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Merge branch 'release_candidate'

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AUTOMATIC1111 2024-07-27 06:53:05 +03:00
parent feee37d75f 8b3d98c5a5
commit c19d044364
95 changed files with 3480 additions and 543 deletions
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2
.github/ISSUE_TEMPLATE/bug_report.yml vendored
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@ -91,7 +91,7 @@ body:
id: logs
attributes:
label: Console logs
description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service.
description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occurred. If it's very long, provide a link to pastebin or similar service.
render: Shell
validations:
required: true

3
.gitignore vendored
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@ -2,6 +2,7 @@ __pycache__
*.ckpt
*.safetensors
*.pth
.DS_Store
/ESRGAN/*
/SwinIR/*
/repositories
@ -39,3 +40,5 @@ notification.mp3
/.coverage*
/test/test_outputs
/cache
trace.json
/sysinfo-????-??-??-??-??.json

113
CHANGELOG.md
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@ -1,7 +1,118 @@
## 1.10.0
### Features:
* A lot of performance improvements (see below in Performance section)
* Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030), [#16164](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16164), [#16212](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16212))
* Recommended Euler sampler; DDIM and other timestamp samplers currently not supported
* T5 text model is disabled by default, enable it in settings
* New schedulers:
* Align Your Steps ([#15751](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15751))
* KL Optimal ([#15608](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608))
* Normal ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
* DDIM ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
* Simple ([#16142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16142))
* Beta ([#16235](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16235))
* New sampler: DDIM CFG++ ([#16035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16035))
### Minor:
* Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607))
* Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742))
* Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682))
* Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679))
* Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602))
* More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627))
* When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610))
* Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978))
* Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992))
* Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001))
* ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065))
* Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
* option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242))
### Extensions and API:
* Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984))
* Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140))
### Performance:
* [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803))
* [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804))
* [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806))
* [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816))
* [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820))
* [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824))
* [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823))
* Check for nans in unet only once, after all steps have been completed
* Added pption to run torch profiler for image generation
### Bug Fixes:
* Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958))
* feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943))
* Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893))
* Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600))
* Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656))
* more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681))
* Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831))
* XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832))
* fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815))
* fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797))
* update pickle safe filenames
* use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757))
* When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750))
* Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738))
* use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705))
* fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664))
* use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680))
* Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657))
* Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654))
* QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632))
* replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968))
* Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976))
* multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988))
* fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118))
* Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151))
* Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116))
* Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092))
* stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085))
* Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079))
* fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062))
* Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054))
* fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
* fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239))
* [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202))
* fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194))
### Other:
* fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907))
* ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864))
* Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851))
* XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836))
* scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830))
* img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817))
* chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783))
* Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739))
* Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730))
* no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641))
* .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980))
* Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144))
* Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119))
* docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102))
* Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192))
* Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059))
* remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061))
* chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060))
* shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170))
* activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231))
* add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218))
* update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178))
* Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173))
* do not send image size on paste inpaint ([#16180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16180))
* Fix noisy DS_Store files for MacOS ([#16166](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16166))
## 1.9.4
### Bug Fixes:
* pin setuptools version to fix the startup error ([#15883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15883))
* pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882))
## 1.9.3

24
README.md
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@ -128,10 +128,32 @@ sudo zypper install wget git python3 libtcmalloc4 libglvnd
# Arch-based:
sudo pacman -S wget git python3
```
If your system is very new, you need to install python3.11 or python3.10:
```bash
# Ubuntu 24.04
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install python3.11
# Manjaro/Arch
sudo pacman -S yay
yay -S python311 # do not confuse with python3.11 package
# Only for 3.11
# Then set up env variable in launch script
export python_cmd="python3.11"
# or in webui-user.sh
python_cmd="python3.11"
```
2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
```
Or just clone the repo wherever you want:
```bash
git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
```
3. Run `webui.sh`.
4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
@ -150,7 +172,7 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
## Credits
Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers, https://github.com/mcmonkey4eva/sd3-ref
- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
- Spandrel - https://github.com/chaiNNer-org/spandrel implementing
- GFPGAN - https://github.com/TencentARC/GFPGAN.git

2
configs/alt-diffusion-inference.yaml
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@ -40,7 +40,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
use_checkpoint: False
legacy: False
first_stage_config:

2
configs/alt-diffusion-m18-inference.yaml
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@ -41,7 +41,7 @@ model:
use_linear_in_transformer: True
transformer_depth: 1
context_dim: 1024
use_checkpoint: True
use_checkpoint: False
legacy: False
first_stage_config:

2
configs/instruct-pix2pix.yaml
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@ -45,7 +45,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
use_checkpoint: False
legacy: False
first_stage_config:

5
configs/sd3-inference.yaml Normal file
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@ -0,0 +1,5 @@
model:
target: modules.models.sd3.sd3_model.SD3Inferencer
params:
shift: 3
state_dict: null

2
configs/sd_xl_inpaint.yaml
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@ -21,7 +21,7 @@ model:
params:
adm_in_channels: 2816
num_classes: sequential
use_checkpoint: True
use_checkpoint: False
in_channels: 9
out_channels: 4
model_channels: 320

2
configs/v1-inference.yaml
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@ -40,7 +40,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
use_checkpoint: False
legacy: False
first_stage_config:

2
configs/v1-inpainting-inference.yaml
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@ -40,7 +40,7 @@ model:
use_spatial_transformer: True
transformer_depth: 1
context_dim: 768
use_checkpoint: True
use_checkpoint: False
legacy: False
first_stage_config:

2
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
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@ -572,7 +572,7 @@ class LatentDiffusionV1(DDPMV1):
:param h: height
:param w: width
:return: normalized distance to image border,
wtith min distance = 0 at border and max dist = 0.5 at image center
with min distance = 0 at border and max dist = 0.5 at image center
"""
lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
arr = self.meshgrid(h, w) / lower_right_corner

23
extensions-builtin/Lora/extra_networks_lora.py
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@ -9,6 +9,8 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
self.errors = {}
"""mapping of network names to the number of errors the network had during operation"""
remove_symbols = str.maketrans('', '', ":,")
def activate(self, p, params_list):
additional = shared.opts.sd_lora
@ -43,22 +45,15 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
if shared.opts.lora_add_hashes_to_infotext:
network_hashes = []
if not getattr(p, "is_hr_pass", False) or not hasattr(p, "lora_hashes"):
p.lora_hashes = {}
for item in networks.loaded_networks:
shorthash = item.network_on_disk.shorthash
if not shorthash:
continue
if item.network_on_disk.shorthash and item.mentioned_name:
p.lora_hashes[item.mentioned_name.translate(self.remove_symbols)] = item.network_on_disk.shorthash
alias = item.mentioned_name
if not alias:
continue
alias = alias.replace(":", "").replace(",", "")
network_hashes.append(f"{alias}: {shorthash}")
if network_hashes:
p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
if p.lora_hashes:
p.extra_generation_params["Lora hashes"] = ', '.join(f'{k}: {v}' for k, v in p.lora_hashes.items())
def deactivate(self, p):
if self.errors:

10
extensions-builtin/Lora/network.py
View File

@ -7,6 +7,7 @@ import torch.nn as nn
import torch.nn.functional as F
from modules import sd_models, cache, errors, hashes, shared
import modules.models.sd3.mmdit
NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
@ -114,7 +115,10 @@ class NetworkModule:
self.sd_key = weights.sd_key
self.sd_module = weights.sd_module
if hasattr(self.sd_module, 'weight'):
if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear):
s = self.sd_module.weight.shape
self.shape = (s[0] // 3, s[1])
elif hasattr(self.sd_module, 'weight'):
self.shape = self.sd_module.weight.shape
elif isinstance(self.sd_module, nn.MultiheadAttention):
# For now, only self-attn use Pytorch's MHA
@ -204,10 +208,12 @@ class NetworkModule:
if ex_bias is not None:
ex_bias = ex_bias * self.multiplier()
updown = updown * self.calc_scale()
if self.dora_scale is not None:
updown = self.apply_weight_decompose(updown, orig_weight)
return updown * self.calc_scale() * self.multiplier(), ex_bias
return updown * self.multiplier(), ex_bias
def calc_updown(self, target):
raise NotImplementedError()

10
extensions-builtin/Lora/network_lora.py
View File

@ -1,6 +1,7 @@
import torch
import lyco_helpers
import modules.models.sd3.mmdit
import network
from modules import devices
@ -10,6 +11,13 @@ class ModuleTypeLora(network.ModuleType):
if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
return NetworkModuleLora(net, weights)
if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]):
w = weights.w.copy()
weights.w.clear()
weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]})
return NetworkModuleLora(net, weights)
return None
@ -29,7 +37,7 @@ class NetworkModuleLora(network.NetworkModule):
if weight is None and none_ok:
return None
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
if is_linear:

151
extensions-builtin/Lora/networks.py
View File

@ -1,3 +1,4 @@
from __future__ import annotations
import gradio as gr
import logging
import os
@ -19,6 +20,7 @@ from typing import Union
from modules import shared, devices, sd_models, errors, scripts, sd_hijack
import modules.textual_inversion.textual_inversion as textual_inversion
import modules.models.sd3.mmdit
from lora_logger import logger
@ -130,7 +132,9 @@ def assign_network_names_to_compvis_modules(sd_model):
network_layer_mapping[network_name] = module
module.network_layer_name = network_name
else:
for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
cond_stage_model = getattr(shared.sd_model.cond_stage_model, 'wrapped', shared.sd_model.cond_stage_model)
for name, module in cond_stage_model.named_modules():
network_name = name.replace(".", "_")
network_layer_mapping[network_name] = module
module.network_layer_name = network_name
@ -143,6 +147,14 @@ def assign_network_names_to_compvis_modules(sd_model):
sd_model.network_layer_mapping = network_layer_mapping
class BundledTIHash(str):
def __init__(self, hash_str):
self.hash = hash_str
def __str__(self):
return self.hash if shared.opts.lora_bundled_ti_to_infotext else ''
def load_network(name, network_on_disk):
net = network.Network(name, network_on_disk)
net.mtime = os.path.getmtime(network_on_disk.filename)
@ -155,11 +167,25 @@ def load_network(name, network_on_disk):
keys_failed_to_match = {}
is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
if hasattr(shared.sd_model, 'diffusers_weight_map'):
diffusers_weight_map = shared.sd_model.diffusers_weight_map
elif hasattr(shared.sd_model, 'diffusers_weight_mapping'):
diffusers_weight_map = {}
for k, v in shared.sd_model.diffusers_weight_mapping():
diffusers_weight_map[k] = v
shared.sd_model.diffusers_weight_map = diffusers_weight_map
else:
diffusers_weight_map = None
matched_networks = {}
bundle_embeddings = {}
for key_network, weight in sd.items():
if diffusers_weight_map:
key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2)
network_part = network_name + '.' + network_weight
else:
key_network_without_network_parts, _, network_part = key_network.partition(".")
if key_network_without_network_parts == "bundle_emb":
@ -172,7 +198,11 @@ def load_network(name, network_on_disk):
emb_dict[vec_name] = weight
bundle_embeddings[emb_name] = emb_dict
if diffusers_weight_map:
key = diffusers_weight_map.get(key_network_without_network_parts, key_network_without_network_parts)
else:
key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
sd_module = shared.sd_model.network_layer_mapping.get(key, None)
if sd_module is None:
@ -229,6 +259,7 @@ def load_network(name, network_on_disk):
for emb_name, data in bundle_embeddings.items():
embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
embedding.loaded = None
embedding.shorthash = BundledTIHash(name)
embeddings[emb_name] = embedding
net.bundle_embeddings = embeddings
@ -260,6 +291,16 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
loaded_networks.clear()
unavailable_networks = []
for name in names:
if name.lower() in forbidden_network_aliases and available_networks.get(name) is None:
unavailable_networks.append(name)
elif available_network_aliases.get(name) is None:
unavailable_networks.append(name)
if unavailable_networks:
update_available_networks_by_names(unavailable_networks)
networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
if any(x is None for x in networks_on_disk):
list_available_networks()
@ -325,6 +366,28 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
purge_networks_from_memory()
def allowed_layer_without_weight(layer):
if isinstance(layer, torch.nn.LayerNorm) and not layer.elementwise_affine:
return True
return False
def store_weights_backup(weight):
if weight is None:
return None
return weight.to(devices.cpu, copy=True)
def restore_weights_backup(obj, field, weight):
if weight is None:
setattr(obj, field, None)
return
getattr(obj, field).copy_(weight)
def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
weights_backup = getattr(self, "network_weights_backup", None)
bias_backup = getattr(self, "network_bias_backup", None)
@ -334,21 +397,15 @@ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Li
if weights_backup is not None:
if isinstance(self, torch.nn.MultiheadAttention):
self.in_proj_weight.copy_(weights_backup[0])
self.out_proj.weight.copy_(weights_backup[1])
restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
restore_weights_backup(self.out_proj, 'weight', weights_backup[1])
else:
self.weight.copy_(weights_backup)
restore_weights_backup(self, 'weight', weights_backup)
if bias_backup is not None:
if isinstance(self, torch.nn.MultiheadAttention):
self.out_proj.bias.copy_(bias_backup)
restore_weights_backup(self.out_proj, 'bias', bias_backup)
else:
self.bias.copy_(bias_backup)
else:
if isinstance(self, torch.nn.MultiheadAttention):
self.out_proj.bias = None
else:
self.bias = None
restore_weights_backup(self, 'bias', bias_backup)
def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
@ -367,24 +424,30 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
weights_backup = getattr(self, "network_weights_backup", None)
if weights_backup is None and wanted_names != ():
if current_names != ():
raise RuntimeError("no backup weights found and current weights are not unchanged")
if current_names != () and not allowed_layer_without_weight(self):
raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged")
if isinstance(self, torch.nn.MultiheadAttention):
weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight))
else:
weights_backup = self.weight.to(devices.cpu, copy=True)
weights_backup = store_weights_backup(self.weight)
self.network_weights_backup = weights_backup
bias_backup = getattr(self, "network_bias_backup", None)
if bias_backup is None:
if bias_backup is None and wanted_names != ():
if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
bias_backup = store_weights_backup(self.out_proj.bias)
elif getattr(self, 'bias', None) is not None:
bias_backup = self.bias.to(devices.cpu, copy=True)
bias_backup = store_weights_backup(self.bias)
else:
bias_backup = None
# Unlike weight which always has value, some modules don't have bias.
# Only report if bias is not None and current bias are not unchanged.
if bias_backup is not None and current_names != ():
raise RuntimeError("no backup bias found and current bias are not unchanged")
self.network_bias_backup = bias_backup
if current_names != wanted_names:
@ -392,7 +455,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
for net in loaded_networks:
module = net.modules.get(network_layer_name, None)
if module is not None and hasattr(self, 'weight'):
if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear):
try:
with torch.no_grad():
if getattr(self, 'fp16_weight', None) is None:
@ -452,6 +515,24 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
continue
if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v:
try:
with torch.no_grad():
# Send "real" orig_weight into MHA's lora module
qw, kw, vw = self.weight.chunk(3, 0)
updown_q, _ = module_q.calc_updown(qw)
updown_k, _ = module_k.calc_updown(kw)
updown_v, _ = module_v.calc_updown(vw)
del qw, kw, vw
updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
self.weight += updown_qkv
except RuntimeError as e:
logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
continue
if module is None:
continue
@ -566,22 +647,16 @@ def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
def list_available_networks():
available_networks.clear()
available_network_aliases.clear()
forbidden_network_aliases.clear()
available_network_hash_lookup.clear()
forbidden_network_aliases.update({"none": 1, "Addams": 1})
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
def process_network_files(names: list[str] | None = None):
candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
for filename in candidates:
if os.path.isdir(filename):
continue
name = os.path.splitext(os.path.basename(filename))[0]
# if names is provided, only load networks with names in the list
if names and name not in names:
continue
try:
entry = network.NetworkOnDisk(name, filename)
except OSError: # should catch FileNotFoundError and PermissionError etc.
@ -597,6 +672,22 @@ def list_available_networks():
available_network_aliases[entry.alias] = entry
def update_available_networks_by_names(names: list[str]):
process_network_files(names)
def list_available_networks():
available_networks.clear()
available_network_aliases.clear()
forbidden_network_aliases.clear()
available_network_hash_lookup.clear()
forbidden_network_aliases.update({"none": 1, "Addams": 1})
os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
process_network_files()
re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")

