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Merge branch 'diffusers_0.9'

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Qing
2022-12-04 20:50:19 +08:00
parent fe0e13b9f7 eadb79af83
commit e15146dad4
11 changed files with 46 additions and 35 deletions
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4
lama_cleaner/app/build/asset-manifest.json
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@@ -1,7 +1,7 @@
{ {
"files": { "files": {
"main.css": "/static/css/main.bb67386a.chunk.css", "main.css": "/static/css/main.bb67386a.chunk.css",
"main.js": "/static/js/main.3c80dc94.chunk.js", "main.js": "/static/js/main.5cf6948e.chunk.js",
"runtime-main.js": "/static/js/runtime-main.5e86ac81.js", "runtime-main.js": "/static/js/runtime-main.5e86ac81.js",
"static/js/2.ee9dcc6c.chunk.js": "/static/js/2.ee9dcc6c.chunk.js", "static/js/2.ee9dcc6c.chunk.js": "/static/js/2.ee9dcc6c.chunk.js",
"index.html": "/index.html", "index.html": "/index.html",
@@ -12,6 +12,6 @@
"static/js/runtime-main.5e86ac81.js", "static/js/runtime-main.5e86ac81.js",
"static/js/2.ee9dcc6c.chunk.js", "static/js/2.ee9dcc6c.chunk.js",
"static/css/main.bb67386a.chunk.css", "static/css/main.bb67386a.chunk.css",
"static/js/main.3c80dc94.chunk.js" "static/js/main.5cf6948e.chunk.js"
] ]
} }

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lama_cleaner/app/build/index.html
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@@ -1 +1 @@
<!doctype html><html lang="en"><head><meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate"/><meta http-equiv="Pragma" content="no-cache"/><meta http-equiv="Expires" content="0"/><meta charset="utf-8"/><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=0"/><meta name="theme-color" content="#ffffff"/><title>lama-cleaner - Image inpainting powered by SOTA AI model</title><link href="/static/css/main.bb67386a.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,l,a=r[0],f=r[1],i=r[2],p=0,s=[];p<a.length;p++)l=a[p],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in f)Object.prototype.hasOwnProperty.call(f,n)&&(e[n]=f[n]);for(c&&c(r);s.length;)s.shift()();return u.push.apply(u,i||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var f=t[a];0!==o[f]&&(n=!1)}n&&(u.splice(r--,1),e=l(l.s=t[0]))}return e}var n={},o={1:0},u=[];function l(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,l),t.l=!0,t.exports}l.m=e,l.c=n,l.d=function(e,r,t){l.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},l.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,r){if(1&r&&(e=l(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(l.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)l.d(t,n,function(r){return e[r]}.bind(null,n));return t},l.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(r,"a",r),r},l.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},l.p="/";var a=this["webpackJsonplama-cleaner"]=this["webpackJsonplama-cleaner"]||[],f=a.push.bind(a);a.push=r,a=a.slice();for(var i=0;i<a.length;i++)r(a[i]);var c=f;t()}([])</script><script src="/static/js/2.ee9dcc6c.chunk.js"></script><script src="/static/js/main.3c80dc94.chunk.js"></script></body></html> <!doctype html><html lang="en"><head><meta http-equiv="Cache-Control" content="no-cache, no-store, must-revalidate"/><meta http-equiv="Pragma" content="no-cache"/><meta http-equiv="Expires" content="0"/><meta charset="utf-8"/><meta name="viewport" content="width=device-width,initial-scale=1,maximum-scale=1,user-scalable=0"/><meta name="theme-color" content="#ffffff"/><title>lama-cleaner - Image inpainting powered by SOTA AI model</title><link href="/static/css/main.bb67386a.chunk.css" rel="stylesheet"></head><body><noscript>You need to enable JavaScript to run this app.</noscript><div id="root"></div><script>!function(e){function r(r){for(var n,l,a=r[0],f=r[1],i=r[2],p=0,s=[];p<a.length;p++)l=a[p],Object.prototype.hasOwnProperty.call(o,l)&&o[l]&&s.push(o[l][0]),o[l]=0;for(n in f)Object.prototype.hasOwnProperty.call(f,n)&&(e[n]=f[n]);for(c&&c(r);s.length;)s.shift()();return u.push.apply(u,i||[]),t()}function t(){for(var e,r=0;r<u.length;r++){for(var t=u[r],n=!0,a=1;a<t.length;a++){var f=t[a];0!==o[f]&&(n=!1)}n&&(u.splice(r--,1),e=l(l.s=t[0]))}return e}var n={},o={1:0},u=[];function l(r){if(n[r])return n[r].exports;var t=n[r]={i:r,l:!1,exports:{}};return e[r].call(t.exports,t,t.exports,l),t.l=!0,t.exports}l.m=e,l.c=n,l.d=function(e,r,t){l.o(e,r)||Object.defineProperty(e,r,{enumerable:!0,get:t})},l.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},l.t=function(e,r){if(1&r&&(e=l(e)),8&r)return e;if(4&r&&"object"==typeof e&&e&&e.__esModule)return e;var t=Object.create(null);if(l.r(t),Object.defineProperty(t,"default",{enumerable:!0,value:e}),2&r&&"string"!=typeof e)for(var n in e)l.d(t,n,function(r){return e[r]}.bind(null,n));return t},l.n=function(e){var r=e&&e.__esModule?function(){return e.default}:function(){return e};return l.d(r,"a",r),r},l.o=function(e,r){return Object.prototype.hasOwnProperty.call(e,r)},l.p="/";var a=this["webpackJsonplama-cleaner"]=this["webpackJsonplama-cleaner"]||[],f=a.push.bind(a);a.push=r,a=a.slice();for(var i=0;i<a.length;i++)r(a[i]);var c=f;t()}([])</script><script src="/static/js/2.ee9dcc6c.chunk.js"></script><script src="/static/js/main.5cf6948e.chunk.js"></script></body></html>

