Compare commits
| Author | SHA1 | Date | |
|---|---|---|---|
 charrywhite
|
111f9fdffb |
@@ -26,7 +26,6 @@ jobs:
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run: |
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python -m pip install --upgrade pip
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pip install .[dev]
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pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
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- name: Run Linting
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run: |
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ruff check .
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@@ -11,5 +11,3 @@ jobs:
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runs-on: ubuntu-latest
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steps:
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- uses: comfy-org/node-diff@main
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with:
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base_ref: ${{ github.event.repository.default_branch }}
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@@ -100,9 +100,3 @@ cookiecutter-pypackage-env/
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*.code-workspace
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.vscode/
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/.vscode
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# Claude Code
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CLAUDE.md
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.claude/
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.playwright-mcp/
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debug_screenshots/
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@@ -1,197 +0,0 @@
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# AGENTS.md
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This file provides guidance to Codex (Codex.ai/code) when working with code in this repository.
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## Project overview
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LanPaint is a ComfyUI extension that implements a training-free diffusion inpainting sampler based on Langevin dynamics ("think mode"). It lets any diffusion model iterate multiple times within each denoising step before committing to an output, improving inpainting quality without a specialized model.
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## Commands
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```bash
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# Run all tests
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pytest
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# Lint
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ruff check .
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# Format
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ruff format .
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# Type check (requires mypy)
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||||
mypy
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||||
```
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There is no build step — this is installed directly as a ComfyUI custom node by cloning into `custom_nodes/LanPaint`.
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## Architecture
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### Entry point and ComfyUI integration
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`__init__.py` is the ComfyUI entry point. When ComfyUI loads this module, it imports `NODE_CLASS_MAPPINGS` and `NODE_DISPLAY_NAME_MAPPINGS` from `src/LanPaint/nodes.py`. The `WEB_DIRECTORY = "./web"` tells ComfyUI where to find the frontend JS.
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When imported **without** ComfyUI (e.g., in CI), `_install_lightweight_runtime_stubs()` creates dummy `torch`, `comfy`, `nodes`, and `comfyui_version` modules so `nodes.py` can still be imported for node discovery. The real stubs are in `src/LanPaint/types.py` (`LangevinState` NamedTuple).
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|
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### Core algorithm (`src/LanPaint/lanpaint.py`)
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`LanPaint.__call__()` is the main algorithm. It runs `n_steps` Langevin dynamics sub-iterations within each outer denoising step:
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1. Replaces the masked region with the noise-scaled known latent (`scale_latent_inpaint`)
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2. In each inner step, computes a score function via `score_model()` — which calls the diffusion model to get `x_0` and `x_0_BIG` (high-CFG) predictions — then runs one Langevin sub-step
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3. After iterations, denoises the result to produce the final `x_0` output
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|
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The `LanPaintEarlyStopper` (`src/LanPaint/earlystop.py`) can terminate inner iterations early based on semantic convergence or a custom distance function, contributed by `@godnight10061`.
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### Monkey-patching mechanism (`src/LanPaint/nodes.py`)
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|
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`override_sample_function()` is a context manager that temporarily replaces three functions on ComfyUI's `comfy.samplers` module:
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- `CFGGuider.outer_sample` → `CFGGuider_LanPaint.outer_sample` (handles mask preparation and WAN22 video models)
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- `CFGGuider.predict_noise` → `CFGGuider_LanPaint.predict_noise` (dual CFG output — normal + BIG)
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- `KSAMPLER.sample` → `KSAMPLER.sample` (injects `LanPaint` as the paint method for inpainting steps)
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|
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These are monkey-patches, not subclass overrides, because ComfyUI internally constructs `CFGGuider` and `KSAMPLER` instances directly. The monkey-patches are scoped to a single `nodes.common_ksampler()` call.
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|
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### Sampler nodes
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|
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There are four sampler nodes, two "basic" and two "advanced":
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|
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- **LanPaint_KSampler** / **LanPaint_KSamplerAdvanced** — use `nodes.common_ksampler()`. The advanced variant exposes all LanPaint hyperparameters (Lambda, StepSize, Beta, Friction, EarlyStop, InnerThreshold, InnerPatience).
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- **LanPaint_SamplerCustom** / **LanPaint_SamplerCustomAdvanced** — use `comfy.sample.sample_custom()` / `guider.sample()`, for use with custom sigmas/samplers/guiders.
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All sampler nodes attach LanPaint parameters to `model` (the model patcher object) and set `model_options["video_inpainting"]` for video mode.
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|
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Additional nodes:
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- **LanPaint_MaskBlend** — blends before/after images with a Gaussian-smoothed mask for seamless boundaries
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- **LanPaint_UpSale_LatentNoiseMask** — generates a checkerboard noise mask (currently commented out in `NODE_CLASS_MAPPINGS`)
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||||
### Numerical utilities (`src/LanPaint/utils.py`)
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||||
`StochasticHarmonicOscillator` simulates the Langevin dynamics step analytically. It computes the exact mean and covariance of the position/velocity after time `t` and samples from a multivariate normal. The module also contains numerically stable implementations of `(e^x - 1)/x`, `(e^x - 1 - x)/x^2`, hyperbolic functions, and helper coefficients (`zeta1`, `zeta2`, `Zcoefs`).
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||||
|
||||
### Dual CFG
|
||||
|
||||
LanPaint uses two classifier-free guidance scales simultaneously:
|
||||
- `cfg` — the standard CFG scale used for the known-region score
|
||||
- `cfg_BIG` — a second (often higher) CFG scale used for the masked-region score via `score_model()`. In "Prompt First" mode, `cfg_BIG = 0*cfg - 0.5 = -0.5`, which effectively disables the second guidance.
|
||||
|
||||
### Frontend (`web/lanpaint_info.js`)
|
||||
|
||||
A ComfyUI extension that adds a "More Info, Bug Report, Star on GitHub" button to each LanPaint sampler node in the UI.
|
||||
|
||||
### Version compatibility
|
||||
|
||||
`COMFYUI_VERSION_060_OR_NEWER` gates behavior differences for mask reshaping between ComfyUI versions < 0.6.0 and >= 0.6.0, which changed the latent tensor dimension convention.
|
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|
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## Testing
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|
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Tests use pytest. The test suite is designed to run without ComfyUI installed — `conftest.py` adds the project root to `sys.path`, and `_install_lightweight_runtime_stubs()` provides dummy modules. The primary integration test (`test_package_imports_without_comfy`) validates that the package can be imported and node mappings are present.
|
||||
|
||||
CI uses `comfy-org/node-diff` to validate backwards compatibility of node interfaces on PRs.
|
||||
|
||||
## Running ComfyUI on this machine
|
||||
|
||||
### Installed locations
|
||||
|
||||
| What | Path | Version |
|
||||
|---|---|---|
|
||||
| User data (custom_nodes, models, output) | `E:\CompyUI` | — |
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| ComfyUI Desktop app (Electron shell) | `C:\Users\scraed\AppData\Local\Programs\@comfyorgcomfyui-electron` | 0.21.1 bundled |
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||||
| **Actual ComfyUI (used by Desktop)** | `C:\Users\scraed\ComfyUI-Installs\ComfyUI\ComfyUI\` | **0.24.1** |
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| Python venv | `E:\CompyUI\.venv` | Python 3.12.6 |
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||||
| Shared models | `C:\Users\scraed\ComfyUI-Shared\models\` | — |
|
||||
| Extra models (Y drive) | `Y:\ComfyData\models\` | — |
|
||||
|
||||
### Launch headless
|
||||
|
||||
The ComfyUI at `ComfyUI-Installs` is the one to use — the bundled Electron version (0.21.1) is outdated and lacks nodes like `Ideogram4Scheduler`, `DualModelGuider`, `CFGOverride`.
|
||||
|
||||
```powershell
|
||||
$env:PYTHONUTF8=1
|
||||
$env:PYTHONIOENCODING='utf-8'
|
||||
& E:\CompyUI\.venv\Scripts\python.exe `
|
||||
C:\Users\scraed\ComfyUI-Installs\ComfyUI\ComfyUI\main.py `
|
||||
--base-directory E:\CompyUI `
|
||||
--listen 127.0.0.1 --port 8188 `
|
||||
--disable-auto-launch
|
||||
```
|
||||
|
||||
The `PYTHONUTF8` and `PYTHONIOENCODING` env vars are required on this machine (Chinese Windows, GBK codec chokes on emoji in custom node logs).
|
||||
|
||||
### Model paths
|
||||
|
||||
ComfyUI loads `extra_model_paths.yaml` from **the same directory as `main.py`**, not from `--base-directory`. The config at `C:\Users\scraed\ComfyUI-Installs\ComfyUI\ComfyUI\extra_model_paths.yaml` points to both the Shared and Y-drive model collections.
|
||||
|
||||
### GPU
|
||||
|
||||
Dual NVIDIA RTX A6000 (49GB VRAM each), PyTorch 2.9.1+cu130.
|
||||
|
||||
### Workflows
|
||||
|
||||
Saved workflows are at `E:\CompyUI\user\default\workflows\`. There are 74 workflows covering LanPaint inpainting, Qwen image edit, Flux, HunYuan, HiDream, Wan video, and Ideogram4 generation.
|
||||
|
||||
### Running a workflow — practical notes
|
||||
|
||||
**Workflow format:** LanPaint examples are PNGs with embedded workflow JSON (`PIL.Image.open(png).info['workflow']`). Save to `E:\CompyUI\user\default\workflows\` to load them from the web UI.
|
||||
|
||||
**Common errors and their fixes:**
|
||||
|
||||
| Error | Cause | Fix |
|
||||
|---|---|---|
|
||||
| Models missing | `extra_model_paths.yaml` not at the ComfyUI root (same dir as `main.py`) | Create one with `base_path` sections pointing to `C:\Users\scraed\ComfyUI-Shared\` and `Y:\ComfyData\` |
|
||||
| Nodes missing | UI-only nodes (`MarkdownNote`, `PreviewAny`) | Usually safe to ignore |
|
||||
| Missing image input | `LoadImage` node needs a mask | Upload `Masked_Load_Me_in_Loader.png` from the example folder |
|
||||
| "Failed to convert input to FLOAT" | `widgets_values` array is too short for the current node signature — params were added/inserted since the workflow was created | Expand `widgets_values` to match the current number of non-linked inputs, using correct types (INT, FLOAT, STRING, COMBO) |
|
||||
|
||||
**Testing workflow changes:** The easiest way to verify a LanPaint workflow works is to load it in the headless ComfyUI web interface (Playwright or manually), upload the mask if needed, and click Run. Monitor the page title for `[N%]` progress or grep the server log for "Prompt executed".
|
||||
|
||||
**UTF-8 on Chinese Windows:** Always set `$env:PYTHONUTF8=1` and `$env:PYTHONIOENCODING='utf-8'` before launching — the GBK codec cannot handle emoji in custom node logs.
|
||||
|
||||
### Workflow JSON debugging
|
||||
|
||||
**Link format differs by context:** Top-level `links` are arrays `[id, from_node, from_slot, to_node, to_slot, "TYPE"]`. Subgraph links (in `definitions.subgraphs[].links`) are dicts `{"id": N, "origin_id": N, ...}`. Mixing formats causes silent failures.
|
||||
|
||||
**ComfyUI clears link fields on save:** After loading+saving a workflow in ComfyUI, `inputs[].link` and `outputs[].links` fields on nodes are often reset to `null`/`[]` — even though the correct links still exist in the `links` array. This makes the frontend render nodes as disconnected. After any ComfyUI save, audit and restore these fields.
|
||||
|
||||
**Subgraph instance ↔ definition name matching:** Instance inputs (on the subgraph node) must have `name` matching the definition `inputs[].name`. Mismatch causes "No link found in parent graph" errors.
|
||||
|
||||
**Virtual nodes:** Inside subgraphs, `-10` = input node, `-20` = output node. Links from `-10` use the slot matching the definition input index.
|
||||
|
||||
**Node ID uniqueness:** IDs must be unique across top-level AND all subgraph nodes combined. Duplicates cause silent connection failures.
|
||||
|
||||
**VAE dimension alignment:** Always use `VAEEncode → VAEDecode → GetImageSize` to derive target dimensions before the actual encode path that feeds the sampler. VAEs require input dimensions divisible by a model-specific factor (e.g., 8). The round-trip forces alignment and captures the clean dimensions for all downstream `ImageScale` nodes. Without this, the mask and latent may have mismatched dimensions in `SetLatentNoiseMask`, causing cryptic errors. Don't remove this pattern unless you fully understand the VAE's input constraints.
|
||||
|
||||
**Widget value ordering when replacing nodes:** When swapping a node (e.g., `KSampler` → `LanPaint_KSampler`), the old widget values array does not map 1:1 to the new node's `INPUT_TYPES`. Always clear the old array and set widget values to exactly match the new node's non-linked inputs — in the correct order, with the correct count. Appending new params to stale old values shifts everything and produces NaN in the UI.
|
||||
|
||||
### Workflow PNG metadata conventions
|
||||
|
||||
Example directories follow this pattern:
|
||||
| File | Metadata |
|
||||
|------|----------|
|
||||
| `Masked_Load_Me_in_Loader.png` | Plain PNG, no metadata |
|
||||
| `Original_No_Mask.png` | Plain PNG, no metadata |
|
||||
| `InPainted_Drag_Me_to_ComfyUI.png` | Must have embedded `workflow` + `prompt` tEXt chunks (auto from SaveImage) |
|
||||
|
||||
**Strip metadata:** `img = Image.open(src); img.save(dst, 'PNG')` — Pillow drops tEXt chunks on re-save.
|
||||
|
||||
**example_workflows/:** Each workflow gets a `.json` + `.jpg` pair. The `.jpg` is a preview derived from the output PNG: `img.convert('RGB').save('name.jpg', 'JPEG', quality=95)`.
|
||||
|
||||
### Kill server safely
|
||||
|
||||
Match the ComfyUI install path to avoid killing other Python programs:
|
||||
|
||||
```powershell
|
||||
Get-CimInstance Win32_Process -Filter "Name='python.exe'" | Where-Object { $_.CommandLine -match 'ComfyUI-Installs.*main\.py' } | ForEach-Object { Stop-Process -Id $_.ProcessId -Force }
|
||||
```
|
||||
|
||||
### Playwright/Browser automation
|
||||
|
||||
- **Beforeunload dialog:** ComfyUI shows `系统可能不会保存您所做的更改` when navigating away from a modified workflow. Call `browser_handle_dialog(accept=true)` to dismiss.
|
||||
- **File choosers:** LoadImage nodes with IMAGEUPLOAD widgets spawn file chooser modals on page load. Dismiss with `browser_file_upload(paths=[])` — there may be multiple.
|
||||
- **IndexedDB cache:** After modifying a workflow JSON on disk, the frontend may load a cached version. Close the workflow tab and re-open it (hard refresh alone is not sufficient).
|
||||
- **Run button:** Use `page.getByTestId('queue-button').click()` — more reliable than text matching.
|
||||
- **Progress:** `page.evaluate('() => document.title')` — format `[N%][M%] Node`. Completed when title returns to `*WorkflowName - ComfyUI`.
|
||||
- **LoadImage via JS:** `app.graph.getNodeById(id).widgets.find(w => w.name === 'image').callback('filename.png')` to set the image without file picker.
|
||||
|
||||
## Commit conventions
|
||||
|
||||
Do NOT include the `Co-Authored-By: Codex <noreply@anthropic.com>` trailer in commit messages. All commits should be attributed solely to the git user.
|
||||
@@ -7,35 +7,17 @@
|
||||
[](https://huggingface.co/charrywhite/LanPaint)
|
||||
[](https://scraed.github.io/scraedBlog/)
|
||||
[](https://github.com/scraed/LanPaint/stargazers)
|
||||
[](https://discord.gg/yN5wYDE6W4)
|
||||
[](https://discord.gg/aCGZutBV)
|
||||
</div>
|
||||
|
||||
|
||||
Universally applicable inpainting ability for every model. LanPaint sampler lets the model "think" through multiple iterations before denoising, enabling you to invest more computation time for superior inpainting quality.
|
||||
|
||||
## What LanPaint Enables
|
||||
This is the official implementation of ["LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling"](https://arxiv.org/abs/2502.03491), accepted by TMLR. The repository is for ComfyUI extension. Local Python benchmark code is published here: [LanPaintBench](https://github.com/scraed/LanPaintBench).
|
||||
|
||||
- Training-free image inpainting
|
||||
- Outpainting and generative fill
|
||||
- Mask-constrained local image editing
|
||||
- Object / region replacement with text guidance
|
||||
- Character-consistent local generation
|
||||
- Video inpainting and local video editing
|
||||
- Video + audio masked generation
|
||||
## Citation
|
||||
|
||||
## Research & Benchmark
|
||||
|
||||
LanPaint is a training-free partial conditional sampler that enables mask-constrained inpainting and local editing with pretrained diffusion and rectified-flow models, without fine-tuning or backpropagation.
|
||||
|
||||
* 📄 **Paper:** [LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling](https://openreview.net/forum?id=JPC8JyOUSW) — TMLR 2025
|
||||
* 🧩 **ComfyUI Implementation:** This repository
|
||||
* 🐍 **Diffusers Implementation:** [LanPaint-Diffusers](https://github.com/charrywhite/LanPaint-diffusers) by [@charrywhite](https://github.com/charrywhite/)
|
||||
* 🧪 **Benchmark & Reproduction:** [LanPaintBench](https://github.com/scraed/LanPaintBench)
|
||||
* 🌐 **Project Website:** [LanPaint Page](https://scraed.github.io/scraedBlog/lanpaint/)
|
||||
|
||||
### Citation
|
||||
|
||||
```bibtex id="2r5ioa"
|
||||
```
|
||||
@article{
|
||||
zheng2025lanpaint,
|
||||
title={LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling},
|
||||
@@ -43,49 +25,20 @@ author={Candi Zheng and Yuan Lan and Yang Wang},
|
||||
journal={Transactions on Machine Learning Research},
|
||||
issn={2835-8856},
|
||||
year={2025},
|
||||
url={https://openreview.net/forum?id=JPC8JyOUSW}
|
||||
url={https://openreview.net/forum?id=JPC8JyOUSW},
|
||||
note={}
|
||||
}
|
||||
```
|
||||
|
||||
**🎉 NEW 2026: Join our discord!**
|
||||
|
||||
[Join our Discord](https://discord.gg/yN5wYDE6W4) to share experiences, discuss features, and explore future development.
|
||||
|
||||
`v2.1.0` significantly accelerates LanPaint with a new schedule mechanism and fixes MiniMax H3 support on the latest ComfyUI.
|
||||
If your inpainting results have wierd (glowing / broken) mask boundary, check this [issue](https://github.com/scraed/LanPaint/issues/80).
|
||||
|
||||
**🎨 NEW: LanPaint now supports Qwen-Image 2.1 - transparency, and masked image editing!**
|
||||
|
||||
|
||||
|
||||
Qwen 2.1's **image edit** model now works under a LanPaint mask: tell it what to change, paint over the part you want it to touch, and only that part changes. Hand it a second picture to borrow from if you want one. Check our latest [Qwen Image 2.1 Image Edit Example](#example-qwen-image-21-image-edit-masked-inpaintlanpaint-k-sampler-5-steps-of-thinking).
|
||||
|
||||
|
||||
|
||||
And if your picture carries transparency, it gets inpainted too - the rebuilt part comes back with a new outline, not just new colours. Check our latest [Qwen Image 2.1 Example](#example-qwen-image-21-inpaint-with-transparencylanpaint-k-sampler-5-steps-of-thinking).
|
||||
|
||||
**🎬 NEW: LanPaint now supports MiniMax H3 video + audio inpainting!**
|
||||
|
||||
| Masked Input (paint in the editor) | Mask (visible overlay) | Inpainted Result |
|
||||
|:----------------------------------:|:----------------------:|:----------------:|
|
||||
|  |  |  |
|
||||
|
||||
Check our latest [MiniMax H3 Example](#minimax-h3-video--audio-inpainting-av-pipeline): paint per-frame video masks and audio intervals in one editor, inpaint video + audio in a single pass, and export the masks into the video file itself.
|
||||
[Join our Discord](https://discord.gg/aCGZutBV) to share experiences, discuss features, and explore future development.
|
||||
|
||||
**🎬 NEW: LanPaint now supports inpainting and outpainting based on Z-Image!**
|
||||
|
||||
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
**🎬 NEW: LanPaint now supports Z-Image-Base too!**
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
|
||||
**🎬 NEW: LanPaint now supports video inpainting and outpainting based on Wan 2.2!**
|
||||
|
||||
@@ -99,25 +52,7 @@ Check our latest [MiniMax H3 Example](#minimax-h3-video--audio-inpainting-av-pip
|
||||
|
||||
</div>
|
||||
|
||||
**🎬 NEW: LanPaint now supports Anima!**
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
**🎬 NEW: LanPaint now supports Ideogram4!**
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
**🎬 NEW: LanPaint now supports Krea2!**
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
Check our latest [Krea2 Example](#example-krea2-inpaintlanpaint-k-sampler-3-steps-of-thinking), [Ideogram4 Example](#example-ideogram4-inpaintlanpaint-custom-sampler-advanced-5-steps-of-thinking), [Anima Example](#example-anima-inpaintlanpaint-k-sampler-5-steps-of-thinking), [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image Examples](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking), and
|
||||
Check our latest [Wan 2.2 Video Examples](#video-examples-beta), [Wan 2.2 Image Examples](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking), and
|
||||
[Qwen Image Edit 2509](#example-qwen-edit-2509-inpaint) support.
