let5see/IOPaint
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

lots of updates

Browse Source
This commit is contained in:
Qing
2023-01-05 22:07:39 +08:00
parent 2e8e52f7a5
commit a22536becc
21 changed files with 394 additions and 74 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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

44
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['sd_cpu_textencoder']:
logger.info("Run Stable Diffusion TextEncoder on CPU")
self.model.text_encoder = CPUTextEncoderWrapper(self.model.text_encoder, torch_dtype)
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()