big update

lama_cleaner/app/src/adapters/inpainting.ts

@@ -1,10 +1,12 @@
+import { Setting } from '../store/Atoms'
 import { dataURItoBlob } from '../utils'
 
-export const API_ENDPOINT = `${process.env.REACT_APP_INPAINTING_URL}/inpaint`
+export const API_ENDPOINT = `${process.env.REACT_APP_INPAINTING_URL}`
 
 export default async function inpaint(
   imageFile: File,
   maskBase64: string,
+  settings: Setting,
   sizeLimit?: string
 ) {
   // 1080, 2000, Original
@@ -13,13 +15,22 @@ export default async function inpaint(
   const mask = dataURItoBlob(maskBase64)
   fd.append('mask', mask)
 
+  fd.append('ldmSteps', settings.ldmSteps.toString())
+  fd.append('hdStrategy', settings.hdStrategy)
+  fd.append('hdStrategyCropMargin', settings.hdStrategyCropMargin.toString())
+  fd.append(
+    'hdStrategyCropTrigerSize',
+    settings.hdStrategyCropTrigerSize.toString()
+  )
+  fd.append('hdStrategyResizeLimit', settings.hdStrategyResizeLimit.toString())
+
   if (sizeLimit === undefined) {
     fd.append('sizeLimit', '1080')
   } else {
     fd.append('sizeLimit', sizeLimit)
   }
 
-  const res = await fetch(API_ENDPOINT, {
+  const res = await fetch(`${API_ENDPOINT}/inpaint`, {
     method: 'POST',
     body: fd,
   }).then(async r => {
@@ -28,3 +39,12 @@ export default async function inpaint(
 
   return URL.createObjectURL(res)
 }
+
+export function switchModel(name: string) {
+  const fd = new FormData()
+  fd.append('name', name)
+  return fetch(`${API_ENDPOINT}/switch_model`, {
+    method: 'POST',
+    body: fd,
+  })
+}

lama_cleaner/app/src/components/Editor/Editor.tsx

@@ -15,12 +15,14 @@ import {
   TransformComponent,
   TransformWrapper,
 } from 'react-zoom-pan-pinch'
+import { useRecoilValue } from 'recoil'
 import { useWindowSize, useKey, useKeyPressEvent } from 'react-use'
 import inpaint from '../../adapters/inpainting'
 import Button from '../shared/Button'
 import Slider from './Slider'
 import SizeSelector from './SizeSelector'
 import { downloadImage, loadImage, useImage } from '../../utils'
+import { settingState } from '../../store/Atoms'
 
 const TOOLBAR_SIZE = 200
 const BRUSH_COLOR = '#ffcc00bb'
@@ -57,6 +59,7 @@ function drawLines(
 
 export default function Editor(props: EditorProps) {
   const { file } = props
+  const settings = useRecoilValue(settingState)
   const [brushSize, setBrushSize] = useState(40)
   const [original, isOriginalLoaded] = useImage(file)
   const [renders, setRenders] = useState<HTMLImageElement[]>([])
@@ -125,6 +128,7 @@ export default function Editor(props: EditorProps) {
       const res = await inpaint(
         file,
         maskCanvas.toDataURL(),
+        settings,
         sizeLimit.toString()
       )
       if (!res) {
@@ -157,6 +161,7 @@ export default function Editor(props: EditorProps) {
     renders,
     sizeLimit,
     historyLineCount,
+    settings,
   ])
 
   const hadDrawSomething = () => {

lama_cleaner/app/src/components/Header/Header.tsx

@@ -6,7 +6,7 @@ import Button from '../shared/Button'
 import Shortcuts from '../Shortcuts/Shortcuts'
 import useResolution from '../../hooks/useResolution'
 import { ThemeChanger } from './ThemeChanger'
-import SettingIcon from '../Setting/SettingIcon'
+import SettingIcon from '../Settings/SettingIcon'
 
 const Header = () => {
   const [file, setFile] = useRecoilState(fileState)

lama_cleaner/app/src/components/Settings/HDSettingBlock.tsx

@@ -1,50 +1,16 @@
 import React, { ReactNode } from 'react'
 import { useRecoilState } from 'recoil'
 import { settingState } from '../../store/Atoms'
-import NumberInput from '../shared/NumberInput'
 import Selector from '../shared/Selector'
+import NumberInputSetting from './NumberInputSetting'
 import SettingBlock from './SettingBlock'
 
 export enum HDStrategy {
   ORIGINAL = 'Original',
-  REISIZE = 'Resize',
+  RESIZE = 'Resize',
   CROP = 'Crop',
 }
 
-interface PixelSizeInputProps {
-  title: string
-  value: string
-  onValue: (val: string) => void
-}
-
-function PixelSizeInputSetting(props: PixelSizeInputProps) {
-  const { title, value, onValue } = props
-
-  return (
-    <SettingBlock
-      className="sub-setting-block"
-      title={title}
-      input={
-        <div
-          style={{
-            display: 'flex',
-            justifyContent: 'center',
-            alignItems: 'center',
-            gap: '8px',
-          }}
-        >
-          <NumberInput
-            style={{ width: '80px' }}
-            value={`${value}`}
-            onValue={onValue}
-          />
-          <span>pixel</span>
-        </div>
-      }
-    />
-  )
-}
-
 function HDSettingBlock() {
   const [setting, setSettingState] = useRecoilState(settingState)
 
