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Qing
2023-12-01 10:15:35 +08:00
parent 973987dfbb
commit 9a9eb8abfd
55 changed files with 2596 additions and 1251 deletions
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286
lama_cleaner/model/controlnet.py
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@@ -1,5 +1,3 @@
import gc
import PIL.Image
import cv2
import numpy as np
@@ -7,107 +5,26 @@ import torch
from diffusers import ControlNetModel
from loguru import logger
from lama_cleaner.const import DIFFUSERS_MODEL_FP16_REVERSION
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import torch_gc, get_scheduler
from lama_cleaner.schema import Config
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 CPUTextEncoderWrapper(torch.nn.Module):
def __init__(self, text_encoder, torch_dtype):
super().__init__()
self.config = text_encoder.config
self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
self.torch_dtype = torch_dtype
del text_encoder
torch_gc()
def __call__(self, x, **kwargs):
input_device = x.device
return [
self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
.to(input_device)
.to(self.torch_dtype)
]
@property
def dtype(self):
return self.torch_dtype
NAMES_MAP = {
"sd1.5": "runwayml/stable-diffusion-inpainting",
"anything4": "Sanster/anything-4.0-inpainting",
"realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
# 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",
}
NATIVE_NAMES_MAP = {
"sd1.5": "runwayml/stable-diffusion-v1-5",
"anything4": "andite/anything-v4.0",
"realisticVision1.4": "SG161222/Realistic_Vision_V1.4",
}
def make_inpaint_condition(image, image_mask):
"""
image: [H, W, C] RGB
mask: [H, W, 1] 255 means area to repaint
"""
image = image.astype(np.float32) / 255.0
image[image_mask[:, :, -1] > 128] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
def load_from_local_model(
local_model_path, torch_dtype, controlnet, pipe_class, is_native_control_inpaint
):
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
download_from_original_stable_diffusion_ckpt,
)
logger.info(f"Converting {local_model_path} to diffusers controlnet pipeline")
try:
pipe = download_from_original_stable_diffusion_ckpt(
local_model_path,
num_in_channels=4 if is_native_control_inpaint else 9,
from_safetensors=local_model_path.endswith("safetensors"),
device="cpu",
load_safety_checker=False,
)
except Exception as e:
err_msg = str(e)
logger.exception(e)
if is_native_control_inpaint and "[320, 9, 3, 3]" in err_msg:
logger.error(
"control_v11p_sd15_inpaint method requires normal SD model, not inpainting SD model"
)
if not is_native_control_inpaint and "[320, 4, 3, 3]" in err_msg:
logger.error(
f"{controlnet.config['_name_or_path']} method requires inpainting SD model, "
f"you can convert any SD model to inpainting model in AUTO1111: \n"
f"https://www.reddit.com/r/StableDiffusion/comments/zyi24j/how_to_turn_any_model_into_an_inpainting_model/"
)
exit(-1)
inpaint_pipe = pipe_class(
vae=pipe.vae,
text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer,
unet=pipe.unet,
controlnet=controlnet,
scheduler=pipe.scheduler,
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
del pipe
gc.collect()
return inpaint_pipe.to(torch_dtype=torch_dtype)
class ControlNet(DiffusionInpaintModel):
name = "controlnet"
@@ -116,10 +33,16 @@ class ControlNet(DiffusionInpaintModel):
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"]
sd_controlnet_method = controlnet_name_map.get(
sd_controlnet_method, sd_controlnet_method
)
model_kwargs = {
"local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
}
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(
@@ -133,41 +56,39 @@ class ControlNet(DiffusionInpaintModel):
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
sd_controlnet_method = kwargs["sd_controlnet_method"]
self.sd_controlnet_method = sd_controlnet_method
if sd_controlnet_method == "control_v11p_sd15_inpaint":
from diffusers import StableDiffusionControlNetPipeline as PipeClass
self.is_native_control_inpaint = True
else:
from .pipeline import StableDiffusionControlNetInpaintPipeline as PipeClass
self.is_native_control_inpaint = False
if self.is_native_control_inpaint:
model_id = NATIVE_NAMES_MAP[kwargs["name"]]
else:
model_id = NAMES_MAP[kwargs["name"]]
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(
f"lllyasviel/{sd_controlnet_method}", torch_dtype=torch_dtype
sd_controlnet_method, torch_dtype=torch_dtype
)
self.is_local_sd_model = False
if kwargs.get("sd_local_model_path", None):
self.is_local_sd_model = True
self.model = load_from_local_model(
kwargs["sd_local_model_path"],
torch_dtype=torch_dtype,
controlnet=controlnet,
pipe_class=PipeClass,
is_native_control_inpaint=self.is_native_control_inpaint,
)
if model_info.is_single_file_diffusers:
self.model = PipeClass.from_single_file(
model_info.path, controlnet=controlnet
).to(torch_dtype)
else:
self.model = PipeClass.from_pretrained(
model_id,
model_info.path,
controlnet=controlnet,
revision="fp16" if use_gpu and fp16 else "main",
revision="fp16"
if (
model_info.path in DIFFUSERS_MODEL_FP16_REVERSION
and use_gpu
and fp16
)
else "main",
torch_dtype=torch_dtype,
**model_kwargs,
)
@@ -191,6 +112,19 @@ class ControlNet(DiffusionInpaintModel):
self.callback = kwargs.pop("callback", None)
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
@@ -206,84 +140,30 @@ class ControlNet(DiffusionInpaintModel):
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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)
if self.is_native_control_inpaint:
control_image = make_inpaint_condition(image, mask)
output = self.model(
prompt=config.prompt,
image=control_image,
height=img_h,
width=img_w,
num_inference_steps=config.sd_steps,
guidance_scale=config.sd_guidance_scale,
controlnet_conditioning_scale=config.controlnet_conditioning_scale,
negative_prompt=config.negative_prompt,
generator=torch.manual_seed(config.sd_seed),
output_type="np",
callback=self.callback,
).images[0]
else:
if "canny" in self.sd_controlnet_method:
canny_image = cv2.Canny(image, 100, 200)
canny_image = canny_image[:, :, None]
canny_image = np.concatenate(
[canny_image, canny_image, canny_image], axis=2
)
canny_image = PIL.Image.fromarray(canny_image)
control_image = canny_image
elif "openpose" in self.sd_controlnet_method:
from controlnet_aux import OpenposeDetector
processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
control_image = processor(image, hand_and_face=True)
elif "depth" in self.sd_controlnet_method:
from transformers import pipeline
depth_estimator = pipeline("depth-estimation")
depth_image = depth_estimator(PIL.Image.fromarray(image))["depth"]
depth_image = np.array(depth_image)
depth_image = depth_image[:, :, None]
depth_image = np.concatenate(
[depth_image, depth_image, depth_image], axis=2
)
control_image = PIL.Image.fromarray(depth_image)
else:
raise NotImplementedError(
f"{self.sd_controlnet_method} not implemented"
)
mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
image = PIL.Image.fromarray(image)
output = self.model(
image=image,
control_image=control_image,
prompt=config.prompt,
negative_prompt=config.negative_prompt,
mask_image=mask_image,
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 = 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
def forward_post_process(self, result, image, mask, config):
if config.sd_match_histograms:
result = self._match_histograms(result, image[:, :, ::-1], mask)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)
return result, image, mask
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings