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IOPaint/iopaint/schema.py
Qing 80ee1b9941 update
2024-04-29 22:20:44 +08:00

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import random
from enum import Enum
from pathlib import Path
from typing import Optional, Literal, List
from loguru import logger
from iopaint.const import (
INSTRUCT_PIX2PIX_NAME,
KANDINSKY22_NAME,
POWERPAINT_NAME,
ANYTEXT_NAME,
SDXL_CONTROLNET_CHOICES,
SD2_CONTROLNET_CHOICES,
SD_CONTROLNET_CHOICES,
SD_BRUSHNET_CHOICES,
)
from pydantic import BaseModel, Field, computed_field, model_validator
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 ModelInfo(BaseModel):
name: str
path: str
model_type: ModelType
is_single_file_diffusers: bool = False
@computed_field
@property
def need_prompt(self) -> bool:
return self.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SD_INPAINT,
ModelType.DIFFUSERS_SDXL_INPAINT,
] or self.name in [
INSTRUCT_PIX2PIX_NAME,
KANDINSKY22_NAME,
POWERPAINT_NAME,
ANYTEXT_NAME,
]
@computed_field
@property
def controlnets(self) -> List[str]:
if self.model_type in [
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SDXL_INPAINT,
]:
return SDXL_CONTROLNET_CHOICES
if self.model_type in [ModelType.DIFFUSERS_SD, ModelType.DIFFUSERS_SD_INPAINT]:
if "sd2" in self.name.lower():
return SD2_CONTROLNET_CHOICES
else:
return SD_CONTROLNET_CHOICES
if self.name == POWERPAINT_NAME:
return SD_CONTROLNET_CHOICES
return []
@computed_field
@property
def brushnets(self) -> List[str]:
if self.model_type in [ModelType.DIFFUSERS_SD]:
return SD_BRUSHNET_CHOICES
return []
@computed_field
@property
def support_strength(self) -> bool:
return self.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SD_INPAINT,
ModelType.DIFFUSERS_SDXL_INPAINT,
] or self.name in [POWERPAINT_NAME, ANYTEXT_NAME]
@computed_field
@property
def support_outpainting(self) -> bool:
return self.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SD_INPAINT,
ModelType.DIFFUSERS_SDXL_INPAINT,
] or self.name in [KANDINSKY22_NAME, POWERPAINT_NAME]
@computed_field
@property
def support_lcm_lora(self) -> bool:
return self.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SD_INPAINT,
ModelType.DIFFUSERS_SDXL_INPAINT,
]
@computed_field
@property
def support_controlnet(self) -> bool:
return self.model_type in [
ModelType.DIFFUSERS_SD,
ModelType.DIFFUSERS_SDXL,
ModelType.DIFFUSERS_SD_INPAINT,
ModelType.DIFFUSERS_SDXL_INPAINT,
]
@computed_field
@property
def support_brushnet(self) -> bool:
return self.model_type in [
ModelType.DIFFUSERS_SD,
]
@computed_field
@property
def support_powerpaint_v2(self) -> bool:
return (
self.model_type
in [
ModelType.DIFFUSERS_SD,
]
and self.name != POWERPAINT_NAME
)
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 RemoveBGModel(Choices):
u2net = "u2net"
u2netp = "u2netp"
u2net_human_seg = "u2net_human_seg"
u2net_cloth_seg = "u2net_cloth_seg"
silueta = "silueta"
isnet_general_use = "isnet-general-use"
briaai_rmbg_1_4 = "briaai/RMBG-1.4"
class Device(Choices):
cpu = "cpu"
cuda = "cuda"
mps = "mps"
class InteractiveSegModel(Choices):
vit_b = "vit_b"
vit_l = "vit_l"
vit_h = "vit_h"
sam_hq_vit_b = "sam_hq_vit_b"
sam_hq_vit_l = "sam_hq_vit_l"
sam_hq_vit_h = "sam_hq_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 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 PowerPaintTask(Choices):
text_guided = "text-guided"
context_aware = "context-aware"
shape_guided = "shape-guided"
object_remove = "object-remove"
outpainting = "outpainting"
class ApiConfig(BaseModel):
host: str
port: int
inbrowser: bool
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
remove_bg_model: str
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_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"
)
# BrushNet
enable_brushnet: bool = Field(False, description="Enable brushnet")
brushnet_method: str = Field(SD_BRUSHNET_CHOICES[0], description="Brushnet method")
brushnet_conditioning_scale: float = Field(
1.0, description="brushnet conditioning scale", ge=0.0, le=1.0
)
# PowerPaint
enable_powerpaint_v2: bool = Field(False, description="Enable PowerPaint v2")
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,
)
@model_validator(mode="after")
def validate_field(cls, values: "InpaintRequest"):
if values.sd_seed == -1:
values.sd_seed = random.randint(1, 99999999)
logger.info(f"Generate random seed: {values.sd_seed}")
if values.use_extender and values.enable_controlnet:
logger.info("Extender is enabled, set controlnet_conditioning_scale=0")
values.controlnet_conditioning_scale = 0
if values.use_extender:
logger.info("Extender is enabled, set sd_strength=1")
values.sd_strength = 1.0
if values.enable_brushnet:
logger.info("BrushNet is enabled, set enable_controlnet=False")
if values.enable_controlnet:
values.enable_controlnet = False
if values.sd_lcm_lora:
logger.info("BrushNet is enabled, set sd_lcm_lora=False")
values.sd_lcm_lora = False
if values.enable_controlnet:
logger.info("ControlNet is enabled, set enable_brushnet=False")
if values.enable_brushnet:
values.enable_brushnet = False
return values
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]
modelInfos: List[ModelInfo]
removeBGModel: RemoveBGModel
removeBGModels: List[RemoveBGModel]
realesrganModel: RealESRGANModel
realesrganModels: List[RealESRGANModel]
interactiveSegModel: InteractiveSegModel
interactiveSegModels: List[InteractiveSegModel]
enableFileManager: bool
enableAutoSaving: bool
enableControlnet: bool
controlnetMethod: Optional[str]
disableModelSwitch: bool
isDesktop: bool
samplers: List[str]
class SwitchModelRequest(BaseModel):
name: str
class SwitchPluginModelRequest(BaseModel):
plugin_name: str
model_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")
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