diff options
| -rw-r--r-- | README.md | 3 | ||||
| -rw-r--r-- | javascript/contextMenus.js | 172 | ||||
| -rw-r--r-- | javascript/edit-attention.js | 2 | ||||
| -rw-r--r-- | javascript/hints.js | 1 | ||||
| -rw-r--r-- | javascript/imageviewer.js | 3 | ||||
| -rw-r--r-- | javascript/progressbar.js | 20 | ||||
| -rw-r--r-- | modules/esrgan_model.py | 2 | ||||
| -rw-r--r-- | modules/hypernetwork.py | 88 | ||||
| -rw-r--r-- | modules/img2img.py | 2 | ||||
| -rw-r--r-- | modules/processing.py | 7 | ||||
| -rw-r--r-- | modules/prompt_parser.py | 9 | ||||
| -rw-r--r-- | modules/sd_hijack.py | 57 | ||||
| -rw-r--r-- | modules/sd_hijack_optimizations.py | 35 | ||||
| -rw-r--r-- | modules/sd_models.py | 14 | ||||
| -rw-r--r-- | modules/sd_samplers.py | 39 | ||||
| -rw-r--r-- | modules/shared.py | 15 | ||||
| -rw-r--r-- | modules/ui.py | 8 | ||||
| -rw-r--r-- | scripts/xy_grid.py | 10 | ||||
| -rw-r--r-- | style.css | 43 | ||||
| -rw-r--r-- | webui.py | 1 |
20 files changed, 475 insertions, 56 deletions
@@ -16,7 +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
+ - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
- Textual Inversion
@@ -65,6 +65,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - [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`
+- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
## 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.
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js new file mode 100644 index 00000000..2d82269f --- /dev/null +++ b/javascript/contextMenus.js @@ -0,0 +1,172 @@ +
+contextMenuInit = function(){
+ let eventListenerApplied=false;
+ let menuSpecs = new Map();
+
+ const uid = function(){
+ return Date.now().toString(36) + Math.random().toString(36).substr(2);
+ }
+
+ function showContextMenu(event,element,menuEntries){
+ let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
+ let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
+
+ let oldMenu = gradioApp().querySelector('#context-menu')
+ if(oldMenu){
+ oldMenu.remove()
+ }
+
+ let tabButton = gradioApp().querySelector('button')
+ let baseStyle = window.getComputedStyle(tabButton)
+
+ const contextMenu = document.createElement('nav')
+ contextMenu.id = "context-menu"
+ contextMenu.style.background = baseStyle.background
+ contextMenu.style.color = baseStyle.color
+ contextMenu.style.fontFamily = baseStyle.fontFamily
+ contextMenu.style.top = posy+'px'
+ contextMenu.style.left = posx+'px'
+
+
+
+ const contextMenuList = document.createElement('ul')
+ contextMenuList.className = 'context-menu-items';
+ contextMenu.append(contextMenuList);
+
+ menuEntries.forEach(function(entry){
+ let contextMenuEntry = document.createElement('a')
+ contextMenuEntry.innerHTML = entry['name']
+ contextMenuEntry.addEventListener("click", function(e) {
+ entry['func']();
+ })
+ contextMenuList.append(contextMenuEntry);
+
+ })
+
+ gradioApp().getRootNode().appendChild(contextMenu)
+
+ let menuWidth = contextMenu.offsetWidth + 4;
+ let menuHeight = contextMenu.offsetHeight + 4;
+
+ let windowWidth = window.innerWidth;
+ let windowHeight = window.innerHeight;
+
+ if ( (windowWidth - posx) < menuWidth ) {
+ contextMenu.style.left = windowWidth - menuWidth + "px";
+ }
+
+ if ( (windowHeight - posy) < menuHeight ) {
+ contextMenu.style.top = windowHeight - menuHeight + "px";
+ }
+
+ }
+
+ function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
+
+ currentItems = menuSpecs.get(targetEmementSelector)
+
+ if(!currentItems){
+ currentItems = []
+ menuSpecs.set(targetEmementSelector,currentItems);
+ }
+ let newItem = {'id':targetEmementSelector+'_'+uid(),
+ 'name':entryName,
+ 'func':entryFunction,
+ 'isNew':true}
+
+ currentItems.push(newItem)
+ return newItem['id']
+ }
+
+ function removeContextMenuOption(uid){
+ menuSpecs.forEach(function(v,k) {
+ let index = -1
+ v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
+ if(index>=0){
+ v.splice(index, 1);
+ }
+ })
+ }
+
+ function addContextMenuEventListener(){
+ if(eventListenerApplied){
+ return;
+ }
+ gradioApp().addEventListener("click", function(e) {
+ let source = e.composedPath()[0]
+ if(source.id && source.indexOf('check_progress')>-1){
+ return
+ }
+
+ let oldMenu = gradioApp().querySelector('#context-menu')
+ if(oldMenu){
+ oldMenu.remove()
+ }
+ });
+ gradioApp().addEventListener("contextmenu", function(e) {
+ let oldMenu = gradioApp().querySelector('#context-menu')
+ if(oldMenu){
+ oldMenu.remove()
+ }
+ menuSpecs.forEach(function(v,k) {
+ if(e.composedPath()[0].matches(k)){
+ showContextMenu(e,e.composedPath()[0],v)
+ e.preventDefault()
+ return
+ }
+ })
+ });
+ eventListenerApplied=true
+
+ }
+
+ return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
+}
+
+initResponse = contextMenuInit()
+appendContextMenuOption = initResponse[0]
+removeContextMenuOption = initResponse[1]