@@ -84,7 +50,7 @@ function HDSettingBlock() {
           tabIndex={0}
           role="button"
           className="inline-tip"
-          onClick={() => onStrategyChange(HDStrategy.REISIZE)}
+          onClick={() => onStrategyChange(HDStrategy.RESIZE)}
         >
           Resize Strategy
         </div>{' '}
@@ -100,9 +66,10 @@ function HDSettingBlock() {
           Resize the longer side of the image to a specific size(keep ratio),
           then do inpainting on the resized image.
         </div>
-        <PixelSizeInputSetting
+        <NumberInputSetting
           title="Size limit"
           value={`${setting.hdStrategyResizeLimit}`}
+          suffix="pixel"
           onValue={onResizeLimitChange}
         />
       </div>
@@ -117,14 +84,16 @@ function HDSettingBlock() {
           the result back. Mainly for performance and memory reasons on high
           resolution image.
         </div>
-        <PixelSizeInputSetting
+        <NumberInputSetting
           title="Trigger size"
           value={`${setting.hdStrategyCropTrigerSize}`}
+          suffix="pixel"
           onValue={onCropTriggerSizeChange}
         />
-        <PixelSizeInputSetting
+        <NumberInputSetting
           title="Crop margin"
           value={`${setting.hdStrategyCropMargin}`}
+          suffix="pixel"
           onValue={onCropMarginChange}
         />
       </div>
@@ -137,7 +106,7 @@ function HDSettingBlock() {
         return renderOriginalOptionDesc()
       case HDStrategy.CROP:
         return renderCropOptionDesc()
-      case HDStrategy.REISIZE:
+      case HDStrategy.RESIZE:
         return renderResizeOptionDesc()
       default:
         return renderOriginalOptionDesc()

lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx

@@ -2,11 +2,12 @@ import React, { ReactNode } from 'react'
 import { useRecoilState } from 'recoil'
 import { settingState } from '../../store/Atoms'
 import Selector from '../shared/Selector'
+import NumberInputSetting from './NumberInputSetting'
 import SettingBlock from './SettingBlock'
 
 export enum AIModel {
-  LAMA = 'LaMa',
-  LDM = 'LDM',
+  LAMA = 'lama',
+  LDM = 'ldm',
 }
 
 function ModelSettingBlock() {
@@ -24,7 +25,7 @@ function ModelSettingBlock() {
     githubUrl: string
   ) => {
     return (
-      <div style={{ display: 'flex', flexDirection: 'column' }}>
+      <div style={{ display: 'flex', flexDirection: 'column', gap: '4px' }}>
         <a
           className="model-desc-link"
           href={paperUrl}
@@ -34,14 +35,11 @@ function ModelSettingBlock() {
           {name}
         </a>
 
-        <br />
-
         <a
           className="model-desc-link"
           href={githubUrl}
           target="_blank"
           rel="noreferrer noopener"
-          style={{ marginTop: '8px' }}
         >
           {githubUrl}
         </a>
@@ -49,6 +47,28 @@ function ModelSettingBlock() {
     )
   }
 
+  const renderLDMModelDesc = () => {
+    return (
+      <div>
+        {renderModelDesc(
+          'High-Resolution Image Synthesis with Latent Diffusion Models',
+          'https://arxiv.org/abs/2112.10752',
+          'https://github.com/CompVis/latent-diffusion'
+        )}
+        <NumberInputSetting
+          title="Steps"
+          value={`${setting.ldmSteps}`}
+          onValue={value => {
+            const val = value.length === 0 ? 0 : parseInt(value, 10)
+            setSettingState(old => {
+              return { ...old, ldmSteps: val }
+            })
+          }}
+        />
+      </div>
+    )
+  }
+
   const renderOptionDesc = (): ReactNode => {
     switch (setting.model) {
       case AIModel.LAMA:
@@ -58,11 +78,7 @@ function ModelSettingBlock() {
           'https://github.com/saic-mdal/lama'
         )
       case AIModel.LDM:
-        return renderModelDesc(
-          'High-Resolution Image Synthesis with Latent Diffusion Models',
-          'https://arxiv.org/abs/2112.10752',
-          'https://github.com/CompVis/latent-diffusion'
-        )
+        return renderLDMModelDesc()
       default:
         return <></>
     }

lama_cleaner/app/src/components/Settings/NumberInputSetting.tsx

@@ -0,0 +1,40 @@
+import React from 'react'
+import NumberInput from '../shared/NumberInput'
+import SettingBlock from './SettingBlock'
+
+interface NumberInputSettingProps {
+  title: string
+  value: string
+  suffix?: string
+  onValue: (val: string) => void
+}
+
+function NumberInputSetting(props: NumberInputSettingProps) {
+  const { title, value, suffix, onValue } = props
+
+  return (
+    <SettingBlock
+      className="sub-setting-block"
+      title={title}
+      input={
+        <div
+          style={{
+            display: 'flex',
+            justifyContent: 'center',
+            alignItems: 'center',
+            gap: '8px',
+          }}
+        >
+          <NumberInput
+            style={{ width: '80px' }}
+            value={`${value}`}
+            onValue={onValue}
+          />
+          {suffix && <span>{suffix}</span>}
+        </div>
+      }
+    />
+  )
+}
+
+export default NumberInputSetting

lama_cleaner/app/src/components/Settings/SavePathSettingBlock.tsx

@@ -1,14 +1,26 @@
 import React, { ReactNode } from 'react'
 import { useRecoilState } from 'recoil'
+import { settingState } from '../../store/Atoms'
 import { Switch, SwitchThumb } from '../shared/Switch'
 import SettingBlock from './SettingBlock'
 
