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lots of updates

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Qing 2023-01-05 22:07:39 +08:00
parent 2e8e52f7a5
commit a22536becc
21 changed files with 394 additions and 74 deletions
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3
lama_cleaner/app/src/App.tsx
View File

@ -1,8 +1,7 @@
import React, { useCallback, useEffect, useMemo, useState } from 'react'
import React, { useCallback, useEffect, useMemo } from 'react'
import { useRecoilState } from 'recoil'
import { nanoid } from 'nanoid'
import useInputImage from './hooks/useInputImage'
import LandingPage from './components/LandingPage/LandingPage'
import { themeState } from './components/Header/ThemeChanger'
import Workspace from './components/Workspace'
import {

27
lama_cleaner/app/src/adapters/inpainting.ts
View File

@ -1,5 +1,5 @@
import { Rect, Settings } from '../store/Atoms'
import { dataURItoBlob } from '../utils'
import { dataURItoBlob, srcToFile } from '../utils'
export const API_ENDPOINT = `${process.env.REACT_APP_INPAINTING_URL}`
@ -57,6 +57,7 @@ export default async function inpaint(
fd.append('sdSampler', settings.sdSampler.toString())
fd.append('sdSeed', seed ? seed.toString() : '-1')
fd.append('sdMatchHistograms', settings.sdMatchHistograms ? 'true' : 'false')
fd.append('sdScale', (settings.sdScale / 100).toString())
fd.append('cv2Radius', settings.cv2Radius.toString())
fd.append('cv2Flag', settings.cv2Flag.toString())
@ -198,3 +199,27 @@ export async function getMedias() {
const errMsg = await res.text()
throw new Error(errMsg)
}
export async function downloadToOutput(
image: HTMLImageElement,
filename: string,
mimeType: string
) {
const file = await srcToFile(image.src, filename, mimeType)
const fd = new FormData()
fd.append('image', file)
fd.append('filename', filename)
try {
const res = await fetch(`${API_ENDPOINT}/save_image`, {
method: 'POST',
body: fd,
})
if (!res.ok) {
const errMsg = await res.text()
throw new Error(errMsg)
}
} catch (error) {
throw new Error(`Something went wrong: ${error}`)
}
}

36
lama_cleaner/app/src/components/Editor/Editor.tsx
View File

@ -18,7 +18,10 @@ import {
} from 'react-zoom-pan-pinch'
import { useRecoilState, useRecoilValue } from 'recoil'
import { useWindowSize, useKey, useKeyPressEvent } from 'react-use'
import inpaint, { postInteractiveSeg } from '../../adapters/inpainting'
import inpaint, {
downloadToOutput,
postInteractiveSeg,
} from '../../adapters/inpainting'
import Button from '../shared/Button'
import Slider from './Slider'
import SizeSelector from './SizeSelector'
@ -34,7 +37,10 @@ import {
} from '../../utils'
import {
croperState,
enableFileManagerState,
fileState,
imageHeightState,
imageWidthState,
interactiveSegClicksState,
isInpaintingState,
isInteractiveSegRunningState,
@ -173,6 +179,10 @@ export default function Editor() {
const [redoRenders, setRedoRenders] = useState<HTMLImageElement[]>([])
const [redoCurLines, setRedoCurLines] = useState<Line[]>([])
const [redoLineGroups, setRedoLineGroups] = useState<LineGroup[]>([])
const enableFileManager = useRecoilValue(enableFileManagerState)
const [imageWidth, setImageWidth] = useRecoilState(imageWidthState)
const [imageHeight, setImageHeight] = useRecoilState(imageHeightState)
const draw = useCallback(
(render: HTMLImageElement, lineGroup: LineGroup) => {
@ -524,6 +534,9 @@ export default function Editor() {
const rW = windowSize.width / original.naturalWidth
const rH = (windowSize.height - TOOLBAR_SIZE) / original.naturalHeight
setImageWidth(original.naturalWidth)
setImageHeight(original.naturalHeight)
let s = 1.0
if (rW < 1 || rH < 1) {
s = Math.min(rW, rH)
@ -1054,6 +1067,27 @@ export default function Editor() {
if (file === undefined) {
return
}
if (enableFileManager && renders.length > 0) {
try {
downloadToOutput(renders[renders.length - 1], file.name, file.type)
setToastState({
open: true,
desc: `Save image success`,
state: 'success',
duration: 2000,
})
} catch (e: any) {
setToastState({
open: true,
desc: e.message ? e.message : e.toString(),
state: 'error',
duration: 2000,
})
}
return
}
// TODO: download to output directory
const name = file.name.replace(/(\.[\w\d_-]+)$/i, '_cleanup$1')
const curRender = renders[renders.length - 1]
downloadImage(curRender.currentSrc, name)

