IOPaint/lama_cleaner/model/controlnet.py
2023-12-24 15:32:27 +08:00

172 lines
6.3 KiB
Python

import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import ControlNetModel, DiffusionPipeline
from loguru import logger
from lama_cleaner.const import DIFFUSERS_MODEL_FP16_REVERSION
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.helper.controlnet_preprocess import (
make_canny_control_image,
make_openpose_control_image,
make_depth_control_image,
make_inpaint_control_image,
)
from lama_cleaner.model.helper.cpu_text_encoder import CPUTextEncoderWrapper
from lama_cleaner.model.utils import get_scheduler
from lama_cleaner.schema import Config, ModelInfo, ModelType
class ControlNet(DiffusionInpaintModel):
name = "controlnet"
pad_mod = 8
min_size = 512
@property
def lcm_lora_id(self):
if self.model_info.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SD_INPAINT,
]:
return "latent-consistency/lcm-lora-sdv1-5"
if self.model_info.model_type in [
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SDXL_INPAINT,
]:
return "latent-consistency/lcm-lora-sdxl"
raise NotImplementedError(f"Unsupported controlnet lcm model {self.model_info}")
def init_model(self, device: torch.device, **kwargs):
fp16 = not kwargs.get("no_half", False)
model_info: ModelInfo = kwargs["model_info"]
sd_controlnet_method = kwargs["sd_controlnet_method"]
self.model_info = model_info
self.sd_controlnet_method = sd_controlnet_method
model_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,
)
)
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
self.torch_dtype = torch_dtype
if model_info.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SD_INPAINT,
]:
from diffusers import (
StableDiffusionControlNetInpaintPipeline as PipeClass,
)
elif model_info.model_type in [
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SDXL_INPAINT,
]:
from diffusers import (
StableDiffusionXLControlNetInpaintPipeline as PipeClass,
)
controlnet = ControlNetModel.from_pretrained(
sd_controlnet_method, torch_dtype=torch_dtype, resume_download=True
)
if 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 = PipeClass.from_single_file(
model_info.path, controlnet=controlnet, **model_kwargs
).to(torch_dtype)
else:
self.model = PipeClass.from_pretrained(
model_info.path,
controlnet=controlnet,
revision="fp16"
if (
model_info.path in DIFFUSERS_MODEL_FP16_REVERSION
and use_gpu
and fp16
)
else "main",
torch_dtype=torch_dtype,
**model_kwargs,
)
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 switch_controlnet_method(self, new_method: str):
self.sd_controlnet_method = new_method
controlnet = ControlNetModel.from_pretrained(
new_method, torch_dtype=self.torch_dtype, resume_download=True
).to(self.model.device)
self.model.controlnet = controlnet
def _get_control_image(self, image, mask):
if "canny" in self.sd_controlnet_method:
control_image = make_canny_control_image(image)
elif "openpose" in self.sd_controlnet_method:
control_image = make_openpose_control_image(image)
elif "depth" in self.sd_controlnet_method:
control_image = make_depth_control_image(image)
elif "inpaint" in self.sd_controlnet_method:
control_image = make_inpaint_control_image(image, mask)
else:
raise NotImplementedError(f"{self.sd_controlnet_method} not implemented")
return control_image
def forward(self, image, mask, config: Config):
"""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
"""
scheduler_config = self.model.scheduler.config
scheduler = get_scheduler(config.sd_sampler, scheduler_config)
self.model.scheduler = scheduler
img_h, img_w = image.shape[:2]
control_image = self._get_control_image(image, mask)
mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
image = PIL.Image.fromarray(image)
output = self.model(
image=image,
mask_image=mask_image,
control_image=control_image,
prompt=config.prompt,
negative_prompt=config.negative_prompt,
num_inference_steps=config.sd_steps,
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),
controlnet_conditioning_scale=config.controlnet_conditioning_scale,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output