IOPaint/lama_cleaner/model/sd.py

189 lines
7.3 KiB
Python

import random
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import PNDMScheduler, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
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
class CPUTextEncoderWrapper:
def __init__(self, text_encoder, torch_dtype):
self.config = text_encoder.config
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
return [self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0].to(input_device).to(self.torch_dtype)]
class SD(InpaintModel):
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
fp16 = not kwargs.get('no_half', False)
model_kwargs = {"local_files_only": kwargs.get('local_files_only', kwargs['sd_run_local'])}
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
))
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 = StableDiffusionInpaintPipeline.from_pretrained(
self.model_id_or_path,
revision="fp16" if use_gpu and fp16 else "main",
torch_dtype=torch_dtype,
use_auth_token=kwargs["hf_access_token"],
**model_kwargs
)
# 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('enable_xformers', False):
self.model.enable_xformers_memory_efficient_attention()
if kwargs.get('cpu_offload', False) and use_gpu:
# TODO: gpu_id
logger.info("Enable sequential cpu offload")
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
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)
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
mask: [H, W, 1] 255 means area to repaint
return: BGR IMAGE
"""
scheduler_config = self.model.scheduler.config
if config.sd_sampler == SDSampler.ddim:
scheduler = DDIMScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.pndm:
scheduler = PNDMScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.k_lms:
scheduler = LMSDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.k_euler:
scheduler = EulerDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.k_euler_a:
scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler_config)
elif config.sd_sampler == SDSampler.dpm_plus_plus:
scheduler = DPMSolverMultistepScheduler.from_config(scheduler_config)
else:
raise ValueError(config.sd_sampler)
self.model.scheduler = scheduler
seed = config.sd_seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
img_h, img_w = image.shape[:2]
output = self.model(
image=PIL.Image.fromarray(image),
prompt=config.prompt,
negative_prompt=config.negative_prompt,
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
num_inference_steps=config.sd_steps,
guidance_scale=config.sd_guidance_scale,
output_type="np.array",
callback=self.callback,
height=img_h,
width=img_w,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
# 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._scaled_pad_forward(crop_img, crop_mask, config)
inpaint_result = image[:, :, ::-1]
inpaint_result[t:b, l:r, :] = crop_image
else:
inpaint_result = self._scaled_pad_forward(image, mask, config)
return inpaint_result
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True
class SD15(SD):
model_id_or_path = "runwayml/stable-diffusion-inpainting"
class SD2(SD):
model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"