import random from enum import Enum from pathlib import Path from typing import Optional, Literal, List from iopaint.const import ( INSTRUCT_PIX2PIX_NAME, KANDINSKY22_NAME, POWERPAINT_NAME, ANYTEXT_NAME, SDXL_CONTROLNET_CHOICES, SD2_CONTROLNET_CHOICES, SD_CONTROLNET_CHOICES, ) from loguru import logger from pydantic import BaseModel, Field, field_validator, computed_field 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 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_freeu(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] 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" 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 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 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_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] 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")