|
||||
|
||||
|
||||
@@ -125,27 +60,19 @@ Check our latest [Krea2 Example](#example-krea2-inpaintlanpaint-k-sampler-3-step
|
||||
- [Features](#features)
|
||||
- [Quickstart](#quickstart)
|
||||
- [How to Use Examples](#how-to-use-examples)
|
||||
- [Video Examples](#video-examples)
|
||||
- [Video Examples (Beta)](#video-examples-beta)
|
||||
- [Wan 2.2 Video Inpainting](#wan-22-video-inpainting)
|
||||
- [Wan 2.2 5B Video Inpainting](#wan-22-5b-video-inpainting)
|
||||
- [Wan 2.2 Video Outpainting](#wan-22-video-outpainting)
|
||||
- [MiniMax H3 Video + Audio Inpainting](#minimax-h3-video--audio-inpainting-av-pipeline)
|
||||
- [Resource Consumption](#resource-consumption)
|
||||
- [Image Examples](#image-examples)
|
||||
- [Flux.2.Dev](#example-flux2dev-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Flux 2 klein](#example-flux-2-klein-inpaintlanpaint-k-sampler-2-steps-of-thinking)
|
||||
- [Z-image](#example-z-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Z-image-base](#example-z-image-base-inpaintlanpaint-k-sampler-3-steps-of-thinking)
|
||||
- [Ideogram4](#example-ideogram4-inpaintlanpaint-custom-sampler-advanced-5-steps-of-thinking)
|
||||
- [Krea2](#example-krea2-inpaintlanpaint-k-sampler-3-steps-of-thinking)
|
||||
- [Anima](#example-anima-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Hunyuan T2I](#example-hunyuan-t2i-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Wan 2.2 T2I](#example-wan22-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Wan 2.2 T2I with reference](#example-wan22-partial-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Qwen Image Edit 2511 2509](#example-qwen-edit-2509-inpaint)
|
||||
- [Qwen Image Edit 2508](#example-qwen-edit-2508-inpaint)
|
||||
- [Qwen Image 2.1 Image Edit](#example-qwen-image-21-image-edit-masked-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Qwen Image 2.1](#example-qwen-image-21-inpaint-with-transparencylanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [Qwen Image](#example-qwen-image-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [HiDream](#example-hidream-inpaint-lanpaint-k-sampler-5-steps-of-thinking)
|
||||
- [SD 3.5](#example-sd-35-inpaintlanpaint-k-sampler-5-steps-of-thinking)
|
||||
@@ -163,16 +90,13 @@ Check our latest [Krea2 Example](#example-krea2-inpaintlanpaint-k-sampler-3-step
|
||||
|
||||
## Features
|
||||
|
||||
- **Universal Compatibility** – Works instantly with almost any model (**Ideogram4, Krea2, Z-image, Z-image-base, Hunyuan, Wan 2.2, Qwen Image 2.1/Image/Edit, Anima, HiDream, SD 3.5, Flux-series, SDXL, SD 1.5 or custom LoRAs**) and ControlNet.
|
||||
- **Universal Compatibility** – Works instantly with almost any model (**Z-image, Hunyuan, Wan 2.2, Qwen Image/Edit, HiDream, SD 3.5, Flux-series, SDXL, SD 1.5 or custom LoRAs**) and ControlNet.
|
||||
|
||||
- **No Training Needed** – Works out of the box with your existing model.
|
||||
- **Easy to Use** – Same workflow as standard ComfyUI KSampler.
|
||||
- **Flexible Masking** – Supports any mask shape, size, or position for inpainting/outpainting.
|
||||
- **No Workarounds** – Generates 100% new content (no blending or smoothing) without relying on partial denoising.
|
||||
- **Beyond Inpainting** – You can even use it as a simple way to generate consistent characters.
|
||||
- **Video Mask Editor** – Paint per-frame inpainting masks directly on a video inside ComfyUI (`LanPaint_VideoMaskEditor`): pick the video file, open the editor, paint masks on keyframes, and the masks in between are interpolated automatically with a live preview. Mask = 1 regenerates, 0 keeps.
|
||||
- **MiniMax H3 Video + Audio Inpainting** – Inpaint video **and audio** in one pass: paint per-frame video masks and audio intervals in the same editor, encode both streams into one nested latent (`LanPaint_AVEncode`), sample, then `LanPaint_AVDecode` merges the inpainted video/audio back into the original with a mask-blended boundary, preserving the source fps and bit depth.
|
||||
- **Masks Live in the Video** – The editor can export the painted masks into the video file itself (mp4 metadata, via a new "masked" copy - the original is never modified). Share that single mp4 and the recipient gets the masks back automatically when they load it.
|
||||
|
||||
**Warning**: LanPaint has degraded performance on distillation models, such as Flux.dev, due to a similar [issue with LORA training](https://medium.com/@zhiwangshi28/why-flux-lora-so-hard-to-train-and-how-to-overcome-it-a0c70bc59eaf). Please use low flux guidance (1.0-2.0) to mitigate this [issue](https://github.com/scraed/LanPaint/issues/30).
|
||||
|
||||
@@ -202,7 +126,7 @@ Once installed, you'll find the LanPaint nodes under the "sampling" category in
|
||||
- **[VAE Encode for Inpainting](https://comfyanonymous.github.io/ComfyUI_examples/inpaint/)**
|
||||
- **[Set Latent Noise Mask](https://comfyui-wiki.com/en/tutorial/basic/how-to-inpaint-an-image-in-comfyui)**
|
||||
|
||||
## Video Examples
|
||||
## Video Examples (Beta)
|
||||
|
||||
LanPaint now supports video inpainting with Wan 2.2, enabling you to seamlessly inpaint masked regions across video frames while maintaining temporal consistency.
|
||||
|
||||
@@ -238,30 +162,6 @@ Extend your videos beyond their original boundaries with LanPaint's video outpai
|
||||
|
||||
You need to follow the ComfyUI version of [Wan2.2 T2V workflow](https://docs.comfy.org/tutorials/video/wan/wan2_2) to download and install the T2V model.
|
||||
|
||||
### MiniMax H3 Video + Audio Inpainting (AV pipeline)
|
||||
|
||||
LanPaint's AV pipeline inpaints video **and audio** together with the MiniMax H3 model. Paint both masks in one editor session, run a single sampler pass on the nested AV latent, and get back a merged video at the original fps and bit depth.
|
||||
|
||||
*Example: MiniMax H3, 864x480, 124 frames, LanPaint Sampler Custom (Advanced)*
|
||||
|
||||
| Masked Input (paint in the editor) | Mask (visible overlay) | Inpainted Result |
|
||||
|:----------------------------------:|:----------------------:|:----------------:|
|
||||
|  |  |  |
|
||||
|
||||
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_29) · [Workflow JSON](https://github.com/scraed/LanPaint/blob/master/example_workflows/MiniMax_H3_AV_EncodeDecode_Inpaint.json)
|
||||
|
||||
**How it works:**
|
||||
1. `LanPaint_VideoMaskEditor` – pick the video, open the editor, paint per-frame video masks on keyframes (SDF-interpolated in between) and drag audio intervals on the waveform. Mask = 1 regenerates, 0 keeps.
|
||||
2. `LanPaint_AVEncode` – encodes the video frames and the audio track into one nested latent with the masks attached.
|
||||
3. Run the sampler as usual (the audio stream runs on its own shifted sigma schedule).
|
||||
4. `LanPaint_AVDecode` – decodes the nested latent, merges the inpainted video with the original (mask-blended boundary) and the inpainted audio inside the masked intervals (with a short crossfade), and writes the result at the original fps and bit depth.
|
||||
|
||||
**Export masks into the video:** the editor's **Export mask video** button remuxes a new `<name>_masked.mp4` copy with the masks embedded in the file metadata (the original file is never modified). Open that file in the editor later - or share it with someone - and the masks are restored automatically.
|
||||
|
||||
Download the models from [MiniMax H3 on Hugging Face](https://huggingface.co/MiniMaxAI/MiniMax-H3) and follow the [ComfyUI MiniMax H3 docs](https://docs.comfy.org/tutorials/video/minimax/minimax_h3).
|
||||
|
||||
### Resource Consumption
|
||||
|
||||
|
||||
@@ -329,38 +229,6 @@ Download the models from [MiniMax H3 on Hugging Face](https://huggingface.co/Min
|
||||
|
||||
## Image Examples
|
||||
|
||||
### Example Anima: InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
We are excited to announce that LanPaint now supports inpainting with the Anima text-to-image model.
|
||||
|
||||
<details open>
|
||||
<summary>View Original / Masked / Inpainted Comparison</summary>
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
</details>
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_26)
|
||||
|
||||
[Model Used in This Example](https://huggingface.co/circlestone-labs/Anima)
|
||||
|
||||
### Example Ideogram4: InPaint(LanPaint Custom Sampler Advanced, 5 steps of thinking)
|
||||
We are excited to announce that LanPaint now supports inpainting with the Ideogram4 text-to-image model.
|
||||
|
||||
<details open>
|
||||
<summary>View Original / Masked / Inpainted Comparison</summary>
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
</details>
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_27)
|
||||
|
||||
[Model Used in This Example](https://huggingface.co/Comfy-Org/Ideogram-4)
|
||||
|
||||
### Example Hunyuan T2I: InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
We are excited to announce that LanPaint now supports inpainting with Hunyuan text to image generation.
|
||||
|
||||
@@ -405,40 +273,6 @@ LanPaint also supports inpainting with the Z-image text-to-image model.
|
||||
|
||||
You can download the Z-image model for ComfyUI from [Z-image](https://docs.comfy.org/zh-CN/tutorials/image/z-image/z-image-turbo).
|
||||
|
||||
### Example Z-image-base: InPaint(LanPaint K Sampler, 3 steps of thinking)
|
||||
LanPaint also supports inpainting with the Z-image-base model.
|
||||
|
||||
**Warning (stability)**: Z-image-base can easily diverge with LanPaint. Start with **small `LanPaint_StepSize`** and **fewer thinking iterations** (lower `LanPaint_NumSteps`) and increase gradually only if stable.
|
||||
|
||||
<details open>
|
||||
<summary>View Original / Masked / Inpainted Comparison</summary>
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
</details>
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_25)
|
||||
|
||||
Workflow template (JSON): [Z_image_base_Inpaint.json](https://github.com/scraed/LanPaint/blob/master/example_workflows/Z_image_base_Inpaint.json)
|
||||
|
||||
### Example Krea2: InPaint(LanPaint K Sampler, 3 steps of thinking)
|
||||
We are excited to announce that LanPaint now supports inpainting with the Krea2 Turbo text-to-image model.
|
||||
|
||||
<details open>
|
||||
<summary>View Original / Masked / Inpainted Comparison</summary>
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
</details>
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_28)
|
||||
|
||||
[Model Used in This Example](https://huggingface.co/Comfy-Org/Krea-2)
|
||||
|
||||
### Example Wan2.2: Partial InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
Sometimes we don't want to inpaint completely new content, but rather let the inpainted image reference the original image. One option to achieve this is to inpaint with an edit model like Qwen Image Edit. Another option is to perform a partial inpaint: allowing the diffusion process to start at some middle steps rather than from 0.
|
||||
|
||||
@@ -460,20 +294,6 @@ Check [Mased Qwen Edit Workflow](https://github.com/scraed/LanPaint/tree/master/
|
||||
|
||||
|
||||
|
||||
### Example Qwen Image 2.1 Image Edit: Masked InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
|
||||
Qwen-Image 2.1's image edit model now works under a LanPaint mask: write what you want changed, paint over the part it should touch, and only that part changes - everything else, transparency included, comes back exactly as it was. In this example a second picture supplies the material for the earcups, and the headband and stitching stay as they are. Workflow and images are in `examples/Example_32`; drag `InPainted_Drag_Me_to_ComfyUI.png` into ComfyUI to load it. Use your own pictures with the official [Qwen Image 2.1 Image Edit template](https://docs.comfy.org/tutorials/image/qwen/qwen-image-2-1).
|
||||
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_32) · [Workflow JSON](https://github.com/scraed/LanPaint/blob/master/example_workflows/Qwen_Image_2.1_Edit_Masked_Inpaint.json)
|
||||
|
||||
### Example Qwen Image 2.1: InPaint with Transparency(LanPaint K Sampler, 5 steps of thinking)
|
||||
|
||||
Qwen-Image 2.1 inpaints a picture's transparency along with its pixels, so the rebuilt part can come back with a new outline instead of merely new colours - here the boot's sole is replaced and the silhouette grows with it. Workflow and images are in `examples/Example_31`; drag `InPainted_Drag_Me_to_ComfyUI.png` into ComfyUI to load it.
|
||||
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_31) · [Workflow JSON](https://github.com/scraed/LanPaint/blob/master/example_workflows/Transparent_Edit_EncodeDecode_Inpaint.json)
|
||||
|
||||
### Example Qwen Image: InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
|
||||
|
||||
@@ -522,22 +342,6 @@ You need to follow the ComfyUI version of [SD 3.5 workflow](https://comfyui-wiki
|
||||
|
||||
(Note: Prompt First mode is disabled on Flux.2.Dev. As it does not use CFG guidance.)