 function SavePathSettingBlock() {
+  const [setting, setSettingState] = useRecoilState(settingState)
+
+  const onCheckChange = (checked: boolean) => {
+    setSettingState(old => {
+      return { ...old, saveImageBesideOrigin: checked }
+    })
+  }
+
   return (
     <SettingBlock
       title="Download image beside origin image"
       input={
-        <Switch defaultChecked>
+        <Switch
+          checked={setting.saveImageBesideOrigin}
+          onCheckedChange={onCheckChange}
+        >
           <SwitchThumb />
         </Switch>
       }

lama_cleaner/app/src/components/Settings/SettingBlock.scss

@@ -7,7 +7,7 @@
     margin-top: 12px;
     border: 1px solid var(--border-color);
     border-radius: 0.3rem;
-    padding: 2rem;
+    padding: 1rem;
 
     .sub-setting-block {
       margin-top: 8px;

lama_cleaner/app/src/components/Settings/Settings.scss

@@ -8,7 +8,6 @@
   background-color: var(--modal-bg);
   color: var(--modal-text-color);
   box-shadow: 0px 0px 20px rgb(0, 0, 40, 0.2);
-  min-height: 600px;
   width: 700px;
 
   @include mobile {

lama_cleaner/app/src/components/Settings/SettingsModal.tsx

@@ -1,11 +1,11 @@
 import React from 'react'
 
 import { useRecoilState } from 'recoil'
+import { switchModel } from '../../adapters/inpainting'
 import { settingState } from '../../store/Atoms'
 import Modal from '../shared/Modal'
 import HDSettingBlock from './HDSettingBlock'
 import ModelSettingBlock from './ModelSettingBlock'
-import SavePathSettingBlock from './SavePathSettingBlock'
 
 export default function SettingModal() {
   const [setting, setSettingState] = useRecoilState(settingState)
@@ -14,6 +14,8 @@ export default function SettingModal() {
     setSettingState(old => {
       return { ...old, show: false }
     })
+
+    switchModel(setting.model)
   }
 
   return (
@@ -23,7 +25,9 @@ export default function SettingModal() {
       className="modal-setting"
       show={setting.show}
     >
-      <SavePathSettingBlock />
+      {/* It's not possible because this poses a security risk */}
+      {/* https://stackoverflow.com/questions/34870711/download-a-file-at-different-location-using-html5 */}
+      {/* <SavePathSettingBlock /> */}
       <ModelSettingBlock />
       <HDSettingBlock />
     </Modal>

lama_cleaner/app/src/components/Workspace.tsx

@@ -1,9 +1,7 @@
 import React from 'react'
-import { useRecoilValue } from 'recoil'
 import Editor from './Editor/Editor'
-import { shortcutsState } from '../store/Atoms'
 import ShortcutsModal from './Shortcuts/ShortcutsModal'
-import SettingModal from './Setting/SettingModal'
+import SettingModal from './Settings/SettingsModal'
 
 interface WorkspaceProps {
   file: File

lama_cleaner/app/src/components/shared/Modal.tsx

@@ -16,11 +16,15 @@ export default function Modal(props: ModalProps) {
   const ref = useRef(null)
 
   useClickAway(ref, () => {
+    if (show) {
       onClose?.()
+    }
   })
 
   useKeyPressEvent('Escape', e => {
+    if (show) {
       onClose?.()
+    }
   })
 
   return (

lama_cleaner/app/src/store/Atoms.tsx

@@ -1,6 +1,6 @@
 import { atom } from 'recoil'
-import { HDStrategy } from '../components/Setting/HDSettingBlock'
-import { AIModel } from '../components/Setting/ModelSettingBlock'
+import { HDStrategy } from '../components/Settings/HDSettingBlock'
+import { AIModel } from '../components/Settings/ModelSettingBlock'
 
 export const fileState = atom<File | undefined>({
   key: 'fileState',
@@ -16,10 +16,15 @@ export interface Setting {
   show: boolean
   saveImageBesideOrigin: boolean
   model: AIModel
+
+  // For LaMa
   hdStrategy: HDStrategy
   hdStrategyResizeLimit: number
   hdStrategyCropTrigerSize: number
   hdStrategyCropMargin: number
+
+  // For LDM
+  ldmSteps: number
 }
 
 export const settingState = atom<Setting>({
@@ -28,9 +33,10 @@ export const settingState = atom<Setting>({
     show: false,
     saveImageBesideOrigin: false,
     model: AIModel.LAMA,
-    hdStrategy: HDStrategy.ORIGINAL,
+    hdStrategy: HDStrategy.RESIZE,
     hdStrategyResizeLimit: 2048,
     hdStrategyCropTrigerSize: 2048,
     hdStrategyCropMargin: 128,
+    ldmSteps: 50,
   },
 })

lama_cleaner/app/src/styles/_index.scss

@@ -11,7 +11,7 @@
 @use '../components/Header/Header';
 @use '../components/Header/ThemeChanger';
 @use '../components/Shortcuts/Shortcuts';
-@use '../components/Setting/Setting.scss';
+@use '../components/Settings/Settings.scss';
 
 // Shared
 @use '../components/FileSelect/FileSelect';

lama_cleaner/helper.py

@@ -31,7 +31,11 @@ def ceil_modulo(x, mod):
 
 
 def numpy_to_bytes(image_numpy: np.ndarray, ext: str) -> bytes:
-    data = cv2.imencode(f".{ext}", image_numpy)[1]
+    data = cv2.imencode(f".{ext}", image_numpy,
+                        [
+                            int(cv2.IMWRITE_JPEG_QUALITY), 100,
+                            int(cv2.IMWRITE_PNG_COMPRESSION), 0
+                        ])[1]
     image_bytes = data.tobytes()
     return image_bytes
 
@@ -74,13 +78,24 @@ def resize_max_size(
         return np_img
 
 
-def pad_img_to_modulo(img, mod):
-    channels, height, width = img.shape
+def pad_img_to_modulo(img: np.ndarray, mod: int):
+    """
+
+    Args:
+        img: [H, W, C]
+        mod:
+
+    Returns:
+
+    """
+    if len(img.shape) == 2:
+        img = img[:, :, np.newaxis]
+    height, width = img.shape[:2]
     out_height = ceil_modulo(height, mod)
     out_width = ceil_modulo(width, mod)
     return np.pad(
         img,
-        ((0, 0), (0, out_height - height), (0, out_width - width)),
+        ((0, out_height - height), (0, out_width - width), (0, 0)),
         mode="symmetric",
     )
 
@@ -88,15 +103,13 @@ def pad_img_to_modulo(img, mod):
 def boxes_from_mask(mask: np.ndarray) -> List[np.ndarray]:
     """
     Args:
-        mask: (1, h, w)  0~1
+        mask: (h, w, 1)  0~255
 
     Returns:
 
     """
-    height, width = mask.shape[1:]
-    _, thresh = cv2.threshold(
-        (mask.transpose(1, 2, 0) * 255).astype(np.uint8), 127, 255, 0
-    )
+    height, width = mask.shape[:2]
+    _, thresh = cv2.threshold(mask, 127, 255, 0)
     contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
     boxes = []

lama_cleaner/lama/__init__.py

@@ -1,121 +0,0 @@
-import os
-from typing import List
-
-import cv2
-import torch
-import numpy as np
-
-from lama_cleaner.helper import pad_img_to_modulo, download_model, boxes_from_mask
-
-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, crop_trigger_size: List[int], crop_margin: int, device):
-        """
-
-        Args:
-            crop_trigger_size: h, w
-            crop_margin:
-            device:
-        """
-        self.crop_trigger_size = crop_trigger_size
-        self.crop_margin = crop_margin
-        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)
-
-        print(f"Load LaMa model from: {model_path}")
-        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
-        """
-        area = image.shape[1] * image.shape[2]
-        if area < self.crop_trigger_size[0] * self.crop_trigger_size[1]:
-            return self._run(image, mask)
-
-        print("Trigger crop image")
-        boxes = boxes_from_mask(mask)
-        crop_result = []
-        for box in boxes:
-            crop_image, crop_box = self._run_box(image, mask, box)
-            crop_result.append((crop_image, crop_box))
-
-        image = (image.transpose(1, 2, 0) * 255).astype(np.uint8)[:, :, ::-1]
-        for crop_image, crop_box in crop_result:
-            x1, y1, x2, y2 = crop_box
-            image[y1:y2, x1:x2, :] = crop_image
-        return image
-
-    def _run_box(self, image, mask, box):
-        """
-
-        Args:
-            image: [C, H, W] RGB
-            mask: [1, H, W]
-            box: [left,top,right,bottom]
-
-        Returns:
-            BGR IMAGE
-        """
-        box_h = box[3] - box[1]
-        box_w = box[2] - box[0]
-        cx = (box[0] + box[2]) // 2
-        cy = (box[1] + box[3]) // 2
-        img_h, img_w = image.shape[1:]
-
-        w = box_w + self.crop_margin * 2
-        h = box_h + self.crop_margin * 2
-
-        l = max(cx - w // 2, 0)
-        t = max(cy - h // 2, 0)
-        r = min(cx + w // 2, img_w)
-        b = min(cy + h // 2, img_h)
-
-        crop_img = image[:, t:b, l:r]
-        crop_mask = mask[:, t:b, l:r]
-
-        print(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
-
-        return self._run(crop_img, crop_mask), [l, t, r, b]
-
-    def _run(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)
-
-        inpainted_image = self.model(image, mask)
-
-        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_RGB2BGR)
-        return cur_res