+addContextMenuEventListener = initResponse[2]
+
+
+//Start example Context Menu Items
+generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+ let genbutton = gradioApp().querySelector('#txt2img_generate');
+ let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+ if(!interruptbutton.offsetParent){
+ genbutton.click();
+ }
+ clearInterval(window.generateOnRepeatInterval)
+ window.generateOnRepeatInterval = setInterval(function(){
+ if(!interruptbutton.offsetParent){
+ genbutton.click();
+ }
+ },
+ 500)}
+)
+
+cancelGenerateForever = function(){
+ clearInterval(window.generateOnRepeatInterval)
+ let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+ if(interruptbutton.offsetParent){
+ interruptbutton.click();
+ }
+}
+
+appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
+appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
+
+
+appendContextMenuOption('#roll','Roll three',
+ function(){
+ let rollbutton = gradioApp().querySelector('#roll');
+ setTimeout(function(){rollbutton.click()},100)
+ setTimeout(function(){rollbutton.click()},200)
+ setTimeout(function(){rollbutton.click()},300)
+ }
+)
+//End example Context Menu Items
+
+onUiUpdate(function(){
+ addContextMenuEventListener()
+});
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index c67ed579..0280c603 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,5 +1,5 @@ addEventListener('keydown', (event) => {
- let target = event.originalTarget;
+ let target = event.originalTarget || event.composedPath()[0];
if (!target.hasAttribute("placeholder")) return;
if (!target.placeholder.toLowerCase().includes("prompt")) return;
diff --git a/javascript/hints.js b/javascript/hints.js index 8adcd983..8e352e94 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -35,6 +35,7 @@ titles = { "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.", + "Skip": "Stop processing current image and continue processing.", "Interrupt": "Stop processing images and return any results accumulated so far.", "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.", diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index 3a0baac8..4c0e8f4b 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -86,6 +86,9 @@ function showGalleryImage(){ if(fullImg_preview != null){ fullImg_preview.forEach(function function_name(e) { + if (e.dataset.modded) + return; + e.dataset.modded = true; if(e && e.parentElement.tagName == 'DIV'){ e.style.cursor='pointer' diff --git a/javascript/progressbar.js b/javascript/progressbar.js index f9e9290e..4395a215 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -1,8 +1,9 @@ // code related to showing and updating progressbar shown as the image is being made global_progressbars = {} -function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){ +function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){ var progressbar = gradioApp().getElementById(id_progressbar) + var skip = id_skip ? gradioApp().getElementById(id_skip) : null var interrupt = gradioApp().getElementById(id_interrupt) if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){ @@ -32,30 +33,37 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_inte var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; if(!progressDiv){ + if (skip) { + skip.style.display = "none" + } interrupt.style.display = "none" } } - window.setTimeout(function(){ requestMoreProgress(id_part, id_progressbar_span, id_interrupt) }, 500) + window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500) }); mutationObserver.observe( progressbar, { childList:true, subtree:true }) } } onUiUpdate(function(){ - check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') - check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') - check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', 'ti_interrupt', 'ti_preview', 'ti_gallery') + check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery') + check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery') + check_progressbar('ti', 'ti_progressbar', 'ti_progress_span', '', 'ti_interrupt', 'ti_preview', 'ti_gallery') }) -function requestMoreProgress(id_part, id_progressbar_span, id_interrupt){ +function requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt){ btn = gradioApp().getElementById(id_part+"_check_progress"); if(btn==null) return; btn.click(); var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0; + var skip = id_skip ? gradioApp().getElementById(id_skip) : null var interrupt = gradioApp().getElementById(id_interrupt) if(progressDiv && interrupt){ + if (skip) { + skip.style.display = "block" + } interrupt.style.display = "block" } } diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py index d17e730f..28548124 100644 --- a/modules/esrgan_model.py +++ b/modules/esrgan_model.py @@ -111,7 +111,7 @@ class UpscalerESRGAN(Upscaler): print("Unable to load %s from %s" % (self.model_path, filename))