15
lama_cleaner/app/src/components/Editor/Slider.tsx
View File

@ -6,14 +6,23 @@ type SliderProps = {
min?: number
max?: number
onChange: (value: number) => void
onClick: () => void
onClick?: () => void
width?: number
}
export default function Slider(props: SliderProps) {
const { value, onChange, onClick, label, min, max } = props
const { value, onChange, onClick, label, min, max, width } = props
const styles: any = {}
if (width !== undefined) {
styles.width = width
}
const step = ((max || 100) - (min || 0)) / 100
const onMouseUp = (e: React.MouseEvent<HTMLDivElement>) => {
e.currentTarget?.blur()
}
return (
<div className="editor-brush-slider">
<span>{label}</span>
@ -29,6 +38,8 @@ export default function Slider(props: SliderProps) {
onChange(parseInt(ev.currentTarget.value, 10))
}}
onClick={onClick}
style={styles}
onMouseUp={onMouseUp}
/>
</div>
)

4
lama_cleaner/app/src/components/FileManager/FileManager.tsx
View File

@ -5,7 +5,7 @@ import React, {
useState,
useCallback,
} from 'react'
import { useRecoilState, useRecoilValue } from 'recoil'
import { useRecoilState } from 'recoil'
import PhotoAlbum, { RenderPhoto } from 'react-photo-album'
import * as ScrollArea from '@radix-ui/react-scroll-area'
import Modal from '../shared/Modal'
@ -127,7 +127,7 @@ export default function FileManager(props: Props) {
ref={onRefChange}
>
<PhotoAlbum
layout="columns"
layout="masonry"
photos={photos}
renderPhoto={renderPhoto}
spacing={8}

2
lama_cleaner/app/src/components/Settings/SettingBlock.tsx
View File

@ -21,7 +21,7 @@ function SettingBlock(props: SettingBlockProps) {
<div className={`setting-block ${className}`}>
<div className={contentClass}>
<div className="setting-block-content-title">
<div style={{ display: 'flex', alignItems: 'center', gap: '12px' }}>
<div style={{ display: 'flex', alignItems: 'center', gap: '8px' }}>
{desc ? (
<Tooltip content={<div style={{ maxWidth: 400 }}>{desc}</div>}>
<span>{title}</span>

9
lama_cleaner/app/src/components/Settings/SettingsModal.tsx
View File

@ -1,8 +1,5 @@
import React, { FormEvent } from 'react'
import React from 'react'
import { useRecoilState, useRecoilValue } from 'recoil'
import { FolderOpenIcon } from '@heroicons/react/24/outline'
import * as Tabs from '@radix-ui/react-tabs'
import {
isPaintByExampleState,
isSDState,
@ -14,10 +11,6 @@ import HDSettingBlock from './HDSettingBlock'
import ModelSettingBlock from './ModelSettingBlock'
import DownloadMaskSettingBlock from './DownloadMaskSettingBlock'
import useHotKey from '../../hooks/useHotkey'
import SettingBlock from './SettingBlock'
import { Switch, SwitchThumb } from '../shared/Switch'
import Button from '../shared/Button'
import TextInput from '../shared/Input'
declare module 'react' {
interface InputHTMLAttributes<T> extends HTMLAttributes<T> {

58
lama_cleaner/app/src/components/SidePanel/ImageResizeScale.tsx Normal file
View File

