def generate():
latentsStr = flask.request.args.get("latents")
latentsStrX = flask.request.args.get("xlatents")
psi = float(flask.request.args.get("psi", 0.5))
# use_noise = bool(flask.request.args.get('use_noise', True))
randomize_noise = int(flask.request.args.get("randomize_noise", 0))
fromW = int(flask.request.args.get("fromW", 0))
global model_name
global g_Session
global g_dLatentsIn
# print('g_Session.1:', g_Session)
fetched_model_name = flask.request.args.get("model_name", "ffhq")
if model_name != fetched_model_name:
model_name = fetched_model_name
gs, synthesis = loadGs()
latent_len = gs.input_shape[1]
if latentsStrX:
latents = latentCode.decodeFixed16(latentsStrX, g_dLatentsIn.shape[0])
else:
latents = latentCode.decodeFloat32(latentsStr, latent_len)
t0 = time.time()
# Generate image.
fmt = dict(func=dnnlib.tflib.convert_images_to_uint8, nchw_to_nhwc=True)
with g_Session.as_default():
if fromW != 0:
# print('latentsStr:', latentsStr)
# print('shapes:', g_dLatentsIn.shape, latents.shape)
if latents.shape[0] < g_dLatentsIn.shape[0]:
latents = np.tile(latents,
g_dLatentsIn.shape[0] // latents.shape[0])
images = dnnlib.tflib.run(synthesis, {g_dLatentsIn: latents})
image = misc.convert_to_pil_image(misc.create_image_grid(images),
drange=[-1, 1])
else:
latents = latents.reshape([1, latent_len])
images = gs.run(
latents,
None,
truncation_psi=psi,
randomize_noise=randomize_noise != 0,
output_transform=fmt,
)
image = PIL.Image.fromarray(images[0], "RGB")
print("generation cost:", time.time() - t0)
# encode to PNG
fp = io.BytesIO()
image.save(fp, PIL.Image.registered_extensions()[".png"])
return flask.Response(fp.getvalue(), mimetype="image/png")
def mapZtoW():
zStr = flask.request.args.get('z')
psi = flask.request.args.get('psi')
psi = psi and float(psi)
gs, _ = loadGs()
latent_len = gs.input_shape[1]
z = latentCode.decodeFloat32(zStr, latent_len).reshape([1, latent_len])
w = gs.components.mapping.run(z, None)[:, :1, :].reshape([latent_len])
if psi is not None:
proj = loadProjector()
avg = proj._dlatent_avg.reshape([latent_len])
w = avg + psi * (w - avg)
return latentCode.encodeFloat32(w)
def generate():
latentsStr = flask.request.args.get('latents')
latentsStrX = flask.request.args.get('xlatents')
psi = float(flask.request.args.get('psi', 0.5))
#use_noise = bool(flask.request.args.get('use_noise', True))
randomize_noise = int(flask.request.args.get('randomize_noise', 0))
fromW = int(flask.request.args.get('fromW', 0))
global g_Session
global g_dLatentsIn
#print('g_Session.1:', g_Session)
gs, synthesis = loadGs()
latent_len = gs.input_shape[1]
if latentsStrX:
latents = latentCode.decodeFixed16(latentsStrX, g_dLatentsIn.shape[0])
else:
latents = latentCode.decodeFloat32(latentsStr, latent_len)
t0 = time.time()
# Generate image.
fmt = dict(func = dnnlib.tflib.convert_images_to_uint8, nchw_to_nhwc = True)
with g_Session.as_default():
if fromW != 0:
#print('latentsStr:', latentsStr)
#print('shapes:', g_dLatentsIn.shape, latents.shape)
if latents.shape[0] < g_dLatentsIn.shape[0]:
latents = np.tile(latents, g_dLatentsIn.shape[0] // latents.shape[0])
images = dnnlib.tflib.run(synthesis, {g_dLatentsIn: latents})
image = images[0].transpose(1, 2, 0)
image = misc.adjust_dynamic_range(image, [-1,1], [0,255])
image = np.rint(image).clip(0, 255).astype(np.uint8)
# image = misc.convert_to_pil_image(misc.create_image_grid(images), drange = [-1,1])
else:
latents = latents.reshape([1, latent_len])
images = gs.run(latents, None, truncation_psi = psi, randomize_noise = randomize_noise != 0, output_transform = fmt)
image = images[0]
t1 = time.time()
# image = PIL.Image.fromarray(images[0], 'RGB')
# encode to PNG
# fp = io.BytesIO()
# image.save(fp, PIL.Image.registered_extensions()['.png'])
# data = fp.getValue()
# Convert Numpy array to Vips image
dtype_to_format = {
'uint8': 'uchar',
'int8': 'char',
'uint16': 'ushort',
'int16': 'short',
'uint32': 'uint',
'int32': 'int',
'float32': 'float',
'float64': 'double',
'complex64': 'complex',
'complex128': 'dpcomplex',
}
height, width, bands = image.shape
linear = image.reshape(width * height * bands)
vi = pyvips.Image.new_from_memory(linear.data, width, height, bands, dtype_to_format[str(image.dtype)])
# Save to memory buffer as PNG
# compression = 0
# data = vi.write_to_buffer(f".png[compression={compression}]")
# Save to memory buffer as JPG
vi = vi.resize(2, kernel = "lanczos2")
data = vi.write_to_buffer(".jpg")
t2 = time.time()
print(f'Costs:\ngenerator\t{t1 - t0}\nencoder\t\t{t2 - t1}\ntotal\t\t{t2 -t0}')
return flask.Response(data, mimetype = 'image/png')