Commit 537da7a3 authored by Greendayle's avatar Greendayle

Merge branch 'master' into dev/deepdanbooru

parents 4320f386 f7c787eb
......@@ -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.
......
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
......
......@@ -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), ''
......
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
This diff is collapsed.
......@@ -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
......
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):
......
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;
}
\ No newline at end of file
......@@ -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')
......
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