Merge branch 'master' into dev/deepdanbooru
This commit is contained in:
Greendayle
commit
537da7a304
|
|
@ -16,6 +16,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
|
|||
- Attention, specify parts of text that the model should pay more attention to
|
||||
- a man in a ((tuxedo)) - will pay more attention to tuxedo
|
||||
- a man in a (tuxedo:1.21) - alternative syntax
|
||||
- select text and press ctrl+up or ctrl+down to aduotmatically adjust attention to selected text
|
||||
- Loopback, run img2img processing multiple times
|
||||
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
|
||||
- Textual Inversion
|
||||
|
|
@ -61,6 +62,9 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
|
|||
- Reloading checkpoints on the fly
|
||||
- Checkpoint Merger, a tab that allows you to merge two checkpoints into one
|
||||
- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
|
||||
- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
|
||||
- separate prompts using uppercase `AND`
|
||||
- also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
|
||||
|
||||
## Installation and Running
|
||||
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
|
||||
|
|
|
|||
41
javascript/edit-attention.js
Normal file
41
javascript/edit-attention.js
Normal file
|
|
@ -0,0 +1,41 @@
|
|||
addEventListener('keydown', (event) => {
|
||||
let target = event.originalTarget;
|
||||
if (!target.hasAttribute("placeholder")) return;
|
||||
if (!target.placeholder.toLowerCase().includes("prompt")) return;
|
||||
|
||||
let plus = "ArrowUp"
|
||||
let minus = "ArrowDown"
|
||||
if (event.key != plus && event.key != minus) return;
|
||||
|
||||
selectionStart = target.selectionStart;
|
||||
selectionEnd = target.selectionEnd;
|
||||
if(selectionStart == selectionEnd) return;
|
||||
|
||||
event.preventDefault();
|
||||
|
||||
if (selectionStart == 0 || target.value[selectionStart - 1] != "(") {
|
||||
target.value = target.value.slice(0, selectionStart) +
|
||||
"(" + target.value.slice(selectionStart, selectionEnd) + ":1.0)" +
|
||||
target.value.slice(selectionEnd);
|
||||
|
||||
target.focus();
|
||||
target.selectionStart = selectionStart + 1;
|
||||
target.selectionEnd = selectionEnd + 1;
|
||||
|
||||
} else {
|
||||
end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
|
||||
weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
|
||||
if (event.key == minus) weight -= 0.1;
|
||||
if (event.key == plus) weight += 0.1;
|
||||
|
||||
weight = parseFloat(weight.toPrecision(12));
|
||||
|
||||
target.value = target.value.slice(0, selectionEnd + 1) +
|
||||
weight +
|
||||
target.value.slice(selectionEnd + 1 + end - 1);
|
||||
|
||||
target.focus();
|
||||
target.selectionStart = selectionStart;
|
||||
target.selectionEnd = selectionEnd;
|
||||
}
|
||||
});
|
||||
|
|
@ -4,6 +4,21 @@ global_progressbars = {}
|
|||
function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){
|
||||
var progressbar = gradioApp().getElementById(id_progressbar)
|
||||
var interrupt = gradioApp().getElementById(id_interrupt)
|
||||
|
||||
if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
|
||||
if(progressbar.innerText){
|
||||
let newtitle = 'Stable Diffusion - ' + progressbar.innerText
|
||||
if(document.title != newtitle){
|
||||
document.title = newtitle;
|
||||
}
|
||||
}else{
|
||||
let newtitle = 'Stable Diffusion'
|
||||
if(document.title != newtitle){
|
||||
document.title = newtitle;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
|
||||
global_progressbars[id_progressbar] = progressbar
|
||||
|
||||
|
|
|
|||
11
launch.py
11
launch.py
|
|
@ -19,7 +19,7 @@ clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLI
|
|||
|
||||
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
|
||||
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
|
||||
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
|
||||
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
|
||||
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
|
||||
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
|
||||
|
||||
|
|
@ -86,6 +86,15 @@ def git_clone(url, dir, name, commithash=None):
|
|||
# TODO clone into temporary dir and move if successful
|
||||
|
||||
if os.path.exists(dir):
|
||||
if commithash is None:
|
||||
return
|
||||
|
||||
current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
|
||||
if current_hash == commithash:
|
||||
return
|
||||
|
||||
run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
|
||||
run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
|
||||
return
|
||||
|
||||
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
|
||||
|
|
|
|||
|
|
@ -100,6 +100,8 @@ def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_v
|
|||
|
||||
outputs.append(image)
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
return outputs, plaintext_to_html(info), ''
|
||||
|
||||
|
||||
|
|
|
|||
88
modules/hypernetwork.py
Normal file
88
modules/hypernetwork.py
Normal file
|
|
@ -0,0 +1,88 @@
|
|||
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 load_hypernetworks(path):
|
||||
res = {}
|
||||
|
||||
for filename in glob.iglob(path + '**/*.pt', recursive=True):
|
||||
try:
|
||||
hn = Hypernetwork(filename)
|
||||
res[hn.name] = hn
|
||||
except Exception:
|
||||
print(f"Error loading hypernetwork {filename}", file=sys.stderr)
|
||||
print(traceback.format_exc(), file=sys.stderr)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def attention_CrossAttention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
|
||||
hypernetwork = shared.selected_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)
|
||||
|
|
@ -292,18 +292,13 @@ def apply_filename_pattern(x, p, seed, prompt):
|
|||
x = x.replace("[cfg]", str(p.cfg_scale))
|
||||
x = x.replace("[width]", str(p.width))
|
||||
x = x.replace("[height]", str(p.height))
|
||||
|
||||
