Merge branch 'AUTOMATIC1111:master' into deepdanbooru_pre_process
JC-Array
3 years ago
committed by
GitHub
commit
963d986396
@ -0,0 +1 @@
|
|||||||
|
* @AUTOMATIC1111
|
||||||
@ -1,98 +0,0 @@
|
|||||||
import glob
|
|
||||||
import os
|
|
||||||
import sys
|
|
||||||
import traceback
|
|
||||||
|
|
||||||
import torch
|
|
||||||
|
|
||||||
from ldm.util import default
|
|
||||||
from modules import devices, shared
|
|
||||||
import torch
|
|
||||||
from torch import einsum
|
|
||||||
from einops import rearrange, repeat
|
|
||||||
|
|
||||||
|
|
||||||
class HypernetworkModule(torch.nn.Module):
|
|
||||||
def __init__(self, dim, state_dict):
|
|
||||||
super().__init__()
|
|
||||||
|
|
||||||
self.linear1 = torch.nn.Linear(dim, dim * 2)
|
|
||||||
self.linear2 = torch.nn.Linear(dim * 2, dim)
|
|
||||||
|
|
||||||
self.load_state_dict(state_dict, strict=True)
|
|
||||||
self.to(devices.device)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
return x + (self.linear2(self.linear1(x)))
|
|
||||||
|
|
||||||
|
|
||||||
class Hypernetwork:
|
|
||||||
filename = None
|
|
||||||
name = None
|
|
||||||
|
|
||||||
def __init__(self, filename):
|
|
||||||
self.filename = filename
|
|
||||||
self.name = os.path.splitext(os.path.basename(filename))[0]
|
|
||||||
self.layers = {}
|
|
||||||
|
|
||||||
state_dict = torch.load(filename, map_location='cpu')
|
|
||||||
for size, sd in state_dict.items():
|
|
||||||
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
|
|
||||||
|
|
||||||
|
|
||||||
def list_hypernetworks(path):
|
|
||||||
res = {}
|
|
||||||
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
|
||||||
name = os.path.splitext(os.path.basename(filename))[0]
|
|
||||||
res[name] = filename
|
|
||||||
return res
|
|
||||||
|
|
||||||
|
|
||||||
def load_hypernetwork(filename):
|
|
||||||
path = shared.hypernetworks.get(filename, None)
|
|
||||||
if path is not None:
|
|
||||||
print(f"Loading hypernetwork {filename}")
|
|
||||||
try:
|
|
||||||
shared.loaded_hypernetwork = Hypernetwork(path)
|
|
||||||
except Exception:
|
|
||||||
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
|
||||||
print(traceback.format_exc(), file=sys.stderr)
|
|
||||||
else:
|
|
||||||
if shared.loaded_hypernetwork is not None:
|
|
||||||
print(f"Unloading hypernetwork")
|
|
||||||
|
|
||||||
shared.loaded_hypernetwork = None
|
|
||||||
|
|
||||||
|
|
||||||
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
|
||||||
h = self.heads
|
|
||||||
|
|
||||||
q = self.to_q(x)
|
|
||||||
context = default(context, x)
|
|
||||||
|
|
||||||
hypernetwork = shared.loaded_hypernetwork
|
|
||||||
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
|
||||||
|
|
||||||
if hypernetwork_layers is not None:
|
|
||||||
k = self.to_k(hypernetwork_layers[0](context))
|
|
||||||
v = self.to_v(hypernetwork_layers[1](context))
|
|
||||||
else:
|
|
||||||
k = self.to_k(context)
|
|
||||||
v = self.to_v(context)
|
|
||||||
|
|
||||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
|
||||||
|
|
||||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
|
||||||
|
|
||||||
if mask is not None:
|
|
||||||
mask = rearrange(mask, 'b ... -> b (...)')