|
||||
|
||||
### Example Flux 2 klein: InPaint(LanPaint K Sampler, 2 steps of thinking)
|
||||
|
||||
<details open>
|
||||
<summary>View Original / Masked / Inpainted Comparison</summary>
|
||||
|
||||
| Original | Masked | Inpainted |
|
||||
|:--------:|:------:|:---------:|
|
||||
|  |  |  |
|
||||
|
||||
</details>
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_24)
|
||||
|
||||
[Model Used in This Example](https://docs.comfy.org/zh-CN/tutorials/flux/flux-2-klein). If you have quality problem on Comfy 0.11 and 0.12, check [this issue](https://github.com/scraed/LanPaint/issues/80).
|
||||
|
||||
|
||||
### Example Flux: InPaint(LanPaint K Sampler, 5 steps of thinking)
|
||||
|
||||
[View Workflow & Masks](https://github.com/scraed/LanPaint/tree/master/examples/Example_7)
|
||||
@@ -658,26 +462,6 @@ Submit a PR to add your tutorial/video here, or open an [Issue](https://github.c
|
||||
[Working togather with crop&stitch](https://github.com/scraed/LanPaint/issues/46)
|
||||
|
||||
## Updates
|
||||
- 2026/09/28
|
||||
- Add Qwen-Image 2.1 image edit support: masked, instruction-driven editing (Example_32).
|
||||
- Add Qwen-Image 2.1 inpainting support with LanPaint KSampler (Example_31).
|
||||
- Inpainting a picture that carries transparency now works end to end: the 2.1 VAE is 4-in/4-out, so the alpha travels through the latent and is edited alongside the pixels. Keep the inpainting mask in its own greyscale file, since 2.1's alpha channel means image transparency.
|
||||
- `LanPaint_ImageDecode` now matches the decoded channel count to the source image, so an RGBA source comes back RGBA and an RGB source still comes back RGB.
|
||||
- 2026/08/12
|
||||
- `v2.1.0`: Significantly accelerated LanPaint using a new schedule mechanism.
|
||||
- Fix bugs for MiniMax H3 on the latest ComfyUI.
|
||||
- 2026/08/09
|
||||
- Add MiniMax H3 video + audio inpainting support (Example_29): paint per-frame video masks and audio intervals in one editor session, encode both streams into a nested AV latent, sample once, and decode back with the source fps and bit depth preserved.
|
||||
- The mask editor can export the masks into the video itself (mp4 metadata) - share a single video file and the masks travel with it.
|
||||
- 2026/06/27
|
||||
- Add Krea2 inpainting support with LanPaint KSampler (Example_28).
|
||||
- Add Ideogram4 inpainting support with LanPaint Custom Sampler Advanced (Example_27).
|
||||
- 2026/05/20
|
||||
- Add Anima inpainting and outpainting support (Example_26).
|
||||
- 2026/03/02
|
||||
- `v1.5.0`: Fixed a hidden bug that hurt performance and caused image blur (especially on `z-image-base`), and improved overall LanPaint performance on other models too.
|
||||
- 2026/01/30
|
||||
- Add Z-image-base documentation and Example_25 workflow images.
|
||||
- 2025/08/08
|
||||
- Add Qwen image support
|
||||
- 2025/06/21
|
||||
|
||||
+2
-98
@@ -10,103 +10,7 @@ __author__ = """LanPaint"""
|
||||
__email__ = "czhengac@connect.ust.hk"
|
||||
__version__ = "0.0.1"
|
||||
|
||||
|
||||
def _install_lightweight_runtime_stubs() -> None:
|
||||
"""Install lightweight stubs so tooling can import this package without ComfyUI.
|
||||
|
||||
This is used by CI tooling (e.g., comfy-org/node-diff) that imports NODE_CLASS_MAPPINGS
|
||||
in an environment where ComfyUI isn't installed.
|
||||
"""
|
||||
import sys
|
||||
import types
|
||||
|
||||
# `src/LanPaint/nodes.py` uses `torch.Tensor` in type annotations.
|
||||
try:
|
||||
import torch # noqa: F401
|
||||
except ModuleNotFoundError:
|
||||
torch_mod = types.ModuleType("torch")
|
||||
|
||||
class Tensor: # noqa: N801 (match torch naming)
|
||||
pass
|
||||
|
||||
torch_mod.Tensor = Tensor
|
||||
torch_mod.nn = types.SimpleNamespace(functional=types.SimpleNamespace())
|
||||
sys.modules["torch"] = torch_mod
|
||||
|
||||
if "comfyui_version" not in sys.modules:
|
||||
comfyui_version_mod = types.ModuleType("comfyui_version")
|
||||
comfyui_version_mod.__version__ = "0.0.0"
|
||||
sys.modules["comfyui_version"] = comfyui_version_mod
|
||||
|
||||
sys.modules.setdefault("nodes", types.ModuleType("nodes"))
|
||||
sys.modules.setdefault("latent_preview", types.ModuleType("latent_preview"))
|
||||
|
||||
if "comfy" not in sys.modules:
|
||||
comfy_mod = types.ModuleType("comfy")
|
||||
comfy_mod.__path__ = []
|
||||
|
||||
comfy_utils_mod = types.ModuleType("comfy.utils")
|
||||
|
||||
def repeat_to_batch_size(tensor, batch_size): # type: ignore[no-untyped-def]
|
||||
if getattr(tensor, "shape", ())[0] == batch_size:
|
||||
return tensor
|
||||
return tensor
|
||||
|
||||
comfy_utils_mod.repeat_to_batch_size = repeat_to_batch_size
|
||||
|
||||
comfy_samplers_mod = types.ModuleType("comfy.samplers")
|
||||
|
||||
class DummyKSAMPLER: # noqa: N801 (match ComfyUI naming)
|
||||
pass
|
||||
|
||||
class KSampler: # noqa: N801 (match ComfyUI naming)
|
||||
SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic", "kl_optimal", "AYS"]
|
||||
|
||||
comfy_samplers_mod.KSAMPLER = DummyKSAMPLER
|
||||
comfy_samplers_mod.KSampler = KSampler
|
||||
|
||||
comfy_model_base_mod = types.ModuleType("comfy.model_base")
|
||||
|
||||
class ModelType: # noqa: N801 (match ComfyUI naming)
|
||||
FLUX = "FLUX"
|
||||
FLOW = "FLOW"
|
||||
|
||||
class WAN22: # noqa: N801 (match ComfyUI naming)
|
||||
pass
|
||||
|
||||
comfy_model_base_mod.ModelType = ModelType
|
||||
comfy_model_base_mod.WAN22 = WAN22
|
||||
|
||||
comfy_mod.utils = comfy_utils_mod
|
||||
comfy_mod.samplers = comfy_samplers_mod
|
||||
comfy_mod.model_base = comfy_model_base_mod
|
||||
|
||||
sys.modules["comfy"] = comfy_mod
|
||||
sys.modules["comfy.utils"] = comfy_utils_mod
|
||||
sys.modules["comfy.samplers"] = comfy_samplers_mod
|
||||
sys.modules["comfy.model_base"] = comfy_model_base_mod
|
||||
|
||||
|
||||
try:
|
||||
from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
|
||||
from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
|
||||
except ModuleNotFoundError:
|
||||
_install_lightweight_runtime_stubs()
|
||||
from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
|
||||
from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
|
||||
from .src.LanPaint.nodes import NODE_CLASS_MAPPINGS
|
||||
from .src.LanPaint.nodes import NODE_DISPLAY_NAME_MAPPINGS
|
||||
|
||||
WEB_DIRECTORY = "./web"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Server routes — registered only when running inside ComfyUI (the ``server``
|
||||
# module and ``folder_paths`` are ComfyUI internals not available in CI/tests).
|
||||
# ---------------------------------------------------------------------------
|
||||
try:
|
||||
from server import PromptServer # noqa: F811 (re-export for convenience)
|
||||
|
||||
from .src.LanPaint.videometa import register_routes
|
||||
|
||||
register_routes(PromptServer.instance)
|
||||
except Exception:
|
||||
pass # not running inside ComfyUI — routes are not needed
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 1.6 MiB |
File diff suppressed because it is too large
Load Diff
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|
Before Width: | Height: | Size: 293 KiB |
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
Before Width: | Height: | Size: 412 KiB |
File diff suppressed because it is too large
Load Diff
Binary file not shown.
