IOPaint/lama_cleaner/model/sd.py
2023-12-30 23:36:44 +08:00

116 lines
4.0 KiB
Python

import PIL.Image
import cv2
import torch
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.helper.cpu_text_encoder import CPUTextEncoderWrapper
from lama_cleaner.model.utils import handle_from_pretrained_exceptions
from lama_cleaner.schema import InpaintRequest, ModelType
class SD(DiffusionInpaintModel):
pad_mod = 8
min_size = 512
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
def init_model(self, device: torch.device, **kwargs):
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
fp16 = not kwargs.get("no_half", False)
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
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 = StableDiffusionInpaintPipeline.from_single_file(
self.model_id_or_path, dtype=torch_dtype, **model_kwargs
)
else:
self.model = handle_from_pretrained_exceptions(
StableDiffusionInpaintPipeline.from_pretrained,
pretrained_model_name_or_path=self.model_id_or_path,
variant="fp16",
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 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]
output = self.model(
image=PIL.Image.fromarray(image),
prompt=config.prompt,
negative_prompt=config.negative_prompt,
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
num_inference_steps=config.sd_steps,
strength=config.sd_strength,
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),
callback_steps=1,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
class SD15(SD):
name = "runwayml/stable-diffusion-inpainting"
model_id_or_path = "runwayml/stable-diffusion-inpainting"
class Anything4(SD):
name = "Sanster/anything-4.0-inpainting"
model_id_or_path = "Sanster/anything-4.0-inpainting"
class RealisticVision14(SD):
name = "Sanster/Realistic_Vision_V1.4-inpainting"
model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting"
class SD2(SD):
name = "stabilityai/stable-diffusion-2-inpainting"
model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"