lama_cleaner/model/base.py

@@ -0,0 +1,122 @@
+import abc
+
+import cv2
+import torch
+from loguru import logger
+
+from lama_cleaner.helper import boxes_from_mask, resize_max_size, pad_img_to_modulo
+from lama_cleaner.schema import Config, HDStrategy
+
+
+class InpaintModel:
+    pad_mod = 8
+
+    def __init__(self, device):
+        """
+
+        Args:
+            device:
+        """
+        self.device = device
+        self.init_model(device)
+
+    @abc.abstractmethod
+    def init_model(self, device):
+        ...
+
+    @abc.abstractmethod
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W]
+        return: BGR IMAGE
+        """
+        ...
+
+    def _pad_forward(self, image, mask, config: Config):
+        origin_height, origin_width = image.shape[:2]
+        padd_image = pad_img_to_modulo(image, mod=self.pad_mod)
+        padd_mask = pad_img_to_modulo(mask, mod=self.pad_mod)
+        result = self.forward(padd_image, padd_mask, config)
+        result = result[0:origin_height, 0:origin_width, :]
+
+        original_pixel_indices = mask != 255
+        result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
+        return result
+
+    @torch.no_grad()
+    def __call__(self, image, mask, config: Config):
+        """
+        image: [H, W, C] RGB, not normalized
+        mask: [H, W]
+        return: BGR IMAGE
+        """
+        inpaint_result = None
+        logger.info(f"hd_strategy: {config.hd_strategy}")
+        if config.hd_strategy == HDStrategy.CROP:
+            if max(image.shape) > config.hd_strategy_crop_trigger_size:
+                logger.info(f"Run crop strategy")
+                boxes = boxes_from_mask(mask)
+                crop_result = []
+                for box in boxes:
+                    crop_image, crop_box = self._run_box(image, mask, box, config)
+                    crop_result.append((crop_image, crop_box))
+
+                inpaint_result = image[:, :, ::-1]
+                for crop_image, crop_box in crop_result:
+                    x1, y1, x2, y2 = crop_box
+                    inpaint_result[y1:y2, x1:x2, :] = crop_image
+
+        elif config.hd_strategy == HDStrategy.RESIZE:
+            if max(image.shape) > config.hd_strategy_resize_limit:
+                origin_size = image.shape[:2]
+                downsize_image = resize_max_size(image, size_limit=config.hd_strategy_resize_limit)
+                downsize_mask = resize_max_size(mask, size_limit=config.hd_strategy_resize_limit)
+
+                logger.info(f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}")
+                inpaint_result = self._pad_forward(downsize_image, downsize_mask, config)
+
+                # only paste masked area result
+                inpaint_result = cv2.resize(inpaint_result,
+                                            (origin_size[1], origin_size[0]),
+                                            interpolation=cv2.INTER_CUBIC)
+
+                original_pixel_indices = mask != 255
+                inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
+
+        if inpaint_result is None:
+            inpaint_result = self._pad_forward(image, mask, config)
+
+        return inpaint_result
+
+    def _run_box(self, image, mask, box, config: Config):
+        """
+
+        Args:
+            image: [H, W, C] RGB
+            mask: [H, W, 1]
+            box: [left,top,right,bottom]
+
+        Returns:
+            BGR IMAGE
+        """
+        box_h = box[3] - box[1]
+        box_w = box[2] - box[0]
+        cx = (box[0] + box[2]) // 2
+        cy = (box[1] + box[3]) // 2
+        img_h, img_w = image.shape[:2]
+
+        w = box_w + config.hd_strategy_crop_margin * 2
+        h = box_h + config.hd_strategy_crop_margin * 2
+
+        l = max(cx - w // 2, 0)
+        t = max(cy - h // 2, 0)
+        r = min(cx + w // 2, img_w)
+        b = min(cy + h // 2, img_h)
+
+        crop_img = image[t:b, l:r, :]
+        crop_mask = mask[t:b, l:r]
+
+        logger.info(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
+
+        return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]

lama_cleaner/model/lama.py

@@ -0,0 +1,64 @@
+import os
+
+import cv2
+import numpy as np
+import torch
+from loguru import logger
+
+from lama_cleaner.helper import pad_img_to_modulo, download_model, norm_img
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
+
+LAMA_MODEL_URL = os.environ.get(
+    "LAMA_MODEL_URL",
+    "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
+)
+
+
+class LaMa(InpaintModel):
+    pad_mod = 8
+
+    def __init__(self, device):
+        """
+
+        Args:
+            device:
+        """
+        super().__init__(device)
+        self.device = device
+
+    def init_model(self, 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)
+
+        logger.info(f"Load LaMa model from: {model_path}")
+        model = torch.jit.load(model_path, map_location="cpu")
+        model = model.to(device)
+        model.eval()
+        self.model = model
+
+    def forward(self, image, mask, config: Config):
+        """Input image and output image have same size
+        image: [H, W, C] RGB
+        mask: [H, W]
+        return: BGR IMAGE
+        """
+        image = norm_img(image)
+        mask = norm_img(mask)
+
+        mask = (mask > 0) * 1
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
+
+        inpainted_image = self.model(image, mask)
+
+        cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
+        cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
+        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
+        return cur_res

lama_cleaner/model/ldm.py

@@ -2,13 +2,16 @@ import os
 
 import numpy as np
 import torch
+from loguru import logger
+
+from lama_cleaner.model.base import InpaintModel
+from lama_cleaner.schema import Config
 
 torch.manual_seed(42)
 import torch.nn as nn
 from tqdm import tqdm
-import cv2
-from lama_cleaner.helper import pad_img_to_modulo, download_model
-from lama_cleaner.ldm.utils import make_beta_schedule, make_ddim_timesteps, make_ddim_sampling_parameters, noise_like, \
+from lama_cleaner.helper import download_model, norm_img
+from lama_cleaner.model.utils import make_beta_schedule, make_ddim_timesteps, make_ddim_sampling_parameters, noise_like, \
     timestep_embedding
 
 LDM_ENCODE_MODEL_URL = os.environ.get(
@@ -217,7 +220,7 @@ class DDIMSampler(object):
 
         time_range = reversed(range(0, timesteps)) if ddim_use_original_steps else np.flip(timesteps)
         total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
-        print(f"Running DDIM Sampling with {total_steps} timesteps")
+        logger.info(f"Running DDIM Sampling with {total_steps} timesteps")
 
         iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
 
@@ -268,97 +271,39 @@ def load_jit_model(url, device):
     return model
 
 
-class LDM:
-    def __init__(self, device, steps=50):
+class LDM(InpaintModel):
+    pad_mod = 32
+
+    def __init__(self, device):
+        super().__init__(device)
         self.device = device
 
