IOPaint/lama_cleaner/model/controlnet.py

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import PIL.Image
import cv2
import numpy as np
import torch
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from diffusers import ControlNetModel
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from loguru import logger
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from lama_cleaner.const import DIFFUSERS_MODEL_FP16_REVERSION
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from lama_cleaner.model.base import DiffusionInpaintModel
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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
# 为了兼容性
controlnet_name_map = {
"control_v11p_sd15_canny": "lllyasviel/control_v11p_sd15_canny",
"control_v11p_sd15_openpose": "lllyasviel/control_v11p_sd15_openpose",
"control_v11p_sd15_inpaint": "lllyasviel/control_v11p_sd15_inpaint",
"control_v11f1p_sd15_depth": "lllyasviel/control_v11f1p_sd15_depth",
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}
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class ControlNet(DiffusionInpaintModel):
name = "controlnet"
pad_mod = 8
min_size = 512
def init_model(self, device: torch.device, **kwargs):
fp16 = not kwargs.get("no_half", False)
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model_info: ModelInfo = kwargs["model_info"]
sd_controlnet_method = kwargs["sd_controlnet_method"]
sd_controlnet_method = controlnet_name_map.get(
sd_controlnet_method, sd_controlnet_method
)
self.model_info = model_info
self.sd_controlnet_method = sd_controlnet_method
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model_kwargs = {}
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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
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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,
)
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controlnet = ControlNetModel.from_pretrained(
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sd_controlnet_method, torch_dtype=torch_dtype
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)
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if model_info.is_single_file_diffusers:
self.model = PipeClass.from_single_file(
model_info.path, controlnet=controlnet
).to(torch_dtype)
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else:
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self.model = PipeClass.from_pretrained(
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model_info.path,
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controlnet=controlnet,
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revision="fp16"
if (
model_info.path in DIFFUSERS_MODEL_FP16_REVERSION
and use_gpu
and fp16
)
else "main",
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torch_dtype=torch_dtype,
**model_kwargs,
)
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# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
self.model.enable_attention_slicing()
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
if kwargs.get("enable_xformers", False):
self.model.enable_xformers_memory_efficient_attention()
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)
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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
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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
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
img_h, img_w = image.shape[:2]
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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]
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output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True