|
|
||||||
max_neg_value = -torch.finfo(sim.dtype).max
|
|
||||||
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
|
||||||
sim.masked_fill_(~mask, max_neg_value)
|
|
||||||
|
|
||||||
# attention, what we cannot get enough of
|
|
||||||
attn = sim.softmax(dim=-1)
|
|
||||||
|
|
||||||
out = einsum('b i j, b j d -> b i d', attn, v)
|
|
||||||
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
|
||||||
return self.to_out(out)
|
|
||||||
@ -0,0 +1,305 @@
|
|||||||
|
import datetime
|
||||||
|
import glob
|
||||||
|
import html
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import traceback
|
||||||
|
import tqdm
|
||||||
|
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from ldm.util import default
|
||||||
|
from modules import devices, shared, processing, sd_models
|
||||||
|
import torch
|
||||||
|
from torch import einsum
|
||||||
|
from einops import rearrange, repeat
|
||||||
|
import modules.textual_inversion.dataset
|
||||||
|
from modules.textual_inversion.learn_schedule import LearnSchedule
|
||||||
|
|
||||||
|
|
||||||
|
class HypernetworkModule(torch.nn.Module):
|
||||||
|
def __init__(self, dim, state_dict=None):
|
||||||
|
super().__init__()
|
||||||
|
|
||||||
|
self.linear1 = torch.nn.Linear(dim, dim * 2)
|
||||||
|
self.linear2 = torch.nn.Linear(dim * 2, dim)
|
||||||
|
|
||||||
|
if state_dict is not None:
|
||||||
|
self.load_state_dict(state_dict, strict=True)
|
||||||
|
else:
|
||||||
|
|
||||||
|
self.linear1.weight.data.normal_(mean=0.0, std=0.01)
|
||||||
|
self.linear1.bias.data.zero_()
|
||||||
|
self.linear2.weight.data.normal_(mean=0.0, std=0.01)
|
||||||
|
self.linear2.bias.data.zero_()
|
||||||
|
|
||||||
|
self.to(devices.device)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
return x + (self.linear2(self.linear1(x)))
|
||||||
|
|
||||||
|
|
||||||
|
class Hypernetwork:
|
||||||
|
filename = None
|
||||||
|
name = None
|
||||||
|
|
||||||
|
def __init__(self, name=None, enable_sizes=None):
|
||||||
|
self.filename = None
|
||||||
|
self.name = name
|
||||||
|
self.layers = {}
|
||||||
|
self.step = 0
|
||||||
|
self.sd_checkpoint = None
|
||||||
|
self.sd_checkpoint_name = None
|
||||||
|
|
||||||
|
for size in enable_sizes or []:
|
||||||
|
self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
|
||||||
|
|
||||||
|
def weights(self):
|
||||||
|
res = []
|
||||||
|
|
||||||
|
for k, layers in self.layers.items():
|
||||||
|
for layer in layers:
|
||||||
|
layer.train()
|
||||||
|
res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
|
||||||
|
|
||||||
|
return res
|
||||||
|
|
||||||
|
def save(self, filename):
|
||||||
|
state_dict = {}
|
||||||
|
|
||||||
|
for k, v in self.layers.items():
|
||||||
|
state_dict[k] = (v[0].state_dict(), v[1].state_dict())
|
||||||
|
|
||||||
|
state_dict['step'] = self.step
|
||||||
|
state_dict['name'] = self.name
|
||||||
|
state_dict['sd_checkpoint'] = self.sd_checkpoint
|
||||||
|
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
|
||||||
|
|
||||||
|
torch.save(state_dict, filename)
|
||||||
|
|
||||||
|
def load(self, filename):
|
||||||
|
self.filename = filename
|
||||||
|
if self.name is None:
|
||||||
|
self.name = os.path.splitext(os.path.basename(filename))[0]
|
||||||
|
|
||||||
|
state_dict = torch.load(filename, map_location='cpu')
|
||||||
|
|
||||||
|
for size, sd in state_dict.items():
|
||||||
|
if type(size) == int:
|
||||||
|
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
|
||||||
|
|
||||||
|
self.name = state_dict.get('name', self.name)
|
||||||
|
self.step = state_dict.get('step', 0)
|
||||||
|
self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
|
||||||
|
self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
|
||||||
|
|
||||||
|
|
||||||
|
def list_hypernetworks(path):
|
||||||
|
res = {}
|
||||||
|
for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
|
||||||
|
name = os.path.splitext(os.path.basename(filename))[0]
|
||||||
|
res[name] = filename
|
||||||
|
return res
|
||||||
|
|
||||||
|
|
||||||
|
def load_hypernetwork(filename):
|
||||||
|
path = shared.hypernetworks.get(filename, None)
|
||||||
|
if path is not None:
|
||||||
|
print(f"Loading hypernetwork {filename}")
|
||||||
|
try:
|
||||||
|
shared.loaded_hypernetwork = Hypernetwork()
|
||||||
|
shared.loaded_hypernetwork.load(path)
|
||||||
|
|
||||||
|
except Exception:
|
||||||
|
print(f"Error loading hypernetwork {path}", file=sys.stderr)
|
||||||
|
print(traceback.format_exc(), file=sys.stderr)
|
||||||
|
else:
|
||||||
|
if shared.loaded_hypernetwork is not None:
|
||||||
|
print(f"Unloading hypernetwork")
|
||||||
|
|
||||||
|
shared.loaded_hypernetwork = None
|
||||||
|
|
||||||
|
|
||||||
|
def apply_hypernetwork(hypernetwork, context, layer=None):
|
||||||
|
hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
|
||||||
|
|
||||||
|
if hypernetwork_layers is None:
|
||||||
|
return context, context
|
||||||
|
|
||||||
|
if layer is not None:
|
||||||
|
layer.hyper_k = hypernetwork_layers[0]
|
||||||
|
layer.hyper_v = hypernetwork_layers[1]
|
||||||
|
|
||||||
|
context_k = hypernetwork_layers[0](context)
|
||||||
|
context_v = hypernetwork_layers[1](context)
|
||||||
|
return context_k, context_v
|
||||||
|
|
||||||
|
|
||||||
|
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
||||||
|
h = self.heads
|
||||||
|
|
||||||
|
q = self.to_q(x)
|
||||||
|
context = default(context, x)
|
||||||
|
|
||||||
|
context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
|
||||||
|
k = self.to_k(context_k)
|
||||||
|
v = self.to_v(context_v)
|
||||||
|
|
||||||
|
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||||
|
|
||||||
|
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||||
|
|
||||||
|
if mask is not None:
|
||||||
|
mask = rearrange(mask, 'b ... -> b (...)')
|
||||||
|
max_neg_value = -torch.finfo(sim.dtype).max
|
||||||
|
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
||||||
|
sim.masked_fill_(~mask, max_neg_value)
|
||||||
|
|
||||||
|
# attention, what we cannot get enough of
|
||||||
|
attn = sim.softmax(dim=-1)
|
||||||
|
|
||||||
|
out = einsum('b i j, b j d -> b i d', attn, v)
|
||||||
|
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
||||||
|
return self.to_out(out)
|
||||||
|
|
||||||
|
|
||||||
|
def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
|
||||||
|
assert hypernetwork_name, 'embedding not selected'
|
||||||
|
|
||||||
|
path = shared.hypernetworks.get(hypernetwork_name, None)
|
||||||
|
shared.loaded_hypernetwork = Hypernetwork()
|
||||||
|
shared.loaded_hypernetwork.load(path)
|
||||||
|
|
||||||
|
shared.state.textinfo = "Initializing hypernetwork training..."
|
||||||
|
shared.state.job_count = steps
|
||||||
|
|
||||||
|
filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
|
||||||
|
|
||||||
|
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
|
||||||
|
unload = shared.opts.unload_models_when_training
|
||||||
|
|
||||||
|
if save_hypernetwork_every > 0:
|
||||||
|
hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
|
||||||
|
os.makedirs(hypernetwork_dir, exist_ok=True)
|
||||||
|
else:
|
||||||
|
hypernetwork_dir = None
|
||||||
|
|
||||||
|
if create_image_every > 0:
|
||||||
|
images_dir = os.path.join(log_directory, "images")
|
||||||
|
os.makedirs(images_dir, exist_ok=True)
|
||||||
|
else:
|
||||||
|
images_dir = None
|
||||||
|
|
||||||
|
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
|
||||||
|
with torch.autocast("cuda"):
|
||||||
|
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
|
||||||
|
|
||||||
|
if unload:
|
||||||
|
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||||
|
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||||
|
|
||||||
|
hypernetwork = shared.loaded_hypernetwork
|
||||||
|
weights = hypernetwork.weights()
|
||||||
|
for weight in weights:
|
||||||
|
weight.requires_grad = True
|
||||||
|
|
||||||
|
losses = torch.zeros((32,))
|
||||||
|
|
||||||
|
last_saved_file = "<none>"
|
||||||
|
last_saved_image = "<none>"
|
||||||
|
|
||||||
|
ititial_step = hypernetwork.step or 0
|
||||||
|
if ititial_step > steps:
|
||||||
|
return hypernetwork, filename
|
||||||
|
|
||||||
|
schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
|
||||||
|
(learn_rate, end_step) = next(schedules)
|
||||||
|
print(f'Training at rate of {learn_rate} until step {end_step}')
|
||||||
|
|
||||||
|
optimizer = torch.optim.AdamW(weights, lr=learn_rate)
|
||||||
|
|
||||||
|
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
|
||||||
|
for i, (x, text, cond) in pbar:
|
||||||
|
hypernetwork.step = i + ititial_step
|
||||||
|
|
||||||
|
if hypernetwork.step > end_step:
|
||||||
|
try:
|
||||||
|
(learn_rate, end_step) = next(schedules)
|
||||||
|
except Exception:
|
||||||
|
break
|
||||||
|
tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
|
||||||
|
for pg in optimizer.param_groups:
|
||||||
|
pg['lr'] = learn_rate
|
||||||
|
|
||||||
|
if shared.state.interrupted:
|
||||||
|
break
|
||||||
|
|
||||||
|
with torch.autocast("cuda"):
|
||||||
|
cond = cond.to(devices.device)
|
||||||
|
x = x.to(devices.device)
|
||||||
|
loss = shared.sd_model(x.unsqueeze(0), cond)[0]
|
||||||
|
del x
|
||||||
|
del cond
|
||||||
|
|
||||||
|
losses[hypernetwork.step % losses.shape[0]] = loss.item()
|
||||||
|
|
||||||
|
optimizer.zero_grad()
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
pbar.set_description(f"loss: {losses.mean():.7f}")
|
||||||
|
|
||||||
|
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
|
||||||
|
last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
|
||||||
|
hypernetwork.save(last_saved_file)
|
||||||
|
|
||||||
|
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
|
||||||
|
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
|
||||||
|
|
||||||
|
preview_text = text if preview_image_prompt == "" else preview_image_prompt
|
||||||
|
|
||||||
|
optimizer.zero_grad()
|
||||||
|
shared.sd_model.cond_stage_model.to(devices.device)
|
||||||
|
shared.sd_model.first_stage_model.to(devices.device)
|
||||||
|
|
||||||
|
p = processing.StableDiffusionProcessingTxt2Img(
|
||||||
|
sd_model=shared.sd_model,
|
||||||
|
prompt=preview_text,
|
||||||
|
steps=20,
|
||||||
|
do_not_save_grid=True,
|
||||||
|
do_not_save_samples=True,
|
||||||
|
)
|
||||||
|
|
||||||
|
processed = processing.process_images(p)
|
||||||
|
image = processed.images[0]
|
||||||
|
|
||||||
|
if unload:
|
||||||
|
shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||||
|
shared.sd_model.first_stage_model.to(devices.cpu)
|
||||||
|
|
||||||
|
shared.state.current_image = image
|
||||||
|
image.save(last_saved_image)
|
||||||
|
|
||||||
|
last_saved_image += f", prompt: {preview_text}"
|
||||||
|
|
||||||
|
shared.state.job_no = hypernetwork.step
|
||||||
|
|
||||||
|
shared.state.textinfo = f"""
|
||||||
|
<p>
|
||||||
|
Loss: {losses.mean():.7f}<br/>
|
||||||
|
Step: {hypernetwork.step}<br/>
|
||||||
|
Last prompt: {html.escape(text)}<br/>
|
||||||
|
Last saved embedding: {html.escape(last_saved_file)}<br/>
|
||||||
|
Last saved image: {html.escape(last_saved_image)}<br/>
|
||||||
|
</p>
|
||||||
|
"""
|
||||||
|
|
||||||
|
checkpoint = sd_models.select_checkpoint()
|
||||||
|
|
||||||
|
hypernetwork.sd_checkpoint = checkpoint.hash
|
||||||
|
hypernetwork.sd_checkpoint_name = checkpoint.model_name
|
||||||
|
hypernetwork.save(filename)
|
||||||
|
|
||||||
|
return hypernetwork, filename
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,47 @@
|
|||||||
|
import html
|
||||||
|
import os
|
||||||
|
|
||||||
|
import gradio as gr
|
||||||
|
|
||||||
|
import modules.textual_inversion.textual_inversion
|
||||||
|
import modules.textual_inversion.preprocess
|
||||||
|
from modules import sd_hijack, shared, devices
|
||||||
|
from modules.hypernetworks import hypernetwork
|
||||||
|
|
||||||
|
|
||||||
|
def create_hypernetwork(name, enable_sizes):
|
||||||
|
fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
|
||||||
|
assert not os.path.exists(fn), f"file {fn} already exists"
|
||||||
|
|
||||||
|
hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
|
||||||
|
hypernet.save(fn)
|
||||||
|
|
||||||
|
shared.reload_hypernetworks()
|
||||||
|
|
||||||
|
return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
|
||||||
|
|
||||||
|
|
||||||
|
def train_hypernetwork(*args):
|
||||||
|
|
||||||
|
initial_hypernetwork = shared.loaded_hypernetwork
|
||||||
|
|
||||||
|
assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
|
||||||
|
|
||||||
|
try:
|
||||||
|
sd_hijack.undo_optimizations()
|
||||||
|
|
||||||
|
hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
|
||||||
|
|
||||||
|
res = f"""
|
||||||
|
Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
|
||||||
|
Hypernetwork saved to {html.escape(filename)}
|
||||||
|
"""
|
||||||
|
return res, ""
|
||||||
|
except Exception:
|
||||||
|
raise
|
||||||
|
finally:
|
||||||
|
shared.loaded_hypernetwork = initial_hypernetwork
|
||||||
|
shared.sd_model.cond_stage_model.to(devices.device)
|
||||||
|
shared.sd_model.first_stage_model.to(devices.device)
|
||||||
|
sd_hijack.apply_optimizations()
|
||||||
|
|
||||||
@ -0,0 +1,15 @@
|
|||||||
|
from pyngrok import ngrok, conf, exception
|
||||||
|
|
||||||
|
|
||||||
|
def connect(token, port):
|
||||||
|
if token == None:
|
||||||
|
token = 'None'
|
||||||
|
conf.get_default().auth_token = token
|
||||||
|
try:
|
||||||
|
public_url = ngrok.connect(port).public_url
|
||||||
|
except exception.PyngrokNgrokError:
|
||||||
|
print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
|
||||||
|
f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
|
||||||
|
else:
|
||||||
|
print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
|
||||||
|
'You can use this link after the launch is complete.')
|
||||||
@ -0,0 +1,34 @@
|
|||||||
|
|
||||||
|
class LearnSchedule:
|
||||||
|
def __init__(self, learn_rate, max_steps, cur_step=0):
|
||||||
|
pairs = learn_rate.split(',')
|
||||||
|
self.rates = []
|
||||||
|
self.it = 0
|
||||||
|
self.maxit = 0
|
||||||
|
for i, pair in enumerate(pairs):
|
||||||
|
tmp = pair.split(':')
|
||||||
|
if len(tmp) == 2:
|
||||||
|
step = int(tmp[1])
|
||||||
|
if step > cur_step:
|
||||||
|
self.rates.append((float(tmp[0]), min(step, max_steps)))
|
||||||
|
self.maxit += 1
|
||||||
|
if step > max_steps:
|
||||||
|
return
|
||||||
|
elif step == -1:
|
||||||
|
self.rates.append((float(tmp[0]), max_steps))
|
||||||
|
self.maxit += 1
|
||||||
|
return
|
||||||
|
else:
|
||||||
|
self.rates.append((float(tmp[0]), max_steps))
|
||||||
|
self.maxit += 1
|
||||||
|
return
|
||||||
|
|
||||||
|
def __iter__(self):
|
||||||
|
return self
|
||||||
|
|
||||||
|
def __next__(self):
|
||||||
|
if self.it < self.maxit:
|
||||||
|
self.it += 1
|
||||||
|
return self.rates[self.it - 1]
|
||||||
|
else:
|
||||||
|
raise StopIteration
|
||||||
@ -0,0 +1,27 @@
|
|||||||
|
a photo of a [filewords]
|
||||||
|
a rendering of a [filewords]
|
||||||
|
a cropped photo of the [filewords]
|
||||||
|
the photo of a [filewords]
|
||||||
|
a photo of a clean [filewords]
|
||||||
|
a photo of a dirty [filewords]
|
||||||
|
a dark photo of the [filewords]
|
||||||
|
a photo of my [filewords]
|
||||||
|
a photo of the cool [filewords]
|
||||||
|
a close-up photo of a [filewords]
|
||||||
|
a bright photo of the [filewords]
|
||||||
|
a cropped photo of a [filewords]
|
||||||
|
a photo of the [filewords]
|
||||||
|
a good photo of the [filewords]
|
||||||
|
a photo of one [filewords]
|
||||||
|
a close-up photo of the [filewords]
|
||||||
|
a rendition of the [filewords]
|
||||||
|
a photo of the clean [filewords]
|
||||||
|
a rendition of a [filewords]
|
||||||
|
a photo of a nice [filewords]
|
||||||
|
a good photo of a [filewords]
|
||||||
|
a photo of the nice [filewords]
|
||||||
|
a photo of the small [filewords]
|
||||||
|
a photo of the weird [filewords]
|
||||||
|
a photo of the large [filewords]
|
||||||
|
a photo of a cool [filewords]
|
||||||
|
a photo of a small [filewords]
|
||||||
@ -0,0 +1 @@
|
|||||||
|
picture
|
||||||
Loading…
Reference in New Issue