easydiffusion/ui/sd_internal/__init__.py
2022-12-08 11:58:09 +05:30

119 lines
3.7 KiB
Python

import json
class Request:
request_id: str = None
session_id: str = "session"
prompt: str = ""
negative_prompt: str = ""
init_image: str = None # base64
mask: str = None # base64
num_outputs: int = 1
num_inference_steps: int = 50
guidance_scale: float = 7.5
width: int = 512
height: int = 512
seed: int = 42
prompt_strength: float = 0.8
sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms"
# allow_nsfw: bool = False
precision: str = "autocast" # or "full"
save_to_disk_path: str = None
turbo: bool = True
use_full_precision: bool = False
use_face_correction: str = None # or "GFPGANv1.3"
use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B"
use_stable_diffusion_model: str = "sd-v1-4"
use_vae_model: str = None
use_hypernetwork_model: str = None
hypernetwork_strength: float = 1
show_only_filtered_image: bool = False
output_format: str = "jpeg" # or "png"
output_quality: int = 75
stream_progress_updates: bool = False
stream_image_progress: bool = False
def json(self):
return {
"session_id": self.session_id,
"prompt": self.prompt,
"negative_prompt": self.negative_prompt,
"num_outputs": self.num_outputs,
"num_inference_steps": self.num_inference_steps,
"guidance_scale": self.guidance_scale,
"width": self.width,
"height": self.height,
"seed": self.seed,
"prompt_strength": self.prompt_strength,
"sampler": self.sampler,
"use_face_correction": self.use_face_correction,
"use_upscale": self.use_upscale,
"use_stable_diffusion_model": self.use_stable_diffusion_model,
"use_vae_model": self.use_vae_model,
"use_hypernetwork_model": self.use_hypernetwork_model,
"hypernetwork_strength": self.hypernetwork_strength,
"output_format": self.output_format,
"output_quality": self.output_quality,
}
def __str__(self):
return f'''
session_id: {self.session_id}
prompt: {self.prompt}
negative_prompt: {self.negative_prompt}
seed: {self.seed}
num_inference_steps: {self.num_inference_steps}
sampler: {self.sampler}
guidance_scale: {self.guidance_scale}
w: {self.width}
h: {self.height}
precision: {self.precision}
save_to_disk_path: {self.save_to_disk_path}
turbo: {self.turbo}
use_full_precision: {self.use_full_precision}
use_face_correction: {self.use_face_correction}
use_upscale: {self.use_upscale}
use_stable_diffusion_model: {self.use_stable_diffusion_model}
use_vae_model: {self.use_vae_model}
use_hypernetwork_model: {self.use_hypernetwork_model}
hypernetwork_strength: {self.hypernetwork_strength}
show_only_filtered_image: {self.show_only_filtered_image}
output_format: {self.output_format}
output_quality: {self.output_quality}
stream_progress_updates: {self.stream_progress_updates}
stream_image_progress: {self.stream_image_progress}'''
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:
request: Request
images: list
def json(self):
res = {
"status": 'succeeded',
"request": self.request.json(),
"output": [],
}
for image in self.images:
res["output"].append(image.json())
return res