huge changes, inpainting in faces unit, change faces processing, change api, refactor, requires further testing
Tran Xen
2 years ago
parent
1d9b3a64dc
commit
ee7f7d09d2
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from dataclasses import dataclass
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from typing import List
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import gradio as gr
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from client_api import api_utils
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@dataclass
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class InpaintingOptions:
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inpainting_denoising_strengh: float = 0
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inpainting_prompt: str = ""
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inpainting_negative_prompt: str = ""
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inpainting_steps: int = 20
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inpainting_sampler: str = "Euler"
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inpainting_model: str = "Current"
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@staticmethod
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def from_gradio(components: List[gr.components.Component]) -> "InpaintingOptions":
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return InpaintingOptions(*components)
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@staticmethod
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def from_api_dto(dto: api_utils.InpaintingOptions) -> "InpaintingOptions":
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"""
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Converts a InpaintingOptions object from an API DTO (Data Transfer Object).
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:param options: An object of api_utils.InpaintingOptions representing the
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post-processing options as received from the API.
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:return: A InpaintingOptions instance containing the translated values
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from the API DTO.
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"""
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if dto is None:
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# Return default values
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return InpaintingOptions()
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return InpaintingOptions(
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inpainting_denoising_strengh=dto.inpainting_denoising_strengh,
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inpainting_prompt=dto.inpainting_prompt,
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inpainting_negative_prompt=dto.inpainting_negative_prompt,
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inpainting_steps=dto.inpainting_steps,
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inpainting_sampler=dto.inpainting_sampler,
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inpainting_model=dto.inpainting_model,
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)
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from typing import List
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import gradio as gr
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from modules.shared import opts
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from modules import sd_models, sd_samplers
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def face_inpainting_ui(
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name: str, id_prefix: str = "faceswaplab", description: str = ""
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) -> List[gr.components.Component]:
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with gr.Accordion(name, open=False):
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gr.Markdown(description)
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inpainting_denoising_strength = gr.Slider(
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0,
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1,
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0,
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step=0.01,
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elem_id=f"{id_prefix}_pp_inpainting_denoising_strength",
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label="Denoising strenght",
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)
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inpainting_denoising_prompt = gr.Textbox(
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opts.data.get(
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"faceswaplab_pp_default_inpainting_prompt", "Portrait of a [gender]"
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),
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elem_id=f"{id_prefix}_pp_inpainting_denoising_prompt",
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label="Inpainting prompt use [gender] instead of men or woman",
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)
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inpainting_denoising_negative_prompt = gr.Textbox(
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opts.data.get(
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"faceswaplab_pp_default_inpainting_negative_prompt", "blurry"
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),
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elem_id=f"{id_prefix}_pp_inpainting_denoising_neg_prompt",
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label="Inpainting negative prompt use [gender] instead of men or woman",
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)
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with gr.Row():
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samplers_names = [s.name for s in sd_samplers.all_samplers]
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inpainting_sampler = gr.Dropdown(
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choices=samplers_names,
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value=[samplers_names[0]],
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label="Inpainting Sampler",
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elem_id=f"{id_prefix}_pp_inpainting_sampler",
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)
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inpainting_denoising_steps = gr.Slider(
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1,
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150,
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20,
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step=1,
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label="Inpainting steps",
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elem_id=f"{id_prefix}_pp_inpainting_steps",
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)
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inpaiting_model = gr.Dropdown(
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choices=["Current"] + sd_models.checkpoint_tiles(),
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default="Current",
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label="sd model (experimental)",
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elem_id=f"{id_prefix}_pp_inpainting_sd_model",
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)
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gradio_components: List[gr.components.Component] = [
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inpainting_denoising_strength,
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inpainting_denoising_prompt,
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inpainting_denoising_negative_prompt,
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inpainting_denoising_steps,
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inpainting_sampler,
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inpaiting_model,
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]
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return gradio_components
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@ -0,0 +1,10 @@
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from typing import Tuple
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from numpy import uint8
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from numpy.typing import NDArray
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from insightface.app.common import Face as IFace
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from PIL import Image
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PILImage = Image.Image
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CV2ImgU8 = NDArray[uint8]
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Face = IFace
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BoxCoords = Tuple[int, int, int, int]
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@ -0,0 +1,39 @@
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from dataclasses import fields, is_dataclass
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from typing import *
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def dataclass_from_flat_list(cls: type, values: Tuple[Any, ...]) -> Any:
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if not is_dataclass(cls):
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raise TypeError(f"{cls} is not a dataclass")
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idx = 0
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init_values = {}
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for field in fields(cls):
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if is_dataclass(field.type):
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inner_values = [values[idx + i] for i in range(len(fields(field.type)))]
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init_values[field.name] = field.type(*inner_values)
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idx += len(inner_values)
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else:
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value = values[idx]
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init_values[field.name] = value
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idx += 1
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return cls(**init_values)
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def dataclasses_from_flat_list(
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classes_mapping: List[type], values: Tuple[Any, ...]
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) -> List[Any]:
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instances = []
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idx = 0
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for cls in classes_mapping:
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num_fields = sum(
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len(fields(field.type)) if is_dataclass(field.type) else 1
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for field in fields(cls)
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)
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instance = dataclass_from_flat_list(cls, values[idx : idx + num_fields])
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instances.append(instance)
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idx += num_fields
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assert [
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isinstance(i, t) for i, t in zip(instances, classes_mapping)
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], "Instances should match types"
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return instances
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