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Qing
2023-12-30 23:36:44 +08:00
parent 85c3397b97
commit c4abda3942
35 changed files with 969 additions and 854 deletions
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277
lama_cleaner/schema.py
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@@ -1,8 +1,17 @@
import random
from enum import Enum
from typing import Optional
from pathlib import Path
from typing import Optional, Literal, List
from PIL.Image import Image
from pydantic import BaseModel
from pydantic import BaseModel, Field, validator, field_validator
from lama_cleaner.const import Device, InteractiveSegModel, RealESRGANModel
class CV2Flag(str, Enum):
INPAINT_NS = "INPAINT_NS"
INPAINT_TELEA = "INPAINT_TELEA"
class ModelType(str, Enum):
@@ -56,93 +65,215 @@ class PowerPaintTask(str, Enum):
outpainting = "outpainting"
class Config(BaseModel):
class Config:
arbitrary_types_allowed = True
class ApiConfig(BaseModel):
host: str
port: int
model: str
no_half: bool
cpu_offload: bool
disable_nsfw_checker: bool
cpu_textencoder: bool
device: Device
gui: bool
disable_model_switch: bool
input: Path
output_dir: 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
# Configs for ldm model
ldm_steps: int = 20
ldm_sampler: str = LDMSampler.plms
# Configs for zits model
zits_wireframe: bool = True
class InpaintRequest(BaseModel):
image: Optional[str] = Field(..., description="base64 encoded image")
mask: Optional[str] = Field(..., description="base64 encoded mask")
# Configs for High Resolution Strategy(different way to preprocess image)
hd_strategy: str = HDStrategy.CROP # See HDStrategy Enum
hd_strategy_crop_margin: int = 128
# If the longer side of the image is larger than this value, use crop strategy
hd_strategy_crop_trigger_size: int = 800
hd_strategy_resize_limit: int = 1280
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.")
# Configs for Stable Diffusion 1.5
prompt: str = ""
negative_prompt: str = ""
# Crop image to this size before doing sd inpainting
# The value is always on the original image scale
use_croper: bool = False
croper_x: int = None
croper_y: int = None
croper_height: int = None
croper_width: int = None
use_extender: bool = False
extender_x: int = None
extender_y: int = None
extender_height: int = None
extender_width: int = None
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"
)
# Resize the image before doing sd inpainting, the area outside the mask will not lose quality.
# Used by sd models and paint_by_example model
sd_scale: float = 1.0
# Blur the edge of mask area. The higher the number the smoother blend with the original image
sd_mask_blur: int = 0
# Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
# starting point and more noise is added the higher the `strength`. The number of denoising steps depends
# on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
# process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
# essentially ignores `image`.
sd_strength: float = 1.0
# The number of denoising steps. More denoising steps usually lead to a
# higher quality image at the expense of slower inference.
sd_steps: int = 50
# Higher guidance scale encourages to generate images that are closely linked
# to the text prompt, usually at the expense of lower image quality.
sd_guidance_scale: float = 7.5
sd_sampler: str = SDSampler.uni_pc
# -1 mean random seed
sd_seed: int = 42
sd_match_histograms: bool = False
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")
# out-painting
sd_outpainting_softness: float = 20.0
sd_outpainting_space: float = 20.0
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")
# freeu
sd_freeu: bool = False
sd_mask_blur: int = Field(
33,
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",
)
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()
# lcm-lora
sd_lcm_lora: bool = False
sd_lcm_lora: bool = Field(
False,
description="Enable lcm-lora mode. https://huggingface.co/docs/diffusers/main/en/using-diffusers/inference_with_lcm#texttoimage",
)
# preserving the unmasked area at the expense of some more unnatural transitions between the masked and unmasked areas.
sd_prevent_unmasked_area: bool = True
sd_keep_unmasked_area: bool = Field(
True, description="Keep unmasked area unchanged"
)
# Configs for opencv inpainting
# opencv document https://docs.opencv.org/4.6.0/d7/d8b/group__photo__inpaint.html#gga8002a65f5a3328fbf15df81b842d3c3ca05e763003a805e6c11c673a9f4ba7d07
cv2_flag: str = "INPAINT_NS"
cv2_radius: int = 4
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[Image] = None
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 = 1.5
p2p_image_guidance_scale: float = Field(1.5, description="Image guidance scale")
# ControlNet
enable_controlnet: bool = False
controlnet_conditioning_scale: float = 0.4
controlnet_method: str = "lllyasviel/control_v11p_sd15_canny"
enable_controlnet: bool = Field(False, description="Enable controlnet")
controlnet_conditioning_scale: float = Field(0.4, description="Conditioning scale")
controlnet_method: str = Field(
"lllyasviel/control_v11p_sd15_canny", description="Controlnet method"
)
# PowerPaint
powerpaint_task: PowerPaintTask = PowerPaintTask.text_guided
# control the fitting degree of the generated objects to the mask shape.
fitting_degree: float = 1.0
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.",
)
@field_validator("sd_seed")
@classmethod
def sd_seed_validator(cls, v: int) -> int:
if v == -1:
return random.randint(1, 99999999)
return v
class RunPluginRequest(BaseModel):
name: str
image: Optional[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 MediasRequest(BaseModel):
tab: MediaTab
class MediasResponse(BaseModel):
name: str
height: int
width: int
ctime: float
mtime: float
class MediaFileRequest(BaseModel):
tab: MediaTab
filename: str
class MediaThumbnailFileRequest(BaseModel):
tab: MediaTab
filename: str
width: int = 0
height: int = 0
class GenInfoResponse(BaseModel):
prompt: str = ""
negative_prompt: str = ""
class ServerConfigResponse(BaseModel):
plugins: List[str]
enableFileManager: bool
enableAutoSaving: bool
enableControlnet: bool
controlnetMethod: Optional[str]
disableModelSwitch: bool
isDesktop: bool
class SwitchModelRequest(BaseModel):
name: str