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images history improvement

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This commit is contained in:
yfszzx 2022-10-11 20:23:41 +08:00
commit 594ab4ba53
29 changed files with 1678 additions and 214 deletions
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4
javascript/contextMenus.js
View file

@ -147,10 +147,6 @@ generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forev
cancelGenerateForever = function(){
clearInterval(window.generateOnRepeatInterval)
let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
if(interruptbutton.offsetParent){
interruptbutton.click();
}
}
appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)

2
javascript/hints.js
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@ -79,6 +79,8 @@ titles = {
"Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
"Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
"Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
"Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
}

2
launch.py
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@ -127,7 +127,7 @@ def prepare_enviroment():
if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
if platform.system() == "Windows":
run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
elif platform.system() == "Linux":
run_pip("install xformers", "xformers")

6
modules/devices.py
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@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32")
device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
dtype = torch.float16
dtype_vae = torch.float16
def randn(seed, shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
@ -59,9 +60,12 @@ def randn_without_seed(shape):
return torch.randn(shape, device=device)
def autocast():
def autocast(disable=False):
from modules import shared
if disable:
return contextlib.nullcontext()
if dtype == torch.float32 or shared.cmd_opts.precision == "full":
return contextlib.nullcontext()

98
modules/hypernetwork.py
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@ -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)

283
modules/hypernetwork/hypernetwork.py Normal file
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@ -0,0 +1,283 @@
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
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):
self.filename = None
self.name = name
self.layers = {}
self.step = 0
self.sd_checkpoint = None
self.sd_checkpoint_name = None
for size in [320, 640, 768, 1280]:
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)
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
cond_model = shared.sd_model.cond_stage_model
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)
hypernetwork = shared.loaded_hypernetwork
weights = hypernetwork.weights()
for weight in weights:
weight.requires_grad = True
optimizer = torch.optim.AdamW(weights, lr=learn_rate)
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
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
for i, (x, text) in pbar:
hypernetwork.step = i + ititial_step
if hypernetwork.step > steps:
break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
c = cond_model([text])
x = x.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), c)[0]
del x
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
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]
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

43
modules/hypernetwork/ui.py Normal file
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@ -0,0 +1,43 @@
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
from modules.hypernetwork import hypernetwork
def create_hypernetwork(name):
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.hypernetwork.hypernetwork.Hypernetwork(name=name)
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
try:
sd_hijack.undo_optimizations()
hypernetwork, filename = modules.hypernetwork.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
sd_hijack.apply_optimizations()

20
modules/processing.py
View file

@ -207,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
# enables the generation of additional tensors with noise that the sampler will use during its processing.
# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
# produce the same images as with two batches [100], [101].
if p is not None and p.sampler is not None and len(seeds) > 1 and opts.enable_batch_seeds:
if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
else:
sampler_noises = None
@ -247,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
if sampler_noises is not None:
cnt = p.sampler.number_of_needed_noises(p)
if opts.eta_noise_seed_delta > 0:
torch.manual_seed(seed + opts.eta_noise_seed_delta)
for j in range(cnt):
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@ -259,6 +262,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
return x
def decode_first_stage(model, x):
with devices.autocast(disable=x.dtype == devices.dtype_vae):
x = model.decode_first_stage(x)
return x
def get_fixed_seed(seed):
if seed is None or seed == '' or seed == -1:
return int(random.randrange(4294967294))
@ -294,6 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
"Denoising strength": getattr(p, 'denoising_strength', None),
"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
}
generation_params.update(p.extra_generation_params)
@ -398,9 +409,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
# 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)
samples_ddim = samples_ddim.to(devices.dtype_vae)
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
@ -533,7 +543,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
decoded_samples = self.sd_model.decode_first_stage(samples)
decoded_samples = decode_first_stage(self.sd_model, samples)
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

