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add download command

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Qing 2023-11-16 21:12:06 +08:00
parent 20e660aa4a
commit 1d145d1cd6
17 changed files with 233 additions and 67 deletions
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2
lama_cleaner/__init__.py
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@ -11,6 +11,8 @@ from lama_cleaner.parse_args import parse_args
def entry_point():
args = parse_args()
if args is None:
return
# To make os.environ["XDG_CACHE_HOME"] = args.model_cache_dir works for diffusers
# https://github.com/huggingface/diffusers/blob/be99201a567c1ccd841dc16fb24e88f7f239c187/src/diffusers/utils/constants.py#L18
from lama_cleaner.server import main

28
lama_cleaner/diffusers_utils.py Normal file
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@ -0,0 +1,28 @@
import json
from pathlib import Path
from typing import Dict, List
def folder_name_to_show_name(name: str) -> str:
return name.replace("models--", "").replace("--", "/")
def _scan_models(cache_dir, class_name: str) -> List[str]:
cache_dir = Path(cache_dir)
res = []
for it in cache_dir.glob("**/*/model_index.json"):
with open(it, "r", encoding="utf-8") as f:
data = json.load(f)
if data["_class_name"] == class_name:
name = folder_name_to_show_name(it.parent.parent.parent.name)
if name not in res:
res.append(name)
return res
def scan_models(cache_dir) -> List[str]:
return _scan_models(cache_dir, "StableDiffusionPipeline")
def scan_inpainting_models(cache_dir) -> List[str]:
return _scan_models(cache_dir, "StableDiffusionInpaintPipeline")

24
lama_cleaner/download.py Normal file
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@ -0,0 +1,24 @@
import os
from loguru import logger
from pathlib import Path
def cli_download_model(model: str, model_dir: str):
if os.path.isfile(model_dir):
raise ValueError(f"invalid --model-dir: {model_dir} is a file")
if not os.path.exists(model_dir):
logger.info(f"Create model cache directory: {model_dir}")
Path(model_dir).mkdir(exist_ok=True, parents=True)
os.environ["XDG_CACHE_HOME"] = model_dir
from lama_cleaner.model_manager import models
if model in models:
logger.info(f"Downloading {model}...")
models[model].download()
logger.info(f"Done.")
else:
logger.info(f"Downloading model from Huggingface: {model}")

4
lama_cleaner/model/base.py
View File

@ -51,6 +51,10 @@ class InpaintModel:
"""
...
@staticmethod
def download():
...
def _pad_forward(self, image, mask, config: Config):
origin_height, origin_width = image.shape[:2]
pad_image = pad_img_to_modulo(

7
lama_cleaner/model/fcf.py
View File

@ -14,6 +14,7 @@ from lama_cleaner.helper import (
norm_img,
boxes_from_mask,
resize_max_size,
download_model,
)
from lama_cleaner.model.base import InpaintModel
from torch import conv2d, nn
@ -870,7 +871,6 @@ class SpectralTransform(nn.Module):
)
def forward(self, x):
x = self.downsample(x)
x = self.conv1(x)
output = self.fu(x)
@ -1437,7 +1437,6 @@ class SynthesisNetwork(torch.nn.Module):
setattr(self, f"b{res}", block)
def forward(self, x_global, mask, feats, ws, fname=None, **block_kwargs):
img = None
x, img = self.foreword(x_global, ws, feats, img)
@ -1656,6 +1655,10 @@ class FcF(InpaintModel):
self.model = load_model(G, FCF_MODEL_URL, device, FCF_MODEL_MD5)
self.label = torch.zeros([1, self.model.c_dim], device=device)
@staticmethod
def download():
download_model(FCF_MODEL_URL, FCF_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
return os.path.exists(get_cache_path_by_url(FCF_MODEL_URL))

