).

Which method produces the best results?

Manual Stable Diffusion inpainting with a good anime model, when properly configured. You can match art styles, control output with prompts, and generate multiple candidates. The tradeoff is that it requires significant technical knowledge, a capable GPU, and minutes per image instead of seconds.

Can I decensor black-and-white manga?

This is harder than color across all tools. B&W manga uses screentone patterns that are difficult for AI to match. The Er0manga Suite was fine-tuned specifically for manga and handles B&W better than most alternatives. SD inpainting with the right model can also work, but requires more tuning.

Does decensoring work on video?

Yes. DeepMosaicsPlus handles video mosaic removal and supports both NVIDIA and AMD GPUs. JavPlayer (1,200 yen, not free) is a dedicated commercial tool for video. Both work best on light mosaic and produce mediocre results on heavy mosaic.

Is DeepCreamPy still worth using in 2026?

Only if you are already familiar with it and have it running. For new users, the dependency setup (Python 3.6, TensorFlow 1.x) is painful on modern systems. Browser tools are easier, SD inpainting is higher quality, and Er0manga Suite is better for B&W manga. DCP's main advantage was being first, not being best.

Do I need a GPU to decensor manga?

No. Browser-based tools handle everything server-side. IOPaint with LaMa runs on CPU. DeepCreamPy also works on CPU (slowly). You only need a local GPU if you want to run SD inpainting or process large batches with DeepMosaicsPlus at full speed.

---

Last updated: 2026.