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lama_cleaner/app/build/static/js/main.5cf6948e.chunk.js Normal file
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lama_cleaner/app/src/components/Settings/ModelSettingBlock.tsx
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@@ -191,6 +191,8 @@ function ModelSettingBlock() {
return renderFCFModelDesc() return renderFCFModelDesc()
case AIModel.SD15: case AIModel.SD15:
return undefined return undefined
case AIModel.SD2:
return undefined
case AIModel.Mange: case AIModel.Mange:
return undefined return undefined
case AIModel.CV2: case AIModel.CV2:
@@ -234,10 +236,16 @@ function ModelSettingBlock() {
) )
case AIModel.SD15: case AIModel.SD15:
return renderModelDesc( return renderModelDesc(
'Stable Diffusion', 'Stable Diffusion 1.5',
'https://ommer-lab.com/research/latent-diffusion-models/', 'https://ommer-lab.com/research/latent-diffusion-models/',
'https://github.com/CompVis/stable-diffusion' 'https://github.com/CompVis/stable-diffusion'
) )
case AIModel.SD2:
return renderModelDesc(
'Stable Diffusion 2',
'https://ommer-lab.com/research/latent-diffusion-models/',
'https://github.com/Stability-AI/stablediffusion'
)
case AIModel.Mange: case AIModel.Mange:
return renderModelDesc( return renderModelDesc(
'Manga Inpainting', 'Manga Inpainting',