#currently disabled if using the save button, will work otherwise
|
||||
# if enabled it will cause a bug because styles is not included in the save_files data dictionary
|
||||
if hasattr(p, "styles"):
|
||||
x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
|
||||
|
||||
x = x.replace("[styles]", sanitize_filename_part(", ".join([x for x in p.styles if not x == "None"]) or "None", replace_spaces=False))
|
||||
x = x.replace("[sampler]", sanitize_filename_part(sd_samplers.samplers[p.sampler_index].name, replace_spaces=False))
|
||||
|
||||
x = x.replace("[model_hash]", shared.sd_model.sd_model_hash)
|
||||
x = x.replace("[model_hash]", getattr(p, "sd_model_hash", shared.sd_model.sd_model_hash))
|
||||
x = x.replace("[date]", datetime.date.today().isoformat())
|
||||
x = x.replace("[datetime]", datetime.datetime.now().strftime("%Y%m%d%H%M%S"))
|
||||
x = x.replace("[job_timestamp]", shared.state.job_timestamp)
|
||||
x = x.replace("[job_timestamp]", getattr(p, "job_timestamp", shared.state.job_timestamp))
|
||||
|
||||
# Apply [prompt] at last. Because it may contain any replacement word.^M
|
||||
if prompt is not None:
|
||||
|
|
@ -353,7 +348,7 @@ def get_next_sequence_number(path, basename):
|
|||
return result + 1
|
||||
|
||||
|
||||
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix=""):
|
||||
def save_image(image, path, basename, seed=None, prompt=None, extension='png', info=None, short_filename=False, no_prompt=False, grid=False, pnginfo_section_name='parameters', p=None, existing_info=None, forced_filename=None, suffix="", save_to_dirs=None):
|
||||
if short_filename or prompt is None or seed is None:
|
||||
file_decoration = ""
|
||||
elif opts.save_to_dirs:
|
||||
|
|
@ -377,7 +372,8 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||
else:
|
||||
pnginfo = None
|
||||
|
||||
save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
|
||||
if save_to_dirs is None:
|
||||
save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
|
||||
|
||||
if save_to_dirs:
|
||||
dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /')
|
||||
|
|
@ -431,4 +427,4 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
|||
with open(f"{fullfn_without_extension}.txt", "w", encoding="utf8") as file:
|
||||
file.write(info + "\n")
|
||||
|
||||
|
||||
return fullfn
|
||||
|
|
|
|||
|
|
@ -11,9 +11,8 @@ import cv2
|
|||
from skimage import exposure
|
||||
|
||||
import modules.sd_hijack
|
||||
from modules import devices, prompt_parser, masking
|
||||
from modules import devices, prompt_parser, masking, sd_samplers, lowvram
|
||||
from modules.sd_hijack import model_hijack
|
||||
from modules.sd_samplers import samplers, samplers_for_img2img
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
import modules.shared as shared
|
||||
import modules.face_restoration
|
||||
|
|
@ -84,7 +83,7 @@ class StableDiffusionProcessing:
|
|||
self.s_tmin = opts.s_tmin
|
||||
self.s_tmax = float('inf') # not representable as a standard ui option
|
||||
self.s_noise = opts.s_noise
|
||||
|
||||
|
||||
if not seed_enable_extras:
|
||||
self.subseed = -1
|
||||
self.subseed_strength = 0
|
||||
|
|
@ -110,7 +109,7 @@ class Processed:
|
|||
self.width = p.width
|
||||
self.height = p.height
|
||||
self.sampler_index = p.sampler_index
|
||||
self.sampler = samplers[p.sampler_index].name
|
||||
self.sampler = sd_samplers.samplers[p.sampler_index].name
|
||||
self.cfg_scale = p.cfg_scale
|
||||
self.steps = p.steps
|
||||
self.batch_size = p.batch_size
|
||||
|
|
@ -122,6 +121,8 @@ class Processed:
|
|||
self.denoising_strength = getattr(p, 'denoising_strength', None)
|
||||
self.extra_generation_params = p.extra_generation_params
|
||||
self.index_of_first_image = index_of_first_image
|
||||
self.styles = p.styles
|
||||
self.job_timestamp = state.job_timestamp
|
||||
|
||||
self.eta = p.eta
|
||||
self.ddim_discretize = p.ddim_discretize
|
||||
|
|
@ -166,6 +167,8 @@ class Processed:
|
|||
"extra_generation_params": self.extra_generation_params,
|
||||
"index_of_first_image": self.index_of_first_image,
|
||||
"infotexts": self.infotexts,
|
||||
"styles": self.styles,
|
||||
"job_timestamp": self.job_timestamp,
|
||||
}
|
||||
|
||||
return json.dumps(obj)
|
||||
|
|
@ -265,7 +268,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
|
|||
|
||||
generation_params = {
|
||||
"Steps": p.steps,
|
||||
"Sampler": samplers[p.sampler_index].name,
|
||||
"Sampler": sd_samplers.samplers[p.sampler_index].name,
|
||||
"CFG scale": p.cfg_scale,
|
||||
"Seed": all_seeds[index],
|
||||
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
|
||||
|
|
@ -296,7 +299,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
assert(len(p.prompt) > 0)
|
||||
else:
|
||||
assert p.prompt is not None
|
||||
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
seed = get_fixed_seed(p.seed)
|
||||
|
|
@ -359,8 +362,8 @@ 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)
|
||||
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)
|
||||
uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
|
||||
c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
|
||||
|
||||
if len(model_hijack.comments) > 0:
|
||||
for comment in model_hijack.comments:
|
||||
|
|
@ -383,6 +386,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
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)
|
||||
|
||||
del samples_ddim
|
||||
|
||||
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
||||
lowvram.send_everything_to_cpu()
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
if opts.filter_nsfw:
|
||||
import modules.safety as safety
|
||||
x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
|
||||
|
|
@ -424,9 +434,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
if opts.samples_save and not p.do_not_save_samples:
|
||||
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
|
||||
|
||||
infotexts.append(infotext(n, i))
|
||||
text = infotext(n, i)
|
||||
infotexts.append(text)
|
||||
image.info["parameters"] = text
|
||||
output_images.append(image)
|
||||
|
||||
del x_samples_ddim
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
state.nextjob()
|
||||
|
||||
p.color_corrections = None
|
||||
|
|
@ -437,7 +453,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
|
|||
grid = images.image_grid(output_images, p.batch_size)
|
||||
|
||||
if opts.return_grid:
|
||||
infotexts.insert(0, infotext())
|
||||
text = infotext()
|
||||
infotexts.insert(0, text)
|
||||
grid.info["parameters"] = text
|
||||
output_images.insert(0, grid)
|
||||
index_of_first_image = 1
|
||||
|
||||
|
|
@ -478,7 +496,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
self.firstphase_height_truncated = int(scale * self.height)
|
||||
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
|
||||
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
|
||||
|
||||
if not self.enable_hr:
|
||||
x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
|
@ -521,13 +539,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
|
||||
shared.state.nextjob()
|
||||
|
||||
self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
|
||||
|
||||
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
|
||||
|
||||
# GC now before running the next img2img to prevent running out of memory
|
||||
x = None
|
||||
devices.torch_gc()
|
||||
|
||||
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
|
||||
|
||||
return samples
|
||||
|
|
@ -556,7 +575,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.nmask = None
|
||||
|
||||
def init(self, all_prompts, all_seeds, all_subseeds):
|
||||
self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
|
||||
crop_region = None
|
||||
|
||||
if self.image_mask is not None:
|
||||
|
|
@ -663,4 +682,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
if self.mask is not None:
|
||||
samples = samples * self.nmask + self.init_latent * self.mask
|
||||
|
||||
del x
|
||||
devices.torch_gc()
|
||||
|
||||
return samples
|
||||
|
|
|
|||
|
|
@ -1,10 +1,7 @@
|
|||
import re
|
||||
from collections import namedtuple
|
||||
import torch
|
||||
from lark import Lark, Transformer, Visitor
|
||||
import functools
|
||||
|
||||
import modules.shared as shared
|
||||
from typing import List
|
||||
import lark
|
||||
|
||||
# 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):
|
||||
|
|
@ -14,25 +11,48 @@ import modules.shared as shared
|
|||
# [75, 'fantasy landscape with a lake and an oak in background masterful']
|
||||
# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
|
||||
|
||||
schedule_parser = lark.Lark(r"""
|
||||
!start: (prompt | /[][():]/+)*
|
||||
prompt: (emphasized | scheduled | plain | WHITESPACE)*
|
||||
!emphasized: "(" prompt ")"
|
||||
| "(" prompt ":" prompt ")"
|
||||
| "[" prompt "]"
|
||||
scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
|
||||
WHITESPACE: /\s+/
|
||||
plain: /([^\\\[\]():]|\\.)+/
|
||||
%import common.SIGNED_NUMBER -> NUMBER
|
||||
""")
|
||||
|
||||
def get_learned_conditioning_prompt_schedules(prompts, steps):
|
||||
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')
|
||||
>>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
|
||||
>>> g("test")
|
||||
[[10, 'test']]
|
||||
>>> g("a [b:3]")
|
||||
[[3, 'a '], [10, 'a b']]
|
||||
>>> g("a [b: 3]")
|
||||
[[3, 'a '], [10, 'a b']]
|
||||
>>> g("a [[[b]]:2]")
|
||||
[[2, 'a '], [10, 'a [[b]]']]
|
||||
>>> g("[(a:2):3]")
|
||||
[[3, ''], [10, '(a:2)']]
|
||||
>>> g("a [b : c : 1] d")
|
||||
[[1, 'a b d'], [10, 'a c d']]
|
||||
>>> g("a[b:[c:d:2]:1]e")
|
||||
[[1, 'abe'], [2, 'ace'], [10, 'ade']]
|
||||
>>> g("a [unbalanced")
|
||||
[[10, 'a [unbalanced']]
|
||||
>>> g("a [b:.5] c")
|
||||
[[5, 'a c'], [10, 'a b c']]
|
||||
>>> g("a [{b|d{:.5] c") # not handling this right now
|
||||
[[5, 'a c'], [10, 'a {b|d{ c']]
|
||||
>>> g("((a][:b:c [d:3]")
|
||||
[[3, '((a][:b:c '], [10, '((a][:b:c d']]
|
||||
"""
|
||||
|
||||
def collect_steps(steps, tree):
|
||||
l = [steps]
|
||||
class CollectSteps(Visitor):
|
||||
class CollectSteps(lark.Visitor):
|
||||
def scheduled(self, tree):
|
||||
tree.children[-1] = float(tree.children[-1])
|
||||
if tree.children[-1] < 1:
|
||||
|
|
@ -43,13 +63,10 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
|||
return sorted(set(l))
|
||||
|
||||
def at_step(step, tree):
|
||||
class AtStep(Transformer):
|
||||
class AtStep(lark.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
|
||||
before, after, _, when = args
|
||||
yield before or () if step <= when else after
|
||||
def start(self, args):
|
||||
def flatten(x):
|
||||
if type(x) == str:
|
||||
|
|
@ -57,16 +74,22 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
|||
else:
|
||||
for gen in x:
|
||||
yield from flatten(gen)
|
||||
return ''.join(flatten(args[0]))
|
||||
return ''.join(flatten(args))
|
||||
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)
|
||||
|
||||
|
||||
def get_schedule(prompt):
|
||||
tree = parser.parse(prompt)
|
||||
try:
|
||||
tree = schedule_parser.parse(prompt)
|
||||
except lark.exceptions.LarkError as e:
|
||||
if 0:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return [[steps, prompt]]
|
||||
return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
|
||||
|
||||
promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
|
||||
|
|
@ -74,11 +97,26 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
|
|||
|
||||
|
||||
ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
|
||||
ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
|
||||
|
||||
|
||||
def get_learned_conditioning(prompts, steps):
|
||||
def get_learned_conditioning(model, prompts, steps):
|
||||
"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
|
||||
and the sampling step at which this condition is to be replaced by the next one.