95
modules/sd_hijack.py
View file

@ -8,7 +8,7 @@ from torch import einsum
from torch.nn.functional import silu
import modules.textual_inversion.textual_inversion
from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
from modules import prompt_parser, devices, sd_hijack_optimizations, shared
from modules.shared import opts, device, cmd_opts
import ldm.modules.attention
@ -23,7 +23,7 @@ def apply_optimizations():
ldm.modules.diffusionmodules.model.nonlinearity = silu
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and torch.cuda.get_device_capability(shared.device) == (8, 6)):
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
print("Applying xformers cross attention optimization.")
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
@ -37,16 +37,15 @@ def apply_optimizations():
def undo_optimizations():
from modules.hypernetwork import hypernetwork
ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
def get_target_prompt_token_count(token_count):
if token_count < 75:
return 75
return math.ceil(token_count / 10) * 10
return math.ceil(max(token_count, 1) / 75) * 75
class StableDiffusionModelHijack:
@ -110,6 +109,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
self.tokenizer = wrapped.tokenizer
self.token_mults = {}
self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
for text, ident in tokens_with_parens:
mult = 1.0
@ -127,7 +128,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
self.token_mults[ident] = mult
def tokenize_line(self, line, used_custom_terms, hijack_comments):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
if opts.enable_emphasis:
@ -140,6 +140,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
fixes = []
remade_tokens = []
multipliers = []
last_comma = -1
for tokens, (text, weight) in zip(tokenized, parsed):
i = 0
@ -148,13 +149,33 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
if token == self.comma_token:
last_comma = len(remade_tokens)
elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
last_comma += 1
reloc_tokens = remade_tokens[last_comma:]
reloc_mults = multipliers[last_comma:]
remade_tokens = remade_tokens[:last_comma]
length = len(remade_tokens)
rem = int(math.ceil(length / 75)) * 75 - length
remade_tokens += [id_end] * rem + reloc_tokens
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
if embedding is None:
remade_tokens.append(token)
multipliers.append(weight)
i += 1
else:
emb_len = int(embedding.vec.shape[0])
fixes.append((len(remade_tokens), embedding))
iteration = len(remade_tokens) // 75
if (len(remade_tokens) + emb_len) // 75 != iteration:
rem = (75 * (iteration + 1) - len(remade_tokens))
remade_tokens += [id_end] * rem
multipliers += [1.0] * rem
iteration += 1
fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
used_custom_terms.append((embedding.name, embedding.checksum()))
@ -162,10 +183,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
token_count = len(remade_tokens)
prompt_target_length = get_target_prompt_token_count(token_count)
tokens_to_add = prompt_target_length - len(remade_tokens) + 1
tokens_to_add = prompt_target_length - len(remade_tokens)
remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
remade_tokens = remade_tokens + [id_end] * tokens_to_add
multipliers = multipliers + [1.0] * tokens_to_add
return remade_tokens, fixes, multipliers, token_count
@ -260,29 +281,55 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
hijack_fixes.append(fixes)
batch_multipliers.append(multipliers)
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def forward(self, text):
if opts.use_old_emphasis_implementation:
use_old = opts.use_old_emphasis_implementation
if use_old:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
else:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
self.hijack.fixes = hijack_fixes
self.hijack.comments += hijack_comments
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
if use_old:
self.hijack.fixes = hijack_fixes
return self.process_tokens(remade_batch_tokens, batch_multipliers)
z = None
i = 0
while max(map(len, remade_batch_tokens)) != 0:
rem_tokens = [x[75:] for x in remade_batch_tokens]
rem_multipliers = [x[75:] for x in batch_multipliers]
target_token_count = get_target_prompt_token_count(token_count) + 2
self.hijack.fixes = []
for unfiltered in hijack_fixes:
fixes = []
for fix in unfiltered:
if fix[0] == i:
fixes.append(fix[1])
self.hijack.fixes.append(fixes)
z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
z = z1 if z is None else torch.cat((z, z1), axis=-2)
remade_batch_tokens = rem_tokens
batch_multipliers = rem_multipliers
i += 1
return z
def process_tokens(self, remade_batch_tokens, batch_multipliers):
if not opts.use_old_emphasis_implementation:
remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
tokens = torch.asarray(remade_batch_tokens).to(device)
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
if opts.CLIP_stop_at_last_layers > 1:
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
z = self.wrapped.transformer.text_model.final_layer_norm(z)
@ -290,7 +337,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
z = outputs.last_hidden_state
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
original_mean = z.mean()
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)

45
modules/sd_hijack_optimizations.py
View file

@ -9,12 +9,12 @@ from ldm.util import default
from einops import rearrange
from modules import shared
from modules.hypernetwork import hypernetwork
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
try:
import xformers.ops
import functorch
xformers._is_functorch_available = True
shared.xformers_available = True
except Exception:
print("Cannot import xformers", file=sys.stderr)
@ -28,16 +28,10 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
q_in = 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_in = self.to_k(hypernetwork_layers[0](context))
v_in = self.to_v(hypernetwork_layers[1](context))
else:
k_in = self.to_k(context)
v_in = self.to_v(context)
del context, x
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
del context, context_k, context_v, x
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
@ -61,22 +55,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
return self.to_out(r2)
# taken from https://github.com/Doggettx/stable-diffusion
# taken from https://github.com/Doggettx/stable-diffusion and modified
def split_cross_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = 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_in = self.to_k(hypernetwork_layers[0](context))
v_in = self.to_v(hypernetwork_layers[1](context))
else:
k_in = self.to_k(context)
v_in = self.to_v(context)
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
k_in *= self.scale
@ -132,14 +120,11 @@ def xformers_attention_forward(self, x, context=None, mask=None):
h = self.heads
q_in = 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_in = self.to_k(hypernetwork_layers[0](context))
v_in = self.to_v(hypernetwork_layers[1](context))
else:
k_in = self.to_k(context)
v_in = self.to_v(context)
context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
del q_in, k_in, v_in
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)

