Merge branch 'master' into cpu-cmdline-opt

This commit is contained in:
brkirch 2022-10-04 07:42:53 -04:00 committed by GitHub
commit e9e2a7ec9a
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 109 additions and 80 deletions

View File

@ -69,10 +69,14 @@ def setup_model(dirname):
self.net = net
self.face_helper = face_helper
self.net.to(devices.device_codeformer)
return net, face_helper
def send_model_to(self, device):
self.net.to(device)
self.face_helper.face_det.to(device)
self.face_helper.face_parse.to(device)
def restore(self, np_image, w=None):
np_image = np_image[:, :, ::-1]
@ -82,6 +86,8 @@ def setup_model(dirname):
if self.net is None or self.face_helper is None:
return np_image
self.send_model_to(devices.device_codeformer)
self.face_helper.clean_all()
self.face_helper.read_image(np_image)
self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
@ -113,8 +119,10 @@ def setup_model(dirname):
if original_resolution != restored_img.shape[0:2]:
restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
self.face_helper.clean_all()
if shared.opts.face_restoration_unload:
self.net.to(devices.cpu)
self.send_model_to(devices.cpu)
return restored_img

View File

@ -1,3 +1,5 @@
import contextlib
import torch
from modules import errors
@ -56,3 +58,11 @@ def randn_without_seed(shape):
return torch.randn(shape, device=device)
def autocast():
from modules import shared
if dtype == torch.float32 or shared.cmd_opts.precision == "full":
return contextlib.nullcontext()
return torch.autocast("cuda")

View File

@ -36,23 +36,33 @@ def gfpgann():
else:
print("Unable to load gfpgan model!")
return None
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
model.gfpgan.to(devices.device_gfpgan)
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
loaded_gfpgan_model = model
return model
def send_model_to(model, device):
model.gfpgan.to(device)
model.face_helper.face_det.to(device)
model.face_helper.face_parse.to(device)
def gfpgan_fix_faces(np_image):
model = gfpgann()
if model is None:
return np_image
send_model_to(model, devices.device_gfpgan)
np_image_bgr = np_image[:, :, ::-1]
cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
np_image = gfpgan_output_bgr[:, :, ::-1]
model.face_helper.clean_all()
if shared.opts.face_restoration_unload:
model.gfpgan.to(devices.cpu)
send_model_to(model, devices.cpu)
return np_image

View File

@ -1,4 +1,3 @@
import contextlib
import json
import math
import os
@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
infotexts = []
output_images = []
precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
with torch.no_grad(), precision_scope("cuda"), ema_scope():
with torch.no_grad():
p.init(all_prompts, all_seeds, all_subseeds)
if state.job_count == -1:
@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
#c = p.sd_model.get_learned_conditioning(prompts)
uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
c = prompt_parser.get_learned_conditioning(prompts, p.steps)
with devices.autocast():
uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
c = prompt_parser.get_learned_conditioning(prompts, p.steps)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@ -361,13 +360,17 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
if p.n_iter > 1:
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
if state.interrupted:
# if we are interruped, sample returns just noise
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
samples_ddim = samples_ddim.to(devices.dtype)
x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
@ -386,6 +389,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
devices.torch_gc()
x_sample = modules.face_restoration.restore_faces(x_sample)
devices.torch_gc()
image = Image.fromarray(x_sample)