1
extensions-builtin/Lora/scripts/lora_script.py
View File

@ -36,6 +36,7 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
"sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
"lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
"lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
"lora_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'),
"lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
"lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
"lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),

4
extensions-builtin/Lora/ui_edit_user_metadata.py
View File

@ -21,7 +21,9 @@ re_comma = re.compile(r" *, *")
def build_tags(metadata):
tags = {}
for _, tags_dict in metadata.get("ss_tag_frequency", {}).items():
ss_tag_frequency = metadata.get("ss_tag_frequency", {})
if ss_tag_frequency is not None and hasattr(ss_tag_frequency, 'items'):
for _, tags_dict in ss_tag_frequency.items():
for tag, tag_count in tags_dict.items():
tag = tag.strip()
tags[tag] = tags.get(tag, 0) + int(tag_count)

2
extensions-builtin/Lora/ui_extra_networks_lora.py
View File

@ -60,7 +60,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
else:
sd_version = lora_on_disk.sd_version
if shared.opts.lora_show_all or not enable_filter:
if shared.opts.lora_show_all or not enable_filter or not shared.sd_model:
pass
elif sd_version == network.SdVersion.Unknown:
model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1

17
extensions-builtin/hypertile/scripts/hypertile_script.py
View File

@ -1,6 +1,5 @@
import hypertile
from modules import scripts, script_callbacks, shared
from scripts.hypertile_xyz import add_axis_options
class ScriptHypertile(scripts.Script):
@ -93,7 +92,6 @@ def on_ui_settings():
"hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"),
"hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"),
"hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"),
"hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"),
"hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"),
"hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"),
@ -105,5 +103,20 @@ def on_ui_settings():
shared.opts.add_option(name, opt)
def add_axis_options():
xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
xyz_grid.axis_options.extend([
xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, xyz_grid.apply_override('hypertile_enable_unet_secondpass', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_unet"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] Unet Max Depth'), choices=lambda: [str(x) for x in range(4)]),
xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_unet"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] Unet Max Tile Size')),
xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_unet"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] Unet Swap Size')),
xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, xyz_grid.apply_override('hypertile_enable_vae', boolean=True), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, xyz_grid.apply_override("hypertile_max_depth_vae"), confirm=xyz_grid.confirm_range(0, 3, '[Hypertile] VAE Max Depth'), choices=lambda: [str(x) for x in range(4)]),
xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, xyz_grid.apply_override("hypertile_max_tile_vae"), confirm=xyz_grid.confirm_range(0, 512, '[Hypertile] VAE Max Tile Size')),
xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, xyz_grid.apply_override("hypertile_swap_size_vae"), confirm=xyz_grid.confirm_range(0, 64, '[Hypertile] VAE Swap Size')),
])
script_callbacks.on_ui_settings(on_ui_settings)
script_callbacks.on_before_ui(add_axis_options)