7
modules/sd_models.py
View file

@ -149,8 +149,13 @@ def load_model_weights(model, checkpoint_info):
model.half()
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
vae_file = shared.cmd_opts.vae_path
if os.path.exists(vae_file):
print(f"Loading VAE weights from: {vae_file}")
vae_ckpt = torch.load(vae_file, map_location="cpu")
@ -158,6 +163,8 @@ def load_model_weights(model, checkpoint_info):
model.first_stage_model.load_state_dict(vae_dict)
model.first_stage_model.to(devices.dtype_vae)
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info

4
modules/sd_samplers.py
View file

@ -7,7 +7,7 @@ import inspect
import k_diffusion.sampling
import ldm.models.diffusion.ddim
import ldm.models.diffusion.plms
from modules import prompt_parser
from modules import prompt_parser, devices, processing
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None):
def sample_to_image(samples):
x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)

19
modules/shared.py
View file

@ -13,7 +13,8 @@ import modules.memmon
import modules.sd_models
import modules.styles
import modules.devices as devices
from modules import sd_samplers, hypernetwork
from modules import sd_samplers
from modules.hypernetwork import hypernetwork
from modules.paths import models_path, script_path, sd_path
sd_model_file = os.path.join(script_path, 'model.ckpt')
@ -25,9 +26,11 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@ -65,6 +68,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None)
parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
@ -80,10 +84,17 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
xformers_available = False
config_filename = cmd_opts.ui_settings_file
hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
loaded_hypernetwork = None
def reload_hypernetworks():
global hypernetworks
hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
class State:
skipped = False
interrupted = False
@ -171,6 +182,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
@ -224,6 +236,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
@ -236,6 +249,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
"interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
}))
options_templates.update(options_section(('ui', "User interface"), {
@ -259,6 +273,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
}))

35
modules/swinir_model.py
View file

@ -10,6 +10,7 @@ from tqdm import tqdm
from modules import modelloader
from modules.shared import cmd_opts, opts, device
from modules.swinir_model_arch import SwinIR as net
from modules.swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
precision_scope = (
@ -57,22 +58,42 @@ class UpscalerSwinIR(Upscaler):
filename = path
if filename is None or not os.path.exists(filename):
return None
model = net(
if filename.endswith(".v2.pth"):
model = net2(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
depths=[6, 6, 6, 6, 6, 6],
embed_dim=180,
num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
resi_connection="1conv",
)
params = None
else:
model = net(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
embed_dim=240,
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="3conv",
)
params = "params_ema"
pretrained_model = torch.load(filename)
model.load_state_dict(pretrained_model["params_ema"], strict=True)
if params is not None:
model.load_state_dict(pretrained_model[params], strict=True)
else:
model.load_state_dict(pretrained_model, strict=True)
if not cmd_opts.no_half:
model = model.half()
return model

1017
modules/swinir_model_arch_v2.py Normal file
View file

File diff suppressed because it is too large Load diff

3
modules/textual_inversion/dataset.py
View file

@ -15,11 +15,10 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
class PersonalizedBase(Dataset):
def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
self.placeholder_token = placeholder_token
self.size = size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)

19
modules/textual_inversion/preprocess.py
View file

@ -7,8 +7,9 @@ import tqdm
from modules import shared, images
def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
size = 512
def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
width = process_width
height = process_height
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
is_wide = ratio < 1 / 1.35
if process_split and is_tall:
img = img.resize((size, size * img.height // img.width))
img = img.resize((width, height * img.height // img.width))
top = img.crop((0, 0, size, size))
top = img.crop((0, 0, width, height))
save_pic(top, index)
bot = img.crop((0, img.height - size, size, img.height))
bot = img.crop((0, img.height - height, width, img.height))
save_pic(bot, index)
elif process_split and is_wide:
img = img.resize((size * img.width // img.height, size))
img = img.resize((width * img.width // img.height, height))
left = img.crop((0, 0, size, size))
left = img.crop((0, 0, width, height))
save_pic(left, index)
right = img.crop((img.width - size, 0, img.width, size))
right = img.crop((img.width - width, 0, img.width, height))
save_pic(right, index)
else:
img = images.resize_image(1, img, size, size)
img = images.resize_image(1, img, width, height)
save_pic(img, index)
shared.state.nextjob()