+    def init_model(self, device):
         self.diffusion_model = load_jit_model(LDM_DIFFUSION_MODEL_URL, device)
         self.cond_stage_model_decode = load_jit_model(LDM_DECODE_MODEL_URL, device)
         self.cond_stage_model_encode = load_jit_model(LDM_ENCODE_MODEL_URL, device)
 
         model = LatentDiffusion(self.diffusion_model, device)
         self.sampler = DDIMSampler(model)
-        self.steps = steps
 
-    def _norm(self, tensor):
-        return tensor * 2.0 - 1.0
-
-    @torch.no_grad()
-    def __call__(self, image, mask):
+    def forward(self, image, mask, config: Config):
         """
-        image: [C, H, W] RGB
-        mask: [1, H, W]
+        image: [H, W, C] RGB
+        mask: [H, W, 1]
         return: BGR IMAGE
         """
         # image [1,3,512,512] float32
         # mask: [1,1,512,512] float32
         # masked_image: [1,3,512,512] float32
-        origin_height, origin_width = image.shape[1:]
-        image = pad_img_to_modulo(image, mod=32)
-        mask = pad_img_to_modulo(mask, mod=32)
-        padded_height, padded_width = image.shape[1:]
+        steps = config.ldm_steps
+        image = norm_img(image)
+        mask = norm_img(mask)
+
         mask[mask < 0.5] = 0
         mask[mask >= 0.5] = 1
 
-        # crop 512 x 512
-        if padded_width <= 512 or padded_height <= 512:
-            np_img = self._forward(image, mask, self.device)
-        else:
-            print("Try to zoom in")
-            # zoom in
-            # x,y,w,h
-            # box = self.box_from_bitmap(mask)
-            box = self.find_main_content(mask)
-            if box is None:
-                print("No bbox found")
-                np_img = self._forward(image, mask, self.device)
-            else:
-                print(f"box: {box}")
-                box_x, box_y, box_w, box_h = box
-                cx = box_x + box_w // 2
-                cy = box_y + box_h // 2
-
-                # w = max(512, box_w)
-                # h = max(512, box_h)
-                w = box_w + 512
-                h = box_h + 512
-
-                left = max(cx - w // 2, 0)
-                top = max(cy - h // 2, 0)
-                right = min(cx + w // 2, origin_width)
-                bottom = min(cy + h // 2, origin_height)
-
-                x = left
-                y = top
-                w = right - left
-                h = bottom - top
-
-                crop_img = image[:, int(y):int(y + h), int(x):int(x + w)]
-                crop_mask = mask[:, int(y):int(y + h), int(x):int(x + w)]
-
-                print(f"Apply zoom in size width x height: {crop_img.shape}")
-
-                crop_img_height, crop_img_width = crop_img.shape[1:]
-
-                crop_img = pad_img_to_modulo(crop_img, mod=32)
-                crop_mask = pad_img_to_modulo(crop_mask, mod=32)
-                # RGB
-                np_img = self._forward(crop_img, crop_mask, self.device)
-
-                image = (image.transpose(1, 2, 0) * 255).astype(np.uint8)
-                image[int(y): int(y + h), int(x): int(x + w), :] = np_img[0:crop_img_height, 0:crop_img_width, :]
-                np_img = image
-                # BGR to RGB
-                # np_img = image[:, :, ::-1]
-
-        np_img = np_img[0:origin_height, 0:origin_width, :]
-        np_img = np_img[:, :, ::-1]
-
-        return np_img
-
-    def _forward(self, image, mask, device):
-        image = torch.from_numpy(image).unsqueeze(0).to(device)
-        mask = torch.from_numpy(mask).unsqueeze(0).to(device)
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
         masked_image = (1 - mask) * image
 
         image = self._norm(image)
@@ -371,47 +316,20 @@ class LDM:
         c = torch.cat((c, cc), dim=1)  # 1,4,128,128
 
         shape = (c.shape[1] - 1,) + c.shape[2:]
-        samples_ddim = self.sampler.sample(steps=self.steps,
+        samples_ddim = self.sampler.sample(steps=steps,
                                            conditioning=c,
                                            batch_size=c.shape[0],
                                            shape=shape)
         x_samples_ddim = self.cond_stage_model_decode(samples_ddim)  # samples_ddim: 1, 3, 128, 128 float32
 
-        image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
-        mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
-        predicted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
+        # image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
+        # mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
+        inpainted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
-        inpainted = (1 - mask) * image + mask * predicted_image
-        inpainted = inpainted.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
-        np_img = inpainted.astype(np.uint8)
-        return np_img
+        # inpainted = (1 - mask) * image + mask * predicted_image
+        inpainted_image = inpainted_image.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
+        inpainted_image = inpainted_image.astype(np.uint8)[:, :, ::-1]
+        return inpainted_image
 