|
||||
|
||||
Input:
|
||||
(model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
|
||||
|
||||
Output:
|
||||
[
|
||||
[
|
||||
ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0'))
|
||||
],
|
||||
[
|
||||
ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')),
|
||||
ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0'))
|
||||
]
|
||||
]
|
||||
"""
|
||||
res = []
|
||||
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
|
||||
|
|
@ -92,7 +130,7 @@ def get_learned_conditioning(prompts, steps):
|
|||
continue
|
||||
|
||||
texts = [x[1] for x in prompt_schedule]
|
||||
conds = shared.sd_model.get_learned_conditioning(texts)
|
||||
conds = model.get_learned_conditioning(texts)
|
||||
|
||||
cond_schedule = []
|
||||
for i, (end_at_step, text) in enumerate(prompt_schedule):
|
||||
|
|
@ -101,22 +139,109 @@ def get_learned_conditioning(prompts, steps):
|
|||
cache[prompt] = cond_schedule
|
||||
res.append(cond_schedule)
|
||||
|
||||
return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
|
||||
return res
|
||||
|
||||
|
||||
def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
|
||||
res = torch.zeros(c.shape, device=shared.device, dtype=next(shared.sd_model.parameters()).dtype)
|
||||
for i, cond_schedule in enumerate(c.schedules):
|
||||
re_AND = re.compile(r"\bAND\b")
|
||||
re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
|
||||
|
||||
def get_multicond_prompt_list(prompts):
|
||||
res_indexes = []
|
||||
|
||||
prompt_flat_list = []
|
||||
prompt_indexes = {}
|
||||
|
||||
for prompt in prompts:
|
||||
subprompts = re_AND.split(prompt)
|
||||
|
||||
indexes = []
|
||||
for subprompt in subprompts:
|
||||
match = re_weight.search(subprompt)
|
||||
|
||||
text, weight = match.groups() if match is not None else (subprompt, 1.0)
|
||||
|
||||
weight = float(weight) if weight is not None else 1.0
|
||||
|
||||
index = prompt_indexes.get(text, None)
|
||||
if index is None:
|
||||
index = len(prompt_flat_list)
|
||||
prompt_flat_list.append(text)
|
||||
prompt_indexes[text] = index
|
||||
|
||||
indexes.append((index, weight))
|
||||
|
||||
res_indexes.append(indexes)
|
||||
|
||||
return res_indexes, prompt_flat_list, prompt_indexes
|
||||
|
||||
|
||||
class ComposableScheduledPromptConditioning:
|
||||
def __init__(self, schedules, weight=1.0):
|
||||
self.schedules: List[ScheduledPromptConditioning] = schedules
|
||||
self.weight: float = weight
|
||||
|
||||
|
||||
class MulticondLearnedConditioning:
|
||||
def __init__(self, shape, batch):
|
||||
self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
|
||||
self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
|
||||
|
||||
def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
|
||||
"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
|
||||
For each prompt, the list is obtained by splitting the prompt using the AND separator.
|
||||
|
||||
https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
|
||||
"""
|
||||
|
||||
res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
|
||||
|
||||
learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
|
||||
|
||||
res = []
|
||||
for indexes in res_indexes:
|
||||
res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
|
||||
|
||||
return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
|
||||
|
||||
|
||||
def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
|
||||
param = c[0][0].cond
|
||||
res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
|
||||
for i, cond_schedule in enumerate(c):
|
||||
target_index = 0
|
||||
for curret_index, (end_at, cond) in enumerate(cond_schedule):
|
||||
for current, (end_at, cond) in enumerate(cond_schedule):
|
||||
if current_step <= end_at:
|
||||
target_index = curret_index
|
||||
target_index = current
|
||||
break
|
||||
res[i] = cond_schedule[target_index].cond
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
|
||||
param = c.batch[0][0].schedules[0].cond
|
||||
|
||||
tensors = []
|
||||
conds_list = []
|
||||
|
||||
for batch_no, composable_prompts in enumerate(c.batch):
|
||||
conds_for_batch = []
|
||||
|
||||
for cond_index, composable_prompt in enumerate(composable_prompts):
|
||||
target_index = 0
|
||||
for current, (end_at, cond) in enumerate(composable_prompt.schedules):
|
||||
if current_step <= end_at:
|
||||
target_index = current
|
||||
break
|
||||
|
||||
conds_for_batch.append((len(tensors), composable_prompt.weight))
|
||||
tensors.append(composable_prompt.schedules[target_index].cond)
|
||||
|
||||
conds_list.append(conds_for_batch)
|
||||
|
||||
return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
|
||||
|
||||
|
||||
re_attention = re.compile(r"""
|
||||
\\\(|
|
||||
\\\)|
|
||||
|
|
@ -148,23 +273,26 @@ def parse_prompt_attention(text):
|
|||
\\ - literal character '\'
|
||||
anything else - just text
|
||||
|
||||
Example:
|
||||
|
||||
'a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).'