@ -0,0 +1,58 @@
import React from 'react'
import { useRecoilState, useRecoilValue } from 'recoil'
import { appState, croperState, settingState } from '../../store/Atoms'
import Slider from '../Editor/Slider'
import SettingBlock from '../Settings/SettingBlock'
const ImageResizeScale = () => {
const [setting, setSettingState] = useRecoilState(settingState)
const app = useRecoilValue(appState)
const croper = useRecoilValue(croperState)
const handleSliderChange = (value: number) => {
setSettingState(old => {
return { ...old, sdScale: value }
})
}
const scaledWidth = () => {
let width = app.imageWidth
if (setting.showCroper) {
width = croper.width
}
return Math.round((width * setting.sdScale) / 100)
}
const scaledHeight = () => {
let height = app.imageHeight
if (setting.showCroper) {
height = croper.height
}
return Math.round((height * setting.sdScale) / 100)
}
return (
<SettingBlock
className="sub-setting-block"
title="Resize"
titleSuffix={
<div
style={{ width: 86 }}
>{`(${scaledWidth()}x${scaledHeight()})`}</div>
}
desc="Resize the image before inpainting, the area outside the mask will not lose quality."
input={
<Slider
label=""
width={70}
min={50}
max={100}
value={setting.sdScale}
onChange={handleSliderChange}
/>
}
/>
)
}
export default ImageResizeScale

19
lama_cleaner/app/src/components/SidePanel/PESidePanel.tsx
View File

@ -14,6 +14,7 @@ import { Switch, SwitchThumb } from '../shared/Switch'
import Button from '../shared/Button'
import emitter, { EVENT_PAINT_BY_EXAMPLE } from '../../event'
import { useImage } from '../../utils'
import ImageResizeScale from './ImageResizeScale'
const INPUT_WIDTH = 30
@ -68,22 +69,6 @@ const PESidePanel = () => {
</PopoverPrimitive.Trigger>
<PopoverPrimitive.Portal>
<PopoverPrimitive.Content className="side-panel-content">
<SettingBlock
title="Croper"
input={
<Switch
checked={setting.showCroper}
onCheckedChange={value => {
setSettingState(old => {
return { ...old, showCroper: value }
})
}}
>
<SwitchThumb />
</Switch>
}
/>
<NumberInputSetting
title="Steps"
width={INPUT_WIDTH}
@ -97,6 +82,8 @@ const PESidePanel = () => {
}}
/>
<ImageResizeScale />
<NumberInputSetting
title="Guidance Scale"
width={INPUT_WIDTH}

21
lama_cleaner/app/src/components/SidePanel/SidePanel.tsx
View File

@ -1,4 +1,4 @@
import React, { FormEvent, useState } from 'react'
import React, { FormEvent } from 'react'
import { useRecoilState, useRecoilValue } from 'recoil'
import * as PopoverPrimitive from '@radix-ui/react-popover'
import { useToggle } from 'react-use'
@ -15,6 +15,7 @@ import Selector from '../shared/Selector'
import { Switch, SwitchThumb } from '../shared/Switch'
import TextAreaInput from '../shared/Textarea'
import emitter, { EVENT_PROMPT } from '../../event'
import ImageResizeScale from './ImageResizeScale'
const INPUT_WIDTH = 30
@ -71,6 +72,9 @@ const SidePanel = () => {
</Switch>
}
/>
<ImageResizeScale />
{/*
<NumberInputSetting
title="Num Samples"
@ -98,21 +102,6 @@ const SidePanel = () => {
}}
/>
{/* <NumberInputSetting
title="Strength"
width={INPUT_WIDTH}
allowFloat
value={`${setting.sdStrength}`}
desc="TODO"
onValue={value => {
const val = value.length === 0 ? 0 : parseFloat(value)
console.log(val)
setSettingState(old => {
return { ...old, sdStrength: val }
})
}}
/> */}
<NumberInputSetting
title="Guidance Scale"
width={INPUT_WIDTH}

