Files
PaintPlus/README.md
T
Claude 59198a6be2 Rewrite README with accurate current state of the project
Old README described the original React/Fabric.js UI and listed
"No local GPU inference" as a non-goal. Updated to reflect:
- miniPaint-based editor with SAM brush selection
- GPU quick-start (nvidia-container-toolkit prereqs, docker-compose.gpu.yml)
- Cloud API quick-start
- GPU tier auto-selection table (FLUX/SDXL/SD by VRAM)
- Full feature list (selection actions, print tools, progress bars)
- Correct clone URL and update commands
- Troubleshooting section

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM
2026-06-13 17:15:33 +00:00

198 lines
6.6 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
# EditmaskwithAI
A self-hosted, web-based AI photo editor. Paint over any object, describe what you want, and the AI replaces just that region — every pixel outside your selection stays untouched.
## Quick Start
### GPU machine (recommended — free inference, best quality)
```bash
# Prerequisites: Docker + nvidia-container-toolkit
# Install toolkit once (Ubuntu/Debian):
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-ctk.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-ctk.gpg] https://#g' \
| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
# Verify GPU passes through into Docker:
docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
# Clone and run:
git clone https://github.com/outis1one/editmaskwithai
cd editmaskwithai
docker compose -f docker-compose.gpu.yml up --build
```
Open **http://localhost:3080**
**First startup downloads the AI model for your GPU (520 GB, one time).** Models are cached in a Docker volume and survive rebuilds.
---
### Cloud API (no GPU required)
```bash
git clone https://github.com/outis1one/editmaskwithai
cd editmaskwithai
cp .env.example .env
# Edit .env: set AI_PROVIDER and your API key (see .env.example for options)
docker compose up -d --build
```
Open **http://localhost:3080**
---
### Updates (any machine)
```bash
git pull
# GPU:
docker compose -f docker-compose.gpu.yml up -d --build
# or cloud:
docker compose up -d --build
```
---
## AI Providers
| Provider | Setup | Cost | Quality |
|---|---|---|---|
| `local_gpu` | GPU machine + nvidia-container-toolkit | Free | Best (SDXL/FLUX auto-selected by VRAM) |
| `openai` | `OPENAI_API_KEY=sk-...` | ~$0.020.04/image | DALL-E 3 |
| `replicate` | `REPLICATE_API_KEY=r8_...` | ~$0.0020.03/image | Multiple models |
| `invokeai` | InvokeAI running on another machine | Self-hosted | FLUX/SDXL |
| `comfyui` | ComfyUI running on another machine | Self-hosted | Any model |
You can also mix: set a default provider in `.env` and override per-operation in the **Image → AI Provider Settings** dialog inside the app.
---
## GPU Tier Auto-Selection
The app detects your GPU at startup and picks the best model it can run:
| Effective VRAM | Model selected | Notes |
|---|---|---|
| ≥ 24 GB | FLUX.1-schnell | Best quality, 4-step generation |
| 1224 GB | SDXL | Excellent quality |
| 812 GB | SDXL + xformers | Good quality |
| 68 GB | SDXL + attention slicing | Good quality, slightly slower |
| 46 GB | SDXL + CPU offload | Good quality, slower (GTX 1060 6GB range) |
| 24 GB | SD 1.5 | Fast, lower detail |
| < 2 GB | SD 1.5 + CPU offload | Very slow — consider a cloud provider |
Override the auto-selected model with `HF_MODEL_TXT2IMG`, `HF_MODEL_INPAINT` in `.env`.
---
## What it can do
### Selection
- **Smart Select (SAM brush)** — paint over an object, AI detects its exact boundaries
- **Smart Select (click)** — click any object, SAM selects it
- **Rectangle / Ellipse / Lasso** — classic selection tools
### After selecting
- **AI Edit** — describe what to change ("add a scar", "make it look aged")
- **Make less symmetrical** — AI adds natural organic variation
- **Replace with clipboard** — paste any image into the selection shape
- **Scale by %** — make the selected object bigger/smaller, AI fills the gap
- **Copy / Cut to layer** — non-destructive layer workflow
- **Erase** — remove the selected region with AI fill
### Image tools
- **Text → Image** — generate from a text description (GPU or cloud)
- **Upscale** — Real-ESRGAN AI upscaling (genuinely adds detail, not just resize)
- **Prepare for Print** — one-click: AI upscale to target DPI + fit to frame
- **Fit to Frame** — resize/crop/AI-extend to standard print sizes
- **Expand Canvas (Outpaint)** — AI extends the image in any direction
- **Remove Background** — one-click background removal
### Print presets
Frame sizes: 4×6, 5×7, 8×10, 11×14, 16×20, 18×24, 20×24, 24×36 (portrait + landscape)
DPI options: 72, 150, 200, 300 — 200 DPI is fine for 18×24" and larger (viewed from distance)
---
## Progress bars
All AI operations show a real-time progress overlay. For local GPU inference, the bar advances step-by-step as the model denoises (e.g. "Step 14 / 30"). For cloud providers and upscale operations, it animates to indicate activity.
---
## Logs
```bash
# GPU container:
docker compose -f docker-compose.gpu.yml logs -f
# Standard container:
docker compose logs -f
```
---
## File structure
```
EditmaskwithAI/
├── backend/
│ ├── app/
│ │ ├── routers/ # API endpoints (ai_tools, print_tools, …)
│ │ ├── services/ # gpu_detect, local_diffusion, upscale, …
│ │ └── config.py
│ ├── requirements.txt
│ └── requirements.gpu.txt
├── frontend/
│ └── src/js/
│ ├── tools/ # brush_select (SAM paint), smart_select, …
│ ├── modules/
│ │ ├── generate/ # text_to_image, outpaint
│ │ └── image/ # upscale, frame_fit, print_prepare, …
│ └── libs/
│ └── progress_overlay.js
├── docker-compose.yml # Cloud / no-GPU
├── docker-compose.gpu.yml # NVIDIA GPU (recommended)
├── docker-compose.dev.yml # Dev with hot reload
├── Dockerfile
├── Dockerfile.gpu
└── .env.example
```
---
## Troubleshooting
**GPU not detected in Docker**
```bash
# Check toolkit is installed and Docker restarted:
docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
# If that fails, re-run: sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker
```
**Model download stalls or fails**
```bash
# Check logs for HuggingFace errors:
docker compose -f docker-compose.gpu.yml logs -f | grep -E "local_gpu|Error|Failed"
# If a private/gated model: add HF_TOKEN=hf_... to .env
```
**Out of VRAM during generation**
- Reduce `LOCAL_GPU_MAX_PIPELINES=1` in `.env` (default 2)
- Or override to a smaller model: `HF_MODEL_TXT2IMG=runwayml/stable-diffusion-v1-5`
**Settings saved locally only**
- The in-app AI Provider Settings dialog saves to localStorage for the session
- To make settings permanent: edit `.env` and rebuild
**Check API docs**
```
http://localhost:3080/api/docs
```