|
Before Width: | Height: | Size: 280 KiB |
@@ -1,671 +0,0 @@
|
||||
{
|
||||
"id": "978d3a45-3d13-43c6-8ef9-89dc3e74d6ba",
|
||||
"revision": 0,
|
||||
"last_node_id": 84,
|
||||
"last_link_id": 229,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 78,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
314.8565368652344,
|
||||
255.63235473632812
|
||||
],
|
||||
"size": [
|
||||
422.84503173828125,
|
||||
164.31304931640625
|
||||
],
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"label": "clip",
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 194
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"label": "CONDITIONING",
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
195
|
||||
]
|
||||
}
|
||||
],
|
||||
"title": "CLIP Text Encode (Positive Prompt)",
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.3.26",
|
||||
"Node name for S&R": "CLIPTextEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
"cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open placing a fancy black forest cake with candles on top of a dinner table of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere there are paintings on the walls"
|
||||
],
|
||||
"color": "#232",
|
||||
"bgcolor": "#353"
|
||||
},
|
||||
{
|
||||
"id": 80,
|
||||
"type": "CLIPTextEncode",
|
||||
"pos": [
|
||||
362.7684020996094,
|
||||
481.0662536621094
|
||||
],
|
||||
"size": [
|
||||
422.84503173828125,
|
||||
164.31304931640625
|
||||
],
|
||||
"flags": {
|
||||
"collapsed": true
|
||||
},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"label": "clip",
|
||||
"name": "clip",
|
||||
"type": "CLIP",
|
||||
"link": 196
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"label": "CONDITIONING",
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
210
|
||||
]
|
||||
}
|
||||
],
|
||||
"title": "CLIP Text Encode (Negative Prompt)",
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.3.26",
|
||||
"Node name for S&R": "CLIPTextEncode"
|
||||
},
|
||||
"widgets_values": [
|
||||
""
|
||||
],
|
||||
"color": "#322",
|
||||
"bgcolor": "#533"
|
||||
},
|
||||
{
|
||||
"id": 79,
|
||||
"type": "FluxGuidance",
|
||||
"pos": [
|
||||
529.1380615234375,
|
||||
156.20236206054688
|
||||
],
|
||||
"size": [
|
||||
211.60000610351562,
|
||||
58
|
||||
],
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"label": "conditioning",
|
||||
"name": "conditioning",
|
||||
"type": "CONDITIONING",
|
||||
"link": 195
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"label": "CONDITIONING",
|
||||
"name": "CONDITIONING",
|
||||
"type": "CONDITIONING",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
207
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.3.26",
|
||||
"Node name for S&R": "FluxGuidance"
|
||||
},
|
||||
"widgets_values": [
|
||||
3.5
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 77,
|
||||
"type": "CheckpointLoaderSimple",
|
||||
"pos": [
|
||||
-66.25785064697266,
|
||||
484.081787109375
|
||||
],
|
||||
"size": [
|
||||
315,
|
||||
98
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"label": "MODEL",
|
||||
"name": "MODEL",
|
||||
"type": "MODEL",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
197
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "CLIP",
|
||||
"name": "CLIP",
|
||||
"type": "CLIP",
|
||||
"slot_index": 1,
|
||||
"links": [
|
||||
194,
|
||||
196
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "VAE",
|
||||
"name": "VAE",
|
||||
"type": "VAE",
|
||||
"slot_index": 2,
|
||||
"links": [
|
||||
222,
|
||||
226
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.3.26",
|
||||
"Node name for S&R": "CheckpointLoaderSimple"
|
||||
},
|
||||
"widgets_values": [
|
||||
"flux1-dev-fp8.safetensors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 73,
|
||||
"type": "LanPaint_KSampler",
|
||||
"pos": [
|
||||
1005.0399780273438,
|
||||
296.9553527832031
|
||||
],
|
||||
"size": [
|
||||
413.6495666503906,
|
||||
596
|
||||
],
|
||||
"flags": {},
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "model",
|
||||
"type": "MODEL",
|
||||
"link": 197
|
||||
},
|
||||
{
|
||||
"name": "positive",
|
||||
"type": "CONDITIONING",
|
||||
"link": 207
|
||||
},
|
||||
{
|
||||
"name": "negative",
|
||||
"type": "CONDITIONING",
|
||||
"link": 210
|
||||
},
|
||||
{
|
||||
"name": "latent_image",
|
||||
"type": "LATENT",
|
||||
"link": 224
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "LATENT",
|
||||
"type": "LATENT",
|
||||
"slot_index": 0,
|
||||
"links": [
|
||||
225
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "LanPaint",
|
||||
"ver": "56bd6c04e89124cd06682b304245d6ddf8b20522",
|
||||
"Node name for S&R": "LanPaint_KSampler"
|
||||
},
|
||||
"widgets_values": [
|
||||
0,
|
||||
"fixed",
|
||||
30,
|
||||
1,
|
||||
"euler",
|
||||
"simple",
|
||||
1,
|
||||
5,
|
||||
"Image First",
|
||||
"LanPaint KSampler. Recommend steps 50, LanPaint NumSteps 1-20 depending on the difficulty of task. LanPaint_EndSigma = 3.0 for anime style, 0.6 for realistic style. For more information, visit https://github.com/scraed/LanPaint",
|
||||
"\ud83d\uddbc\ufe0f Image Inpainting"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 48,
|
||||
"type": "SaveImage",
|
||||
"pos": [
|
||||
1075.49169921875,
|
||||
1167.2703857421875
|
||||
],
|
||||
"size": [
|
||||
311.2532653808594,
|
||||
484.7096252441406
|
||||
],
|
||||
"flags": {},
|
||||
"order": 9,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 229
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.3.23"
|
||||
},
|
||||
"widgets_values": [
|
||||
"ComfyUI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 75,
|
||||
"type": "LoadImage",
|
||||
"pos": [
|
||||
45.954593658447266,
|
||||
1150.45556640625
|
||||
],
|
||||
"size": [
|
||||
266.13720703125,
|
||||
487.1314697265625
|
||||
],
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
221,
|
||||
227
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": [
|
||||
221,
|
||||
227
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.3.27",
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"Example_7_Masked.png",
|
||||
"image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 83,
|
||||
"type": "LanPaint_ImageEncode",
|
||||
"pos": [
|
||||
635.0,
|
||||
317.0
|
||||
],
|
||||
"size": [
|
||||
195.3150390625,
|
||||
66
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 221
|
||||
},
|
||||
{
|
||||
"localized_name": "vae",
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": 222
|
||||
},
|
||||
{
|
||||
"localized_name": "mask",
|
||||
"name": "mask",
|
||||
"shape": 7,
|
||||
"type": "MASK",
|
||||
"link": 223
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "latent",
|
||||
"name": "latent",
|
||||
"type": "LATENT",
|
||||
"links": [
|
||||
224
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LanPaint_ImageEncode"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 84,
|
||||
"type": "LanPaint_ImageDecode",
|
||||
"pos": [
|
||||
1355.0,
|
||||
317.0
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
118
|
||||
],
|
||||
"flags": {},
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "samples",
|
||||
"name": "samples",
|
||||
"type": "LATENT",
|
||||
"link": 225
|
||||
},
|
||||
{
|
||||
"localized_name": "vae",
|
||||
"name": "vae",
|
||||
"type": "VAE",
|
||||
"link": 226
|
||||
},
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"shape": 7,
|
||||
"type": "IMAGE",
|
||||
"link": 227
|
||||
},
|
||||
{
|
||||
"localized_name": "mask",
|
||||
"name": "mask",
|
||||
"shape": 7,
|
||||
"type": "MASK",
|
||||
"link": 228
|
||||
},
|
||||
{
|
||||
"localized_name": "blend_overlap",
|
||||
"name": "blend_overlap",
|
||||
"shape": 7,
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "blend_overlap"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
229
|
||||
]
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LanPaint_ImageDecode"
|
||||
},
|
||||
"widgets_values": [
|
||||
9
|
||||
]
|
||||
}
|
||||
],
|
||||
"links": [
|
||||
[
|
||||
194,
|
||||
77,
|
||||
1,
|
||||
78,
|
||||
0,
|
||||
"CLIP"
|
||||
],
|
||||
[
|
||||
195,
|
||||
78,
|
||||
0,
|
||||
79,
|
||||
0,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
196,
|
||||
77,
|
||||
1,
|
||||
80,
|
||||
0,
|
||||
"CLIP"
|
||||
],
|
||||
[
|
||||
197,
|
||||
77,
|
||||
0,
|
||||
73,
|
||||
0,
|
||||
"MODEL"
|
||||
],
|
||||
[
|
||||
207,
|
||||
79,
|
||||
0,
|
||||
73,
|
||||
1,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
210,
|
||||
80,
|
||||
0,
|
||||
73,
|
||||
2,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
221,
|
||||
75,
|
||||
0,
|
||||
83,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
222,
|
||||
77,
|
||||
2,
|
||||
83,
|
||||
1,
|
||||
"VAE"
|
||||
],
|
||||
[
|
||||
223,
|
||||
75,
|
||||
1,
|
||||
83,
|
||||
2,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
224,
|
||||
83,
|
||||
0,
|
||||
73,
|
||||
3,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
225,
|
||||
73,
|
||||
0,
|
||||
84,
|
||||
0,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
226,
|
||||
77,
|
||||
2,
|
||||
84,
|
||||
1,
|
||||
"VAE"
|
||||
],
|
||||
[
|
||||
227,
|
||||
75,
|
||||
0,
|
||||
84,
|
||||
2,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
228,
|
||||
75,
|
||||
1,
|
||||
84,
|
||||
3,
|
||||
"MASK"
|
||||
],
|
||||
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"## Report workflow issue\n\nIf you found any issues when running this workflow, [report template issue here](https://github.com/Comfy-Org/workflow_templates/issues)\n\n\n## Model links\n\n**text_encoders**\n\n- [qwen_3_4b.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/text_encoders/qwen_3_4b.safetensors)\n\n**loras**\n\n- [pixel_art_style_z_image_turbo.safetensors](https://huggingface.co/tarn59/pixel_art_style_lora_z_image_turbo/resolve/main/pixel_art_style_z_image_turbo.safetensors)\n\n**diffusion_models**\n\n- [z_image_turbo_bf16.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/diffusion_models/z_image_turbo_bf16.safetensors)\n\n**vae**\n\n- [ae.safetensors](https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors)\n\n\nModel Storage Location\n\n```\n\ud83d\udcc2 ComfyUI/\n\u251c\u2500\u2500 \ud83d\udcc2 models/\n\u2502 \u251c\u2500\u2500 \ud83d\udcc2 text_encoders/\n\u2502 \u2502 \u2514\u2500\u2500 qwen_3_4b.safetensors\n\u2502 \u251c\u2500\u2500 \ud83d\udcc2 loras/\n\u2502 \u2502 \u2514\u2500\u2500 pixel_art_style_z_image_turbo.safetensors\n\u2502 \u251c\u2500\u2500 \ud83d\udcc2 diffusion_models/\n\u2502 \u2502 \u2514\u2500\u2500 z_image_turbo_bf16.safetensors\n\u2502 \u2514\u2500\u2500 \ud83d\udcc2 vae/\n\u2502 \u2514\u2500\u2500 ae.safetensors\n```\n"
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"CLIP"
|
||||
],
|
||||
[
|
||||
54,
|
||||
46,
|
||||
0,
|
||||
48,
|
||||
0,
|
||||
"MODEL"
|
||||
],
|
||||
[
|
||||
60,
|
||||
48,
|
||||
0,
|
||||
47,
|
||||
0,
|
||||
"MODEL"
|
||||
],
|
||||
[
|
||||
62,
|
||||
47,
|
||||
0,
|
||||
56,
|
||||
0,
|
||||
"MODEL"
|
||||
],
|
||||
[
|
||||
63,
|
||||
45,
|
||||
0,
|
||||
56,
|
||||
1,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
64,
|
||||
42,
|
||||
0,
|
||||
56,
|
||||
2,
|
||||
"CONDITIONING"
|
||||
],
|
||||
[
|
||||
96,
|
||||
57,
|
||||
0,
|
||||
74,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
97,
|
||||
40,
|
||||
0,
|
||||
74,
|
||||
1,
|
||||
"VAE"
|
||||
],
|
||||
[
|
||||
98,
|
||||
57,
|
||||
1,
|
||||
74,
|
||||
2,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
99,
|
||||
74,
|
||||
0,
|
||||
56,
|
||||
3,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
100,
|
||||
56,
|
||||
0,
|
||||
75,
|
||||
0,
|
||||
"LATENT"
|
||||
],
|
||||
[
|
||||
101,
|
||||
40,
|
||||
0,
|
||||
75,
|
||||
1,
|
||||
"VAE"
|
||||
],
|
||||
[
|
||||
102,
|
||||
57,
|
||||
0,
|
||||
75,
|
||||
2,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
103,
|
||||
57,
|
||||
1,
|
||||
75,
|
||||
3,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
104,
|
||||
75,
|
||||
0,
|
||||
61,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
105,
|
||||
75,
|
||||
0,
|
||||
73,
|
||||
0,
|
||||
"IMAGE"
|
||||
]
|
||||
],
|
||||
"groups": [
|
||||
{
|
||||
"id": 2,
|
||||
"title": "Step2 - Image size",
|
||||
"bounding": [
|
||||
146,
|
||||
770,
|
||||
348,
|
||||
240
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
"flags": {}
|
||||
},
|
||||
{
|
||||
"id": 3,
|
||||
"title": "Step3 - Prompt",
|
||||
"bounding": [
|
||||
518,
|
||||
254,
|
||||
540,
|
||||
648
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
"flags": {}
|
||||
},
|
||||
{
|
||||
"id": 4,
|
||||
"title": "Step1 - Load models",
|
||||
"bounding": [
|
||||
146,
|
||||
254,
|
||||
348,
|
||||
496.32
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
"flags": {}
|
||||
},
|
||||
{
|
||||
"id": 5,
|
||||
"title": "Ctrl-B to enable LoRA input",
|
||||
"bounding": [
|
||||
518,
|
||||
50,
|
||||
528,
|
||||
192
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
"flags": {}
|
||||
}
|
||||
],
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 0.3800835362432756,
|
||||
"offset": [
|
||||
-902.897494105397,
|
||||
-396.4805064253962
|
||||
]
|
||||
},
|
||||
"frontendVersion": "1.33.14",
|
||||
"VHS_latentpreview": false,
|
||||
"VHS_latentpreviewrate": 0,
|
||||
"VHS_MetadataImage": true,
|
||||
"VHS_KeepIntermediate": true,
|
||||
"workflowRendererVersion": "Vue"
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
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+1
-4
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "LanPaint"
|
||||
version = "2.1.0"
|
||||
version = "1.4.9"
|
||||
description = "Achieve seamless inpainting results without needing a specialized inpainting model."