36
lama_cleaner/app/src/store/Atoms.tsx
View File

@ -35,6 +35,8 @@ export interface Rect {
interface AppState {
file: File | undefined
imageHeight: number
imageWidth: number
disableShortCuts: boolean
isInpainting: boolean
isDisableModelSwitch: boolean
@ -49,6 +51,8 @@ export const appState = atom<AppState>({
key: 'appState',
default: {
file: undefined,
imageHeight: 0,
imageWidth: 0,
disableShortCuts: false,
isInpainting: false,
isDisableModelSwitch: false,
@ -82,6 +86,30 @@ export const isInpaintingState = selector({
},
})
export const imageHeightState = selector({
key: 'imageHeightState',
get: ({ get }) => {
const app = get(appState)
return app.imageHeight
},
set: ({ get, set }, newValue: any) => {
const app = get(appState)
set(appState, { ...app, imageHeight: newValue })
},
})
export const imageWidthState = selector({
key: 'imageWidthState',
get: ({ get }) => {
const app = get(appState)
return app.imageWidth
},
set: ({ get, set }, newValue: any) => {
const app = get(appState)
set(appState, { ...app, imageWidth: newValue })
},
})
export const showFileManagerState = selector({
key: 'showFileManager',
get: ({ get }) => {
@ -121,6 +149,12 @@ export const fileState = selector({
isInteractiveSeg: false,
isInteractiveSegRunning: false,
})
const setting = get(settingState)
set(settingState, {
...setting,
sdScale: 100,
})
},
})
@ -282,6 +316,7 @@ export interface Settings {
sdSeedFixed: boolean // true: use sdSeed, false: random generate seed on backend
sdNumSamples: number
sdMatchHistograms: boolean
sdScale: number
// For OpenCV2
cv2Radius: number
@ -409,6 +444,7 @@ export const settingStateDefault: Settings = {
sdSeedFixed: true,
sdNumSamples: 1,
sdMatchHistograms: false,
sdScale: 100,
// CV2
cv2Radius: 5,

22
lama_cleaner/file_manager/file_manager.py
View File

@ -1,11 +1,16 @@
# Copy from https://github.com/silentsokolov/flask-thumbnails/blob/master/flask_thumbnails/thumbnail.py
import os
from functools import lru_cache
from cachetools import TTLCache, cached
import cv2
import time
from io import BytesIO
from pathlib import Path
import numpy as np
from PIL import Image, ImageOps, PngImagePlugin
LARGE_ENOUGH_NUMBER = 100
PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024**2)
PngImagePlugin.MAX_TEXT_CHUNK = LARGE_ENOUGH_NUMBER * (1024 ** 2)
from .storage_backends import FilesystemStorageBackend
from .utils import aspect_to_string, generate_filename, glob_img
@ -18,6 +23,7 @@ class FileManager:
self._default_root_url = "/"
self._default_thumbnail_root_url = "/"
self._default_format = "JPEG"
self.output_dir: Path = None
if app is not None:
self.init_app(app)
@ -41,6 +47,16 @@ class FileManager:
app.config.setdefault("THUMBNAIL_MEDIA_THUMBNAIL_URL", self._default_thumbnail_root_url)
app.config.setdefault("THUMBNAIL_DEFAULT_FORMAT", self._default_format)
def save_to_output_directory(self, image: np.ndarray, filename: str):
fp = Path(filename)
new_name = fp.stem + f"_{int(time.time())}" + fp.suffix
if image.shape[2] == 3:
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
elif image.shape[2] == 4:
image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGRA)
cv2.imwrite(str(self.output_dir / new_name), image)
@property
def root_directory(self):
path = self.app.config["THUMBNAIL_MEDIA_ROOT"]
@ -64,7 +80,7 @@ class FileManager:
return self.app.config["THUMBNAIL_MEDIA_URL"]
@property
@lru_cache()
@cached(cache=TTLCache(maxsize=1024, ttl=30))
def media_names(self):
names = sorted([it.name for it in glob_img(self.root_directory)])
res = []

