IOPaint/iopaint/schema.py
2024-01-31 21:53:14 +08:00

338 lines
10 KiB
Python

import json
import random
from enum import Enum
from pathlib import Path
from typing import Optional, Literal, List
from loguru import logger
from pydantic import BaseModel, Field, field_validator
class Choices(str, Enum):
@classmethod
def values(cls):
return [member.value for member in cls]
class RealESRGANModel(Choices):
realesr_general_x4v3 = "realesr-general-x4v3"
RealESRGAN_x4plus = "RealESRGAN_x4plus"
RealESRGAN_x4plus_anime_6B = "RealESRGAN_x4plus_anime_6B"
class Device(Choices):
cpu = "cpu"
cuda = "cuda"
mps = "mps"
class InteractiveSegModel(Choices):
vit_b = "vit_b"
vit_l = "vit_l"
vit_h = "vit_h"
mobile_sam = "mobile_sam"
class PluginInfo(BaseModel):
name: str
support_gen_image: bool = False
support_gen_mask: bool = False
class CV2Flag(str, Enum):
INPAINT_NS = "INPAINT_NS"
INPAINT_TELEA = "INPAINT_TELEA"
class ModelType(str, Enum):
INPAINT = "inpaint" # LaMa, MAT...
DIFFUSERS_SD = "diffusers_sd"
DIFFUSERS_SD_INPAINT = "diffusers_sd_inpaint"
DIFFUSERS_SDXL = "diffusers_sdxl"
DIFFUSERS_SDXL_INPAINT = "diffusers_sdxl_inpaint"
DIFFUSERS_OTHER = "diffusers_other"
class HDStrategy(str, Enum):
# Use original image size
ORIGINAL = "Original"
# Resize the longer side of the image to a specific size(hd_strategy_resize_limit),
# then do inpainting on the resized image. Finally, resize the inpainting result to the original size.
# The area outside the mask will not lose quality.
RESIZE = "Resize"
# Crop masking area(with a margin controlled by hd_strategy_crop_margin) from the original image to do inpainting
CROP = "Crop"
class LDMSampler(str, Enum):
ddim = "ddim"
plms = "plms"
class SDSampler(str, Enum):
dpm_plus_plus_2m = "DPM++ 2M"
dpm_plus_plus_2m_karras = "DPM++ 2M Karras"
dpm_plus_plus_2m_sde = "DPM++ 2M SDE"
dpm_plus_plus_2m_sde_karras = "DPM++ 2M SDE Karras"
dpm_plus_plus_sde = "DPM++ SDE"
dpm_plus_plus_sde_karras = "DPM++ SDE Karras"
dpm2 = "DPM2"
dpm2_karras = "DPM2 Karras"
dpm2_a = "DPM2 a"
dpm2_a_karras = "DPM2 a Karras"
euler = "Euler"
euler_a = "Euler a"
heun = "Heun"
lms = "LMS"
lms_karras = "LMS Karras"
ddim = "DDIM"
pndm = "PNDM"
uni_pc = "UniPC"
lcm = "LCM"
class FREEUConfig(BaseModel):
s1: float = 0.9
s2: float = 0.2
b1: float = 1.2
b2: float = 1.4
class PowerPaintTask(str, Enum):
text_guided = "text-guided"
shape_guided = "shape-guided"
object_remove = "object-remove"
outpainting = "outpainting"
class ApiConfig(BaseModel):
host: str
port: int
model: str
no_half: bool
low_mem: bool
cpu_offload: bool
disable_nsfw_checker: bool
local_files_only: bool
cpu_textencoder: bool
device: Device
input: Optional[Path]
output_dir: Optional[Path]
quality: int
enable_interactive_seg: bool
interactive_seg_model: InteractiveSegModel
interactive_seg_device: Device
enable_remove_bg: bool
enable_anime_seg: bool
enable_realesrgan: bool
realesrgan_device: Device
realesrgan_model: RealESRGANModel
enable_gfpgan: bool
gfpgan_device: Device
enable_restoreformer: bool
restoreformer_device: Device
class InpaintRequest(BaseModel):
image: Optional[str] = Field(None, description="base64 encoded image")
mask: Optional[str] = Field(None, description="base64 encoded mask")
ldm_steps: int = Field(20, description="Steps for ldm model.")
ldm_sampler: str = Field(LDMSampler.plms, discription="Sampler for ldm model.")
zits_wireframe: bool = Field(True, description="Enable wireframe for zits model.")
hd_strategy: str = Field(
HDStrategy.CROP,
description="Different way to preprocess image, only used by erase models(e.g. lama/mat)",
)
hd_strategy_crop_trigger_size: int = Field(
800,
description="Crop trigger size for hd_strategy=CROP, if the longer side of the image is larger than this value, use crop strategy",
)
hd_strategy_crop_margin: int = Field(
128, description="Crop margin for hd_strategy=CROP"
)
hd_strategy_resize_limit: int = Field(
1280, description="Resize limit for hd_strategy=RESIZE"
)
prompt: str = Field("", description="Prompt for diffusion models.")
negative_prompt: str = Field(
"", description="Negative prompt for diffusion models."