|
||||
authors = [
|
||||
{name = "LanPaint", email = "czhengac@connect.ust.hk"}
|
||||
@@ -75,8 +75,5 @@ select = [
|
||||
# See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
|
||||
]
|
||||
|
||||
[tool.ruff.lint.per-file-ignores]
|
||||
"src/LanPaint/nodes.py" = ["F403", "F405"]
|
||||
|
||||
[tool.ruff.lint.flake8-quotes]
|
||||
inline-quotes = "double"
|
||||
|
||||
@@ -1,337 +0,0 @@
|
||||
"""
|
||||
Early Stop Logic Contributed by `https://github.com/godnight10061`.
|
||||
"""
|
||||
|
||||
import inspect
|
||||
from typing import Any, Callable, Optional
|
||||
|
||||
import torch
|
||||
|
||||
from .types import LangevinState
|
||||
|
||||
|
||||
def _clamp01(val: float) -> float:
|
||||
if val <= 0.0:
|
||||
return 0.0
|
||||
if val >= 1.0:
|
||||
return 1.0
|
||||
return val
|
||||
|
||||
|
||||
def _abt_scale(abt_val: float) -> float:
|
||||
"""
|
||||
Smooth, parameter-free scale based on outer-step noise level.
|
||||
|
||||
- 0 at abt=0/1 (disable at extreme noise / extreme tail)
|
||||
- 1 at abt=0.5 (mid-schedule)
|
||||
"""
|
||||
abt_val = _clamp01(abt_val)
|
||||
return _clamp01(4.0 * abt_val * (1.0 - abt_val))
|
||||
|
||||
|
||||
def _boundary_weight(latent_mask: torch.Tensor, inpaint_weight: torch.Tensor) -> Optional[torch.Tensor]:
|
||||
"""
|
||||
Return a 4-neighbor boundary weight: unknown pixels adjacent to known pixels.
|
||||
|
||||
This replaces the previous dilation-based "ring" (kernel/padding) and has no tunable hyperparameters.
|
||||
"""
|
||||
if latent_mask.dim() != 4:
|
||||
return None
|
||||
|
||||
known = latent_mask > 0.5
|
||||
neighbor_known = torch.zeros_like(known)
|
||||
neighbor_known[:, :, 1:, :] |= known[:, :, :-1, :]
|
||||
neighbor_known[:, :, :-1, :] |= known[:, :, 1:, :]
|
||||
neighbor_known[:, :, :, 1:] |= known[:, :, :, :-1]
|
||||
neighbor_known[:, :, :, :-1] |= known[:, :, :, 1:]
|
||||
|
||||
boundary = (~known) & neighbor_known
|
||||
return boundary.to(dtype=torch.float32) * inpaint_weight
|
||||
|
||||
|
||||
def _weighted_mse(t1: torch.Tensor, t2: torch.Tensor, weight: torch.Tensor) -> float:
|
||||
diff_sq = (t1.to(dtype=torch.float32) - t2.to(dtype=torch.float32)) ** 2
|
||||
denom = torch.sum(weight) + 1e-12
|
||||
return float((torch.sum(diff_sq * weight) / denom).item())
|
||||
|
||||
|
||||
class LanPaintEarlyStopper:
|
||||
"""
|
||||
Per-step early-stop logic for LanPaint inner (Langevin) iterations.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def from_options(
|
||||
cls,
|
||||
*,
|
||||
model_options: Optional[dict],
|
||||
latent_mask: torch.Tensor,
|
||||
abt: torch.Tensor,
|
||||
default_threshold: float,
|
||||
default_patience: int,
|
||||
default_distance_fn: Optional[Callable[..., Any]],
|
||||
) -> Optional["LanPaintEarlyStopper"]:
|
||||
semantic_stop = model_options.get("lanpaint_semantic_stop") if isinstance(model_options, dict) else None
|
||||
|
||||
threshold = float(default_threshold)
|
||||
patience = int(default_patience)
|
||||
distance_fn = default_distance_fn
|
||||
# distance_fn contract: return None (use default metric) or a scalar (Python number / 0-d (1-element) torch.Tensor)
|
||||
|
||||
if isinstance(semantic_stop, dict):
|
||||
threshold = float(semantic_stop.get("threshold", threshold))
|
||||
patience = int(semantic_stop.get("patience", patience))
|
||||
distance_fn = semantic_stop.get("distance_fn", distance_fn)
|
||||
|
||||
# Backward compatibility: map legacy 'min_steps' to a patience floor so it is not an independent knob.
|
||||
if patience > 0:
|
||||
min_steps = semantic_stop.get("min_steps")
|
||||
if min_steps is not None:
|
||||
try:
|
||||
min_steps_int = int(min_steps)
|
||||
except (TypeError, ValueError):
|
||||
min_steps_int = 0
|
||||
if min_steps_int > 1:
|
||||
patience = max(patience, min_steps_int - 1)
|
||||
|
||||
enabled_early_stop = (threshold > 0.0) and (patience > 0)
|
||||
# Require N+1 consecutive stable checks:
|
||||
# - the first stable step sets patience_counter to 1
|
||||
# - `patience=1` therefore stops after 2 stable steps
|
||||
patience_eff = max(1, patience) + 1
|
||||
threshold_eff = threshold
|
||||
inpaint_weight = ring_weight = trace = abt_val = None
|
||||
|
||||
if enabled_early_stop:
|
||||
try:
|
||||
abt_val = float(torch.mean(abt).item())
|
||||
except (TypeError, ValueError):
|
||||
abt_val = 0.0
|
||||
|
||||
threshold_eff = threshold * _abt_scale(abt_val)
|
||||
if threshold_eff <= 0.0:
|
||||
enabled_early_stop = False
|
||||
else:
|
||||
inpaint_weight = (1 - latent_mask).to(dtype=torch.float32)
|
||||
if float(torch.sum(inpaint_weight).item()) < 1e-6:
|
||||
enabled_early_stop = False
|
||||
else:
|
||||
ring_weight = _boundary_weight(latent_mask, inpaint_weight)
|
||||
if isinstance(model_options, dict):
|
||||
trace = model_options.get("lanpaint_semantic_trace")
|
||||
|
||||
if not enabled_early_stop:
|
||||
return None
|
||||
|
||||
# Pre-fetch trace keys to avoid repeated dict lookups
|
||||
bench_case_id = bench_outer_step = bench_timestep = None
|
||||
if isinstance(trace, list) and isinstance(model_options, dict):
|
||||
bench_case_id = model_options.get("bench_case_id")
|
||||
bench_outer_step = model_options.get("bench_outer_step")
|
||||
bench_timestep = model_options.get("bench_timestep")
|
||||
|
||||
return cls(
|
||||
enabled=enabled_early_stop,
|
||||
threshold=threshold,
|
||||
threshold_eff=threshold_eff,
|
||||
patience_eff=patience_eff,
|
||||
inpaint_weight=inpaint_weight,
|
||||
ring_weight=ring_weight,
|
||||
distance_fn=distance_fn,
|
||||
trace=trace,
|
||||
bench_case_id=bench_case_id,
|
||||
bench_outer_step=bench_outer_step,
|
||||
bench_timestep=bench_timestep,
|
||||
abt_val=abt_val,
|
||||
)
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
enabled: bool,
|
||||
threshold: float,
|
||||
threshold_eff: float,
|
||||
patience_eff: int,
|
||||
inpaint_weight: Optional[torch.Tensor],
|
||||
ring_weight: Optional[torch.Tensor],
|
||||
distance_fn: Optional[Callable[..., Any]] = None,
|
||||
trace: Optional[list] = None,
|
||||
bench_case_id: Any = None,
|
||||
bench_outer_step: Any = None,
|
||||
bench_timestep: Any = None,
|
||||
abt_val: Optional[float] = None,
|
||||
) -> None:
|
||||
self.enabled = bool(enabled)
|
||||
self.threshold = float(threshold)
|
||||
self.threshold_eff = float(threshold_eff)
|
||||
self.patience_eff = int(patience_eff)
|
||||
|
||||
self.inpaint_weight = inpaint_weight
|
||||
self.ring_weight = ring_weight
|
||||
|
||||
self.trace = trace
|
||||
self.bench_case_id = bench_case_id
|
||||
self.bench_outer_step = bench_outer_step
|
||||
self.bench_timestep = bench_timestep
|
||||
self.abt_val = abt_val
|
||||
|
||||
self.patience_counter = 0
|
||||
self.x0_anchor = None
|
||||
|
||||
self._dist_wrapper = self._wrap_distance_fn(distance_fn) if self.enabled else None
|
||||
|
||||
@property
|
||||
def has_custom_distance_fn(self) -> bool:
|
||||
return self._dist_wrapper is not None
|
||||
|
||||
@staticmethod
|
||||
def _wrap_distance_fn(distance_fn: Optional[Callable[..., Any]]):
|
||||
"""
|
||||
Wrap a user-provided `distance_fn` into a normalized callable: fn(prev, cur, ctx) -> dist|None.
|
||||
|
||||
Supported signatures:
|
||||
- 3+ positional (or *args): `distance_fn(prev, cur, ctx)`
|
||||
- explicit / **kwargs ctx: `distance_fn(prev, cur, ctx=ctx)`
|
||||
- default 2-arg: `distance_fn(cur, prev)`
|
||||
|
||||
Return contract: None (use default metric) or a scalar (Python number / 0-d (1-element) torch.Tensor).
|
||||
"""
|
||||
if not callable(distance_fn):
|
||||
return None
|
||||
|
||||
try:
|
||||
sig = inspect.signature(distance_fn)
|
||||
params = list(sig.parameters.values())
|
||||
|
||||
has_ctx_param = "ctx" in sig.parameters
|
||||
has_var_kw = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in params)
|
||||
has_var_pos = any(p.kind == inspect.Parameter.VAR_POSITIONAL for p in params)
|
||||
|
||||
pos_params = [
|
||||
p
|
||||
for p in params
|
||||
if p.kind in (inspect.Parameter.POSITIONAL_ONLY, inspect.Parameter.POSITIONAL_OR_KEYWORD)
|
||||
]
|
||||
|
||||
if len(pos_params) >= 3 or has_var_pos:
|
||||
# 3-arg positional: fn(prev, cur, ctx)
|
||||
return lambda p, c, ctx: distance_fn(p, c, ctx)
|
||||
if has_ctx_param or has_var_kw:
|
||||
# keyword ctx: fn(prev, cur, ctx=ctx)
|
||||
return lambda p, c, ctx: distance_fn(p, c, ctx=ctx)
|
||||
|
||||
# Default 2-arg: fn(cur, prev)
|
||||
return lambda p, c, ctx: distance_fn(c, p)
|
||||
except (ValueError, TypeError):
|
||||
# Fallback for built-ins or complex callables.
|
||||
def fallback_wrapper(p, c, ctx):
|
||||
try:
|
||||
return distance_fn(p, c, ctx)
|
||||
except TypeError as e:
|
||||
tb = e.__traceback__
|
||||
if tb is not None and tb.tb_frame.f_code is not fallback_wrapper.__code__:
|
||||
raise
|
||||
return distance_fn(c, p)
|
||||
|
||||
return fallback_wrapper
|
||||
|
||||
def step(
|
||||
self,
|
||||
*,
|
||||
i: int,
|
||||
n_steps: int,
|
||||
x_t_before: torch.Tensor,
|
||||
x_t_after: torch.Tensor,
|
||||
x_t_prev_for_custom: Optional[torch.Tensor],
|
||||
prev_args: Any,
|
||||
args: Any,
|
||||
ctx: dict,
|
||||
) -> bool:
|
||||
if not self.enabled:
|
||||
return False
|
||||
|
||||
# 'inpaint_weight' is guaranteed to be set when enabled is True in the caller.
|
||||
inpaint = self.inpaint_weight
|
||||
if inpaint is None:
|
||||
return False
|
||||
|
||||
dist = None
|
||||
custom_dist = False
|
||||
dist_inpaint = dist_ring = dist_drift = x0_prev = x0_cur = None
|
||||
|
||||
if self._dist_wrapper is not None:
|
||||
dist = self._dist_wrapper(x_t_prev_for_custom, x_t_after, ctx)
|
||||
if dist is not None:
|
||||
if isinstance(dist, torch.Tensor):
|
||||
if dist.numel() != 1:
|
||||
raise TypeError("distance_fn must return None or a scalar / 0-d (1-element) tensor")
|
||||
dist = float(dist.item())
|
||||
else:
|
||||
dist = float(dist)
|
||||
custom_dist = dist is not None
|
||||
|
||||
if dist is None:
|
||||
def _get_x0(arg: Any) -> Optional[torch.Tensor]:
|
||||
if isinstance(arg, LangevinState):
|
||||
return arg.x0
|
||||
if isinstance(arg, tuple) and len(arg) >= 3:
|
||||
return arg[2]
|
||||
return None
|
||||
|
||||
x0_prev = _get_x0(prev_args)
|
||||
x0_cur = _get_x0(args)
|
||||
|
||||
if x0_prev is not None and x0_cur is not None:
|
||||
dist_inpaint = _weighted_mse(x0_cur, x0_prev, inpaint)
|
||||
dist_ring = _weighted_mse(x0_cur, x0_prev, self.ring_weight) if self.ring_weight is not None else None
|
||||
dist = dist_inpaint if dist_ring is None else max(dist_inpaint, dist_ring)
|
||||
else:
|
||||
dist_inpaint = _weighted_mse(x_t_after, x_t_before, inpaint)
|
||||
dist = dist_inpaint
|
||||
|
||||
threshold_used = self.threshold if custom_dist else self.threshold_eff
|
||||
|
||||
# Drift guard (only for default metric with x0_cur).
|
||||
if x0_cur is not None and not custom_dist:
|
||||
if dist <= threshold_used:
|
||||
if self.x0_anchor is None:
|
||||
self.x0_anchor = x0_cur.detach()
|
||||
else:
|
||||
drift_inpaint = _weighted_mse(x0_cur, self.x0_anchor, inpaint)
|
||||
drift_ring = _weighted_mse(x0_cur, self.x0_anchor, self.ring_weight) if self.ring_weight is not None else None
|
||||
dist_drift = drift_inpaint if drift_ring is None else max(drift_inpaint, drift_ring)
|
||||
dist = max(dist, dist_drift)
|
||||
else:
|
||||
self.x0_anchor = None
|
||||
|
||||
if dist <= threshold_used:
|
||||
self.patience_counter += 1
|
||||
else:
|
||||
self.patience_counter = 0
|
||||
self.x0_anchor = None
|
||||
|
||||
should_stop = self.patience_counter >= self.patience_eff
|
||||
|
||||
if isinstance(self.trace, list):
|
||||
self.trace.append(
|
||||
{
|
||||
"case_id": self.bench_case_id,
|
||||
"outer_step": self.bench_outer_step,
|
||||
"bench_timestep": self.bench_timestep,
|
||||
"inner_step": i + 1,
|
||||
"dist": dist,
|
||||
"dist_inpaint": None if dist_inpaint is None else float(dist_inpaint),
|
||||
"dist_ring": None if dist_ring is None else float(dist_ring),
|
||||
"dist_drift": None if dist_drift is None else float(dist_drift),
|
||||
"threshold": float(threshold_used),
|
||||
"threshold_eff": float(self.threshold_eff),
|
||||
"patience_counter": int(self.patience_counter),
|
||||
"patience_eff": int(self.patience_eff),
|
||||
"abt": None if self.abt_val is None else float(self.abt_val),
|
||||
"custom_dist": bool(custom_dist),
|
||||
"stopped": bool(should_stop),
|
||||
}
|
||||
)
|
||||
|
||||
return bool(should_stop)
|
||||
|
||||
+49
-196
@@ -1,11 +1,9 @@
|
||||
import torch
|
||||
# from .utils import StochasticHarmonicOscillator # second-order scheme, not used
|
||||
from .utils import *
|
||||
from functools import partial
|
||||
from .earlystop import LanPaintEarlyStopper
|
||||
from .types import LangevinState
|
||||
|
||||
class LanPaint():
|
||||
def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False, EarlyStopThreshold = 0.0, EarlyStopPatience = 1, EarlyStopHook = None, MinStepFrac = 0.0):
|
||||
def __init__(self, Model, NSteps, Friction, Lambda, Beta, StepSize, IS_FLUX = False, IS_FLOW = False):
|
||||
self.n_steps = NSteps
|
||||
self.chara_lamb = Lambda
|
||||
self.IS_FLUX = IS_FLUX
|
||||
@@ -14,84 +12,39 @@ class LanPaint():
|
||||
self.inner_model = Model
|
||||
self.friction = Friction
|
||||
self.chara_beta = Beta
|
||||
self.min_step_frac = MinStepFrac
|
||||
self.img_dim_size = None
|
||||
self.early_stop_threshold = EarlyStopThreshold
|
||||
self.early_stop_patience = EarlyStopPatience
|
||||
self.early_stop_hook = EarlyStopHook
|
||||
|
||||
def add_none_dims(self, array):
|
||||
# Broadcast to the latent's dimensionality. Identical to the tuple-index
|
||||
# form for scalar/[B] inputs; per-row (already broadcast) tensors pass
|
||||
# through unchanged.
|
||||
while array.ndim < self.img_dim_size:
|
||||
array = array.unsqueeze(array.ndim)
|
||||
return array
|
||||
# Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
|
||||
index = (slice(None),) + (None,) * (self.img_dim_size-1)
|
||||
return array[index]
|
||||
def remove_none_dims(self, array):
|
||||
# Create a tuple with ':' for the first dimension and 'None' repeated num_nones times
|
||||
index = (slice(None),) + (0,) * (self.img_dim_size-1)
|
||||
return array[index]
|
||||
def unpack_model_output(self, output):
|
||||
# Some guider/model wrappers return one denoised latent, others return
|
||||
# both the normal and BIG-guidance denoised latents.
|
||||
if isinstance(output, (tuple, list)):
|
||||
if len(output) >= 2:
|
||||
return output[0], output[1]
|
||||
if len(output) == 1:
|
||||
return output[0], output[0]
|
||||
raise ValueError("Model output is empty")
|
||||
return output, output
|
||||
def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None, current_times_audio=None, audio_indicator=None, audio_correction=None):
|
||||
def __call__(self, x, latent_image, noise, sigma, latent_mask, current_times, model_options, seed, n_steps=None):
|
||||
self.img_dim_size = len(x.shape)
|
||||
self.latent_image = latent_image
|
||||
self.noise = noise
|
||||
self.audio_indicator = audio_indicator
|
||||
self.current_times_audio = current_times_audio
|
||||
self.audio_correction = audio_correction
|
||||
if torch.mean(torch.abs(self.noise)) < 1e-8:
|
||||
self.noise = torch.randn_like(self.noise)
|
||||
if n_steps is None:
|
||||
n_steps = self.n_steps
|
||||
return self.LanPaint(x, sigma, latent_mask, current_times, n_steps, model_options, seed, self.IS_FLUX, self.IS_FLOW)
|
||||
def LanPaint(self, x, sigma, latent_mask, current_times, n_steps, model_options, seed, IS_FLUX, IS_FLOW):
|
||||
input_x = x
|
||||
VE_Sigma, abt, Flow_t = current_times
|
||||
|
||||
# MiniMax H3 AV packs: the audio rows of the flat pack run on their own
|
||||
# shifted sigma schedule (sigma_audio = time_shift_sigma(sigma_video,
|
||||
# shift_v, shift_a)). Blend the per-stream times so every downstream
|
||||
# consumer (x_t conversions, score, dynamics coefficients, replace
|
||||
# step) uses the audio schedule on the audio rows and the video
|
||||
# schedule elsewhere. Flow_t stays the video timestep -- the DiT
|
||||
# derives the audio schedule from it internally.
|
||||
replace_sigma = sigma
|
||||
if self.audio_indicator is not None and self.current_times_audio is not None:
|
||||
VE_a, abt_a, Flow_a = self.current_times_audio
|
||||
ai = self.audio_indicator
|
||||
VE_Sigma = VE_Sigma * (1 - ai) + VE_a * ai
|
||||
abt = abt * (1 - ai) + abt_a * ai
|
||||
replace_sigma = sigma * (1 - ai) + Flow_a * ai
|
||||
current_times = (VE_Sigma, abt, Flow_t)
|
||||
|
||||
# Above MinStepFrac the step size scales with the remaining noise
|
||||
# fraction (1 - abt); below it the step size is pinned at
|
||||
# StepSize*MinStepFrac and the inner-step count ramps down instead
|
||||
# (see KSamplerX0Inpaint.__call__). 0.0 disables the pin (the step
|
||||
# size keeps shrinking to zero as before).
|
||||
step_size = self.step_size * (1 - abt).clamp(min=self.min_step_frac)
|
||||
|
||||
step_size = self.step_size * (1 - abt)
|
||||
step_size = self.add_none_dims(step_size)
|
||||
# self.inner_model.inner_model.scale_latent_inpaint returns variance exploding x_t values
|
||||
# This is the replace step
|
||||
def scale_latent_inpaint(x, sigma, noise, latent_image):
|
||||
s = self.add_none_dims(sigma)
|
||||
if s.numel() == 1:
|
||||
return self.inner_model.inner_model.model_sampling.noise_scaling(s, noise, latent_image)
|
||||
# per-row (audio) sigma: model_sampling.noise_scaling requires a
|
||||
# scalar sigma, so emulate its flow form elementwise
|
||||
ns = getattr(self.inner_model.inner_model.model_sampling, "noise_scale", 1.0)
|
||||
return s * (ns * noise) + (1.0 - s) * latent_image
|
||||
return self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
|
||||
|
||||
x = x * (1 - latent_mask) + scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
|
||||
|
||||
|
||||
x = x * (1 - latent_mask) + scale_latent_inpaint(x=x, sigma=replace_sigma, noise=self.noise, latent_image=self.latent_image)* latent_mask
|
||||
|
||||
if IS_FLUX or IS_FLOW:
|
||||
x_t = x * ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
|
||||
@@ -101,86 +54,32 @@ class LanPaint():
|
||||
############ LanPaint Iterations Start ###############
|
||||
# after noise_scaling, noise = latent_image + noise * sigma, which is x_t in the variance exploding diffusion model notation for the known region.