51
extensions-builtin/hypertile/scripts/hypertile_xyz.py
View File

@ -1,51 +0,0 @@
from modules import scripts
from modules.shared import opts
xyz_grid = [x for x in scripts.scripts_data if x.script_class.__module__ == "xyz_grid.py"][0].module
def int_applier(value_name:str, min_range:int = -1, max_range:int = -1):
"""
Returns a function that applies the given value to the given value_name in opts.data.
"""
def validate(value_name:str, value:str):
value = int(value)
# validate value
if not min_range == -1:
assert value >= min_range, f"Value {value} for {value_name} must be greater than or equal to {min_range}"
if not max_range == -1:
assert value <= max_range, f"Value {value} for {value_name} must be less than or equal to {max_range}"
def apply_int(p, x, xs):
validate(value_name, x)
opts.data[value_name] = int(x)
return apply_int
def bool_applier(value_name:str):
"""
Returns a function that applies the given value to the given value_name in opts.data.
"""
def validate(value_name:str, value:str):
assert value.lower() in ["true", "false"], f"Value {value} for {value_name} must be either true or false"
def apply_bool(p, x, xs):
validate(value_name, x)
value_boolean = x.lower() == "true"
opts.data[value_name] = value_boolean
return apply_bool
def add_axis_options():
extra_axis_options = [
xyz_grid.AxisOption("[Hypertile] Unet First pass Enabled", str, bool_applier("hypertile_enable_unet"), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] Unet Second pass Enabled", str, bool_applier("hypertile_enable_unet_secondpass"), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] Unet Max Depth", int, int_applier("hypertile_max_depth_unet", 0, 3), choices=lambda: [str(x) for x in range(4)]),
xyz_grid.AxisOption("[Hypertile] Unet Max Tile Size", int, int_applier("hypertile_max_tile_unet", 0, 512)),
xyz_grid.AxisOption("[Hypertile] Unet Swap Size", int, int_applier("hypertile_swap_size_unet", 0, 64)),
xyz_grid.AxisOption("[Hypertile] VAE Enabled", str, bool_applier("hypertile_enable_vae"), choices=xyz_grid.boolean_choice(reverse=True)),
xyz_grid.AxisOption("[Hypertile] VAE Max Depth", int, int_applier("hypertile_max_depth_vae", 0, 3), choices=lambda: [str(x) for x in range(4)]),
xyz_grid.AxisOption("[Hypertile] VAE Max Tile Size", int, int_applier("hypertile_max_tile_vae", 0, 512)),
xyz_grid.AxisOption("[Hypertile] VAE Swap Size", int, int_applier("hypertile_swap_size_vae", 0, 64)),
]
set_a = {opt.label for opt in xyz_grid.axis_options}
set_b = {opt.label for opt in extra_axis_options}
if set_a.intersection(set_b):
return
xyz_grid.axis_options.extend(extra_axis_options)

9
extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
View File

@ -3,6 +3,7 @@ import gradio as gr
import math
from modules.ui_components import InputAccordion
import modules.scripts as scripts
from modules.torch_utils import float64
class SoftInpaintingSettings:
@ -79,13 +80,11 @@ def latent_blend(settings, a, b, t):
# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
# 64-bit operations are used here to allow large exponents.
current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(float64(image_interp)).add_(0.00001)
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
settings.inpaint_detail_preservation) * one_minus_t3
b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
settings.inpaint_detail_preservation) * t3
a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(float64(a)).pow_(settings.inpaint_detail_preservation) * one_minus_t3
b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(float64(b)).pow_(settings.inpaint_detail_preservation) * t3
desired_magnitude = a_magnitude
desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
del a_magnitude, b_magnitude, t3, one_minus_t3

35
javascript/contextMenus.js
View File

@ -8,9 +8,6 @@ var contextMenuInit = function() {
};
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();
@ -23,10 +20,8 @@ var contextMenuInit = function() {
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';
contextMenu.style.top = event.pageY + 'px';
contextMenu.style.left = event.pageX + 'px';
const contextMenuList = document.createElement('ul');
contextMenuList.className = 'context-menu-items';
@ -43,21 +38,6 @@ var contextMenuInit = function() {
});
gradioApp().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(targetElementSelector, entryName, entryFunction) {
@ -107,18 +87,25 @@ var contextMenuInit = function() {
oldMenu.remove();
}
});
gradioApp().addEventListener("contextmenu", function(e) {
['contextmenu', 'touchstart'].forEach((eventType) => {
gradioApp().addEventListener(eventType, function(e) {
let ev = e;
if (eventType.startsWith('touch')) {
if (e.touches.length !== 2) return;
ev = e.touches[0];
}
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);
showContextMenu(ev, e.composedPath()[0], v);
e.preventDefault();
}
});
});
});
eventListenerApplied = true;
}

11
javascript/dragdrop.js vendored
View File

@ -56,6 +56,15 @@ function eventHasFiles(e) {
return false;
}
function isURL(url) {
try {
const _ = new URL(url);
return true;
} catch {
return false;
}
}
function dragDropTargetIsPrompt(target) {
if (target?.placeholder && target?.placeholder.indexOf("Prompt") >= 0) return true;
if (target?.parentNode?.parentNode?.className?.indexOf("prompt") > 0) return true;
@ -77,7 +86,7 @@ window.document.addEventListener('dragover', e => {
window.document.addEventListener('drop', async e => {
const target = e.composedPath()[0];
const url = e.dataTransfer.getData('text/uri-list') || e.dataTransfer.getData('text/plain');
if (!eventHasFiles(e) && !url) return;
if (!eventHasFiles(e) && !isURL(url)) return;
if (dragDropTargetIsPrompt(target)) {
e.stopPropagation();

