Unverified Commit 649d79a8 authored by guaneec's avatar guaneec Committed by GitHub

Merge branch 'master' into hn-activation

parents 877d94f9 757264c4
This diff is collapsed.
......@@ -7,6 +7,7 @@ import uvicorn
from fastapi import Body, APIRouter, HTTPException
from fastapi.responses import JSONResponse
from pydantic import BaseModel, Field, Json
from typing import List
import json
import io
import base64
......@@ -15,12 +16,12 @@ from PIL import Image
sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
class TextToImageResponse(BaseModel):
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: Json
info: Json
class ImageToImageResponse(BaseModel):
images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
parameters: Json
info: Json
......@@ -65,7 +66,7 @@ class Api:
i.save(buffer, format="png")
b64images.append(base64.b64encode(buffer.getvalue()))
return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=json.dumps(processed.info))
return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
......@@ -111,7 +112,11 @@ class Api:
i.save(buffer, format="png")
b64images.append(base64.b64encode(buffer.getvalue()))
return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info))
if (not img2imgreq.include_init_images):
img2imgreq.init_images = None
img2imgreq.mask = None
return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
def extrasapi(self):
raise NotImplementedError
......
......@@ -31,6 +31,7 @@ class ModelDef(BaseModel):
field_alias: str
field_type: Any
field_value: Any
field_exclude: bool = False
class PydanticModelGenerator:
......@@ -78,7 +79,8 @@ class PydanticModelGenerator:
field=underscore(fields["key"]),
field_alias=fields["key"],
field_type=fields["type"],
field_value=fields["default"]))
field_value=fields["default"],
field_exclude=fields["exclude"] if "exclude" in fields else False))
def generate_model(self):
"""
......@@ -86,7 +88,7 @@ class PydanticModelGenerator:
from the json and overrides provided at initialization
"""
fields = {
d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias)) for d in self._model_def
d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
}
DynamicModel = create_model(self._model_name, **fields)
DynamicModel.__config__.allow_population_by_field_name = True
......@@ -102,5 +104,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
"StableDiffusionProcessingImg2Img",
StableDiffusionProcessingImg2Img,
[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}]
[{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
).generate_model()
\ No newline at end of file
......@@ -5,6 +5,7 @@ import html
import os
import sys
import traceback
import inspect
import modules.textual_inversion.dataset
import torch
......@@ -15,10 +16,12 @@ from modules import devices, processing, sd_models, shared
from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
from collections import defaultdict, deque
from statistics import stdev, mean
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
activation_dict = {
......@@ -26,9 +29,12 @@ class HypernetworkModule(torch.nn.Module):
"leakyrelu": torch.nn.LeakyReLU,
"elu": torch.nn.ELU,
"swish": torch.nn.Hardswish,
"tanh": torch.nn.Tanh,
"sigmoid": torch.nn.Sigmoid,
}
activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):
def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False):
super().__init__()
assert layer_structure is not None, "layer_structure must not be None"
......@@ -65,9 +71,24 @@ class HypernetworkModule(torch.nn.Module):
else:
for layer in self.linear:
if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
layer.weight.data.normal_(mean=0.0, std=0.01)
layer.bias.data.zero_()
w, b = layer.weight.data, layer.bias.data
if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
normal_(w, mean=0.0, std=0.01)
normal_(b, mean=0.0, std=0.005)
elif weight_init == 'XavierUniform':
xavier_uniform_(w)
zeros_(b)
elif weight_init == 'XavierNormal':
xavier_normal_(w)
zeros_(b)
elif weight_init == 'KaimingUniform':
kaiming_uniform_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
zeros_(b)
elif weight_init == 'KaimingNormal':
kaiming_normal_(w, nonlinearity='leaky_relu' if 'leakyrelu' == activation_func else 'relu')
zeros_(b)
else:
raise KeyError(f"Key {weight_init} is not defined as initialization!")
self.to(devices.device)
def fix_old_state_dict(self, state_dict):
......@@ -105,7 +126,7 @@ class Hypernetwork:
filename = None
name = None
def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False, activate_output=False):
def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False)
self.filename = None
self.name = name
self.layers = {}
......@@ -114,14 +135,15 @@ class Hypernetwork:
self.sd_checkpoint_name = None
self.layer_structure = layer_structure
self.activation_func = activation_func
self.weight_init = weight_init
self.add_layer_norm = add_layer_norm
self.use_dropout = use_dropout
self.activate_output = activate_output
for size in enable_sizes or []:
self.layers[size] = (
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
)
def weights(self):
......@@ -145,6 +167,7 @@ class Hypernetwork:
state_dict['layer_structure'] = self.layer_structure
state_dict['activation_func'] = self.activation_func
state_dict['is_layer_norm'] = self.add_layer_norm
state_dict['weight_initialization'] = self.weight_init
state_dict['use_dropout'] = self.use_dropout
state_dict['sd_checkpoint'] = self.sd_checkpoint
state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
......@@ -160,16 +183,22 @@ class Hypernetwork:
state_dict = torch.load(filename, map_location='cpu')
self.layer_structure = state_dict.get('layer_structure', [1, 2, 1])
print(self.layer_structure)
self.activation_func = state_dict.get('activation_func', None)
print(f"Activation function is {self.activation_func}")
self.weight_init = state_dict.get('weight_initialization', 'Normal')
print(f"Weight initialization is {self.weight_init}")
self.add_layer_norm = state_dict.get('is_layer_norm', False)
self.use_dropout = state_dict.get('use_dropout', False)
print(f"Layer norm is set to {self.add_layer_norm}")
self.use_dropout = state_dict.get('use_dropout', False
print(f"Dropout usage is set to {self.use_dropout}" )
self.activate_output = state_dict.get('activate_output', True)
for size, sd in state_dict.items():
if type(size) == int:
self.layers[size] = (
HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout, self.activate_output),
HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
)
self.name = state_dict.get('name', self.name)
......