|
||||
|
||||
produces:
|
||||
|
||||
[
|
||||
['a ', 1.0],
|
||||
['house', 1.5730000000000004],
|
||||
[' ', 1.1],
|
||||
['on', 1.0],
|
||||
[' a ', 1.1],
|
||||
['hill', 0.55],
|
||||
[', sun, ', 1.1],
|
||||
['sky', 1.4641000000000006],
|
||||
['.', 1.1]
|
||||
]
|
||||
>>> parse_prompt_attention('normal text')
|
||||
[['normal text', 1.0]]
|
||||
>>> parse_prompt_attention('an (important) word')
|
||||
[['an ', 1.0], ['important', 1.1], [' word', 1.0]]
|
||||
>>> parse_prompt_attention('(unbalanced')
|
||||
[['unbalanced', 1.1]]
|
||||
>>> parse_prompt_attention('\(literal\]')
|
||||
[['(literal]', 1.0]]
|
||||
>>> parse_prompt_attention('(unnecessary)(parens)')
|
||||
[['unnecessaryparens', 1.1]]
|
||||
>>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
|
||||
[['a ', 1.0],
|
||||
['house', 1.5730000000000004],
|
||||
[' ', 1.1],
|
||||
['on', 1.0],
|
||||
[' a ', 1.1],
|
||||
['hill', 0.55],
|
||||
[', sun, ', 1.1],
|
||||
['sky', 1.4641000000000006],
|
||||
['.', 1.1]]
|
||||
"""
|
||||
|
||||
res = []
|
||||
|
|
@ -206,4 +334,19 @@ def parse_prompt_attention(text):
|
|||
if len(res) == 0:
|
||||
res = [["", 1.0]]
|
||||
|
||||
# merge runs of identical weights
|
||||
i = 0
|
||||
while i + 1 < len(res):
|
||||
if res[i][1] == res[i + 1][1]:
|
||||
res[i][0] += res[i + 1][0]
|
||||
res.pop(i + 1)
|
||||
else:
|
||||
i += 1
|
||||
|
||||
return res
|
||||
|
||||
if __name__ == "__main__":
|
||||
import doctest
|
||||
doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
|
||||
else:
|
||||
import torch # doctest faster
|
||||
|
|
|
|||
|
|
@ -5,9 +5,10 @@ import traceback
|
|||
import torch
|
||||
import numpy as np
|
||||
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
|
||||
from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
|
||||
from modules.shared import opts, device, cmd_opts
|
||||
|
||||
import ldm.modules.attention
|
||||
|
|
@ -19,16 +20,19 @@ diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.At
|
|||
|
||||
|
||||
def apply_optimizations():
|
||||
undo_optimizations()
|
||||
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = silu
|
||||
|
||||
if cmd_opts.opt_split_attention_v1:
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = sd_hijack_optimizations.nonlinearity_hijack
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
|
||||
|
||||
|
||||
def undo_optimizations():
|
||||
ldm.modules.attention.CrossAttention.forward = attention_CrossAttention_forward
|
||||
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
|
||||
|
||||
|
|
|
|||
|
|
@ -5,6 +5,8 @@ from torch import einsum
|
|||
from ldm.util import default
|
||||
from einops import rearrange
|
||||
|
||||
from modules import shared
|
||||
|
||||
|
||||
# see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
|
||||
def split_cross_attention_forward_v1(self, x, context=None, mask=None):
|
||||
|
|
@ -42,8 +44,19 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
q_in = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k_in = self.to_k(context) * self.scale
|
||||
v_in = self.to_v(context)
|
||||
|
||||
hypernetwork = shared.selected_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)
|
||||
|
||||
k_in *= self.scale
|
||||
|
||||
del context, 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))
|
||||
|
|
@ -92,14 +105,6 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
return self.to_out(r2)
|
||||
|
||||
def nonlinearity_hijack(x):
|
||||
# swish
|
||||
t = torch.sigmoid(x)
|
||||
x *= t
|
||||
del t
|
||||
|
||||
return x
|
||||
|
||||
def cross_attention_attnblock_forward(self, x):
|
||||
h_ = x
|
||||
h_ = self.norm(h_)
|
||||
|
|
|
|||
|
|
@ -134,6 +134,14 @@ def load_model_weights(model, checkpoint_file, sd_model_hash):
|
|||
|
||||
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
|
||||
|
||||
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
|
||||
if os.path.exists(vae_file):
|
||||
print(f"Loading VAE weights from: {vae_file}")
|
||||
vae_ckpt = torch.load(vae_file, map_location="cpu")
|
||||
vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
|
||||
|
||||
model.first_stage_model.load_state_dict(vae_dict)
|
||||
|
||||
model.sd_model_hash = sd_model_hash
|
||||
model.sd_model_checkpint = checkpoint_file
|
||||
|
||||
|
|
|
|||
|
|
@ -13,31 +13,57 @@ from modules.shared import opts, cmd_opts, state
|
|||
import modules.shared as shared
|
||||
|
||||
|
||||
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
|
||||
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
|
||||
|
||||
samplers_k_diffusion = [
|
||||
('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
|
||||
('Euler', 'sample_euler', ['k_euler']),
|
||||
('LMS', 'sample_lms', ['k_lms']),
|
||||
('Heun', 'sample_heun', ['k_heun']),
|
||||
('DPM2', 'sample_dpm_2', ['k_dpm_2']),
|
||||
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
|
||||
('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
|
||||
('Euler', 'sample_euler', ['k_euler'], {}),
|
||||
('LMS', 'sample_lms', ['k_lms'], {}),
|
||||
('Heun', 'sample_heun', ['k_heun'], {}),
|
||||
('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
|
||||
('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
|
||||
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
|
||||
]
|
||||
|
||||
samplers_data_k_diffusion = [
|
||||
SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
|
||||
for label, funcname, aliases in samplers_k_diffusion
|
||||
SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
|
||||
for label, funcname, aliases, options in samplers_k_diffusion
|
||||
if hasattr(k_diffusion.sampling, funcname)
|
||||
]
|
||||
|
||||
samplers = [
|
||||
all_samplers = [
|
||||
*samplers_data_k_diffusion,
|
||||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
|
||||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
|
||||
]
|
||||
samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
|
||||
|
||||
samplers = []
|
||||
samplers_for_img2img = []
|
||||
|
||||
|
||||
def create_sampler_with_index(list_of_configs, index, model):
|
||||
config = list_of_configs[index]
|
||||
sampler = config.constructor(model)
|
||||
sampler.config = config
|
||||
|
||||