return None
- pretrained_net = torch.load(filename, map_location='cpu' if shared.device.type == 'mps' else None)
+ pretrained_net = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)
pretrained_net = fix_model_layers(crt_model, pretrained_net)
diff --git a/modules/hypernetwork.py b/modules/hypernetwork.py new file mode 100644 index 00000000..7f062242 --- /dev/null +++ b/modules/hypernetwork.py @@ -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(os.path.join(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)
diff --git a/modules/img2img.py b/modules/img2img.py index da212d72..24126774 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -32,6 +32,8 @@ def process_batch(p, input_dir, output_dir, args): for i, image in enumerate(images):
state.job = f"{i+1} out of {len(images)}"
+ if state.skipped:
+ state.skipped = False
if state.interrupted:
break
diff --git a/modules/processing.py b/modules/processing.py index f773a30e..8240ee27 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -141,6 +141,7 @@ class Processed: self.all_subseeds = all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
+
def js(self):
obj = {
"prompt": self.prompt,
@@ -312,6 +313,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: os.makedirs(p.outpath_grids, exist_ok=True)
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
+ modules.sd_hijack.model_hijack.clear_comments()
comments = {}
@@ -349,6 +351,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: state.job_count = p.n_iter
for n in range(p.n_iter):
+ if state.skipped:
+ state.skipped = False
+
if state.interrupted:
break
@@ -375,7 +380,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
- if state.interrupted:
+ if state.interrupted or state.skipped:
# if we are interruped, sample returns just noise
# use the image collected previously in sampler loop
diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py index f00256f2..15666073 100644 --- a/modules/prompt_parser.py +++ b/modules/prompt_parser.py @@ -239,6 +239,15 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step): conds_list.append(conds_for_batch)
+ # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
+ # and won't be able to torch.stack them. So this fixes that.
+ token_count = max([x.shape[0] for x in tensors])
+ for i in range(len(tensors)):
+ if tensors[i].shape[0] != token_count:
+ last_vector = tensors[i][-1:]
+ last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
+ tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
+
return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 5b30539f..5d93f7f6 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -8,7 +8,7 @@ from torch import einsum from torch.nn.functional import silu
import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
from modules.shared import opts, device, cmd_opts
import ldm.modules.attention
@@ -18,8 +18,9 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
-
def apply_optimizations():
+ undo_optimizations()
+
ldm.modules.diffusionmodules.model.nonlinearity = silu
if not cmd_opts.disable_opt_xformers_attention and not (cmd_opts.opt_split_attention or torch.version.hip) and shared.xformers_available:
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
@@ -32,11 +33,18 @@ def apply_optimizations(): 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
+def get_target_prompt_token_count(token_count):
+ if token_count < 75:
+ return 75
+
+ return math.ceil(token_count / 10) * 10
+
+
class StableDiffusionModelHijack:
fixes = None
comments = []
@@ -82,10 +90,12 @@ class StableDiffusionModelHijack: for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]:
layer.padding_mode = 'circular' if enable else 'zeros'
+ def clear_comments(self):
+ self.comments = []
+
def tokenize(self, text):
- max_length = self.clip.max_length - 2
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
- return remade_batch_tokens[0], token_count, max_length
+ return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
@@ -94,7 +104,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.wrapped = wrapped
self.hijack: StableDiffusionModelHijack = hijack
self.tokenizer = wrapped.tokenizer
- self.max_length = wrapped.max_length
self.token_mults = {}
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
@@ -116,7 +125,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def tokenize_line(self, line, used_custom_terms, hijack_comments):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
- maxlen = self.wrapped.max_length
if opts.enable_emphasis:
parsed = prompt_parser.parse_prompt_attention(line)
@@ -148,19 +156,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): used_custom_terms.append((embedding.name, embedding.checksum()))
i += embedding_length_in_tokens
- if len(remade_tokens) > maxlen - 2:
- vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