-    def find_main_content(self, bitmap: np.ndarray):
-        th2 = bitmap[0].astype(np.uint8)
-        row_sum = th2.sum(1)
-        col_sum = th2.sum(0)
-        xmin = max(0, np.argwhere(col_sum != 0).min() - 20)
-        xmax = min(np.argwhere(col_sum != 0).max() + 20, th2.shape[1])
-        ymin = max(0, np.argwhere(row_sum != 0).min() - 20)
-        ymax = min(np.argwhere(row_sum != 0).max() + 20, th2.shape[0])
-
-        left, top, right, bottom = int(xmin), int(ymin), int(xmax), int(ymax)
-        return left, top, right - left, bottom - top
-
-    def box_from_bitmap(self, bitmap):
-        """
-        bitmap: single map with shape (NUM_CLASSES, H, W),
-            whose values are binarized as {0, 1}
-        """
-        contours, _ = cv2.findContours(
-            (bitmap[0] * 255).astype(np.uint8), cv2.RETR_FLOODFILL, cv2.CHAIN_APPROX_NONE
-        )
-
-        contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
-        num_contours = len(contours)
-        print(f"contours size: {num_contours}")
-        if num_contours != 1:
-            return None
-
-        # x,y,w,h
-        return cv2.boundingRect(contours[0])
+    def _norm(self, tensor):
+        return tensor * 2.0 - 1.0

lama_cleaner/model_manager.py

@@ -0,0 +1,34 @@
+from lama_cleaner.model.lama import LaMa
+from lama_cleaner.model.ldm import LDM
+from lama_cleaner.schema import Config
+
+
+class ModelManager:
+    LAMA = 'lama'
+    LDM = 'ldm'
+
+    def __init__(self, name: str, device):
+        self.name = name
+        self.device = device
+        self.model = self.init_model(name, device)
+
+    def init_model(self, name: str, device):
+        if name == self.LAMA:
+            model = LaMa(device)
+        elif name == self.LDM:
+            model = LDM(device)
+        else:
+            raise NotImplementedError(f"Not supported model: {name}")
+        return model
+
+    def __call__(self, image, mask, config: Config):
+        return self.model(image, mask, config)
+
+    def switch(self, new_name: str):
+        if new_name == self.name:
+            return
+        try:
+            self.model = self.init_model(new_name, self.device)
+            self.name = new_name
+        except NotImplementedError as e:
+            raise e

lama_cleaner/schema.py

@@ -0,0 +1,17 @@
+from enum import Enum
+
+from pydantic import BaseModel
+
+
+class HDStrategy(str, Enum):
+    ORIGINAL = 'Original'
+    RESIZE = 'Resize'
+    CROP = 'Crop'
+
+
+class Config(BaseModel):
+    ldm_steps: int
+    hd_strategy: str
+    hd_strategy_crop_margin: int
+    hd_strategy_crop_trigger_size: int
+    hd_strategy_resize_limit: int

lama_cleaner/tests/test_boxes_from_mask.py

@@ -1,15 +0,0 @@
-import cv2
-import numpy as np
-
-from lama_cleaner.helper import boxes_from_mask
-
-
-def test_boxes_from_mask():
-    mask = cv2.imread("mask.jpg", cv2.IMREAD_GRAYSCALE)
-    mask = mask[:, :, np.newaxis]
-    mask = (mask / 255).transpose(2, 0, 1)
-    boxes = boxes_from_mask(mask)
-    print(boxes)
-
-
-test_boxes_from_mask()

lama_cleaner/tests/test_model.py

@@ -0,0 +1,55 @@
+import os
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pytest
+
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import Config, HDStrategy
+
+current_dir = Path(__file__).parent.absolute().resolve()
+
+
+def get_data():
+    img = cv2.imread(str(current_dir / 'image.png'))
+    img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
+    mask = cv2.imread(str(current_dir / 'mask.png'), cv2.IMREAD_GRAYSCALE)
+    return img, mask
+
+
+def get_config(strategy):
+    return Config(
+        ldm_steps=1,
+        hd_strategy=strategy,
+        hd_strategy_crop_margin=32,
+        hd_strategy_crop_trigger_size=200,
+        hd_strategy_resize_limit=200,
+    )
+
+
+def assert_equal(model, config, gt_name):
+    img, mask = get_data()
+    res = model(img, mask, config)
+    # cv2.imwrite(gt_name, res,
+    #             [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0])
+
+    """
+    Note that JPEG is lossy compression, so even if it is the highest quality 100, 
+    when the saved image is reloaded, a difference occurs with the original pixel value. 
+    If you want to save the original image as it is, save it as PNG or BMP.
+    """
+    gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED)
+    assert np.array_equal(res, gt)
+
+
+@pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP])
+def test_lama(strategy):
+    model = ModelManager(name='lama', device='cpu')
+    assert_equal(model, get_config(strategy), f'lama_{strategy[0].upper() + strategy[1:]}_result.png')
+
+
+@pytest.mark.parametrize('strategy', [HDStrategy.ORIGINAL, HDStrategy.RESIZE, HDStrategy.CROP])
+def test_ldm(strategy):
+    model = ModelManager(name='ldm', device='cpu')
+    assert_equal(model, get_config(strategy), f'ldm_{strategy[0].upper() + strategy[1:]}_result.png')

main.py

@@ -2,19 +2,21 @@
 
 import argparse
 import io
+import logging
 import multiprocessing
 import os
 import time
 import imghdr
+from pathlib import Path
 from typing import Union
 
 import cv2
 import torch
 import numpy as np
-from lama_cleaner.lama import LaMa
-from lama_cleaner.ldm import LDM
+from loguru import logger
 