return sampler
|
||||
|
||||
|
||||
def set_samplers():
|
||||
global samplers, samplers_for_img2img
|
||||
|
||||
hidden = set(opts.hide_samplers)
|
||||
hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
|
||||
|
||||
samplers = [x for x in all_samplers if x.name not in hidden]
|
||||
samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
|
||||
|
||||
|
||||
set_samplers()
|
||||
|
||||
sampler_extra_params = {
|
||||
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
||||
|
|
@ -104,14 +130,18 @@ class VanillaStableDiffusionSampler:
|
|||
self.step = 0
|
||||
self.eta = None
|
||||
self.default_eta = 0.0
|
||||
self.config = None
|
||||
|
||||
def number_of_needed_noises(self, p):
|
||||
return 0
|
||||
|
||||
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
|
||||
cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
|
||||
|
||||
assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
|
||||
cond = tensor
|
||||
|
||||
if self.mask is not None:
|
||||
img_orig = self.sampler.model.q_sample(self.init_latent, ts)
|
||||
x_dec = img_orig * self.mask + self.nmask * x_dec
|
||||
|
|
@ -183,19 +213,31 @@ class CFGDenoiser(torch.nn.Module):
|
|||
self.step = 0
|
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale):
|
||||
cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
|
||||
|
||||
batch_size = len(conds_list)
|
||||
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
||||
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_in = torch.cat([x] * 2)
|
||||
sigma_in = torch.cat([sigma] * 2)
|
||||
cond_in = torch.cat([uncond, cond])
|
||||
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
|
||||
denoised = uncond + (cond - uncond) * cond_scale
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
else:
|
||||
uncond = self.inner_model(x, sigma, cond=uncond)
|
||||
cond = self.inner_model(x, sigma, cond=cond)
|
||||
denoised = uncond + (cond - uncond) * cond_scale
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
|
||||
denoised_uncond = x_out[-batch_size:]
|
||||
denoised = torch.clone(denoised_uncond)
|
||||
|
||||
for i, conds in enumerate(conds_list):
|
||||
for cond_index, weight in conds:
|
||||
denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
|
||||
|
||||
if self.mask is not None:
|
||||
denoised = self.init_latent * self.mask + self.nmask * denoised
|
||||
|
|
@ -250,6 +292,7 @@ class KDiffusionSampler:
|
|||
self.stop_at = None
|
||||
self.eta = None
|
||||
self.default_eta = 1.0
|
||||
self.config = None
|
||||
|
||||
def callback_state(self, d):
|
||||
store_latent(d["denoised"])
|
||||
|
|
@ -295,9 +338,11 @@ class KDiffusionSampler:
|
|||
steps, t_enc = setup_img2img_steps(p, steps)
|
||||
|
||||
if p.sampler_noise_scheduler_override:
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
||||
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
|
||||
else:
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
noise = noise * sigmas[steps - t_enc - 1]
|
||||
xi = x + noise
|
||||
|
|
@ -314,9 +359,12 @@ class KDiffusionSampler:
|
|||
steps = steps or p.steps
|
||||
|
||||
if p.sampler_noise_scheduler_override:
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
||||
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
|
||||
else:
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
x = x * sigmas[0]
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
|
|
|
|||
|
|
@ -13,11 +13,11 @@ import modules.memmon
|
|||
import modules.sd_models
|
||||
import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules.paths import script_path, sd_path
|
||||
from modules import sd_samplers, hypernetwork
|
||||
from modules.paths import models_path, script_path, sd_path
|
||||
|
||||
sd_model_file = os.path.join(script_path, 'model.ckpt')
|
||||
default_sd_model_file = sd_model_file
|
||||
model_path = os.path.join(script_path, 'models')
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--config", type=str, default=os.path.join(sd_path, "configs/stable-diffusion/v1-inference.yaml"), help="path to config which constructs model",)
|
||||
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
|
||||
|
|
@ -35,14 +35,14 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
|
|||
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
|
||||
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
|
||||
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
|
||||
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(model_path, 'Codeformer'))
|
||||
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(model_path, 'GFPGAN'))
|
||||
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(model_path, 'ESRGAN'))
|
||||
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(model_path, 'BSRGAN'))
|
||||
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(model_path, 'RealESRGAN'))
|
||||
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(model_path, 'ScuNET'))
|
||||
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(model_path, 'SwinIR'))
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(model_path, 'LDSR'))
|
||||
parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
|
||||
parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
|
||||
parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
|
||||
parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
|
||||
parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
|
||||
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
|
||||
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
|
||||
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
|
||||
parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
|
||||
parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
|
||||
|
|
@ -55,6 +55,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
|
|||
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
|
||||
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
|
||||
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
|
||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
|
||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||
|
|
@ -75,6 +76,12 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
|
|||
|
||||