20
modules/textual_inversion/textual_inversion.py
View file

@ -156,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
return fn
def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file, preview_image_prompt):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
@ -182,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
hijack = sd_hijack.model_hijack
@ -200,6 +200,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
if ititial_step > steps:
return embedding, filename
tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
epoch_len = (tr_img_len * num_repeats) + tr_img_len
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
embedding.step = i + ititial_step
@ -223,7 +226,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
loss.backward()
optimizer.step()
pbar.set_description(f"loss: {losses.mean():.7f}")
epoch_num = embedding.step // epoch_len
epoch_step = embedding.step - (epoch_num * epoch_len) + 1
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@ -232,10 +238,14 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
preview_text = text if preview_image_prompt == "" else preview_image_prompt
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
prompt=text,
prompt=preview_text,
steps=20,
height=training_height,
width=training_width,
do_not_save_grid=True,
do_not_save_samples=True,
)
@ -246,7 +256,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
shared.state.current_image = image
image.save(last_saved_image)
last_saved_image += f", prompt: {text}"
last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = embedding.step

1
modules/textual_inversion/ui.py
View file

@ -22,7 +22,6 @@ def preprocess(*args):
def train_embedding(*args):
try:
sd_hijack.undo_optimizations()

83
modules/ui.py
View file

@ -39,6 +39,7 @@ import modules.generation_parameters_copypaste
from modules import prompt_parser
from modules.images import save_image
import modules.textual_inversion.ui
import modules.hypernetwork.ui
import modules.images_history as img_his
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
@ -312,7 +313,7 @@ def interrogate(image):
def interrogate_deepbooru(image):
prompt = get_deepbooru_tags(image)
prompt = get_deepbooru_tags(image, opts.interrogate_deepbooru_score_threshold)
return gr_show(True) if prompt is None else prompt
@ -525,7 +526,7 @@ def create_ui(wrap_gradio_gpu_call):
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
@ -712,7 +713,7 @@ def create_ui(wrap_gradio_gpu_call):
tiling = gr.Checkbox(label='Tiling', value=False)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
with gr.Group():
@ -964,7 +965,7 @@ def create_ui(wrap_gradio_gpu_call):
extras_send_to_inpaint.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery_img2img",
_js="extract_image_from_gallery_inpaint",
inputs=[result_images],
outputs=[init_img_with_mask],
)
@ -1025,13 +1026,27 @@ def create_ui(wrap_gradio_gpu_call):
gr.HTML(value="")
with gr.Column():
create_embedding = gr.Button(value="Create", variant='primary')
create_embedding = gr.Button(value="Create embedding", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
new_hypernetwork_name = gr.Textbox(label="Name")
with gr.Row():
with gr.Column(scale=3):
gr.HTML(value="")
with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
process_src = gr.Textbox(label='Source directory')
process_dst = gr.Textbox(label='Destination directory')
process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
@ -1046,24 +1061,25 @@ def create_ui(wrap_gradio_gpu_call):
run_preprocess = gr.Button(value="Preprocess", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
steps = gr.Number(label='Max steps', value=100000, precision=0)
num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
with gr.Row():
with gr.Column(scale=2):
gr.HTML(value="")
with gr.Column():
with gr.Row():
interrupt_training = gr.Button(value="Interrupt")
train_embedding = gr.Button(value="Train", variant='primary')
interrupt_training = gr.Button(value="Interrupt")
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
train_embedding = gr.Button(value="Train Embedding", variant='primary')
with gr.Column():
progressbar = gr.HTML(elem_id="ti_progressbar")
@ -1089,12 +1105,26 @@ def create_ui(wrap_gradio_gpu_call):
]
)
create_hypernetwork.click(
fn=modules.hypernetwork.ui.create_hypernetwork,
inputs=[
new_hypernetwork_name,
],
outputs=[
train_hypernetwork_name,
ti_output,
ti_outcome,
]
)
run_preprocess.click(
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
process_src,
process_dst,
process_width,
process_height,
process_flip,
process_split,
process_caption,
@ -1113,10 +1143,34 @@ def create_ui(wrap_gradio_gpu_call):
learn_rate,
dataset_directory,
log_directory,
training_width,
training_height,
steps,
num_repeats,
create_image_every,
save_embedding_every,
template_file,
preview_image_prompt,
],
outputs=[
ti_output,
ti_outcome,
]
)
train_hypernetwork.click(
fn=wrap_gradio_gpu_call(modules.hypernetwork.ui.train_hypernetwork, extra_outputs=[gr.update()]),
_js="start_training_textual_inversion",
inputs=[
train_hypernetwork_name,
learn_rate,
dataset_directory,
log_directory,
steps,
create_image_every,
save_embedding_every,
template_file,
preview_image_prompt,
],
outputs=[
ti_output,
@ -1130,6 +1184,7 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[],
)
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
@ -1335,7 +1390,7 @@ Requested path was: {f}
with gr.Tabs() as tabs:
for interface, label, ifid in interfaces:
with gr.TabItem(label, id=ifid):
with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
interface.render()
if os.path.exists(os.path.join(script_path, "notification.mp3")):