26
javascript/imageviewer.js
View File

@ -6,6 +6,8 @@ function closeModal() {
function showModal(event) {
const source = event.target || event.srcElement;
const modalImage = gradioApp().getElementById("modalImage");
const modalToggleLivePreviewBtn = gradioApp().getElementById("modal_toggle_live_preview");
modalToggleLivePreviewBtn.innerHTML = opts.js_live_preview_in_modal_lightbox ? "&#x1F5C7;" : "&#x1F5C6;";
const lb = gradioApp().getElementById("lightboxModal");
modalImage.src = source.src;
if (modalImage.style.display === 'none') {
@ -51,14 +53,7 @@ function modalImageSwitch(offset) {
var galleryButtons = all_gallery_buttons();
if (galleryButtons.length > 1) {
var currentButton = selected_gallery_button();
var result = -1;
galleryButtons.forEach(function(v, i) {
if (v == currentButton) {
result = i;
}
});
var result = selected_gallery_index();
if (result != -1) {
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)];
@ -159,6 +154,13 @@ function modalZoomToggle(event) {
event.stopPropagation();
}
function modalLivePreviewToggle(event) {
const modalToggleLivePreview = gradioApp().getElementById("modal_toggle_live_preview");
opts.js_live_preview_in_modal_lightbox = !opts.js_live_preview_in_modal_lightbox;
modalToggleLivePreview.innerHTML = opts.js_live_preview_in_modal_lightbox ? "&#x1F5C7;" : "&#x1F5C6;";
event.stopPropagation();
}
function modalTileImageToggle(event) {
const modalImage = gradioApp().getElementById("modalImage");
const modal = gradioApp().getElementById("lightboxModal");
@ -216,6 +218,14 @@ document.addEventListener("DOMContentLoaded", function() {
modalSave.title = "Save Image(s)";
modalControls.appendChild(modalSave);
const modalToggleLivePreview = document.createElement('span');
modalToggleLivePreview.className = 'modalToggleLivePreview cursor';
modalToggleLivePreview.id = "modal_toggle_live_preview";
modalToggleLivePreview.innerHTML = "&#x1F5C6;";
modalToggleLivePreview.onclick = modalLivePreviewToggle;
modalToggleLivePreview.title = "Toggle live preview";
modalControls.appendChild(modalToggleLivePreview);
const modalClose = document.createElement('span');
modalClose.className = 'modalClose cursor';
modalClose.innerHTML = '&times;';

22
javascript/progressbar.js
View File

@ -76,6 +76,26 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
var dateStart = new Date();
var wasEverActive = false;
var parentProgressbar = progressbarContainer.parentNode;
var wakeLock = null;
var requestWakeLock = async function() {
if (!opts.prevent_screen_sleep_during_generation || wakeLock) return;
try {
wakeLock = await navigator.wakeLock.request('screen');
} catch (err) {
console.error('Wake Lock is not supported.');
}
};
var releaseWakeLock = async function() {
if (!opts.prevent_screen_sleep_during_generation || !wakeLock) return;
try {
await wakeLock.release();
wakeLock = null;
} catch (err) {
console.error('Wake Lock release failed', err);
}
};
var divProgress = document.createElement('div');
divProgress.className = 'progressDiv';
@ -89,6 +109,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
var livePreview = null;
var removeProgressBar = function() {
releaseWakeLock();
if (!divProgress) return;
setTitle("");
@ -100,6 +121,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
};
var funProgress = function(id_task) {
requestWakeLock();
request("./internal/progress", {id_task: id_task, live_preview: false}, function(res) {
if (res.completed) {
removeProgressBar();

11
javascript/ui.js
View File

@ -26,6 +26,14 @@ function selected_gallery_index() {
return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected'));
}
function gallery_container_buttons(gallery_container) {
return gradioApp().querySelectorAll(`#${gallery_container} .thumbnail-item.thumbnail-small`);
}
function selected_gallery_index_id(gallery_container) {
return Array.from(gallery_container_buttons(gallery_container)).findIndex(elem => elem.classList.contains('selected'));
}
function extract_image_from_gallery(gallery) {
if (gallery.length == 0) {
return [null];
@ -299,6 +307,7 @@ onAfterUiUpdate(function() {
var jsdata = textarea.value;
opts = JSON.parse(jsdata);
executeCallbacks(optionsAvailableCallbacks); /*global optionsAvailableCallbacks*/
executeCallbacks(optionsChangedCallbacks); /*global optionsChangedCallbacks*/
Object.defineProperty(textarea, 'value', {
@ -337,8 +346,8 @@ onOptionsChanged(function() {
let txt2img_textarea, img2img_textarea = undefined;
function restart_reload() {
document.body.style.backgroundColor = "var(--background-fill-primary)";
document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
var requestPing = function() {
requestGet("./internal/ping", {}, function(data) {
location.reload();

