142 lines
5.0 KiB
Python
142 lines
5.0 KiB
Python
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 transformers import CLIPTextModel, CLIPTokenizer
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import numpy as np
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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 (
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get_torch_dtype,
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enable_low_mem,
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is_local_files_only,
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handle_from_pretrained_exceptions,
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)
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from .powerpaint_tokenizer import task_to_prompt
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from iopaint.schema import InpaintRequest
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from .v2.BrushNet_CA import BrushNetModel
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from .v2.unet_2d_condition import UNet2DConditionModel
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class PowerPaintV2(DiffusionInpaintModel):
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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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hf_model_id = "Sanster/PowerPaint_v2"
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def init_model(self, device: torch.device, **kwargs):
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from .v2.pipeline_PowerPaint_Brushnet_CA import (
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StableDiffusionPowerPaintBrushNetPipeline,
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)
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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 = {"local_files_only": is_local_files_only(**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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text_encoder_brushnet = CLIPTextModel.from_pretrained(
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self.hf_model_id,
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subfolder="text_encoder_brushnet",
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variant="fp16",
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torch_dtype=torch_dtype,
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local_files_only=model_kwargs["local_files_only"],
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)
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unet = handle_from_pretrained_exceptions(
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UNet2DConditionModel.from_pretrained,
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pretrained_model_name_or_path=self.model_id_or_path,
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subfolder="unet",
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variant="fp16",
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torch_dtype=torch_dtype,
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local_files_only=model_kwargs["local_files_only"],
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)
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brushnet = BrushNetModel.from_pretrained(
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self.hf_model_id,
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subfolder="PowerPaint_Brushnet",
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variant="fp16",
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torch_dtype=torch_dtype,
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local_files_only=model_kwargs["local_files_only"],
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)
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pipe = handle_from_pretrained_exceptions(
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StableDiffusionPowerPaintBrushNetPipeline.from_pretrained,
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pretrained_model_name_or_path=self.model_id_or_path,
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torch_dtype=torch_dtype,
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unet=unet,
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brushnet=brushnet,
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text_encoder_brushnet=text_encoder_brushnet,
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variant="fp16",
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**model_kwargs,
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)
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pipe.tokenizer = PowerPaintTokenizer(
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CLIPTokenizer.from_pretrained(self.hf_model_id, subfolder="tokenizer")
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)
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self.model = pipe
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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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image = image * (1 - mask / 255.0)
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img_h, img_w = image.shape[:2]
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image = PIL.Image.fromarray(image.astype(np.uint8))
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mask = PIL.Image.fromarray(mask[:, :, -1], mode="L").convert("RGB")
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promptA, promptB, negative_promptA, negative_promptB = task_to_prompt(
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config.powerpaint_task
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)
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output = self.model(
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image=image,
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mask=mask,
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promptA=promptA,
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promptB=promptB,
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promptU=config.prompt,
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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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negative_promptU=config.negative_prompt,
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num_inference_steps=config.sd_steps,
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# strength=config.sd_strength,
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brushnet_conditioning_scale=1.0,
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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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