13
lama_cleaner/app/src/store/Atoms.tsx
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@@ -10,6 +10,7 @@ export enum AIModel {
MAT = 'mat', MAT = 'mat',
FCF = 'fcf', FCF = 'fcf',
SD15 = 'sd1.5', SD15 = 'sd1.5',
SD2 = 'sd2',
CV2 = 'cv2', CV2 = 'cv2',
Mange = 'manga', Mange = 'manga',
} }
@@ -294,7 +295,14 @@ const defaultHDSettings: ModelsHDSettings = {
hdStrategyResizeLimit: 768, hdStrategyResizeLimit: 768,
hdStrategyCropTrigerSize: 512, hdStrategyCropTrigerSize: 512,
hdStrategyCropMargin: 128, hdStrategyCropMargin: 128,
enabled: true, enabled: false,
},
[AIModel.SD2]: {
hdStrategy: HDStrategy.ORIGINAL,
hdStrategyResizeLimit: 768,
hdStrategyCropTrigerSize: 512,
hdStrategyCropMargin: 128,
enabled: false,
}, },
[AIModel.Mange]: { [AIModel.Mange]: {
hdStrategy: HDStrategy.CROP, hdStrategy: HDStrategy.CROP,
@@ -318,6 +326,7 @@ export enum SDSampler {
klms = 'k_lms', klms = 'k_lms',
kEuler = 'k_euler', kEuler = 'k_euler',
kEulerA = 'k_euler_a', kEulerA = 'k_euler_a',
dpmPlusPlus = 'dpm++',
} }
export enum SDMode { export enum SDMode {
@@ -422,7 +431,7 @@ export const isSDState = selector({
key: 'isSD', key: 'isSD',
get: ({ get }) => { get: ({ get }) => {
const settings = get(settingState) const settings = get(settingState)
return settings.model === AIModel.SD15 return settings.model === AIModel.SD15 || settings.model === AIModel.SD2
}, },
}) })

36
lama_cleaner/model/sd.py
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@@ -5,7 +5,7 @@ import cv2
import numpy as np import numpy as np
import torch import torch
from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \ from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
EulerAncestralDiscreteScheduler EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler
from loguru import logger from loguru import logger
from lama_cleaner.model.base import InpaintModel from lama_cleaner.model.base import InpaintModel
@@ -102,27 +102,20 @@ class SD(InpaintModel):
# image = torch.from_numpy(image).unsqueeze(0).to(self.device) # image = torch.from_numpy(image).unsqueeze(0).to(self.device)
# mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) # mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
scheduler_kwargs = dict( scheduler_config = self.model.scheduler.config
beta_schedule="scaled_linear",
beta_start=0.00085,
beta_end=0.012,
num_train_timesteps=1000,
)
if config.sd_sampler == SDSampler.ddim: if config.sd_sampler == SDSampler.ddim:
scheduler = DDIMScheduler( scheduler = DDIMScheduler.from_config(scheduler_config)
**scheduler_kwargs,
clip_sample=False,
set_alpha_to_one=False,
)
elif config.sd_sampler == SDSampler.pndm: elif config.sd_sampler == SDSampler.pndm:
scheduler = PNDMScheduler(**scheduler_kwargs, skip_prk_steps=True) scheduler = PNDMScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.k_lms: elif config.sd_sampler == SDSampler.k_lms:
scheduler = LMSDiscreteScheduler(**scheduler_kwargs) scheduler = LMSDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.k_euler: elif config.sd_sampler == SDSampler.k_euler:
scheduler = EulerDiscreteScheduler(**scheduler_kwargs) scheduler = EulerDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.k_euler_a: elif config.sd_sampler == SDSampler.k_euler_a:
scheduler = EulerAncestralDiscreteScheduler(**scheduler_kwargs) scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.dpm_plus_plus:
scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config)
else: else:
raise ValueError(config.sd_sampler) raise ValueError(config.sd_sampler)
@@ -138,13 +131,10 @@ class SD(InpaintModel):
k = 2 * config.sd_mask_blur + 1 k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis] mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
_kwargs = {
self.image_key: PIL.Image.fromarray(image),
}
img_h, img_w = image.shape[:2] img_h, img_w = image.shape[:2]
output = self.model( output = self.model(
image=PIL.Image.fromarray(image),
prompt=config.prompt, prompt=config.prompt,
negative_prompt=config.negative_prompt, negative_prompt=config.negative_prompt,
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
@@ -155,7 +145,6 @@ class SD(InpaintModel):
callback=self.callback, callback=self.callback,
height=img_h, height=img_h,
width=img_w, width=img_w,
**_kwargs
).images[0] ).images[0]
output = (output * 255).round().astype("uint8") output = (output * 255).round().astype("uint8")
@@ -217,4 +206,7 @@ class SD(InpaintModel):
class SD15(SD): class SD15(SD):
model_id_or_path = "runwayml/stable-diffusion-inpainting" model_id_or_path = "runwayml/stable-diffusion-inpainting"
image_key = "image"
class SD2(SD):
model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"

