IOPaint/iopaint/model/brushnet/brushnet_wrapper.py

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2024-04-12 05:07:41 +02:00
import PIL.Image
import cv2
import torch
from loguru import logger
import numpy as np
from ..base import DiffusionInpaintModel
from ..helper.cpu_text_encoder import CPUTextEncoderWrapper
from ..original_sd_configs import get_config_files
from ..utils import (
handle_from_pretrained_exceptions,
get_torch_dtype,
enable_low_mem,
is_local_files_only,
)
from .brushnet import BrushNetModel
from .brushnet_unet_forward import brushnet_unet_forward
from .unet_2d_blocks import CrossAttnDownBlock2D_forward, DownBlock2D_forward, CrossAttnUpBlock2D_forward, \
UpBlock2D_forward
from ...schema import InpaintRequest, ModelType
class BrushNetWrapper(DiffusionInpaintModel):
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
from .pipeline_brushnet import StableDiffusionBrushNetPipeline
self.model_info = kwargs["model_info"]
self.brushnet_method = kwargs["brushnet_method"]
use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
self.torch_dtype = torch_dtype
model_kwargs = {
**kwargs.get("pipe_components", {}),
"local_files_only": is_local_files_only(**kwargs),
}
self.local_files_only = model_kwargs["local_files_only"]
disable_nsfw_checker = kwargs["disable_nsfw"] or kwargs.get(
"cpu_offload", False
)
if disable_nsfw_checker:
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(
dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
)
logger.info(f"Loading BrushNet model from {self.brushnet_method}")
brushnet = BrushNetModel.from_pretrained(self.brushnet_method, torch_dtype=torch_dtype)
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
self.model = StableDiffusionBrushNetPipeline.from_single_file(
self.model_id_or_path,
torch_dtype=torch_dtype,
load_safety_checker=not disable_nsfw_checker,
original_config_file=get_config_files()['v1'],
2024-04-12 05:07:41 +02:00
brushnet=brushnet,
**model_kwargs,
)
else:
self.model = handle_from_pretrained_exceptions(
StableDiffusionBrushNetPipeline.from_pretrained,
pretrained_model_name_or_path=self.model_id_or_path,
variant="fp16",
torch_dtype=torch_dtype,
brushnet=brushnet,
**model_kwargs,
)
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 = brushnet_unet_forward.__get__(self.model.unet, self.model.unet.__class__)
for down_block in self.model.brushnet.down_blocks:
down_block.forward = DownBlock2D_forward.__get__(down_block, down_block.__class__)
for up_block in self.model.brushnet.up_blocks:
up_block.forward = UpBlock2D_forward.__get__(up_block, up_block.__class__)
# Monkey patch unet down_blocks to use CrossAttnDownBlock2D_forward
for down_block in self.model.unet.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 self.model.unet.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 switch_brushnet_method(self, new_method: str):
self.brushnet_method = new_method
brushnet = BrushNetModel.from_pretrained(
new_method,
resume_download=True,
local_files_only=self.local_files_only,
torch_dtype=self.torch_dtype,
).to(self.model.device)
self.model.brushnet = brushnet
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]
normalized_mask = mask[:, :].astype("float32") / 255.0
image = image * (1 - normalized_mask)
image = image.astype(np.uint8)
output = self.model(
image=PIL.Image.fromarray(image),
prompt=config.prompt,
negative_prompt=config.negative_prompt,
mask=PIL.Image.fromarray(mask[:, :, -1], mode="L").convert("RGB"),
num_inference_steps=config.sd_steps,
# strength=config.sd_strength,
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),
brushnet_conditioning_scale=config.brushnet_conditioning_scale,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output