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from PIL import Image
import PIL.Image
import cv2
import torch
from loguru import logger
from ..base import DiffusionInpaintModel
from ..helper.cpu_text_encoder import CPUTextEncoderWrapper
from ..utils import (
handle_from_pretrained_exceptions,
get_torch_dtype,
enable_low_mem,
is_local_files_only,
)
from iopaint.schema import InpaintRequest
from .powerpaint_tokenizer import add_task_to_prompt
from ...const import POWERPAINT_NAME
class PowerPaint(DiffusionInpaintModel):
name = POWERPAINT_NAME
pad_mod = 8
min_size = 512
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
def init_model(self, device: torch.device, **kwargs):
from .pipeline_powerpaint import StableDiffusionInpaintPipeline
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,
)
)
self.model = handle_from_pretrained_exceptions(
StableDiffusionInpaintPipeline.from_pretrained,
pretrained_model_name_or_path=self.name,
variant="fp16",
torch_dtype=torch_dtype,
**model_kwargs,
)
self.model.tokenizer = PowerPaintTokenizer(self.model.tokenizer)
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)
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)
img_h, img_w = image.shape[:2]
promptA, promptB, negative_promptA, negative_promptB = add_task_to_prompt(
config.prompt, config.negative_prompt, config.powerpaint_task
)
output = self.model(
image=PIL.Image.fromarray(image),
promptA=promptA,
promptB=promptB,
tradoff=config.fitting_degree,
tradoff_nag=config.fitting_degree,
negative_promptA=negative_promptA,
negative_promptB=negative_promptB,
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
num_inference_steps=config.sd_steps,
strength=config.sd_strength,
guidance_scale=config.sd_guidance_scale,
output_type="np",
callback=self.callback,
height=img_h,
width=img_w,
generator=torch.manual_seed(config.sd_seed),
callback_steps=1,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
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