36
lama_cleaner/model/instruct_pix2pix.py
View File

@ -4,7 +4,6 @@ import torch
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import set_seed
from lama_cleaner.schema import Config
@ -15,18 +14,21 @@ class InstructPix2Pix(DiffusionInpaintModel):
def init_model(self, device: torch.device, **kwargs):
from diffusers import StableDiffusionInstructPix2PixPipeline
fp16 = not kwargs.get('no_half', False)
model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
fp16 = not kwargs.get("no_half", False)
model_kwargs = {"local_files_only": kwargs.get("local_files_only", False)}
if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False
))
model_kwargs.update(
dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
)
use_gpu = device == torch.device('cuda') and torch.cuda.is_available()
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix",
@ -36,15 +38,23 @@ class InstructPix2Pix(DiffusionInpaintModel):
)
self.model.enable_attention_slicing()
if kwargs.get('enable_xformers', False):
if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
if kwargs.get('cpu_offload', False) and use_gpu:
if kwargs.get("cpu_offload", False) and use_gpu:
logger.info("Enable sequential cpu offload")
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
@staticmethod
def download():
from diffusers import StableDiffusionInstructPix2PixPipeline
StableDiffusionInstructPix2PixPipeline.from_pretrained(
"timbrooks/instruct-pix2pix", revision="fp16"
)
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
@ -60,7 +70,7 @@ class InstructPix2Pix(DiffusionInpaintModel):
image_guidance_scale=config.p2p_image_guidance_scale,
guidance_scale=config.p2p_guidance_scale,
output_type="np",
generator=torch.manual_seed(config.sd_seed)
generator=torch.manual_seed(config.sd_seed),
).images[0]
output = (output * 255).round().astype("uint8")

8
lama_cleaner/model/kandinsky.py
View File

@ -76,3 +76,11 @@ class Kandinsky(DiffusionInpaintModel):
class Kandinsky22(Kandinsky):
name = "kandinsky2.2"
model_name = "kandinsky-community/kandinsky-2-2-decoder-inpaint"
@staticmethod
def download():
from diffusers import AutoPipelineForInpainting
AutoPipelineForInpainting.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder-inpaint"
)

5
lama_cleaner/model/lama.py
View File

@ -8,6 +8,7 @@ from lama_cleaner.helper import (
norm_img,
get_cache_path_by_url,
load_jit_model,
download_model,
)
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config
@ -23,6 +24,10 @@ class LaMa(InpaintModel):
name = "lama"
pad_mod = 8
@staticmethod
def download():
download_model(LAMA_MODEL_URL, LAMA_MODEL_MD5)
def init_model(self, device, **kwargs):
self.model = load_jit_model(LAMA_MODEL_URL, device, LAMA_MODEL_MD5).eval()

6
lama_cleaner/model/ldm.py
View File

@ -260,6 +260,12 @@ class LDM(InpaintModel):
self.model = LatentDiffusion(self.diffusion_model, device)
@staticmethod
def download():
download_model(LDM_DIFFUSION_MODEL_URL, LDM_DIFFUSION_MODEL_MD5)
download_model(LDM_DECODE_MODEL_URL, LDM_DECODE_MODEL_MD5)
download_model(LDM_ENCODE_MODEL_URL, LDM_ENCODE_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
model_paths = [

7
lama_cleaner/model/manga.py
View File

@ -7,7 +7,7 @@ import torch
import time
from loguru import logger
from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
from lama_cleaner.helper import get_cache_path_by_url, load_jit_model, download_model
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.schema import Config
@ -42,6 +42,11 @@ class Manga(InpaintModel):
)
self.seed = 42
@staticmethod
def download():
download_model(MANGA_INPAINTOR_MODEL_URL, MANGA_INPAINTOR_MODEL_MD5)
download_model(MANGA_LINE_MODEL_URL, MANGA_LINE_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
model_paths = [

53
lama_cleaner/model/mat.py
View File

@ -8,7 +8,12 @@ import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from lama_cleaner.helper import load_model, get_cache_path_by_url, norm_img
from lama_cleaner.helper import (
load_model,
get_cache_path_by_url,
norm_img,
download_model,
)
from lama_cleaner.model.base import InpaintModel
from lama_cleaner.model.utils import (
setup_filter,
@ -52,7 +57,7 @@ class ModulatedConv2d(nn.Module):
)
self.out_channels = out_channels
self.kernel_size = kernel_size
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
self.padding = self.kernel_size // 2
self.up = up
self.down = down
@ -213,7 +218,7 @@ class DecBlockFirst(nn.Module):
super().__init__()
self.fc = FullyConnectedLayer(
in_features=in_channels * 2,
out_features=in_channels * 4 ** 2,
out_features=in_channels * 4**2,
activation=activation,
)
self.conv = StyleConv(
@ -312,7 +317,7 @@ class DecBlock(nn.Module):
in_channels=in_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
up=2,
use_noise=use_noise,
@ -323,7 +328,7 @@ class DecBlock(nn.Module):
in_channels=out_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
use_noise=use_noise,
activation=activation,
@ -507,7 +512,7 @@ class Discriminator(torch.nn.Module):
self.img_channels = img_channels
resolution_log2 = int(np.log2(img_resolution))
assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
assert img_resolution == 2**resolution_log2 and img_resolution >= 4
self.resolution_log2 = resolution_log2
def nf(stage):
@ -543,7 +548,7 @@ class Discriminator(torch.nn.Module):
)
self.Dis = nn.Sequential(*Dis)
self.fc0 = FullyConnectedLayer(nf(2) * 4 ** 2, nf(2), activation=activation)
self.fc0 = FullyConnectedLayer(nf(2) * 4**2, nf(2), activation=activation)
self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
def forward(self, images_in, masks_in, c):
@ -562,7 +567,7 @@ class Discriminator(torch.nn.Module):
def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512):
NF = {512: 64, 256: 128, 128: 256, 64: 512, 32: 512, 16: 512, 8: 512, 4: 512}
return NF[2 ** stage]
return NF[2**stage]
class Mlp(nn.Module):
@ -659,7 +664,7 @@ class Conv2dLayerPartial(nn.Module):
)
self.weight_maskUpdater = torch.ones(1, 1, kernel_size, kernel_size)
self.slide_winsize = kernel_size ** 2
self.slide_winsize = kernel_size**2
self.stride = down
self.padding = kernel_size // 2 if kernel_size % 2 == 1 else 0
@ -715,7 +720,7 @@ class WindowAttention(nn.Module):
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = qk_scale or head_dim**-0.5
self.q = FullyConnectedLayer(in_features=dim, out_features=dim)
self.k = FullyConnectedLayer(in_features=dim, out_features=dim)
@ -1211,7 +1216,7 @@ class Encoder(nn.Module):
self.resolution = []
for idx, i in enumerate(range(res_log2, 3, -1)): # from input size to 16x16
res = 2 ** i
res = 2**i
self.resolution.append(res)
if i == res_log2:
block = EncFromRGB(img_channels * 2 + 1, nf(i), activation)
@ -1296,7 +1301,7 @@ class DecBlockFirstV2(nn.Module):
