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
198 lines
6.6 KiB
Markdown
198 lines
6.6 KiB
Markdown
# EditmaskwithAI
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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.
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## Quick Start
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### GPU machine (recommended — free inference, best quality)
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```bash
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# Prerequisites: Docker + nvidia-container-toolkit
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# Install toolkit once (Ubuntu/Debian):
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curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \
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| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-ctk.gpg
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curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \
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| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-ctk.gpg] https://#g' \
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| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
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sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
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sudo nvidia-ctk runtime configure --runtime=docker
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sudo systemctl restart docker
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# Verify GPU passes through into Docker:
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docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
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# Clone and run:
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git clone https://github.com/outis1one/editmaskwithai
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cd editmaskwithai
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docker compose -f docker-compose.gpu.yml up --build
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```
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Open **http://localhost:3080**
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**First startup downloads the AI model for your GPU (5–20 GB, one time).** Models are cached in a Docker volume and survive rebuilds.
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---
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### Cloud API (no GPU required)
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```bash
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git clone https://github.com/outis1one/editmaskwithai
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cd editmaskwithai
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cp .env.example .env
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# Edit .env: set AI_PROVIDER and your API key (see .env.example for options)
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docker compose up -d --build
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```
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Open **http://localhost:3080**
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---
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### Updates (any machine)
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```bash
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git pull
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# GPU:
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docker compose -f docker-compose.gpu.yml up -d --build
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# or cloud:
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docker compose up -d --build
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```
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---
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## AI Providers
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| Provider | Setup | Cost | Quality |
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|---|---|---|---|
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| `local_gpu` | GPU machine + nvidia-container-toolkit | Free | Best (SDXL/FLUX auto-selected by VRAM) |
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| `openai` | `OPENAI_API_KEY=sk-...` | ~$0.02–0.04/image | DALL-E 3 |
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| `replicate` | `REPLICATE_API_KEY=r8_...` | ~$0.002–0.03/image | Multiple models |
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| `invokeai` | InvokeAI running on another machine | Self-hosted | FLUX/SDXL |
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| `comfyui` | ComfyUI running on another machine | Self-hosted | Any model |
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You can also mix: set a default provider in `.env` and override per-operation in the **Image → AI Provider Settings** dialog inside the app.
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---
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## GPU Tier Auto-Selection
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The app detects your GPU at startup and picks the best model it can run:
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| Effective VRAM | Model selected | Notes |
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|---|---|---|
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| ≥ 24 GB | FLUX.1-schnell | Best quality, 4-step generation |
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| 12–24 GB | SDXL | Excellent quality |
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| 8–12 GB | SDXL + xformers | Good quality |
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| 6–8 GB | SDXL + attention slicing | Good quality, slightly slower |
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| 4–6 GB | SDXL + CPU offload | Good quality, slower (GTX 1060 6GB range) |
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| 2–4 GB | SD 1.5 | Fast, lower detail |
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| < 2 GB | SD 1.5 + CPU offload | Very slow — consider a cloud provider |
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Override the auto-selected model with `HF_MODEL_TXT2IMG`, `HF_MODEL_INPAINT` in `.env`.
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---
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## What it can do
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### Selection
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- **Smart Select (SAM brush)** — paint over an object, AI detects its exact boundaries
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- **Smart Select (click)** — click any object, SAM selects it
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- **Rectangle / Ellipse / Lasso** — classic selection tools
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### After selecting
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- **AI Edit** — describe what to change ("add a scar", "make it look aged")
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- **Make less symmetrical** — AI adds natural organic variation
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- **Replace with clipboard** — paste any image into the selection shape
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- **Scale by %** — make the selected object bigger/smaller, AI fills the gap
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- **Copy / Cut to layer** — non-destructive layer workflow
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- **Erase** — remove the selected region with AI fill
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### Image tools
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- **Text → Image** — generate from a text description (GPU or cloud)
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- **Upscale** — Real-ESRGAN AI upscaling (genuinely adds detail, not just resize)
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- **Prepare for Print** — one-click: AI upscale to target DPI + fit to frame
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- **Fit to Frame** — resize/crop/AI-extend to standard print sizes
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- **Expand Canvas (Outpaint)** — AI extends the image in any direction
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- **Remove Background** — one-click background removal
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### Print presets
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Frame sizes: 4×6, 5×7, 8×10, 11×14, 16×20, 18×24, 20×24, 24×36 (portrait + landscape)
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DPI options: 72, 150, 200, 300 — 200 DPI is fine for 18×24" and larger (viewed from distance)
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---
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## Progress bars
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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.
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---
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## Logs
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```bash
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# GPU container:
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docker compose -f docker-compose.gpu.yml logs -f
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# Standard container:
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docker compose logs -f
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```
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---
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## File structure
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```
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EditmaskwithAI/
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├── backend/
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│ ├── app/
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│ │ ├── routers/ # API endpoints (ai_tools, print_tools, …)
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│ │ ├── services/ # gpu_detect, local_diffusion, upscale, …
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│ │ └── config.py
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│ ├── requirements.txt
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│ └── requirements.gpu.txt
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├── frontend/
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│ └── src/js/
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│ ├── tools/ # brush_select (SAM paint), smart_select, …
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│ ├── modules/
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│ │ ├── generate/ # text_to_image, outpaint
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│ │ └── image/ # upscale, frame_fit, print_prepare, …
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│ └── libs/
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│ └── progress_overlay.js
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├── docker-compose.yml # Cloud / no-GPU
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├── docker-compose.gpu.yml # NVIDIA GPU (recommended)
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├── docker-compose.dev.yml # Dev with hot reload
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├── Dockerfile
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├── Dockerfile.gpu
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└── .env.example
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```
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---
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## Troubleshooting
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**GPU not detected in Docker**
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```bash
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# Check toolkit is installed and Docker restarted:
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docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
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# If that fails, re-run: sudo nvidia-ctk runtime configure --runtime=docker && sudo systemctl restart docker
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```
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**Model download stalls or fails**
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```bash
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# Check logs for HuggingFace errors:
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docker compose -f docker-compose.gpu.yml logs -f | grep -E "local_gpu|Error|Failed"
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# If a private/gated model: add HF_TOKEN=hf_... to .env
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```
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**Out of VRAM during generation**
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- Reduce `LOCAL_GPU_MAX_PIPELINES=1` in `.env` (default 2)
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- Or override to a smaller model: `HF_MODEL_TXT2IMG=runwayml/stable-diffusion-v1-5`
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**Settings saved locally only**
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- The in-app AI Provider Settings dialog saves to localStorage for the session
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- To make settings permanent: edit `.env` and rebuild
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**Check API docs**
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```
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http://localhost:3080/api/docs
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```
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