- ovf = remade_tokens[maxlen - 2:]
- overflowing_words = [vocab.get(int(x), "") for x in ovf]
- overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
- hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
-
token_count = len(remade_tokens)
- remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
- remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
+ prompt_target_length = get_target_prompt_token_count(token_count)
+ tokens_to_add = prompt_target_length - len(remade_tokens) + 1
- multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
- multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
+ remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
+ multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
return remade_tokens, fixes, multipliers, token_count
@@ -177,7 +178,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if line in cache:
remade_tokens, fixes, multipliers = cache[line]
else:
- remade_tokens, fixes, multipliers, token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+ remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
+ token_count = max(current_token_count, token_count)
cache[line] = (remade_tokens, fixes, multipliers)
@@ -191,7 +193,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def process_text_old(self, text):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
- maxlen = self.wrapped.max_length
+ maxlen = self.wrapped.max_length # you get to stay at 77
used_custom_terms = []
remade_batch_tokens = []
overflowing_words = []
@@ -263,17 +265,24 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
self.hijack.fixes = hijack_fixes
- self.hijack.comments = hijack_comments
+ self.hijack.comments += hijack_comments
if len(used_custom_terms) > 0:
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
- tokens = torch.asarray(remade_batch_tokens).to(device)
- outputs = self.wrapped.transformer(input_ids=tokens)
+ target_token_count = get_target_prompt_token_count(token_count) + 2
+
+ position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
+ position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
+
+ remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
+ tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
+ outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids)
z = outputs.last_hidden_state
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
- batch_multipliers = torch.asarray(batch_multipliers).to(device)
+ batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
+ batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
original_mean = z.mean()
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
new_mean = z.mean()
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py index be09ec8f..e43e2c7a 100644 --- a/modules/sd_hijack_optimizations.py +++ b/modules/sd_hijack_optimizations.py @@ -12,18 +12,29 @@ except: 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):
h = self.heads
- q = self.to_q(x)
+ q_in = self.to_q(x)
context = default(context, x)
- k = self.to_k(context)
- v = 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)
del context, x
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+ del q_in, k_in, v_in
r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
for i in range(0, q.shape[0], 2):
@@ -36,6 +47,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
del s2
+ del q, k, v
r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
del r1
@@ -49,8 +61,19 @@ 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))
diff --git a/modules/sd_models.py b/modules/sd_models.py index 5f992064..9409d070 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -122,7 +122,11 @@ def load_model_weights(model, checkpoint_file, sd_model_hash): pl_sd = torch.load(checkpoint_file, map_location="cpu")
if "global_step" in pl_sd:
print(f"Global Step: {pl_sd['global_step']}")
- sd = pl_sd["state_dict"]
+
+ if "state_dict" in pl_sd:
+ sd = pl_sd["state_dict"]
+ else:
+ sd = pl_sd
model.load_state_dict(sd, strict=False)
@@ -134,6 +138,14 @@ 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
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index df17e93c..eade0dbb 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -106,7 +106,7 @@ def extended_tdqm(sequence, *args, desc=None, **kwargs): seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
for x in seq:
- if state.interrupted:
+ if state.interrupted or state.skipped:
break
yield x
@@ -142,6 +142,16 @@ class VanillaStableDiffusionSampler: assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
cond = tensor
+ # for DDIM, shapes must match, we can't just process cond and uncond independently;
+ # filling unconditional_conditioning with repeats of the last vector to match length is
+ # not 100% correct but should work well enough
+ if unconditional_conditioning.shape[1] < cond.shape[1]:
+ last_vector = unconditional_conditioning[:, -1:]
