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from pydantic import BaseModel
from typing import Any
class GenerateImageRequest(BaseModel):
prompt: str = ""
negative_prompt: str = ""
seed: int = 42
width: int = 512
height: int = 512
num_outputs: int = 1
num_inference_steps: int = 50
guidance_scale: float = 7.5
init_image: Any = None
init_image_mask: Any = None
prompt_strength: float = 0.8
preserve_init_image_color_profile = False
sampler_name: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
hypernetwork_strength: float = 0
class TaskData(BaseModel):
request_id: str = None
session_id: str = "session"
save_to_disk_path: str = None
vram_usage_level: str = "balanced" # or "low" or "medium"
use_face_correction: str = None # or "GFPGANv1.3"
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
upscale_amount: int = 4 # or 2
use_stable_diffusion_model: str = "sd-v1-4"
# use_stable_diffusion_config: str = "v1-inference"
use_vae_model: str = None
use_hypernetwork_model: str = None
show_only_filtered_image: bool = False
block_nsfw: bool = False
output_format: str = "jpeg" # or "png" or "webp"
output_quality: int = 75
metadata_output_format: str = "txt" # or "json"
stream_image_progress: bool = False
stream_image_progress_interval: int = 5
class MergeRequest(BaseModel):
model0: str = None
model1: str = None
ratio: float = None
out_path: str = "mix"
use_fp16 = True
class Image:
data: str # base64
seed: int
is_nsfw: bool
path_abs: str = None
def __init__(self, data, seed):
self.data = data
self.seed = seed
def json(self):
return {
"data": self.data,
"seed": self.seed,
"path_abs": self.path_abs,
}
class Response:
render_request: GenerateImageRequest
task_data: TaskData
images: list
def __init__(self, render_request: GenerateImageRequest, task_data: TaskData, images: list):
self.render_request = render_request
self.task_data = task_data
self.images = images
def json(self):
del self.render_request.init_image
del self.render_request.init_image_mask
res = {
"status": "succeeded",
"render_request": self.render_request.dict(),
"task_data": self.task_data.dict(),
"output": [],
}
for image in self.images:
res["output"].append(image.json())
return res
class UserInitiatedStop(Exception):
pass
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