35
lama_cleaner/model/paint_by_example.py
View File

@ -6,6 +6,9 @@ import cv2
import numpy as np
import torch
from diffusers import DiffusionPipeline
from loguru import logger
from lama_cleaner.helper import resize_max_size
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config
@ -15,15 +18,20 @@ class PaintByExample(InpaintModel):
min_size = 512
def init_model(self, device: torch.device, **kwargs):
fp16 = not kwargs['no_half']
fp16 = not kwargs.get('no_half', False)
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
self.model = DiffusionPipeline.from_pretrained(
"Fantasy-Studio/Paint-by-Example",
torch_dtype=torch_dtype,
**model_kwargs
)
self.model.enable_attention_slicing()
self.model = self.model.to(device)
self.model.enable_attention_slicing()
# TODO: gpu_id
if kwargs.get('cpu_offload', False) and torch.cuda.is_available():
self.model.enable_sequential_cpu_offload(gpu_id=0)
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
@ -49,6 +57,25 @@ class PaintByExample(InpaintModel):
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
downsize_image = resize_max_size(image, size_limit=longer_side_length)
downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
logger.info(
f"Resize image to do paint_by_example: {image.shape} -> {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 < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return inpaint_result
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
@ -58,11 +85,11 @@ class PaintByExample(InpaintModel):
"""
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
crop_image = self._pad_forward(crop_img, crop_mask, config)
crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._pad_forward(image, mask, config)
inpaint_result = self._scaled_pad_forward(image, mask, config)
return inpaint_result

38
lama_cleaner/model/sd.py
View File

@ -8,7 +8,9 @@ from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerD
EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
from loguru import logger
from lama_cleaner.helper import resize_max_size
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.model.utils import torch_gc
from lama_cleaner.schema import Config, SDSampler
@ -18,6 +20,8 @@ class CPUTextEncoderWrapper:
self.text_encoder = text_encoder.to(torch.device('cpu'), non_blocking=True)
self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
self.torch_dtype = torch_dtype
del text_encoder
torch_gc()
def __call__(self, x, **kwargs):
input_device = x.device
@ -30,9 +34,9 @@ class SD(InpaintModel):
def init_model(self, device: torch.device, **kwargs):
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
fp16 = not kwargs['no_half']
fp16 = not kwargs.get('no_half', False)
model_kwargs = {"local_files_only": kwargs['sd_run_local']}
model_kwargs = {"local_files_only": kwargs.get('local_files_only', kwargs['sd_run_local'])}
if kwargs['sd_disable_nsfw']:
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(dict(
@ -48,19 +52,43 @@ class SD(InpaintModel):
use_auth_token=kwargs["hf_access_token"],
**model_kwargs
)
self.model = self.model.to(device)
# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
self.model.enable_attention_slicing()
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
if kwargs.get('sd_enable_xformers', False):
self.model.enable_xformers_memory_efficient_attention()
self.model = self.model.to(device)
if kwargs.get('cpu_offload', False) and torch.cuda.is_available():
# TODO: gpu_id
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
if kwargs['sd_cpu_textencoder']:
logger.info("Run Stable Diffusion TextEncoder on CPU")
self.model.text_encoder = CPUTextEncoderWrapper(self.model.text_encoder, torch_dtype)
self.callback = kwargs.pop("callback", None)
def _scaled_pad_forward(self, image, mask, config: Config):
longer_side_length = int(config.sd_scale * max(image.shape[:2]))
origin_size = image.shape[:2]
downsize_image = resize_max_size(image, size_limit=longer_side_length)
downsize_mask = resize_max_size(mask, size_limit=longer_side_length)
logger.info(
f"Resize image to do sd inpainting: {image.shape} -> {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 < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return inpaint_result
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
@ -126,11 +154,11 @@ class SD(InpaintModel):
# boxes = boxes_from_mask(mask)
if config.use_croper:
crop_img, crop_mask, (l, t, r, b) = self._apply_cropper(image, mask, config)
crop_image = self._pad_forward(crop_img, crop_mask, config)
crop_image = self._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._pad_forward(image, mask, config)
inpaint_result = self._scaled_pad_forward(image, mask, config)
return inpaint_result

6
lama_cleaner/model/utils.py
View File

@ -707,3 +707,9 @@ class Conv2dLayer(torch.nn.Module):
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
out = bias_act(x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp)
return out
def torch_gc():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()

13
lama_cleaner/parse_args.py
View File

@ -15,7 +15,9 @@ def parse_args():
default="lama",
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.5", "cv2", "manga", "sd2", "paint_by_example"],
)
parser.add_argument("--no-half", action="store_true", help="SD/PaintByExample model no half precision")
parser.add_argument("--no-half", action="store_true", help="sd/paint_by_example model no half precision")
parser.add_argument("--cpu-offload", action="store_true",
help="sd/paint_by_example model, offloads all models to CPU, significantly reducing vRAM usage.")
parser.add_argument(
"--hf_access_token",
default="",
@ -34,7 +36,12 @@ def parse_args():
parser.add_argument(
"--sd-run-local",
action="store_true",
help="After first time Stable Diffusion model downloaded, you can add this arg and remove --hf_access_token",
help="After first time Stable Diffusion model downloaded, you can add this arg and remove --hf_access_token.",
)
parser.add_argument(
"--local-files-only",
action="store_true",
help="sd/paint_by_example model. Use local files only, not connect to huggingface server",
)
parser.add_argument(
"--sd-enable-xformers",
@ -80,7 +87,7 @@ def parse_args():
if not output_dir.is_dir():
parser.error(f"invalid --output-dir: {output_dir} is not a directory")
if args.model == 'sd1.5' and not args.sd_run_local:
if args.model == 'sd1.5' and not (args.sd_run_local or args.local_files_only):
if not args.hf_access_token.startswith("hf_"):
parser.error(
f"sd(stable-diffusion) model requires huggingface access token. Check how to get token from: https://huggingface.co/docs/hub/security-tokens"

3
lama_cleaner/schema.py
View File

@ -58,6 +58,9 @@ class Config(BaseModel):
croper_height: int = None
croper_width: int = None
# Resize the image before doing sd inpainting, the area outside the mask will not lose quality.
# Used by sd models and paint_by_example model
sd_scale: float = 1.0
# Blur the edge of mask area. The higher the number the smoother blend with the original image
sd_mask_blur: int = 0
# Ignore this value, it's useless for inpainting

18
lama_cleaner/server.py
View File

@ -31,7 +31,6 @@ except:
pass
from flask import Flask, request, send_file, cli, make_response, send_from_directory, jsonify
from flask_caching import Cache
# Disable ability for Flask to display warning about using a development server in a production environment.
# https://gist.github.com/jerblack/735b9953ba1ab6234abb43174210d356
@ -67,12 +66,9 @@ class NoFlaskwebgui(logging.Filter):
logging.getLogger("werkzeug").addFilter(NoFlaskwebgui())
cache = Cache(config={'CACHE_TYPE': 'SimpleCache'}, with_jinja2_ext=False)
app = Flask(__name__, static_folder=os.path.join(BUILD_DIR, "static"))
app.config["JSON_AS_ASCII"] = False
CORS(app, expose_headers=["Content-Disposition"])
cache.init_app(app)
model: ModelManager = None
thumb = FileManager(app)
@ -96,6 +92,16 @@ def diffuser_callback(i, t, latents):
# socketio.emit('diffusion_step', {'diffusion_step': step})
@app.route("/save_image", methods=["POST"])
def save_image():
# all image in output directory
input = request.files
origin_image_bytes = input["image"].read() # RGB
image, _ = load_img(origin_image_bytes)
thumb.save_to_output_directory(image, request.form["filename"])
return 'ok', 200
@app.route("/medias")
def medias():
# all images in input folder
@ -172,6 +178,7 @@ def process():
croper_y=form["croperY"],
croper_height=form["croperHeight"],
croper_width=form["croperWidth"],
sd_scale=form["sdScale"],
sd_mask_blur=form["sdMaskBlur"],
sd_strength=form["sdStrength"],
sd_steps=form["sdSteps"],
@ -345,6 +352,7 @@ def main(args):
app.config["THUMBNAIL_MEDIA_ROOT"] = args.input
app.config["THUMBNAIL_MEDIA_THUMBNAIL_ROOT"] = os.path.join(args.output_dir, 'thumbnails')
is_enable_file_manager = True
thumb.output_dir = Path(args.output_dir)
else:
input_image_path = args.input
@ -356,6 +364,8 @@ def main(args):
sd_disable_nsfw=args.sd_disable_nsfw,
sd_cpu_textencoder=args.sd_cpu_textencoder,
sd_run_local=args.sd_run_local,
local_files_only=args.local_files_only,
cpu_offload=args.cpu_offload,
sd_enable_xformers=args.sd_enable_xformers,
callback=diffuser_callback,
)

