97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
from PIL import Image
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import PIL.Image
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import cv2
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import torch
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from loguru import logger
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from ..base import DiffusionInpaintModel
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from ..helper.cpu_text_encoder import CPUTextEncoderWrapper
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from ..utils import handle_from_pretrained_exceptions, get_torch_dtype, enable_low_mem
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from iopaint.schema import InpaintRequest
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from .powerpaint_tokenizer import add_task_to_prompt
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from ...const import POWERPAINT_NAME
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class PowerPaint(DiffusionInpaintModel):
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name = POWERPAINT_NAME
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pad_mod = 8
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min_size = 512
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lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
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def init_model(self, device: torch.device, **kwargs):
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from .pipeline_powerpaint import StableDiffusionInpaintPipeline
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from .powerpaint_tokenizer import PowerPaintTokenizer
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use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
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model_kwargs = {}
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if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
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logger.info("Disable Stable Diffusion Model NSFW checker")
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model_kwargs.update(
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dict(
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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)
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)
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self.model = handle_from_pretrained_exceptions(
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StableDiffusionInpaintPipeline.from_pretrained,
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pretrained_model_name_or_path=self.name,
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variant="fp16",
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torch_dtype=torch_dtype,
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**model_kwargs,
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)
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self.model.tokenizer = PowerPaintTokenizer(self.model.tokenizer)
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enable_low_mem(self.model, kwargs.get("low_mem", False))
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if kwargs.get("cpu_offload", False) and use_gpu:
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logger.info("Enable sequential cpu offload")
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self.model.enable_sequential_cpu_offload(gpu_id=0)
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else:
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self.model = self.model.to(device)
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if kwargs["sd_cpu_textencoder"]:
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logger.info("Run Stable Diffusion TextEncoder on CPU")
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self.model.text_encoder = CPUTextEncoderWrapper(
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self.model.text_encoder, torch_dtype
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)
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self.callback = kwargs.pop("callback", None)
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def forward(self, image, mask, config: InpaintRequest):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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self.set_scheduler(config)
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img_h, img_w = image.shape[:2]
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promptA, promptB, negative_promptA, negative_promptB = add_task_to_prompt(
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config.prompt, config.negative_prompt, config.powerpaint_task
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)
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output = self.model(
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image=PIL.Image.fromarray(image),
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promptA=promptA,
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promptB=promptB,
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tradoff=config.fitting_degree,
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tradoff_nag=config.fitting_degree,
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negative_promptA=negative_promptA,
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negative_promptB=negative_promptB,
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
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num_inference_steps=config.sd_steps,
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strength=config.sd_strength,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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callback=self.callback,
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height=img_h,
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width=img_w,
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generator=torch.manual_seed(config.sd_seed),
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callback_steps=1,
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).images[0]
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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