config_filename = cmd_opts.ui_settings_file
|
||||
|
||||
hypernetworks = hypernetwork.load_hypernetworks(os.path.join(models_path, 'hypernetworks'))
|
||||
|
||||
|
||||
def selected_hypernetwork():
|
||||
return hypernetworks.get(opts.sd_hypernetwork, None)
|
||||
|
||||
|
||||
class State:
|
||||
interrupted = False
|
||||
|
|
@ -205,6 +212,7 @@ options_templates.update(options_section(('system', "System"), {
|
|||
|
||||
options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
||||
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
|
||||
"sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
|
||||
|
|
@ -234,17 +242,20 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
'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}),
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
'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}),
|
||||
}))
|
||||
|
||||
|
||||
class Options:
|
||||
data = None
|
||||
data_labels = options_templates
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
import os
|
||||
from PIL import Image, ImageOps
|
||||
import platform
|
||||
import sys
|
||||
import tqdm
|
||||
|
||||
from modules import shared, images
|
||||
|
|
@ -10,7 +12,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
|||
src = os.path.abspath(process_src)
|
||||
dst = os.path.abspath(process_dst)
|
||||
|
||||
assert src != dst, 'same directory specified as source and desitnation'
|
||||
assert src != dst, 'same directory specified as source and destination'
|
||||
|
||||
os.makedirs(dst, exist_ok=True)
|
||||
|
||||
|
|
@ -25,6 +27,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
|||
def save_pic_with_caption(image, index):
|
||||
if process_caption:
|
||||
caption = "-" + shared.interrogator.generate_caption(image)
|
||||
caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
|
||||
else:
|
||||
caption = filename
|
||||
caption = os.path.splitext(caption)[0]
|
||||
|
|
@ -75,3 +78,27 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
|||
|
||||
if process_caption:
|
||||
shared.interrogator.send_blip_to_ram()
|
||||
|
||||
def sanitize_caption(base_path, original_caption, suffix):
|
||||
operating_system = platform.system().lower()
|
||||
if (operating_system == "windows"):
|
||||
invalid_path_characters = "\\/:*?\"<>|"
|
||||
max_path_length = 259
|
||||
else:
|
||||
invalid_path_characters = "/" #linux/macos
|
||||
max_path_length = 1023
|
||||
caption = original_caption
|
||||
for invalid_character in invalid_path_characters:
|
||||
caption = caption.replace(invalid_character, "")
|
||||
fixed_path_length = len(base_path) + len(suffix)
|
||||
if fixed_path_length + len(caption) <= max_path_length:
|
||||
return caption
|
||||
caption_tokens = caption.split()
|
||||
new_caption = ""
|
||||
for token in caption_tokens:
|
||||
last_caption = new_caption
|
||||
new_caption = new_caption + token + " "
|
||||
if (len(new_caption) + fixed_path_length - 1 > max_path_length):
|
||||
break
|
||||
print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
|
||||
return last_caption.strip()
|
||||
|
|
|
|||
|
|
@ -35,8 +35,8 @@ import modules.gfpgan_model
|
|||
import modules.codeformer_model
|
||||
import modules.styles
|
||||
import modules.generation_parameters_copypaste
|
||||
from modules.prompt_parser import get_learned_conditioning_prompt_schedules
|
||||
from modules.images import apply_filename_pattern, get_next_sequence_number
|
||||
from modules import prompt_parser
|
||||
from modules.images import save_image
|
||||
import modules.textual_inversion.ui
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
|
||||
|
|
@ -115,20 +115,13 @@ def save_files(js_data, images, index):
|
|||
p = MyObject(data)
|
||||
path = opts.outdir_save
|
||||
save_to_dirs = opts.use_save_to_dirs_for_ui
|
||||
|
||||
if save_to_dirs:
|
||||
dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, p.seed, p.prompt)
|
||||
path = os.path.join(opts.outdir_save, dirname)
|
||||
|
||||
os.makedirs(path, exist_ok=True)
|
||||
|
||||
extension: str = opts.samples_format
|
||||
start_index = 0
|
||||
|
||||
if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
|
||||
images = [images[index]]
|
||||
infotexts = [data["infotexts"][index]]
|
||||
else:
|
||||
infotexts = data["infotexts"]
|
||||
start_index = index
|
||||
|
||||
with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
||||
at_start = file.tell() == 0
|
||||
|
|
@ -136,37 +129,18 @@ def save_files(js_data, images, index):
|
|||
if at_start:
|
||||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
file_decoration = opts.samples_filename_pattern or "[seed]-[prompt_spaces]"
|
||||
if file_decoration != "":
|
||||
file_decoration = "-" + file_decoration.lower()
|
||||
file_decoration = apply_filename_pattern(file_decoration, p, p.seed, p.prompt)
|
||||
truncated = (file_decoration[:240] + '..') if len(file_decoration) > 240 else file_decoration
|
||||
filename_base = truncated
|
||||
extension = opts.samples_format.lower()
|
||||
|
||||
basecount = get_next_sequence_number(path, "")
|
||||
for i, filedata in enumerate(images):
|
||||
file_number = f"{basecount+i:05}"
|
||||
filename = file_number + filename_base + f".{extension}"
|
||||
filepath = os.path.join(path, filename)
|
||||
|
||||
|
||||
for image_index, filedata in enumerate(images, start_index):
|
||||
if filedata.startswith("data:image/png;base64,"):
|
||||
filedata = filedata[len("data:image/png;base64,"):]
|
||||
|
||||
image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
|
||||
if opts.enable_pnginfo and extension == 'png':
|
||||
pnginfo = PngImagePlugin.PngInfo()
|
||||
pnginfo.add_text('parameters', infotexts[i])
|
||||
image.save(filepath, pnginfo=pnginfo)
|
||||
else:
|
||||
image.save(filepath, quality=opts.jpeg_quality)
|
||||
|
||||
if opts.enable_pnginfo and extension in ("jpg", "jpeg", "webp"):
|
||||
piexif.insert(piexif.dump({"Exif": {
|
||||
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(infotexts[i], encoding="unicode")
|
||||
}}), filepath)
|
||||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
|
@ -197,6 +171,11 @@ def wrap_gradio_call(func, extra_outputs=None):
|
|||
res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