-from flaskwebgui import FlaskUI
+from lama_cleaner.model_manager import ModelManager
+from lama_cleaner.schema import Config
 
 try:
     torch._C._jit_override_can_fuse_on_cpu(False)
@@ -29,7 +31,6 @@ from flask_cors import CORS
 
 from lama_cleaner.helper import (
     load_img,
-    norm_img,
     numpy_to_bytes,
     resize_max_size,
 )
@@ -46,11 +47,19 @@ if os.environ.get("CACHE_DIR"):
 
 BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "./lama_cleaner/app/build")
 
+
+class InterceptHandler(logging.Handler):
+    def emit(self, record):
+        logger_opt = logger.opt(depth=6, exception=record.exc_info)
+        logger_opt.log(record.levelno, record.getMessage())
+
+
 app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
 app.config["JSON_AS_ASCII"] = False
-CORS(app)
+app.logger.addHandler(InterceptHandler())
+CORS(app, expose_headers=["Content-Disposition"])
 
-model = None
+model: ModelManager = None
 device = None
 input_image_path: str = None
 
@@ -72,24 +81,31 @@ def process():
     original_shape = image.shape
     interpolation = cv2.INTER_CUBIC
 
-    size_limit: Union[int, str] = request.form.get("sizeLimit", "1080")
+    form = request.form
+    size_limit: Union[int, str] = form.get("sizeLimit", "1080")
     if size_limit == "Original":
         size_limit = max(image.shape)
     else:
         size_limit = int(size_limit)
 
-    print(f"Origin image shape: {original_shape}")
+    config = Config(
+        ldm_steps=form['ldmSteps'],
+        hd_strategy=form['hdStrategy'],
+        hd_strategy_crop_margin=form['hdStrategyCropMargin'],
+        hd_strategy_crop_trigger_size=form['hdStrategyCropTrigerSize'],
+        hd_strategy_resize_limit=form['hdStrategyResizeLimit'],
+    )
+
+    logger.info(f"Origin image shape: {original_shape}")
     image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
-    print(f"Resized image shape: {image.shape}")
-    image = norm_img(image)
+    logger.info(f"Resized image shape: {image.shape}")
 
     mask, _ = load_img(input["mask"].read(), gray=True)
     mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
-    mask = norm_img(mask)
 
     start = time.time()
-    res_np_img = model(image, mask)
-    print(f"process time: {(time.time() - start) * 1000}ms")
+    res_np_img = model(image, mask, config)
+    logger.info(f"process time: {(time.time() - start) * 1000}ms")
 
     torch.cuda.empty_cache()
 
@@ -109,6 +125,19 @@ def process():
     )
 
 
+@app.route("/switch_model", methods=["POST"])
+def switch_model():
+    new_name = request.form.get("name")
+    if new_name == model.name:
+        return "Same model", 200
+
+    try:
+        model.switch(new_name)
+    except NotImplementedError:
+        return f"{new_name} not implemented", 403
+    return f"ok, switch to {new_name}", 200
+
+
 @app.route("/")
 def index():
     return send_file(os.path.join(BUILD_DIR, "index.html"))
@@ -120,7 +149,9 @@ def set_input_photo():
         with open(input_image_path, "rb") as f:
             image_in_bytes = f.read()
         return send_file(
-            io.BytesIO(image_in_bytes),
+            input_image_path,
+            as_attachment=True,
+            download_name=Path(input_image_path).name,
             mimetype=f"image/{get_image_ext(image_in_bytes)}",
         )
     else:
@@ -135,29 +166,6 @@ def get_args_parser():
     parser.add_argument("--host", default="127.0.0.1")
     parser.add_argument("--port", default=8080, type=int)
     parser.add_argument("--model", default="lama", choices=["lama", "ldm"])
-    parser.add_argument(
-        "--crop-trigger-size",
-        default=[2042, 2042],
-        nargs=2,
-        type=int,
-        help="If image size large then crop-trigger-size, "
-        "crop each area from original image to do inference."
-        "Mainly for performance and memory reasons"
-        "Only for lama",
-    )
-    parser.add_argument(
-        "--crop-margin",
-        type=int,
-        default=256,
-        help="Margin around bounding box of painted stroke when crop mode triggered",
-    )
-    parser.add_argument(
-        "--ldm-steps",
-        default=50,
-        type=int,
-        help="Steps for DDIM sampling process."
-        "The larger the value, the better the result, but it will be more time-consuming",
-    )
     parser.add_argument("--device", default="cuda", type=str)
     parser.add_argument("--gui", action="store_true", help="Launch as desktop app")
     parser.add_argument(
@@ -188,19 +196,11 @@ def main():
     device = torch.device(args.device)
     input_image_path = args.input
 
-    if args.model == "lama":
-        model = LaMa(
-            crop_trigger_size=args.crop_trigger_size,
-            crop_margin=args.crop_margin,
-            device=device,
-        )
-    elif args.model == "ldm":
-        model = LDM(device, steps=args.ldm_steps)
-    else:
-        raise NotImplementedError(f"Not supported model: {args.model}")
+    model = ModelManager(name=args.model, device=device)
 
     if args.gui:
         app_width, app_height = args.gui_size
+        from flaskwebgui import FlaskUI
         ui = FlaskUI(app, width=app_width, height=app_height)
         ui.run()
     else:

requirements.txt

@@ -1,6 +1,9 @@
 torch>=1.8.2
 opencv-python
 flask_cors
-flask
+flask==2.1.1
 flaskwebgui
 tqdm
+pydantic
+loguru
+pytest