in_channels=in_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
use_noise=use_noise,
activation=activation,
@ -1341,7 +1346,7 @@ class DecBlock(nn.Module):
in_channels=in_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
up=2,
use_noise=use_noise,
@ -1352,7 +1357,7 @@ class DecBlock(nn.Module):
in_channels=out_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
use_noise=use_noise,
activation=activation,
@ -1389,7 +1394,7 @@ class Decoder(nn.Module):
for res in range(5, res_log2 + 1):
setattr(
self,
"Dec_%dx%d" % (2 ** res, 2 ** res),
"Dec_%dx%d" % (2**res, 2**res),
DecBlock(
res,
nf(res - 1),
@ -1406,7 +1411,7 @@ class Decoder(nn.Module):
def forward(self, x, ws, gs, E_features, noise_mode="random"):
x, img = self.Dec_16x16(x, ws, gs, E_features, noise_mode=noise_mode)
for res in range(5, self.res_log2 + 1):
block = getattr(self, "Dec_%dx%d" % (2 ** res, 2 ** res))
block = getattr(self, "Dec_%dx%d" % (2**res, 2**res))
x, img = block(x, img, ws, gs, E_features, noise_mode=noise_mode)
return img
@ -1431,7 +1436,7 @@ class DecStyleBlock(nn.Module):
in_channels=in_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
up=2,
use_noise=use_noise,
@ -1442,7 +1447,7 @@ class DecStyleBlock(nn.Module):
in_channels=out_channels,
out_channels=out_channels,
style_dim=style_dim,
resolution=2 ** res,
resolution=2**res,
kernel_size=3,
use_noise=use_noise,
activation=activation,
@ -1640,7 +1645,7 @@ class SynthesisNet(nn.Module):
):
super().__init__()
resolution_log2 = int(np.log2(img_resolution))
assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
assert img_resolution == 2**resolution_log2 and img_resolution >= 4
self.num_layers = resolution_log2 * 2 - 3 * 2
self.img_resolution = img_resolution
@ -1781,7 +1786,7 @@ class Discriminator(torch.nn.Module):
self.img_channels = img_channels
resolution_log2 = int(np.log2(img_resolution))
assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
assert img_resolution == 2**resolution_log2 and img_resolution >= 4
self.resolution_log2 = resolution_log2
if cmap_dim == None:
@ -1812,7 +1817,7 @@ class Discriminator(torch.nn.Module):
)
self.Dis = nn.Sequential(*Dis)
self.fc0 = FullyConnectedLayer(nf(2) * 4 ** 2, nf(2), activation=activation)
self.fc0 = FullyConnectedLayer(nf(2) * 4**2, nf(2), activation=activation)
self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
# for 64x64
@ -1837,7 +1842,7 @@ class Discriminator(torch.nn.Module):
self.Dis_stg1 = nn.Sequential(*Dis_stg1)
self.fc0_stg1 = FullyConnectedLayer(
nf(2) // 2 * 4 ** 2, nf(2) // 2, activation=activation
nf(2) // 2 * 4**2, nf(2) // 2, activation=activation
)
self.fc1_stg1 = FullyConnectedLayer(
nf(2) // 2, 1 if cmap_dim == 0 else cmap_dim
@ -1898,6 +1903,10 @@ class MAT(InpaintModel):
self.label = torch.zeros([1, self.model.c_dim], device=device).to(self.torch_dtype)
# fmt: on
@staticmethod
def download():
download_model(MAT_MODEL_URL, MAT_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
return os.path.exists(get_cache_path_by_url(MAT_MODEL_URL))

