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68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
from pydantic import BaseModel
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from diffusionkit.types import GenerateImageRequest
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class TaskData(BaseModel):
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request_id: str = None
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session_id: str = "session"
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save_to_disk_path: str = None
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vram_usage_level: str = "balanced" # or "low" or "medium"
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use_face_correction: str = None # or "GFPGANv1.3"
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use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
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use_stable_diffusion_model: str = "sd-v1-4"
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use_vae_model: str = None
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use_hypernetwork_model: str = None
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show_only_filtered_image: bool = False
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output_format: str = "jpeg" # or "png"
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output_quality: int = 75
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metadata_output_format: str = "txt" # or "json"
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stream_image_progress: bool = False
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class Image:
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data: str # base64
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seed: int
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is_nsfw: bool
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path_abs: str = None
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def __init__(self, data, seed):
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self.data = data
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self.seed = seed
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def json(self):
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return {
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"data": self.data,
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"seed": self.seed,
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"path_abs": self.path_abs,
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}
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class Response:
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render_request: GenerateImageRequest
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task_data: TaskData
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images: list
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def __init__(self, render_request: GenerateImageRequest, task_data: TaskData, images: list):
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self.render_request = render_request
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self.task_data = task_data
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self.images = images
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def json(self):
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del self.render_request.init_image
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del self.render_request.init_image_mask
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res = {
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"status": 'succeeded',
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"render_request": self.render_request.dict(),
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"task_data": self.task_data.dict(),
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"output": [],
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}
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for image in self.images:
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res["output"].append(image.json())
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return res
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class UserInitiatedStop(Exception):
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pass
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