|
||||
args = None
|
||||
stopper = LanPaintEarlyStopper.from_options(
|
||||
model_options=model_options if isinstance(model_options, dict) else None,
|
||||
latent_mask=latent_mask,
|
||||
abt=abt,
|
||||
default_threshold=self.early_stop_threshold,
|
||||
default_patience=self.early_stop_patience,
|
||||
default_distance_fn=self.early_stop_hook,
|
||||
)
|
||||
|
||||
for i in range(n_steps):
|
||||
score_func = partial( self.score_model, y = self.latent_image, mask = latent_mask, abt = self.add_none_dims(abt), sigma = self.add_none_dims(VE_Sigma), tflow = self.add_none_dims(Flow_t), model_options = model_options, seed = seed )
|
||||
|
||||
prev_args = args
|
||||
x_t_prev = x_t.detach() if (stopper is not None and stopper.has_custom_distance_fn) else None
|
||||
x_t_before = x_t if (stopper is not None and stopper.enabled) else None
|
||||
|
||||
x_t, args = self.langevin_dynamics(x_t, score_func , latent_mask, step_size , current_times, sigma_x = self.add_none_dims(self.sigma_x(abt)), sigma_y = self.add_none_dims(self.sigma_y(abt)), args = args)
|
||||
|
||||
if stopper is not None:
|
||||
ctx = {
|
||||
"step": i,
|
||||
"steps_done": i + 1,
|
||||
"n_steps": n_steps,
|
||||
"mask": latent_mask,
|
||||
"latent_image": self.latent_image,
|
||||
"current_times": current_times,
|
||||
"seed": seed,
|
||||
}
|
||||
if stopper.step(
|
||||
i=i,
|
||||
n_steps=n_steps,
|
||||
x_t_before=x_t_before,
|
||||
x_t_after=x_t,
|
||||
x_t_prev_for_custom=x_t_prev,
|
||||
prev_args=prev_args,
|
||||
args=args,
|
||||
ctx=ctx,
|
||||
):
|
||||
break
|
||||
|
||||
if IS_FLUX or IS_FLOW:
|
||||
x = x_t / ( self.add_none_dims(abt)**0.5 + (1-self.add_none_dims(abt))**0.5 )
|
||||
else:
|
||||
x = x_t * ( 1+self.add_none_dims(VE_Sigma)**2 )**0.5 # switch to variance perserving x_t values
|
||||
############ LanPaint Iterations End ###############
|
||||
# out is x_0
|
||||
|
||||
out, _ = self.unpack_model_output(
|
||||
self.inner_model(x, sigma, model_options=model_options, seed=seed)
|
||||
)
|
||||
out, _ = self.inner_model(x, sigma, model_options=model_options, seed=seed)
|
||||
out = out * (1-latent_mask) + self.latent_image * latent_mask
|
||||
|
||||
input_x.copy_(x)
|
||||
return out
|
||||
|
||||
def score_model(self, x_t, y, mask, abt, sigma, tflow, model_options, seed):
|
||||
|
||||
lamb = self.chara_lamb
|
||||
if self.IS_FLUX or self.IS_FLOW:
|
||||
# compute t for flow model, with a small epsilon compensating for numerical error.
|
||||
x = x_t / ( abt**0.5 + (1-abt)**0.5 ) # switch to Gaussian flow matching
|
||||
x_0, x_0_BIG = self.unpack_model_output(
|
||||
self.inner_model(x, self.remove_none_dims(tflow), model_options=model_options, seed=seed)
|
||||
)
|
||||
x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(tflow), model_options=model_options, seed=seed)
|
||||
else:
|
||||
x = x_t * ( 1+sigma**2 )**0.5 # switch to variance exploding
|
||||
x_0, x_0_BIG = self.unpack_model_output(
|
||||
self.inner_model(x, self.remove_none_dims(sigma), model_options=model_options, seed=seed)
|
||||
)
|
||||
|
||||
if getattr(self, "audio_correction", None) is not None:
|
||||
# The flat-grid model output for the audio rows is the slope-scaled
|
||||
# velocity estimate, which overshoots the true denoised audio by
|
||||
# sigma_v*slope/sigma_a. Pull the Langevin target back to the true
|
||||
# audio denoised: x0_true = x + c*(x0_flat - x), c = 1 on video rows
|
||||
# (video is untouched).
|
||||
x_0 = x + self.audio_correction * (x_0 - x)
|
||||
x_0_BIG = x + self.audio_correction * (x_0_BIG - x)
|
||||
x_0, x_0_BIG = self.inner_model(x, self.remove_none_dims(sigma), model_options=model_options, seed=seed)
|
||||
|
||||
score_x = -(x_t - x_0)
|
||||
score_y = - (1 + lamb) * ( x_t - y ) + lamb * (x_t - x_0_BIG)
|
||||
score_y = - (1 + lamb) * ( x_t - y ) + lamb * (x_t - x_0_BIG)
|
||||
return score_x * (1 - mask) + score_y * mask
|
||||
def sigma_x(self, abt):
|
||||
# the time scale for the x_t update
|
||||
@@ -190,13 +89,6 @@ class LanPaint():
|
||||
return beta
|
||||
|
||||
def langevin_dynamics(self, x_t, score, mask, step_size, current_times, sigma_x=1, sigma_y=0, args=None):
|
||||
if args is not None and not isinstance(args, LangevinState):
|
||||
if isinstance(args, tuple):
|
||||
if len(args) == 2:
|
||||
# Backwards compat: older state was (v, C) without x0.
|
||||
args = LangevinState(args[0], args[1], None)
|
||||
elif len(args) >= 3:
|
||||
args = LangevinState(args[0], args[1], args[2])
|
||||
# prepare the step size and time parameters
|
||||
with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
|
||||
step_sizes = self.prepare_step_size(current_times, step_size, sigma_x, sigma_y)
|
||||
@@ -212,85 +104,40 @@ class LanPaint():
|
||||
A = A_x * (1-mask) + A_y * mask
|
||||
D = D_x * (1-mask) + D_y * mask
|
||||
dt = dtx * (1-mask) + dty * mask
|
||||
# Gamma = Gamma_x * (1-mask) + Gamma_y * mask # only used by the disabled second-order scheme
|
||||
Gamma = Gamma_x * (1-mask) + Gamma_y * mask
|
||||
|
||||
|
||||
def Coef_C(x_t):
|
||||
x0 = x_t + score(x_t)
|
||||
x0 = self.x0_evalutation(x_t, score, sigma, args)
|
||||
C = (abt**0.5 * x0 - x_t )/ (1-abt) + A * x_t
|
||||
return C, x0
|
||||
# Second-order damped-oscillator update (position + velocity via
|
||||
# StochasticHarmonicOscillator) -- kept for reference, not used.
|
||||
# def advance_time(x_t, v, dt, Gamma, A, C, D):
|
||||
# dtype = x_t.dtype
|
||||
# with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
|
||||
# osc = StochasticHarmonicOscillator(Gamma, A, C, D )
|
||||
# x_t, v = osc.dynamics(x_t, v, dt )
|
||||
# x_t = x_t.to(dtype)
|
||||
# v = v.to(dtype)
|
||||
# return x_t, v
|
||||
|
||||
def advance_time_overdamped(x_t, dt, A, C, D):
|
||||
"""
|
||||
Overdamped (Gamma -> infinity) limit:
|
||||
dx = -A x dt + C dt + D dW_t
|
||||
with C treated as constant over this substep.
|
||||
"""
|
||||
return C
|
||||
def advance_time(x_t, v, dt, Gamma, A, C, D):
|
||||
dtype = x_t.dtype
|
||||
with torch.autocast(device_type=x_t.device.type, dtype=torch.float32):
|
||||
A_dt = A * dt
|
||||
exp_neg = torch.exp(-A_dt)
|
||||
osc = StochasticHarmonicOscillator(Gamma, A, C, D )
|
||||
x_t, v = osc.dynamics(x_t, v, dt )
|
||||
x_t = x_t.to(dtype)
|
||||
v = v.to(dtype)
|
||||
return x_t, v
|
||||
if args is None:
|
||||
#v = torch.zeros_like(x_t)
|
||||
v = None
|
||||
C = Coef_C(x_t)
|
||||
#print(torch.squeeze(dtx), torch.squeeze(dty))
|
||||
x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
|
||||
else:
|
||||
v, C = args
|
||||
|
||||
eps = 1e-8
|
||||
abs_A = torch.abs(A)
|
||||
# k = (1 - exp(-A dt)) / A -> dt when A -> 0
|
||||
k = torch.where(abs_A < eps, dt, (-torch.expm1(-A_dt)) / A)
|
||||
# k2 = (1 - exp(-2 A dt)) / (2 A) -> dt when A -> 0
|
||||
k2 = torch.where(abs_A < eps, dt, (-torch.expm1(-2 * A_dt)) / (2 * A))
|
||||
x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
|
||||
|
||||
mean = exp_neg * x_t + k * C
|
||||
var = (D ** 2) * k2
|
||||
noise = torch.randn_like(x_t) * torch.sqrt(torch.clamp(var, min=0.0))
|
||||
x_t = mean + noise
|
||||
return x_t.to(dtype)
|
||||
C_new = Coef_C(x_t)
|
||||
v = v + Gamma**0.5 * ( C_new - C) *dt
|
||||
|
||||
# Second-order damped-oscillator scheme (position + velocity) -- kept
|
||||
# for reference, not used.
|
||||
# def run_damped(x_t, args):
|
||||
# if args is None:
|
||||
# v = None
|
||||
# C, x0 = Coef_C(x_t)
|
||||
# x_t, v = advance_time(x_t, v, dt, Gamma, A, C, D)
|
||||
# else:
|
||||
# v = args.v
|
||||
# C = args.C
|
||||
# x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
|
||||
# C_new, x0 = Coef_C(x_t)
|
||||
# v = v + Gamma**0.5 * ( C_new - C) *dt
|
||||
# x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
|
||||
# C = C_new
|
||||
# # args is (v, C, x0) for the next inner step.
|
||||
# return x_t, LangevinState(v, C, x0)
|
||||
x_t, v = advance_time(x_t, v, dt/2, Gamma, A, C, D)
|
||||
|
||||
def run_overdamped(x_t, args):
|
||||
if args is None:
|
||||
C, x0 = Coef_C(x_t)
|
||||
x_t = advance_time_overdamped(x_t, dt, A, C, D)
|
||||
else:
|
||||
C = args.C
|
||||
x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
|
||||
C_new, x0 = Coef_C(x_t)
|
||||
x_t = x_t + (C_new - C) * dt
|
||||
x_t = advance_time_overdamped(x_t, dt / 2, A, C, D)
|
||||
C = C_new
|
||||
# args is (v, C, x0); v is None in the overdamped fallback.
|
||||
return x_t, LangevinState(None, C, x0)
|
||||
|
||||
# Only the first-order (overdamped) scheme is used; the second-order
|
||||
# damped-oscillator scheme is kept commented out above.
|
||||
x_t, state = run_overdamped(x_t, args)
|
||||
|
||||
# args is (v, C, x0); v is always None in the overdamped scheme.
|
||||
return x_t, state
|
||||
C = C_new
|
||||
|
||||
return x_t, (v, C)
|
||||
|
||||
def prepare_step_size(self, current_times, step_size, sigma_x, sigma_y):
|
||||
# -------------------------------------------------------------------------
|
||||
@@ -301,7 +148,7 @@ class LanPaint():
|
||||
# Compute time step (dtx, dty) for x and y branches.
|
||||
dtx = 2 * step_size * sigma_x
|
||||
dty = 2 * step_size * sigma_y
|
||||
|
||||
|
||||
# -------------------------------------------------------------------------
|
||||
# Define friction parameter Gamma_hat for each branch.
|
||||
# Using dtx**0 provides a tensor of the proper device/dtype.
|
||||
@@ -326,3 +173,9 @@ class LanPaint():
|
||||
D_x = (2 * abt**0 )**0.5
|
||||
D_y = (2 * abt**0 )**0.5
|
||||
return sigma, abt, dtx/2, dty/2, Gamma_x, Gamma_y, A_x, A_y, D_x, D_y
|
||||
|
||||
|
||||
|
||||
def x0_evalutation(self, x_t, score, sigma, args):
|
||||
x0 = x_t + score(x_t)
|
||||
return x0
|
||||
+274
-925
File diff suppressed because it is too large
Load Diff
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Reference in New Issue
Block a user