39
lama_cleaner/model/paint_by_example.py
View File

@ -2,11 +2,9 @@ import PIL
import PIL.Image
import cv2
import torch
from diffusers import DiffusionPipeline
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import set_seed
from lama_cleaner.schema import Config
@ -16,35 +14,40 @@ class PaintByExample(DiffusionInpaintModel):
min_size = 512
def init_model(self, device: torch.device, **kwargs):
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)}
from diffusers import DiffusionPipeline
if kwargs['disable_nsfw'] or kwargs.get('cpu_offload', False):
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)}
if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
logger.info("Disable Paint By Example Model NSFW checker")
model_kwargs.update(dict(
safety_checker=None,
requires_safety_checker=False
))
model_kwargs.update(
dict(safety_checker=None, requires_safety_checker=False)
)
self.model = DiffusionPipeline.from_pretrained(
"Fantasy-Studio/Paint-by-Example",
torch_dtype=torch_dtype,
**model_kwargs
"Fantasy-Studio/Paint-by-Example", torch_dtype=torch_dtype, **model_kwargs
)
self.model.enable_attention_slicing()
if kwargs.get('enable_xformers', False):
if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
# TODO: gpu_id
if kwargs.get('cpu_offload', False) and use_gpu:
if kwargs.get("cpu_offload", False) and use_gpu:
self.model.image_encoder = self.model.image_encoder.to(device)
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
@staticmethod
def download():
from diffusers import DiffusionPipeline
DiffusionPipeline.from_pretrained("Fantasy-Studio/Paint-by-Example")
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
@ -56,8 +59,8 @@ class PaintByExample(DiffusionInpaintModel):
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
example_image=config.paint_by_example_example_image,
num_inference_steps=config.paint_by_example_steps,
output_type='np.array',
generator=torch.manual_seed(config.paint_by_example_seed)
output_type="np.array",
generator=torch.manual_seed(config.paint_by_example_seed),
).images[0]
output = (output * 255).round().astype("uint8")

6
lama_cleaner/model/sd.py
View File

@ -132,6 +132,12 @@ class SD(DiffusionInpaintModel):
# model will be downloaded when app start, and can't switch in frontend settings
return True
@classmethod
def download(cls):
from diffusers import StableDiffusionInpaintPipeline
StableDiffusionInpaintPipeline.from_pretrained(cls.model_id_or_path)
class SD15(SD):
name = "sd1.5"

10
lama_cleaner/model/sdxl.py
View File

@ -5,7 +5,6 @@ import torch
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import torch_gc, get_scheduler
from lama_cleaner.schema import Config
@ -51,6 +50,14 @@ class SDXL(DiffusionInpaintModel):
self.callback = kwargs.pop("callback", None)
@staticmethod
def download():
from diffusers import AutoPipelineForInpainting
AutoPipelineForInpainting.from_pretrained(
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
)
def forward(self, image, mask, config: Config):
"""Input image and output image have same size
image: [H, W, C] RGB
@ -85,7 +92,6 @@ class SDXL(DiffusionInpaintModel):
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings

42
lama_cleaner/model/zits.py
View File

@ -5,7 +5,7 @@ import cv2
import torch
import torch.nn.functional as F
from lama_cleaner.helper import get_cache_path_by_url, load_jit_model
from lama_cleaner.helper import get_cache_path_by_url, load_jit_model, download_model
from lama_cleaner.schema import Config
import numpy as np
@ -171,14 +171,19 @@ def load_image(img, mask, device, sigma256=3.0):
try:
import skimage
gray_256 = skimage.color.rgb2gray(img_256)
edge_256 = skimage.feature.canny(gray_256, sigma=3.0, mask=None).astype(float)
# cv2.imwrite("skimage_gray.jpg", (gray_256*255).astype(np.uint8))
# cv2.imwrite("skimage_edge.jpg", (edge_256*255).astype(np.uint8))
except:
gray_256 = cv2.cvtColor(img_256, cv2.COLOR_RGB2GRAY)
gray_256_blured = cv2.GaussianBlur(gray_256, ksize=(7, 7), sigmaX=sigma256, sigmaY=sigma256)
edge_256 = cv2.Canny(gray_256_blured, threshold1=int(255*0.1), threshold2=int(255*0.2))
gray_256_blured = cv2.GaussianBlur(
gray_256, ksize=(7, 7), sigmaX=sigma256, sigmaY=sigma256
)
edge_256 = cv2.Canny(
gray_256_blured, threshold1=int(255 * 0.1), threshold2=int(255 * 0.2)
)
# cv2.imwrite("opencv_edge.jpg", edge_256)
@ -233,12 +238,27 @@ class ZITS(InpaintModel):
self.sample_edge_line_iterations = 1
def init_model(self, device, **kwargs):
self.wireframe = load_jit_model(ZITS_WIRE_FRAME_MODEL_URL, device, ZITS_WIRE_FRAME_MODEL_MD5)
self.edge_line = load_jit_model(ZITS_EDGE_LINE_MODEL_URL, device, ZITS_EDGE_LINE_MODEL_MD5)
self.wireframe = load_jit_model(
ZITS_WIRE_FRAME_MODEL_URL, device, ZITS_WIRE_FRAME_MODEL_MD5
)
self.edge_line = load_jit_model(
ZITS_EDGE_LINE_MODEL_URL, device, ZITS_EDGE_LINE_MODEL_MD5
)
self.structure_upsample = load_jit_model(
ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, device, ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5
)
self.inpaint = load_jit_model(ZITS_INPAINT_MODEL_URL, device, ZITS_INPAINT_MODEL_MD5)
self.inpaint = load_jit_model(
ZITS_INPAINT_MODEL_URL, device, ZITS_INPAINT_MODEL_MD5
)
@staticmethod
def download():
download_model(ZITS_WIRE_FRAME_MODEL_URL, ZITS_WIRE_FRAME_MODEL_MD5)
download_model(ZITS_EDGE_LINE_MODEL_URL, ZITS_EDGE_LINE_MODEL_MD5)
download_model(
ZITS_STRUCTURE_UPSAMPLE_MODEL_URL, ZITS_STRUCTURE_UPSAMPLE_MODEL_MD5
)
download_model(ZITS_INPAINT_MODEL_URL, ZITS_INPAINT_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
@ -385,12 +405,20 @@ class ZITS(InpaintModel):
if score > mask_th:
try:
import skimage
rr, cc, value = skimage.draw.line_aa(
*to_int(line[0:2]), *to_int(line[2:4])
)
lmap[rr, cc] = np.maximum(lmap[rr, cc], value)
except:
cv2.line(lmap, to_int(line[0:2][::-1]), to_int(line[2:4][::-1]), (1, 1, 1), 1, cv2.LINE_AA)
cv2.line(
lmap,
to_int(line[0:2][::-1]),
to_int(line[2:4][::-1]),
(1, 1, 1),
1,
cv2.LINE_AA,
)
lmap = np.clip(lmap * 255, 0, 255).astype(np.uint8)
lines_tensor.append(to_tensor(lmap).unsqueeze(0))

21
lama_cleaner/parse_args.py
View File

@ -6,13 +6,29 @@ from pathlib import Path
from loguru import logger
from lama_cleaner.const import *
from lama_cleaner.download import cli_download_model
from lama_cleaner.runtime import dump_environment_info
DOWNLOAD_SUBCOMMAND = "download"
def download_parse_args(parser):
subparsers = parser.add_subparsers(dest="subcommand")
subparser = subparsers.add_parser(DOWNLOAD_SUBCOMMAND, help="Download models")
subparser.add_argument(
"--model", help="Erase model name(lama/mat...) or model id on huggingface"
)
subparser.add_argument(
"--model-dir", type=str, default=DEFAULT_MODEL_DIR, help=MODEL_DIR_HELP
)
def parse_args():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
download_parse_args(parser)
parser.add_argument("--host", default="127.0.0.1")
parser.add_argument("--port", default=8080, type=int)
@ -166,9 +182,12 @@ def parse_args():
)
args = parser.parse_args()
# collect system info to help debug
dump_environment_info()
if args.subcommand == DOWNLOAD_SUBCOMMAND:
cli_download_model(args.model, args.model_dir)
return
if args.install_plugins_package:
from lama_cleaner.installer import install_plugins_package

2
lama_cleaner/web_config.py
View File

@ -185,7 +185,7 @@ def main(config_file: str):
)
sd_controlnet_method = gr.Radio(
SD_CONTROLNET_CHOICES,
lable="ControlNet method",
label="ControlNet method",
value=init_config.sd_controlnet_method,
)
no_half = gr.Checkbox(init_config.no_half, label=f"{NO_HALF_HELP}")