View File

@ -1,20 +1,11 @@
import re
from collections import namedtuple
import torch
from lark import Lark, Transformer, Visitor
import functools
import modules.shared as shared
re_prompt = re.compile(r'''
(.*?)
\[
([^]:]+):
(?:([^]:]*):)?
([0-9]*\.?[0-9]+)
]
|
(.+)
''', re.X)
# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
# will be represented with prompt_schedule like this (assuming steps=100):
# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
@ -25,61 +16,57 @@ re_prompt = re.compile(r'''
def get_learned_conditioning_prompt_schedules(prompts, steps):
res = []
cache = {}
for prompt in prompts:
prompt_schedule: list[list[str | int]] = [[steps, ""]]
cached = cache.get(prompt, None)
if cached is not None:
res.append(cached)
continue
for m in re_prompt.finditer(prompt):
plaintext = m.group(1) if m.group(5) is None else m.group(5)
concept_from = m.group(2)
concept_to = m.group(3)
if concept_to is None:
concept_to = concept_from
concept_from = ""
swap_position = float(m.group(4)) if m.group(4) is not None else None
if swap_position is not None:
if swap_position < 1:
swap_position = swap_position * steps
swap_position = int(min(swap_position, steps))
swap_index = None
found_exact_index = False
for i in range(len(prompt_schedule)):
end_step = prompt_schedule[i][0]
prompt_schedule[i][1] += plaintext
if swap_position is not None and swap_index is None:
if swap_position == end_step:
swap_index = i
found_exact_index = True
if swap_position < end_step:
swap_index = i
if swap_index is not None:
if not found_exact_index:
prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
for i in range(len(prompt_schedule)):
end_step = prompt_schedule[i][0]
must_replace = swap_position < end_step
prompt_schedule[i][1] += concept_to if must_replace else concept_from
res.append(prompt_schedule)
cache[prompt] = prompt_schedule
#for t in prompt_schedule:
# print(t)
return res
grammar = r"""
start: prompt
prompt: (emphasized | scheduled | weighted | plain)*
!emphasized: "(" prompt ")"
| "(" prompt ":" prompt ")"
| "[" prompt "]"
scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
!weighted: "{" weighted_item ("|" weighted_item)* "}"
!weighted_item: prompt (":" prompt)?
plain: /([^\\\[\](){}:|]|\\.)+/
%import common.SIGNED_NUMBER -> NUMBER
"""
parser = Lark(grammar, parser='lalr')
def collect_steps(steps, tree):
l = [steps]
class CollectSteps(Visitor):
def scheduled(self, tree):
tree.children[-1] = float(tree.children[-1])
if tree.children[-1] < 1:
tree.children[-1] *= steps
tree.children[-1] = min(steps, int(tree.children[-1]))
l.append(tree.children[-1])
CollectSteps().visit(tree)
return sorted(set(l))
def at_step(step, tree):
class AtStep(Transformer):
def scheduled(self, args):
if len(args) == 2:
before, after, when = (), *args
else:
before, after, when = args
yield before if step <= when else after
def start(self, args):
def flatten(x):
if type(x) == str:
yield x
else:
for gen in x:
yield from flatten(gen)
return ''.join(flatten(args[0]))
def plain(self, args):
yield args[0].value
def __default__(self, data, children, meta):
for child in children:
yield from child
return AtStep().transform(tree)
@functools.cache
def get_schedule(prompt):
tree = parser.parse(prompt)
return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
return [get_schedule(prompt) for prompt in prompts]
ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])

View File

@ -386,14 +386,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
outputs=[seed, dummy_component]
)
def update_token_counter(text, steps):
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
try:
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
except Exception:
# a parsing error can happen here during typing, and we don't want to bother the user with
# messages related to it in console
prompt_schedules = [[[steps, text]]]
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
prompts = [prompt_text for step,prompt_text in flat_prompts]
prompts = [prompt_text for step, prompt_text in flat_prompts]
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
style_class = ' class="red"' if (token_count > max_length) else ""
return f"<span {style_class}>{token_count}/{max_length}</span>"
def create_toprow(is_img2img):
id_part = "img2img" if is_img2img else "txt2img"

View File

@ -22,3 +22,4 @@ clean-fid
resize-right
torchdiffeq
kornia
lark

View File

@ -21,3 +21,4 @@ clean-fid==0.1.29
resize-right==0.0.2
torchdiffeq==0.2.3
kornia==0.6.7
lark==1.1.2