IOPaint/iopaint/model/sd.py

130 lines
4.3 KiB
Python
Raw Normal View History

2022-09-15 16:21:27 +02:00
import PIL.Image
import cv2
import torch
from loguru import logger
2024-01-05 09:40:06 +01:00
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,
)
2024-01-05 08:19:23 +01:00
from iopaint.schema import InpaintRequest, ModelType
2022-09-15 16:21:27 +02:00
2023-01-27 13:59:22 +01:00
class SD(DiffusionInpaintModel):
pad_mod = 8
2022-09-15 16:21:27 +02:00
min_size = 512
2023-11-15 01:50:35 +01:00
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
2022-09-15 16:21:27 +02:00
def init_model(self, device: torch.device, **kwargs):
2022-10-20 15:01:14 +02:00
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
2022-09-15 16:21:27 +02:00
use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
2022-09-29 03:42:19 +02:00
model_kwargs = {
**kwargs.get("pipe_components", {}),
"local_files_only": is_local_files_only(**kwargs),
}
disable_nsfw_checker = kwargs["disable_nsfw"] or kwargs.get(
"cpu_offload", False
)
if disable_nsfw_checker:
2023-02-07 14:00:19 +01:00
logger.info("Disable Stable Diffusion Model NSFW checker")
model_kwargs.update(
dict(
safety_checker=None,
feature_extractor=None,
requires_safety_checker=False,
)
)
2023-03-29 16:05:34 +02:00
2023-12-15 05:40:29 +01:00
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
2023-11-16 07:09:08 +01:00
self.model = StableDiffusionInpaintPipeline.from_single_file(
self.model_id_or_path,
dtype=torch_dtype,
load_safety_checker=not disable_nsfw_checker,
config_files=get_config_files(),
**model_kwargs,
2023-03-29 16:05:34 +02:00
)
else:
2023-12-27 15:00:07 +01:00
self.model = handle_from_pretrained_exceptions(
StableDiffusionInpaintPipeline.from_pretrained,
pretrained_model_name_or_path=self.model_id_or_path,
variant="fp16",
dtype=torch_dtype,
2023-03-29 16:05:34 +02:00
**model_kwargs,
)
2023-01-05 15:07:39 +01:00
2024-01-09 15:42:48 +01:00
enable_low_mem(self.model, kwargs.get("low_mem", False))
2023-02-07 14:00:19 +01:00
if kwargs.get("cpu_offload", False) and use_gpu:
2023-01-07 01:52:11 +01:00
logger.info("Enable sequential cpu offload")
2023-01-05 15:07:39 +01:00
self.model.enable_sequential_cpu_offload(gpu_id=0)
else:
self.model = self.model.to(device)
2023-02-07 14:00:19 +01:00
if kwargs["sd_cpu_textencoder"]:
2023-01-05 15:07:39 +01:00
logger.info("Run Stable Diffusion TextEncoder on CPU")
2023-02-07 14:00:19 +01:00
self.model.text_encoder = CPUTextEncoderWrapper(
self.model.text_encoder, torch_dtype
)
2022-09-29 06:20:55 +02:00
2022-10-15 16:32:25 +02:00
self.callback = kwargs.pop("callback", None)
2022-09-15 16:21:27 +02:00
2023-12-30 16:36:44 +01:00
def forward(self, image, mask, config: InpaintRequest):
2022-09-15 16:21:27 +02:00
"""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
"""
2023-11-15 01:50:35 +01:00
self.set_scheduler(config)
2022-09-22 06:38:32 +02:00
img_h, img_w = image.shape[:2]
2022-09-15 16:21:27 +02:00
output = self.model(
2022-12-04 06:41:48 +01:00
image=PIL.Image.fromarray(image),
2022-09-15 16:21:27 +02:00
prompt=config.prompt,
2022-11-08 14:58:48 +01:00
negative_prompt=config.negative_prompt,
2022-09-15 16:21:27 +02:00
mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
num_inference_steps=config.sd_steps,
2023-11-14 07:02:15 +01:00
strength=config.sd_strength,
2022-09-15 16:21:27 +02:00
guidance_scale=config.sd_guidance_scale,
2023-08-30 15:30:11 +02:00
output_type="np",
2024-01-02 10:13:11 +01:00
callback_on_step_end=self.callback,
height=img_h,
width=img_w,
2023-03-01 14:44:02 +01:00
generator=torch.manual_seed(config.sd_seed),
2022-09-15 16:21:27 +02:00
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
class SD15(SD):
2023-12-27 15:00:07 +01:00
name = "runwayml/stable-diffusion-inpainting"
2022-10-20 15:01:14 +02:00
model_id_or_path = "runwayml/stable-diffusion-inpainting"
2022-12-04 06:41:48 +01:00
2023-03-01 14:44:02 +01:00
class Anything4(SD):
2023-12-27 15:00:07 +01:00
name = "Sanster/anything-4.0-inpainting"
2023-03-01 14:44:02 +01:00
model_id_or_path = "Sanster/anything-4.0-inpainting"
class RealisticVision14(SD):
2023-12-27 15:00:07 +01:00
name = "Sanster/Realistic_Vision_V1.4-inpainting"
2023-03-01 14:44:02 +01:00
model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting"
2022-12-04 06:41:48 +01:00
class SD2(SD):
2023-12-27 15:00:07 +01:00
name = "stabilityai/stable-diffusion-2-inpainting"
2022-12-04 06:41:48 +01:00
model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"