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onnx-web
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add seed to output filename, apply pep8 to server

This commit is contained in:
Sean Sube 2023-01-05 11:19:42 -06:00
parent d8b6d7fc15
commit d93d4659fa
1 changed files with 64 additions and 54 deletions
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16
api/serve.py
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@ -41,9 +41,11 @@ scheduler_list = {
'dpm-multi': DPMSolverMultistepScheduler.from_pretrained(model_path, subfolder="scheduler"), 'dpm-multi': DPMSolverMultistepScheduler.from_pretrained(model_path, subfolder="scheduler"),
} }
def get_and_clamp(args, key, default_value, max_value, min_value=1): def get_and_clamp(args, key, default_value, max_value, min_value=1):
return min(max(int(args.get(key, default_value)), min_value), max_value) return min(max(int(args.get(key, default_value)), min_value), max_value)
def get_from_map(args, key, values, default): def get_from_map(args, key, values, default):
selected = args.get(key, default) selected = args.get(key, default)
if selected in values: if selected in values:
@ -51,6 +53,7 @@ def get_from_map(args, key, values, default):
else: else:
return values[default] return values[default]
def get_latents_from_seed(seed: int, width: int, height: int) -> np.ndarray: def get_latents_from_seed(seed: int, width: int, height: int) -> np.ndarray:
# 1 is batch size # 1 is batch size
latents_shape = (1, 4, height // 8, width // 8) latents_shape = (1, 4, height // 8, width // 8)
@ -59,6 +62,7 @@ def get_latents_from_seed(seed: int, width: int, height: int) -> np.ndarray:
image_latents = rng.standard_normal(latents_shape).astype(np.float32) image_latents = rng.standard_normal(latents_shape).astype(np.float32)
return image_latents return image_latents
# setup # setup
if not path.exists(model_path): if not path.exists(model_path):
raise RuntimeError('model path must exist') raise RuntimeError('model path must exist')
@ -69,16 +73,20 @@ if not path.exists(output_path):
app = Flask(__name__) app = Flask(__name__)
# routes # routes
@app.route('/') @app.route('/')
def hello(): def hello():
return 'Hello, %s' % (__name__) return 'Hello, %s' % (__name__)
@app.route('/txt2img') @app.route('/txt2img')
def txt2img(): def txt2img():
user = request.remote_addr user = request.remote_addr
prompt = request.args.get('prompt', default_prompt) prompt = request.args.get('prompt', default_prompt)
scheduler = get_from_map(request.args, 'scheduler', scheduler_list, 'euler-a') scheduler = get_from_map(request.args, 'scheduler',
scheduler_list, 'euler-a')
cfg = get_and_clamp(request.args, 'cfg', default_cfg, max_cfg, 0) cfg = get_and_clamp(request.args, 'cfg', default_cfg, max_cfg, 0)
steps = get_and_clamp(request.args, 'steps', default_steps, max_steps) steps = get_and_clamp(request.args, 'steps', default_steps, max_steps)
height = get_and_clamp(request.args, 'height', default_height, max_height) height = get_and_clamp(request.args, 'height', default_height, max_height)
@ -90,7 +98,8 @@ def txt2img():
latents = get_latents_from_seed(seed, width, height) 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)) print("txt2img from %s: %s/%s, %sx%s, %s, %s" %
(user, cfg, steps, width, height, seed, prompt))
pipe = OnnxStableDiffusionPipeline.from_pretrained( pipe = OnnxStableDiffusionPipeline.from_pretrained(
model_path, model_path,
@ -107,7 +116,8 @@ def txt2img():
latents=latents latents=latents
).images[0] ).images[0]
output = '%s/txt2img_%s.png' % (output_path, spinalcase(prompt[0:64])) output = '%s/txt2img_%s_%s.png' % (output_path,
seed, spinalcase(prompt[0:64]))
print("txt2img output: %s" % (output)) print("txt2img output: %s" % (output))
image.save(output) image.save(output)