1
requirements.txt
View file

@ -23,4 +23,3 @@ resize-right
torchdiffeq
kornia
lark
functorch

1
requirements_versions.txt
View file

@ -22,4 +22,3 @@ resize-right==0.0.2
torchdiffeq==0.2.3
kornia==0.6.7
lark==1.1.2
functorch==0.2.1

23
script.js
View file

@ -6,6 +6,10 @@ function get_uiCurrentTab() {
return gradioApp().querySelector('.tabs button:not(.border-transparent)')
}
function get_uiCurrentTabContent() {
return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])')
}
uiUpdateCallbacks = []
uiTabChangeCallbacks = []
let uiCurrentTab = null
@ -40,6 +44,25 @@ document.addEventListener("DOMContentLoaded", function() {
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
});
/**
* Add a ctrl+enter as a shortcut to start a generation
*/
document.addEventListener('keydown', function(e) {
var handled = false;
if (e.key !== undefined) {
if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
} else if (e.keyCode !== undefined) {
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
}
if (handled) {
button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
if (button) {
button.click();
}
e.preventDefault();
}
})
/**
* checks that a UI element is not in another hidden element or tab content
*/

4
scripts/loopback.py
View file

@ -38,6 +38,7 @@ class Script(scripts.Script):
grids = []
all_images = []
original_init_image = p.init_images
state.job_count = loops * batch_count
initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
@ -45,6 +46,9 @@ class Script(scripts.Script):
for n in range(batch_count):
history = []
# Reset to original init image at the start of each batch
p.init_images = original_init_image
for i in range(loops):
p.n_iter = 1
p.batch_size = 1

11
scripts/xy_grid.py
View file

@ -10,7 +10,8 @@ import numpy as np
import modules.scripts as scripts
import gradio as gr
from modules import images, hypernetwork
from modules import images
from modules.hypernetwork import hypernetwork
from modules.processing import process_images, Processed, get_correct_sampler
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@ -27,6 +28,9 @@ def apply_field(field):
def apply_prompt(p, x, xs):
if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
p.prompt = p.prompt.replace(xs[0], x)
p.negative_prompt = p.negative_prompt.replace(xs[0], x)
@ -205,7 +209,10 @@ class Script(scripts.Script):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
p.batch_size = 1
if not opts.return_grid:
p.batch_size = 1
CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
def process_axis(opt, vals):

12
style.css
View file

@ -1,3 +1,7 @@
.container {
max-width: 100%;
}
.output-html p {margin: 0 0.5em;}
.row > *,
@ -463,6 +467,10 @@ input[type="range"]{
max-width: 32em;
padding: 0;
}
.images-history-hidden{
display: none;
canvas[key="mask"] {
z-index: 12 !important;
filter: invert();
mix-blend-mode: multiply;
pointer-events: none;
}

27
textual_inversion_templates/hypernetwork.txt Normal file
View file

@ -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]

1
textual_inversion_templates/none.txt Normal file
View file

@ -0,0 +1 @@
picture

6
webui.py
View file

@ -29,6 +29,7 @@ from modules import devices
from modules import modelloader
from modules.paths import script_path
from modules.shared import cmd_opts
import modules.hypernetwork.hypernetwork
modelloader.cleanup_models()
modules.sd_models.setup_model()
@ -82,8 +83,7 @@ modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
shared.sd_model = modules.sd_models.load_model()
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
loaded_hypernetwork = modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
def webui():
@ -108,7 +108,7 @@ def webui():
prevent_thread_lock=True
)
app.add_middleware(GZipMiddleware,minimum_size=1000)
app.add_middleware(GZipMiddleware, minimum_size=1000)
while 1:
time.sleep(0.5)