18
modules/api/api.py
View File

@ -43,7 +43,7 @@ def script_name_to_index(name, scripts):
def validate_sampler_name(name):
config = sd_samplers.all_samplers_map.get(name, None)
if config is None:
raise HTTPException(status_code=404, detail="Sampler not found")
raise HTTPException(status_code=400, detail="Sampler not found")
return name
@ -113,7 +113,7 @@ def encode_pil_to_base64(image):
image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
if image.mode == "RGBA":
if image.mode in ("RGBA", "P"):
image = image.convert("RGB")
parameters = image.info.get('parameters', None)
exif_bytes = piexif.dump({
@ -372,7 +372,7 @@ class Api:
return {}
possible_fields = infotext_utils.paste_fields[tabname]["fields"]
set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have differenrt names for this
set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True) # pydantic v1/v2 have different names for this
params = infotext_utils.parse_generation_parameters(request.infotext)
def get_field_value(field, params):
@ -438,15 +438,19 @@ class Api:
self.apply_infotext(txt2imgreq, "txt2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)
sampler, scheduler = sd_samplers.get_sampler_and_scheduler(txt2imgreq.sampler_name or txt2imgreq.sampler_index, txt2imgreq.scheduler)
populate = txt2imgreq.copy(update={ # Override __init__ params
"sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
"sampler_name": validate_sampler_name(sampler),
"do_not_save_samples": not txt2imgreq.save_images,
"do_not_save_grid": not txt2imgreq.save_images,
})
if populate.sampler_name:
populate.sampler_index = None # prevent a warning later on
if not populate.scheduler and scheduler != "Automatic":
populate.scheduler = scheduler
args = vars(populate)
args.pop('script_name', None)
args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
@ -502,9 +506,10 @@ class Api:
self.apply_infotext(img2imgreq, "img2img", script_runner=script_runner, mentioned_script_args=infotext_script_args)
selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
sampler, scheduler = sd_samplers.get_sampler_and_scheduler(img2imgreq.sampler_name or img2imgreq.sampler_index, img2imgreq.scheduler)
populate = img2imgreq.copy(update={ # Override __init__ params
"sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
"sampler_name": validate_sampler_name(sampler),
"do_not_save_samples": not img2imgreq.save_images,
"do_not_save_grid": not img2imgreq.save_images,
"mask": mask,
@ -512,6 +517,9 @@ class Api:
if populate.sampler_name:
populate.sampler_index = None # prevent a warning later on
if not populate.scheduler and scheduler != "Automatic":
populate.scheduler = scheduler
args = vars(populate)
args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
args.pop('script_name', None)

35
modules/call_queue.py
View File

@ -1,8 +1,9 @@
import os.path
from functools import wraps
import html
import time
from modules import shared, progress, errors, devices, fifo_lock
from modules import shared, progress, errors, devices, fifo_lock, profiling
queue_lock = fifo_lock.FIFOLock()
@ -46,6 +47,22 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
@wraps(func)
def f(*args, **kwargs):
try:
res = func(*args, **kwargs)
finally:
shared.state.skipped = False
shared.state.interrupted = False
shared.state.stopping_generation = False
shared.state.job_count = 0
shared.state.job = ""
return res
return wrap_gradio_call_no_job(f, extra_outputs, add_stats)
def wrap_gradio_call_no_job(func, extra_outputs=None, add_stats=False):
@wraps(func)
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
@ -65,9 +82,6 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
errors.report(f"{message}\n{arg_str}", exc_info=True)
shared.state.job = ""
shared.state.job_count = 0
if extra_outputs_array is None:
extra_outputs_array = [None, '']
@ -76,11 +90,6 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
devices.torch_gc()
shared.state.skipped = False
shared.state.interrupted = False
shared.state.stopping_generation = False
shared.state.job_count = 0
if not add_stats:
return tuple(res)
@ -111,9 +120,15 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
else:
vram_html = ''
if shared.opts.profiling_enable and os.path.exists(shared.opts.profiling_filename):
profiling_html = f"<p class='profile'> [ <a href='{profiling.webpath()}' download>Profile</a> ] </p>"
else:
profiling_html = ''
# last item is always HTML
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr><span class='measurement'>{elapsed_text}</span></p>{vram_html}</div>"
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr><span class='measurement'>{elapsed_text}</span></p>{vram_html}{profiling_html}</div>"
return tuple(res)
return f

5
modules/cmd_args.py
View File

@ -20,6 +20,7 @@ parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argum
parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
parser.add_argument("--data-dir", type=normalized_filepath, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
parser.add_argument("--models-dir", type=normalized_filepath, default=None, help="base path where models are stored; overrides --data-dir")
parser.add_argument("--config", type=normalized_filepath, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=normalized_filepath, 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",)
parser.add_argument("--ckpt-dir", type=normalized_filepath, default=None, help="Path to directory with stable diffusion checkpoints")
@ -29,7 +30,7 @@ parser.add_argument("--gfpgan-model", type=normalized_filepath, help="GFPGAN mod
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
parser.add_argument("--max-batch-count", type=int, default=16, help="does not do anything")
parser.add_argument("--embeddings-dir", type=normalized_filepath, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
parser.add_argument("--textual-inversion-templates-dir", type=normalized_filepath, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
parser.add_argument("--hypernetwork-dir", type=normalized_filepath, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
@ -41,7 +42,7 @@ parser.add_argument("--lowvram", action='store_true', help="enable stable diffus
parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
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("--precision", type=str, help="evaluate at this precision", choices=["full", "half", "autocast"], default="autocast")
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)

2
modules/deepbooru.py
View File

@ -57,7 +57,7 @@ class DeepDanbooru:
a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
with torch.no_grad(), devices.autocast():
x = torch.from_numpy(a).to(devices.device)
x = torch.from_numpy(a).to(devices.device, devices.dtype)
y = self.model(x)[0].detach().cpu().numpy()
probability_dict = {}

