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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
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