158 lines
6.2 KiB
Python
158 lines
6.2 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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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 ..original_sd_configs import get_config_files
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from ..utils import (
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handle_from_pretrained_exceptions,
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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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)
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from .brushnet import BrushNetModel
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from .brushnet_unet_forward import brushnet_unet_forward
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from .unet_2d_blocks import CrossAttnDownBlock2D_forward, DownBlock2D_forward, CrossAttnUpBlock2D_forward, \
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UpBlock2D_forward
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from ...schema import InpaintRequest, ModelType
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class BrushNetWrapper(DiffusionInpaintModel):
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pad_mod = 8
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min_size = 512
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def init_model(self, device: torch.device, **kwargs):
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from .pipeline_brushnet import StableDiffusionBrushNetPipeline
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self.model_info = kwargs["model_info"]
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self.brushnet_method = kwargs["brushnet_method"]
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use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
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self.torch_dtype = torch_dtype
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model_kwargs = {
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**kwargs.get("pipe_components", {}),
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"local_files_only": is_local_files_only(**kwargs),
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}
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self.local_files_only = model_kwargs["local_files_only"]
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disable_nsfw_checker = kwargs["disable_nsfw"] or kwargs.get(
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"cpu_offload", False
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)
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if disable_nsfw_checker:
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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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logger.info(f"Loading BrushNet model from {self.brushnet_method}")
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brushnet = BrushNetModel.from_pretrained(self.brushnet_method, torch_dtype=torch_dtype)
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if self.model_info.is_single_file_diffusers:
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if self.model_info.model_type == ModelType.DIFFUSERS_SD:
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model_kwargs["num_in_channels"] = 4
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else:
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model_kwargs["num_in_channels"] = 9
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self.model = StableDiffusionBrushNetPipeline.from_single_file(
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self.model_id_or_path,
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torch_dtype=torch_dtype,
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load_safety_checker=not disable_nsfw_checker,
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original_config_file=get_config_files()['v1'],
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brushnet=brushnet,
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**model_kwargs,
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)
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else:
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self.model = handle_from_pretrained_exceptions(
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StableDiffusionBrushNetPipeline.from_pretrained,
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pretrained_model_name_or_path=self.model_id_or_path,
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variant="fp16",
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torch_dtype=torch_dtype,
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brushnet=brushnet,
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**model_kwargs,
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)
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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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# Monkey patch the forward method of the UNet to use the brushnet_unet_forward method
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self.model.unet.forward = brushnet_unet_forward.__get__(self.model.unet, self.model.unet.__class__)
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for down_block in self.model.brushnet.down_blocks:
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down_block.forward = DownBlock2D_forward.__get__(down_block, down_block.__class__)
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for up_block in self.model.brushnet.up_blocks:
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up_block.forward = UpBlock2D_forward.__get__(up_block, up_block.__class__)
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# Monkey patch unet down_blocks to use CrossAttnDownBlock2D_forward
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for down_block in self.model.unet.down_blocks:
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if down_block.__class__.__name__ == "CrossAttnDownBlock2D":
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down_block.forward = CrossAttnDownBlock2D_forward.__get__(down_block, down_block.__class__)
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else:
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down_block.forward = DownBlock2D_forward.__get__(down_block, down_block.__class__)
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for up_block in self.model.unet.up_blocks:
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if up_block.__class__.__name__ == "CrossAttnUpBlock2D":
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up_block.forward = CrossAttnUpBlock2D_forward.__get__(up_block, up_block.__class__)
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else:
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up_block.forward = UpBlock2D_forward.__get__(up_block, up_block.__class__)
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def switch_brushnet_method(self, new_method: str):
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self.brushnet_method = new_method
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brushnet = BrushNetModel.from_pretrained(
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new_method,
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resume_download=True,
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local_files_only=self.local_files_only,
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torch_dtype=self.torch_dtype,
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).to(self.model.device)
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self.model.brushnet = brushnet
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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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normalized_mask = mask[:, :].astype("float32") / 255.0
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image = image * (1 - normalized_mask)
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image = image.astype(np.uint8)
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output = self.model(
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image=PIL.Image.fromarray(image),
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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mask=PIL.Image.fromarray(mask[:, :, -1], mode="L").convert("RGB"),
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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_on_step_end=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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brushnet_conditioning_scale=config.brushnet_conditioning_scale,
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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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