......@@ -8,8 +8,9 @@ import modules.textual_inversion.textual_inversion
from modules import devices, sd_hijack, shared
from modules.hypernetworks import hypernetwork
keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
# Remove illegal characters from name.
name = "".join( x for x in name if (x.isalnum() or x in "._- "))
......@@ -25,6 +26,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
enable_sizes=[int(x) for x in enable_sizes],
layer_structure=layer_structure,
activation_func=activation_func,
weight_init=weight_init,
add_layer_norm=add_layer_norm,
use_dropout=use_dropout,
)
......
......@@ -277,7 +277,7 @@ invalid_filename_chars = '<>:"/\\|?*\n'
invalid_filename_prefix = ' '
invalid_filename_postfix = ' .'
re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
re_pattern = re.compile(r"([^\[\]]+|\[([^]]+)]|[\[\]]*)")
re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
max_filename_part_length = 128
......@@ -343,7 +343,7 @@ class FilenameGenerator:
def datetime(self, *args):
time_datetime = datetime.datetime.now()
time_format = args[0] if len(args) > 0 else self.default_time_format
time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
try:
time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
except pytz.exceptions.UnknownTimeZoneError as _:
......@@ -362,9 +362,9 @@ class FilenameGenerator:
for m in re_pattern.finditer(x):
text, pattern = m.groups()
res += text
if pattern is None:
res += text
continue
pattern_args = []
......@@ -385,12 +385,9 @@ class FilenameGenerator:
print(f"Error adding [{pattern}] to filename", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
if replacement is None:
res += f'[{pattern}]'
else:
if replacement is not None:
res += str(replacement)
continue
continue
res += f'[{pattern}]'
......
This diff is collapsed.
......@@ -7,12 +7,14 @@ import tqdm
import time
from modules import shared, images
from modules.paths import models_path
from modules.shared import opts, cmd_opts
from modules.textual_inversion import autocrop
if cmd_opts.deepdanbooru:
import modules.deepbooru as deepbooru
def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):
def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
try:
if process_caption:
shared.interrogator.load()
......@@ -22,7 +24,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio)
preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)
finally:
......@@ -34,7 +36,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2):
def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):
width = process_width
height = process_height
src = os.path.abspath(process_src)
......@@ -113,6 +115,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
splitted = image.crop((0, y, to_w, y + to_h))
yield splitted
for index, imagefile in enumerate(tqdm.tqdm(files)):
subindex = [0]
filename = os.path.join(src, imagefile)
......@@ -137,11 +140,36 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
ratio = (img.height * width) / (img.width * height)
inverse_xy = True
process_default_resize = True
if process_split and ratio < 1.0 and ratio <= split_threshold:
for splitted in split_pic(img, inverse_xy):
save_pic(splitted, index, existing_caption=existing_caption)
else:
process_default_resize = False
if process_focal_crop and img.height != img.width:
dnn_model_path = None
try:
dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
except Exception as e:
print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)
autocrop_settings = autocrop.Settings(
crop_width = width,
crop_height = height,
face_points_weight = process_focal_crop_face_weight,
entropy_points_weight = process_focal_crop_entropy_weight,
corner_points_weight = process_focal_crop_edges_weight,
annotate_image = process_focal_crop_debug,
dnn_model_path = dnn_model_path,
)
for focal in autocrop.crop_image(img, autocrop_settings):
save_pic(focal, index, existing_caption=existing_caption)
process_default_resize = False
if process_default_resize:
img = images.resize_image(1, img, width, height)
save_pic(img, index, existing_caption=existing_caption)
shared.state.nextjob()
shared.state.nextjob()
\ No newline at end of file
......@@ -157,6 +157,9 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
with devices.autocast():
cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
......
......@@ -1238,7 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
new_hypernetwork_name = gr.Textbox(label="Name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"])
new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
......@@ -1260,6 +1261,7 @@ def create_ui(wrap_gradio_gpu_call):
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
process_split = gr.Checkbox(label='Split oversized images')
process_focal_crop = gr.Checkbox(label='Auto focal point crop')
process_caption = gr.Checkbox(label='Use BLIP for caption')
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
......@@ -1267,6 +1269,12 @@ def create_ui(wrap_gradio_gpu_call):
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05)
with gr.Row(visible=False) as process_focal_crop_row:
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05)
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05)
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05)
process_focal_crop_debug = gr.Checkbox(label='Create debug image')
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
......@@ -1280,6 +1288,12 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[process_split_extra_row],
)
process_focal_crop.change(
fn=lambda show: gr_show(show),
inputs=[process_focal_crop],
outputs=[process_focal_crop_row],
)
with gr.Tab(label="Train"):
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
with gr.Row():
......@@ -1342,6 +1356,7 @@ def create_ui(wrap_gradio_gpu_call):
overwrite_old_hypernetwork,
new_hypernetwork_layer_structure,
new_hypernetwork_activation_func,
new_hypernetwork_initialization_option,
new_hypernetwork_add_layer_norm,
new_hypernetwork_use_dropout
],
......@@ -1367,6 +1382,11 @@ def create_ui(wrap_gradio_gpu_call):
process_caption_deepbooru,
process_split_threshold,
process_overlap_ratio,
process_focal_crop,
process_focal_crop_face_weight,
process_focal_crop_entropy_weight,
process_focal_crop_edges_weight,
process_focal_crop_debug,
],
outputs=[
ti_output,
......
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