)
use_croper: bool = Field(
False, description="Crop image before doing diffusion inpainting"
)
croper_x: int = Field(0, description="Crop x for croper")
croper_y: int = Field(0, description="Crop y for croper")
croper_height: int = Field(512, description="Crop height for croper")
croper_width: int = Field(512, description="Crop width for croper")
use_extender: bool = Field(
False, description="Extend image before doing sd outpainting"
)
extender_x: int = Field(0, description="Extend x for extender")
extender_y: int = Field(0, description="Extend y for extender")
extender_height: int = Field(640, description="Extend height for extender")
extender_width: int = Field(640, description="Extend width for extender")
sd_scale: float = Field(
1.0,
description="Resize the image before doing sd inpainting, the area outside the mask will not lose quality.",
gt=0.0,
le=1.0,
)
sd_mask_blur: int = Field(
11,
description="Blur the edge of mask area. The higher the number the smoother blend with the original image",
)
sd_strength: float = Field(
1.0,
description="Strength is a measure of how much noise is added to the base image, which influences how similar the output is to the base image. Higher value means more noise and more different from the base image",
le=1.0,
)
sd_steps: int = Field(
50,
description="The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.",
)
sd_guidance_scale: float = Field(
7.5,
help="Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.",
)
sd_sampler: str = Field(
SDSampler.uni_pc, description="Sampler for diffusion model."
)
sd_seed: int = Field(
42,
description="Seed for diffusion model. -1 mean random seed",
validate_default=True,
)
sd_match_histograms: bool = Field(
False,
description="Match histograms between inpainting area and original image.",
)
sd_outpainting_softness: float = Field(20.0)
sd_outpainting_space: float = Field(20.0)
sd_freeu: bool = Field(
False,
description="Enable freeu mode. https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu",
)
sd_freeu_config: FREEUConfig = FREEUConfig()
sd_lcm_lora: bool = Field(
False,
description="Enable lcm-lora mode. https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm#texttoimage",
)
sd_keep_unmasked_area: bool = Field(
True, description="Keep unmasked area unchanged"
)
cv2_flag: CV2Flag = Field(
CV2Flag.INPAINT_NS,
description="Flag for opencv inpainting: https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07",
)
cv2_radius: int = Field(
4,
description="Radius of a circular neighborhood of each point inpainted that is considered by the algorithm",
)
# Paint by Example
paint_by_example_example_image: Optional[str] = Field(
None, description="Base64 encoded example image for paint by example model"
)
# InstructPix2Pix
p2p_image_guidance_scale: float = Field(1.5, description="Image guidance scale")
# ControlNet
enable_controlnet: bool = Field(False, description="Enable controlnet")
controlnet_conditioning_scale: float = Field(
0.4, description="Conditioning scale", ge=0.0, le=1.0
)
controlnet_method: str = Field(
"lllyasviel/control_v11p_sd15_canny", description="Controlnet method"
)
# PowerPaint
powerpaint_task: PowerPaintTask = Field(
PowerPaintTask.text_guided, description="PowerPaint task"
)
fitting_degree: float = Field(
1.0,
description="Control the fitting degree of the generated objects to the mask shape.",
gt=0.0,
le=1.0,
)
@field_validator("sd_seed")
@classmethod
def sd_seed_validator(cls, v: int) -> int:
if v == -1:
return random.randint(1, 99999999)
return v
@field_validator("controlnet_conditioning_scale")
@classmethod
def validate_field(cls, v: float, values):
use_extender = values.data["use_extender"]
enable_controlnet = values.data["enable_controlnet"]
if use_extender and enable_controlnet:
logger.info(f"Extender is enabled, set controlnet_conditioning_scale=0")
return 0
return v
class RunPluginRequest(BaseModel):
name: str
image: str = Field(..., description="base64 encoded image")
clicks: List[List[int]] = Field(
[], description="Clicks for interactive seg, [[x,y,0/1], [x2,y2,0/1]]"
)
scale: float = Field(2.0, description="Scale for upscaling")
MediaTab = Literal["input", "output"]
class MediasResponse(BaseModel):
name: str
height: int
width: int
ctime: float
mtime: float
class GenInfoResponse(BaseModel):
prompt: str = ""
negative_prompt: str = ""
class ServerConfigResponse(BaseModel):
plugins: List[PluginInfo]
enableFileManager: bool
enableAutoSaving: bool
enableControlnet: bool
controlnetMethod: Optional[str]
disableModelSwitch: bool
isDesktop: bool
samplers: List[str]
class SwitchModelRequest(BaseModel):
name: str
AdjustMaskOperate = Literal["expand", "shrink", "reverse"]
class AdjustMaskRequest(BaseModel):
mask: str = Field(
..., description="base64 encoded mask. 255 means area to do inpaint"
)
operate: AdjustMaskOperate = Field(..., description="expand/shrink/reverse")
kernel_size: int = Field(5, description="Kernel size for expanding mask")