5
lama_cleaner/model_manager.py
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@@ -5,12 +5,13 @@ from lama_cleaner.model.lama import LaMa
from lama_cleaner.model.ldm import LDM from lama_cleaner.model.ldm import LDM
from lama_cleaner.model.manga import Manga from lama_cleaner.model.manga import Manga
from lama_cleaner.model.mat import MAT from lama_cleaner.model.mat import MAT
from lama_cleaner.model.sd import SD15 from lama_cleaner.model.sd import SD15, SD2
from lama_cleaner.model.zits import ZITS from lama_cleaner.model.zits import ZITS
from lama_cleaner.model.opencv2 import OpenCV2 from lama_cleaner.model.opencv2 import OpenCV2
from lama_cleaner.schema import Config from lama_cleaner.schema import Config
models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.5": SD15, "cv2": OpenCV2, "manga": Manga} models = {"lama": LaMa, "ldm": LDM, "zits": ZITS, "mat": MAT, "fcf": FcF, "sd1.5": SD15, "cv2": OpenCV2, "manga": Manga,
"sd2": SD2}
class ModelManager: class ModelManager:

6
lama_cleaner/parse_args.py
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@@ -10,7 +10,7 @@ def parse_args():
parser.add_argument( parser.add_argument(
"--model", "--model",
default="lama", default="lama",
choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.5", "cv2", "manga"], choices=["lama", "ldm", "zits", "mat", "fcf", "sd1.5", "cv2", "manga", "sd2"],
) )
parser.add_argument( parser.add_argument(
"--hf_access_token", "--hf_access_token",
@@ -37,7 +37,7 @@ def parse_args():
action="store_true", action="store_true",
help="Enable xFormers optimizations. Requires that xformers package has been installed. See: https://github.com/facebookresearch/xformers" help="Enable xFormers optimizations. Requires that xformers package has been installed. See: https://github.com/facebookresearch/xformers"
) )
parser.add_argument("--device", default="cuda", type=str, choices=["cuda", "cpu"]) parser.add_argument("--device", default="cuda", type=str, choices=["cuda", "cpu", "mps"])
parser.add_argument("--gui", action="store_true", help="Launch as desktop app") parser.add_argument("--gui", action="store_true", help="Launch as desktop app")
parser.add_argument( parser.add_argument(
"--gui-size", "--gui-size",
@@ -59,7 +59,7 @@ def parse_args():
if imghdr.what(args.input) is None: if imghdr.what(args.input) is None:
parser.error(f"invalid --input: {args.input} is not a valid image file") parser.error(f"invalid --input: {args.input} is not a valid image file")
if args.model.startswith("sd") and not args.sd_run_local: if args.model == 'sd1.5' and not args.sd_run_local:
if not args.hf_access_token.startswith("hf_"): if not args.hf_access_token.startswith("hf_"):
parser.error( parser.error(
f"sd(stable-diffusion) model requires huggingface access token. Check how to get token from: https://huggingface.co/docs/hub/security-tokens" f"sd(stable-diffusion) model requires huggingface access token. Check how to get token from: https://huggingface.co/docs/hub/security-tokens"

1
lama_cleaner/schema.py
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@@ -25,6 +25,7 @@ class SDSampler(str, Enum):
k_lms = "k_lms" k_lms = "k_lms"
k_euler = 'k_euler' k_euler = 'k_euler'
k_euler_a = 'k_euler_a' k_euler_a = 'k_euler_a'
dpm_plus_plus = 'dpm++'
class Config(BaseModel): class Config(BaseModel):

2
requirements.txt
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@@ -10,5 +10,5 @@ pytest
yacs yacs
markupsafe==2.0.1 markupsafe==2.0.1
scikit-image==0.19.3 scikit-image==0.19.3
diffusers[torch]==0.7.2 diffusers[torch]==0.9
transformers==4.21.0 transformers==4.21.0