+ last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+ unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+ elif unconditional_conditioning.shape[1] > cond.shape[1]:
+ unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
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
@@ -221,18 +231,29 @@ class CFGDenoiser(torch.nn.Module): 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_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+ if tensor.shape[1] == uncond.shape[1]:
+ cond_in = torch.cat([tensor, uncond])
+
+ if shared.batch_cond_uncond:
+ x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
+ else:
+ 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])
else:
x_out = torch.zeros_like(x_in)
- for batch_offset in range(0, x_out.shape[0], batch_size):
+ batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+ for batch_offset in range(0, tensor.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])
+ b = min(a + batch_size, tensor.shape[0])
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
+
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
- denoised_uncond = x_out[-batch_size:]
+ denoised_uncond = x_out[-uncond.shape[0]:]
denoised = torch.clone(denoised_uncond)
for i, conds in enumerate(conds_list):
@@ -254,7 +275,7 @@ def extended_trange(sampler, count, *args, **kwargs): seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
for x in seq:
- if state.interrupted:
+ if state.interrupted or state.skipped:
break
if sampler.stop_at is not None and x > sampler.stop_at:
diff --git a/modules/shared.py b/modules/shared.py index 6ed4b802..d68df751 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -13,7 +13,7 @@ import modules.memmon import modules.sd_models
import modules.styles
import modules.devices as devices
-from modules import sd_samplers
+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')
@@ -77,8 +77,15 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False
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:
+ skipped = False
interrupted = False
job = ""
job_no = 0
@@ -91,6 +98,9 @@ class State: current_image_sampling_step = 0
textinfo = None
+ def skip(self):
+ self.skipped = True
+
def interrupt(self):
self.interrupted = True
@@ -113,8 +123,6 @@ prompt_styles = modules.styles.StyleDatabase(styles_filename) interrogator = modules.interrogate.InterrogateModels("interrogate")
face_restorers = []
-# This was moved to webui.py with the other model "setup" calls.
-# modules.sd_models.list_models()
def realesrgan_models_names():
@@ -207,6 +215,7 @@ 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)."),
diff --git a/modules/ui.py b/modules/ui.py index 4f18126f..e3e62fdd 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -191,6 +191,7 @@ def wrap_gradio_call(func, extra_outputs=None): # last item is always HTML
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
+ shared.state.skipped = False
shared.state.interrupted = False
shared.state.job_count = 0
@@ -411,9 +412,16 @@ def create_toprow(is_img2img): with gr.Column(scale=1):
with gr.Row():
+ skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
+ skip.click(
+ fn=lambda: shared.state.skip(),
+ inputs=[],
+ outputs=[],
+ )
+
interrupt.click(
fn=lambda: shared.state.interrupt(),
inputs=[],
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 6344e612..c0c364df 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -77,6 +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)
@@ -122,6 +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),
@@ -193,6 +199,8 @@ 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]
@@ -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
@@ -393,10 +393,20 @@ input[type="range"]{ #txt2img_interrupt, #img2img_interrupt{
position: absolute;
- width: 100%;
+ width: 50%;
height: 72px;
background: #b4c0cc;
- border-radius: 8px;
+ border-radius: 0px;
+ display: none;
+}
+
+#txt2img_skip, #img2img_skip{
+ position: absolute;
+ width: 50%;
+ right: 0px;
+ height: 72px;
+ background: #b4c0cc;
+ border-radius: 0px;
display: none;
}
@@ -410,4 +420,31 @@ input[type="range"]{ #img2img_image div.h-60{
height: 480px;
-}
\ No newline at end of file +}
+
+#context-menu{
+ z-index:9999;
+ position:absolute;
+ display:block;
+ padding:0px 0;
+ border:2px solid #a55000;
+ border-radius:8px;
+ box-shadow:1px 1px 2px #CE6400;
+ width: 200px;
+}
+
+.context-menu-items{
+ list-style: none;
+ margin: 0;
+ padding: 0;
+}
+
+.context-menu-items a{
+ display:block;
+ padding:5px;
+ cursor:pointer;
+}
+
+.context-menu-items a:hover{
+ background: #a55000;
+}
@@ -58,6 +58,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): shared.state.current_latent = None
shared.state.current_image = None
shared.state.current_image_sampling_step = 0
+ shared.state.skipped = False
shared.state.interrupted = False
shared.state.textinfo = None
|