32
lama_cleaner/tests/test_paint_by_example.py
View File

@ -38,7 +38,7 @@ def assert_equal(
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
def test_paint_by_example(strategy):
model = ModelManager(name="paint_by_example", device=device)
cfg = get_config(strategy, paint_by_example_steps=30 if device == 'cuda' else 1)
cfg = get_config(strategy, paint_by_example_steps=30)
assert_equal(
model,
cfg,
@ -46,5 +46,35 @@ def test_paint_by_example(strategy):
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fy=0.9,
fx=1.3,
)
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
def test_paint_by_example_sd_scale(strategy):
model = ModelManager(name="paint_by_example", device=device)
cfg = get_config(strategy, paint_by_example_steps=30, sd_scale=0.85)
assert_equal(
model,
cfg,
f"paint_by_example_{strategy.capitalize()}_sdscale.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fy=0.9,
fx=1.3
)
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
def test_paint_by_example_cpu_offload(strategy):
model = ModelManager(name="paint_by_example", device=device, cpu_offload=True)
cfg = get_config(strategy, paint_by_example_steps=30, sd_scale=0.85)
assert_equal(
model,
cfg,
f"paint_by_example_{strategy.capitalize()}_cpu_offload.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fy=0.9,
fx=1.3
)

64
lama_cleaner/tests/test_sd_model.py
View File

@ -99,8 +99,8 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
sd_disable_nsfw=True,
sd_cpu_textencoder=True,
sd_disable_nsfw=False,
sd_cpu_textencoder=False,
callback=callback)
cfg = get_config(
strategy,
@ -121,3 +121,63 @@ def test_runway_sd_1_5_negative_prompt(sd_device, strategy, sampler):
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1
)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
@pytest.mark.parametrize("cpu_textencoder", [False])
@pytest.mark.parametrize("disable_nsfw", [False])
def test_runway_sd_1_5_sd_scale(sd_device, strategy, sampler, cpu_textencoder, disable_nsfw):
if sd_device == 'cuda' and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
sd_disable_nsfw=disable_nsfw,
sd_cpu_textencoder=cpu_textencoder)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps, sd_scale=0.85)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}_cpu_textencoder_{cpu_textencoder}_disnsfw_{disable_nsfw}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}_sdscale.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
fx=1.3
)
@pytest.mark.parametrize("sd_device", ['cuda'])
@pytest.mark.parametrize("strategy", [HDStrategy.ORIGINAL])
@pytest.mark.parametrize("sampler", [SDSampler.k_euler_a])
def test_runway_sd_1_5_cpu_offload(sd_device, strategy, sampler):
if sd_device == 'cuda' and not torch.cuda.is_available():
return
sd_steps = 50 if sd_device == 'cuda' else 1
model = ModelManager(name="sd1.5",
device=torch.device(sd_device),
hf_access_token="",
sd_run_local=True,
sd_disable_nsfw=False,
sd_cpu_textencoder=False,
cpu_offload=True)
cfg = get_config(strategy, prompt='a fox sitting on a bench', sd_steps=sd_steps, sd_scale=0.85)
cfg.sd_sampler = sampler
name = f"device_{sd_device}_{sampler}"
assert_equal(
model,
cfg,
f"runway_sd_{strategy.capitalize()}_{name}_cpu_offload.png",
img_p=current_dir / "overture-creations-5sI6fQgYIuo.png",
mask_p=current_dir / "overture-creations-5sI6fQgYIuo_mask.png",
)

1
requirements.txt
View File

@ -12,3 +12,4 @@ markupsafe==2.0.1
scikit-image==0.19.3
diffusers[torch]==0.10.2
transformers>=4.25.1
cachetools==5.2.0