187 lines
6.8 KiB
Python
187 lines
6.8 KiB
Python
from itertools import chain
|
|
|
|
import PIL.Image
|
|
import cv2
|
|
import torch
|
|
from iopaint.model.original_sd_configs import get_config_files
|
|
from loguru import logger
|
|
from transformers import CLIPTextModel, CLIPTokenizer
|
|
import numpy as np
|
|
|
|
from ..base import DiffusionInpaintModel
|
|
from ..helper.cpu_text_encoder import CPUTextEncoderWrapper
|
|
from ..utils import (
|
|
get_torch_dtype,
|
|
enable_low_mem,
|
|
is_local_files_only,
|
|
handle_from_pretrained_exceptions,
|
|
)
|
|
from .powerpaint_tokenizer import task_to_prompt
|
|
from iopaint.schema import InpaintRequest, ModelType
|
|
from .v2.BrushNet_CA import BrushNetModel
|
|
from .v2.unet_2d_condition import UNet2DConditionModel_forward
|
|
from .v2.unet_2d_blocks import (
|
|
CrossAttnDownBlock2D_forward,
|
|
DownBlock2D_forward,
|
|
CrossAttnUpBlock2D_forward,
|
|
UpBlock2D_forward,
|
|
)
|
|
|
|
|
|
class PowerPaintV2(DiffusionInpaintModel):
|
|
pad_mod = 8
|
|
min_size = 512
|
|
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
|
|
hf_model_id = "Sanster/PowerPaint_v2"
|
|
|
|
def init_model(self, device: torch.device, **kwargs):
|
|
from .v2.pipeline_PowerPaint_Brushnet_CA import (
|
|
StableDiffusionPowerPaintBrushNetPipeline,
|
|
)
|
|
from .powerpaint_tokenizer import PowerPaintTokenizer
|
|
|
|
use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
|
|
model_kwargs = {"local_files_only": is_local_files_only(**kwargs)}
|
|
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,
|
|
)
|
|
)
|
|
|
|
text_encoder_brushnet = CLIPTextModel.from_pretrained(
|
|
self.hf_model_id,
|
|
subfolder="text_encoder_brushnet",
|
|
variant="fp16",
|
|
torch_dtype=torch_dtype,
|
|
local_files_only=model_kwargs["local_files_only"],
|
|
)
|
|
|
|
brushnet = BrushNetModel.from_pretrained(
|
|
self.hf_model_id,
|
|
subfolder="PowerPaint_Brushnet",
|
|
variant="fp16",
|
|
torch_dtype=torch_dtype,
|
|
local_files_only=model_kwargs["local_files_only"],
|
|
)
|
|
|
|
if self.model_info.is_single_file_diffusers:
|
|
if self.model_info.model_type == ModelType.DIFFUSERS_SD:
|
|
model_kwargs["num_in_channels"] = 4
|
|
else:
|
|
model_kwargs["num_in_channels"] = 9
|
|
|
|
pipe = StableDiffusionPowerPaintBrushNetPipeline.from_single_file(
|
|
self.model_id_or_path,
|
|
torch_dtype=torch_dtype,
|
|
load_safety_checker=False,
|
|
original_config_file=get_config_files()["v1"],
|
|
brushnet=brushnet,
|
|
text_encoder_brushnet=text_encoder_brushnet,
|
|
**model_kwargs,
|
|
)
|
|
else:
|
|
pipe = handle_from_pretrained_exceptions(
|
|
StableDiffusionPowerPaintBrushNetPipeline.from_pretrained,
|
|
pretrained_model_name_or_path=self.model_id_or_path,
|
|
torch_dtype=torch_dtype,
|
|
brushnet=brushnet,
|
|
text_encoder_brushnet=text_encoder_brushnet,
|
|
variant="fp16",
|
|
**model_kwargs,
|
|
)
|
|
pipe.tokenizer = PowerPaintTokenizer(
|
|
CLIPTokenizer.from_pretrained(self.hf_model_id, subfolder="tokenizer")
|
|
)
|
|
self.model = pipe
|
|
|
|
enable_low_mem(self.model, kwargs.get("low_mem", False))
|
|
|
|
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)
|
|
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)
|
|
|
|
# Monkey patch the forward method of the UNet to use the brushnet_unet_forward method
|
|
self.model.unet.forward = UNet2DConditionModel_forward.__get__(
|
|
self.model.unet, self.model.unet.__class__
|
|
)
|
|
|
|
# Monkey patch unet down_blocks to use CrossAttnDownBlock2D_forward
|
|
for down_block in chain(
|
|
self.model.unet.down_blocks, self.model.brushnet.down_blocks
|
|
):
|
|
if down_block.__class__.__name__ == "CrossAttnDownBlock2D":
|
|
down_block.forward = CrossAttnDownBlock2D_forward.__get__(
|
|
down_block, down_block.__class__
|
|
)
|
|
else:
|
|
down_block.forward = DownBlock2D_forward.__get__(
|
|
down_block, down_block.__class__
|
|
)
|
|
|
|
for up_block in chain(self.model.unet.up_blocks, self.model.brushnet.up_blocks):
|
|
if up_block.__class__.__name__ == "CrossAttnUpBlock2D":
|
|
up_block.forward = CrossAttnUpBlock2D_forward.__get__(
|
|
up_block, up_block.__class__
|
|
)
|
|
else:
|
|
up_block.forward = UpBlock2D_forward.__get__(
|
|
up_block, up_block.__class__
|
|
)
|
|
|
|
def forward(self, image, mask, config: InpaintRequest):
|
|
"""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
|
|
"""
|
|
self.set_scheduler(config)
|
|
|
|
image = image * (1 - mask / 255.0)
|
|
img_h, img_w = image.shape[:2]
|
|
|
|
image = PIL.Image.fromarray(image.astype(np.uint8))
|
|
mask = PIL.Image.fromarray(mask[:, :, -1], mode="L").convert("RGB")
|
|
|
|
promptA, promptB, negative_promptA, negative_promptB = task_to_prompt(
|
|
config.powerpaint_task
|
|
)
|
|
|
|
output = self.model(
|
|
image=image,
|
|
mask=mask,
|
|
promptA=promptA,
|
|
promptB=promptB,
|
|
promptU=config.prompt,
|
|
tradoff=config.fitting_degree,
|
|
tradoff_nag=config.fitting_degree,
|
|
negative_promptA=negative_promptA,
|
|
negative_promptB=negative_promptB,
|
|
negative_promptU=config.negative_prompt,
|
|
num_inference_steps=config.sd_steps,
|
|
# strength=config.sd_strength,
|
|
brushnet_conditioning_scale=1.0,
|
|
guidance_scale=config.sd_guidance_scale,
|
|
output_type="np",
|
|
callback_on_step_end=self.callback,
|
|
height=img_h,
|
|
width=img_w,
|
|
generator=torch.manual_seed(config.sd_seed),
|
|
).images[0]
|
|
|
|
output = (output * 255).round().astype("uint8")
|
|
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
|
|
return output
|