add ldm model

README.md

@@ -1,37 +1,58 @@
-# Lama-cleaner: Image inpainting tool powered by [LaMa](https://github.com/saic-mdal/lama)
-
-This project is mainly used for selfhosting LaMa model, some interaction improvements may be added later.
+# Lama-cleaner: Image inpainting tool powered by SOTA AI model
 
 https://user-images.githubusercontent.com/3998421/153323093-b664bb68-2928-480b-b59b-7c1ee24a4507.mp4
 
-
+- [x] Support multiple model architectures
+    1. [LaMa](https://github.com/saic-mdal/lama)
+    1. [LDM](https://github.com/CompVis/latent-diffusion)
 - [x] High resolution support
 - [x] Multi stroke support. Press and hold the `cmd/ctrl` key to enable multi stroke mode.
 - [x] Zoom & Pan
 - [ ] Keep image EXIF data
 
 ## Quick Start
 
-- Install requirements: `pip3 install -r requirements.txt`
-- Start server: `python3 main.py --device=cuda --port=8080`
+Install requirements: `pip3 install -r requirements.txt`
+
+### Start server with LaMa model
+
+```bash
+python3 main.py --device=cuda --port=8080 --model=lama
+```
+
+### Start server with LDM model
+
+```bash
+python3 main.py --device=cuda --port=8080 --model=ldm --ldm-steps=50
+```
+
+`--ldm-steps`: The larger the value, the better the result, but it will be more time-consuming
+
+Diffusion model is **MUCH MORE** slower than GANs(1080x720 image takes 8s on 3090), but it's possible to get better
+results than LaMa.
+
+Blogs about diffusion models:
+
+- https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
+- https://yang-song.github.io/blog/2021/score/
 
 ## Development
 
 Only needed if you plan to modify the frontend and recompile yourself.
 
 ### Fronted
 
-Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures),
-You can experience their great online services [here](https://cleanup.pictures/).
+Frontend code are modified from [cleanup.pictures](https://github.com/initml/cleanup.pictures), You can experience their
+great online services [here](https://cleanup.pictures/).
 
 - Install dependencies:`cd lama_cleaner/app/ && yarn`
 - Start development server: `yarn dev`
 - Build: `yarn build`
 
 ## Docker
 
-Run within a Docker container. Set the `CACHE_DIR` to models location path.
-Optionally add a `-d` option to the `docker run` command below to run as a daemon.
+Run within a Docker container. Set the `CACHE_DIR` to models location path. Optionally add a `-d` option to
+the `docker run` command below to run as a daemon.
 
 ### Build Docker image
 
@@ -54,6 +75,7 @@ docker run --gpus all -p 8080:8080 -e CACHE_DIR=/app/models -v $(pwd)/models:/ap
 Then open [http://localhost:8080](http://localhost:8080)
 
 ## Like My Work?
+
 <a href="https://www.buymeacoffee.com/Sanster"> 
   <img height="50em" src="https://cdn.buymeacoffee.com/buttons/v2/default-blue.png" alt="Sanster" />
 </a>

lama_cleaner/helper.py

@@ -7,13 +7,8 @@
 import torch
 from torch.hub import download_url_to_file, get_dir
 
-LAMA_MODEL_URL = os.environ.get(
-    "LAMA_MODEL_URL",
-    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
-)
 
-
-def download_model(url=LAMA_MODEL_URL):
+def download_model(url):
     parts = urlparse(url)
     hub_dir = get_dir()
     model_dir = os.path.join(hub_dir, "checkpoints")

lama_cleaner/lama/__init__.py

@@ -0,0 +1,56 @@
+import os
+import time
+
+import cv2
+import torch
+import numpy as np
+
+from lama_cleaner.helper import pad_img_to_modulo, download_model
+
+LAMA_MODEL_URL = os.environ.get(
+    "LAMA_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+)
+
+
+class LaMa:
+    def __init__(self, device):
+        self.device = device
+
+        if os.environ.get("LAMA_MODEL"):
+            model_path = os.environ.get("LAMA_MODEL")
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(f"lama torchscript model not found: {model_path}")
+        else:
+            model_path = download_model(LAMA_MODEL_URL)
+
+        model = torch.jit.load(model_path, map_location="cpu")
+        model = model.to(device)
+        model.eval()
+        self.model = model
+
+    @torch.no_grad()
+    def __call__(self, image, mask):
+        """
+        image: [C, H, W] RGB
+        mask: [1, H, W]
+        return: BGR IMAGE
+        """
+        device = self.device
+        origin_height, origin_width = image.shape[1:]
+        image = pad_img_to_modulo(image, mod=8)
+        mask = pad_img_to_modulo(mask, mod=8)
+
+        mask = (mask > 0) * 1
+        image = torch.from_numpy(image).unsqueeze(0).to(device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(device)
+
+        start = time.time()
+        inpainted_image = self.model(image, mask)
+
+        print(f"process time: {(time.time() - start) * 1000}ms")
+        cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
+        cur_res = cur_res[0:origin_height, 0:origin_width, :]
+        cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_BGR2RGB)
+        return cur_res