|
||||
|
||||
elapsed = time.perf_counter() - t
|
||||
elapsed_m = int(elapsed // 60)
|
||||
elapsed_s = elapsed % 60
|
||||
elapsed_text = f"{elapsed_s:.2f}s"
|
||||
if (elapsed_m > 0):
|
||||
elapsed_text = f"{elapsed_m}m "+elapsed_text
|
||||
|
||||
if run_memmon:
|
||||
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
|
||||
|
|
@ -211,7 +190,7 @@ def wrap_gradio_call(func, extra_outputs=None):
|
|||
vram_html = ''
|
||||
|
||||
# last item is always HTML
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed:.2f}s</p>{vram_html}</div>"
|
||||
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
|
||||
|
||||
shared.state.interrupted = False
|
||||
shared.state.job_count = 0
|
||||
|
|
@ -395,7 +374,9 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
|
|||
|
||||
def update_token_counter(text, steps):
|
||||
try:
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
|
||||
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
|
||||
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, 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
|
||||
|
|
@ -652,7 +633,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
|
||||
with gr.TabItem('img2img', id='img2img'):
|
||||
init_img = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil")
|
||||
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool)
|
||||
|
||||
with gr.TabItem('Inpaint', id='inpaint'):
|
||||
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA")
|
||||
|
|
@ -1219,6 +1200,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
)
|
||||
|
||||
def request_restart():
|
||||
shared.state.interrupt()
|
||||
settings_interface.gradio_ref.do_restart = True
|
||||
|
||||
restart_gradio.click(
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ import gradio as gr
|
|||
|
||||
from modules import processing, shared, sd_samplers, prompt_parser
|
||||
from modules.processing import Processed
|
||||
from modules.sd_samplers import samplers
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
|
||||
import torch
|
||||
|
|
@ -159,7 +158,7 @@ class Script(scripts.Script):
|
|||
|
||||
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
|
||||
|
||||
sampler = samplers[p.sampler_index].constructor(p.sd_model)
|
||||
sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
|
||||
|
||||
sigmas = sampler.model_wrap.get_sigmas(p.steps)
|
||||
|
||||
|
|
|
|||
|
|
@ -85,8 +85,11 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
|
|||
src_dist = np.absolute(src_fft)
|
||||
src_phase = src_fft / src_dist
|
||||
|
||||
# create a generator with a static seed to make outpainting deterministic / only follow global seed
|
||||
rng = np.random.default_rng(0)
|
||||
|
||||
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
|
||||
noise_rgb = np.random.random_sample((width, height, num_channels))
|
||||
noise_rgb = rng.random((width, height, num_channels))
|
||||
noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
|
||||
noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
|
||||
for c in range(num_channels):
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
from collections import namedtuple
|
||||
from copy import copy
|
||||
from itertools import permutations
|
||||
from itertools import permutations, chain
|
||||
import random
|
||||
|
||||
import csv
|
||||
from io import StringIO
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
|
|
@ -76,6 +77,11 @@ def apply_checkpoint(p, x, xs):
|
|||
modules.sd_models.reload_model_weights(shared.sd_model, info)
|
||||
|
||||
|
||||
def apply_hypernetwork(p, x, xs):
|
||||
hn = shared.hypernetworks.get(x, None)
|
||||
opts.data["sd_hypernetwork"] = hn.name if hn is not None else 'None'
|
||||
|
||||
|
||||
def format_value_add_label(p, opt, x):
|
||||
if type(x) == float:
|
||||
x = round(x, 8)
|
||||
|
|
@ -121,6 +127,7 @@ axis_options = [
|
|||
AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
|
||||
AxisOption("Sampler", str, apply_sampler, format_value),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
|
||||
AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
|
||||
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
|
||||
|
|
@ -168,7 +175,6 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
|
|||
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
|
||||
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "X/Y plot"
|
||||
|
|
@ -193,11 +199,13 @@ class Script(scripts.Script):
|
|||
modules.processing.fix_seed(p)
|
||||
p.batch_size = 1
|
||||
|
||||
initial_hn = opts.sd_hypernetwork
|
||||
|
||||
def process_axis(opt, vals):
|
||||
if opt.label == 'Nothing':
|
||||
return [0]
|
||||
|
||||
valslist = [x.strip() for x in vals.split(",")]
|
||||
valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]
|
||||
|
||||
if opt.type == int:
|
||||
valslist_ext = []
|
||||
|
|
@ -300,4 +308,6 @@ class Script(scripts.Script):
|
|||
# restore checkpoint in case it was changed by axes
|
||||
modules.sd_models.reload_model_weights(shared.sd_model)
|
||||
|
||||
opts.data["sd_hypernetwork"] = initial_hn
|
||||
|
||||
return processed
|
||||
|
|
|
|||
|
|
@ -408,3 +408,11 @@ input[type="range"]{
|
|||
.red {
|
||||
color: red;
|
||||
}
|
||||
|
||||
.gallery-item {
|
||||
--tw-bg-opacity: 0 !important;
|
||||
}
|
||||
|
||||
#img2img_image div.h-60{
|
||||
height: 480px;
|
||||
}
|
||||
7
webui.py
7
webui.py
|
|
@ -2,11 +2,12 @@ import os
|
|||
import threading
|
||||
import time
|
||||
import importlib
|
||||
from modules import devices
|
||||
from modules.paths import script_path
|
||||
import signal
|
||||
import threading
|
||||
|
||||
from modules.paths import script_path
|
||||
|
||||
from modules import devices, sd_samplers
|
||||
import modules.codeformer_model as codeformer
|
||||
import modules.extras
|
||||
import modules.face_restoration
|
||||
|
|
@ -109,6 +110,8 @@ def webui():
|
|||
time.sleep(0.5)
|
||||
break
|
||||
|
||||
sd_samplers.set_samplers()
|
||||
|
||||
print('Reloading Custom Scripts')
|
||||
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
|
||||
print('Reloading modules: modules.ui')
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user