add seed and latents stuff
This commit is contained in:
parent
ea9f929bf1
commit
4548a44ca3
34
api/serve.py
34
api/serve.py
|
@ -12,18 +12,19 @@ from flask import Flask, make_response, request, send_file
|
|||
from stringcase import spinalcase
|
||||
from io import BytesIO
|
||||
from os import environ, path, makedirs
|
||||
import numpy as np
|
||||
|
||||
# defaults
|
||||
default_prompt = "a photo of an astronaut eating a hamburger"
|
||||
default_cfg = 8
|
||||
default_steps = 20
|
||||
default_height = 512
|
||||
default_width = 512
|
||||
default_steps = 20
|
||||
default_cfg = 8
|
||||
|
||||
max_cfg = 30
|
||||
max_steps = 150
|
||||
max_height = 512
|
||||
max_width = 512
|
||||
max_steps = 150
|
||||
max_cfg = 30
|
||||
|
||||
# paths
|
||||
model_path = environ.get('ONNX_WEB_MODEL_PATH', "../../stable_diffusion_onnx")
|
||||
|
@ -50,6 +51,14 @@ def get_from_map(args, key, values, default):
|
|||
else:
|
||||
return values[default]
|
||||
|
||||
def get_latents_from_seed(seed: int, width: int, height: int) -> np.ndarray:
|
||||
# 1 is batch size
|
||||
latents_shape = (1, 4, height // 8, width // 8)
|
||||
# Gotta use numpy instead of torch, because torch's randn() doesn't support DML
|
||||
rng = np.random.default_rng(seed)
|
||||
image_latents = rng.standard_normal(latents_shape).astype(np.float32)
|
||||
return image_latents
|
||||
|
||||
# setup
|
||||
if not path.exists(model_path):
|
||||
raise RuntimeError('model path must exist')
|
||||
|
@ -68,14 +77,20 @@ def hello():
|
|||
def txt2img():
|
||||
user = request.remote_addr
|
||||
|
||||
cfg = get_and_clamp(request.args, 'cfg', default_cfg, max_cfg)
|
||||
height = get_and_clamp(request.args, 'height', default_height, max_height)
|
||||
prompt = request.args.get('prompt', default_prompt)
|
||||
steps = get_and_clamp(request.args, 'steps', default_steps, max_steps)
|
||||
scheduler = get_from_map(request.args, 'scheduler', scheduler_list, 'euler-a')
|
||||
cfg = get_and_clamp(request.args, 'cfg', default_cfg, max_cfg, 0)
|
||||
steps = get_and_clamp(request.args, 'steps', default_steps, max_steps)
|
||||
height = get_and_clamp(request.args, 'height', default_height, max_height)
|
||||
width = get_and_clamp(request.args, 'width', default_width, max_width)
|
||||
|
||||
print("txt2img from %s: %s/%s, %sx%s, %s" % (user, cfg, steps, width, height, prompt))
|
||||
seed = int(request.args.get('seed', -1))
|
||||
if seed == -1:
|
||||
seed = np.random.randint(np.iinfo(np.int32).max)
|
||||
|
||||
latents = get_latents_from_seed(seed, width, height)
|
||||
|
||||
print("txt2img from %s: %s/%s, %sx%s, %s, %s" % (user, cfg, steps, width, height, seed, prompt))
|
||||
|
||||
pipe = OnnxStableDiffusionPipeline.from_pretrained(
|
||||
model_path,
|
||||
|
@ -88,7 +103,8 @@ def txt2img():
|
|||
height,
|
||||
width,
|
||||
num_inference_steps=steps,
|
||||
guidance_scale=cfg
|
||||
guidance_scale=cfg,
|
||||
latents=latents
|
||||
).images[0]
|
||||
|
||||
output = '%s/txt2img_%s.png' % (output_path, spinalcase(prompt[0:64]))
|
||||
|
|
Loading…
Reference in New Issue