34
modules/devices.py
View File

@ -114,6 +114,9 @@ errors.run(enable_tf32, "Enabling TF32")
cpu: torch.device = torch.device("cpu")
fp8: bool = False
# Force fp16 for all models in inference. No casting during inference.
# This flag is controlled by "--precision half" command line arg.
force_fp16: bool = False
device: torch.device = None
device_interrogate: torch.device = None
device_gfpgan: torch.device = None
@ -127,6 +130,8 @@ unet_needs_upcast = False
def cond_cast_unet(input):
if force_fp16:
return input.to(torch.float16)
return input.to(dtype_unet) if unet_needs_upcast else input
@ -206,6 +211,11 @@ def autocast(disable=False):
if disable:
return contextlib.nullcontext()
if force_fp16:
# No casting during inference if force_fp16 is enabled.
# All tensor dtype conversion happens before inference.
return contextlib.nullcontext()
if fp8 and device==cpu:
return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
@ -233,22 +243,22 @@ def test_for_nans(x, where):
if shared.cmd_opts.disable_nan_check:
return
if not torch.all(torch.isnan(x)).item():
if not torch.isnan(x[(0, ) * len(x.shape)]):
return
if where == "unet":
message = "A tensor with all NaNs was produced in Unet."
message = "A tensor with NaNs was produced in Unet."
if not shared.cmd_opts.no_half:
message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."
elif where == "vae":
message = "A tensor with all NaNs was produced in VAE."
message = "A tensor with NaNs was produced in VAE."
if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
else:
message = "A tensor with all NaNs was produced."
message = "A tensor with NaNs was produced."
message += " Use --disable-nan-check commandline argument to disable this check."
@ -258,7 +268,7 @@ def test_for_nans(x, where):
@lru_cache
def first_time_calculation():
"""
just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
just do any calculation with pytorch layers - the first time this is done it allocates about 700MB of memory and
spends about 2.7 seconds doing that, at least with NVidia.
"""
@ -269,3 +279,17 @@ def first_time_calculation():
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)
def force_model_fp16():
"""
ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which
force conversion of input to float32. If force_fp16 is enabled, we need to
prevent this casting.
"""
assert force_fp16
import sgm.modules.diffusionmodules.util as sgm_util
import ldm.modules.diffusionmodules.util as ldm_util
sgm_util.GroupNorm32 = torch.nn.GroupNorm
ldm_util.GroupNorm32 = torch.nn.GroupNorm
print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.")

9
modules/extensions.py
View File

@ -191,8 +191,9 @@ class Extension:
def check_updates(self):
repo = Repo(self.path)
branch_name = f'{repo.remote().name}/{self.branch}'
for fetch in repo.remote().fetch(dry_run=True):
if self.branch and fetch.name != f'{repo.remote().name}/{self.branch}':
if self.branch and fetch.name != branch_name:
continue
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
@ -200,7 +201,7 @@ class Extension:
return
try:
origin = repo.rev_parse('origin')
origin = repo.rev_parse(branch_name)
if repo.head.commit != origin:
self.can_update = True
self.status = "behind HEAD"
@ -213,8 +214,10 @@ class Extension:
self.can_update = False
self.status = "latest"
def fetch_and_reset_hard(self, commit='origin'):
def fetch_and_reset_hard(self, commit=None):
repo = Repo(self.path)
if commit is None:
commit = f'{repo.remote().name}/{self.branch}'
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch(all=True)

4
modules/gfpgan_model.py
View File

@ -36,13 +36,11 @@ class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
ext_filter=['.pth'],
):
if 'GFPGAN' in os.path.basename(model_path):
model = modelloader.load_spandrel_model(
return modelloader.load_spandrel_model(
model_path,
device=self.get_device(),
expected_architecture='GFPGAN',
).model
model.different_w = True # see https://github.com/chaiNNer-org/spandrel/pull/81
return model
raise ValueError("No GFPGAN model found")
def restore(self, np_image):

25
modules/images.py
View File

@ -54,11 +54,14 @@ def image_grid(imgs, batch_size=1, rows=None):
params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
script_callbacks.image_grid_callback(params)
w, h = imgs[0].size
grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black')
w, h = map(max, zip(*(img.size for img in imgs)))
grid_background_color = ImageColor.getcolor(opts.grid_background_color, 'RGB')
grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color=grid_background_color)
for i, img in enumerate(params.imgs):
grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
img_w, img_h = img.size
w_offset, h_offset = 0 if img_w == w else (w - img_w) // 2, 0 if img_h == h else (h - img_h) // 2
grid.paste(img, box=(i % params.cols * w + w_offset, i // params.cols * h + h_offset))
return grid
@ -377,6 +380,7 @@ def get_sampler_scheduler(p, sampler):
class FilenameGenerator:
replacements = {
'basename': lambda self: self.basename or 'img',
'seed': lambda self: self.seed if self.seed is not None else '',
'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
'seed_last': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.all_seeds[-1],
@ -413,12 +417,13 @@ class FilenameGenerator:
}
default_time_format = '%Y%m%d%H%M%S'
def __init__(self, p, seed, prompt, image, zip=False):
def __init__(self, p, seed, prompt, image, zip=False, basename=""):
self.p = p
self.seed = seed
self.prompt = prompt
self.image = image
self.zip = zip
self.basename = basename
def get_vae_filename(self):
"""Get the name of the VAE file."""
@ -606,9 +611,10 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(geninfo or "", encoding="unicode")
},
})
else:
exif_bytes = None
image.save(filename,format=image_format, exif=exif_bytes)
image.save(filename,format=image_format, quality=opts.jpeg_quality, exif=exif_bytes)
elif extension.lower() == ".gif":
image.save(filename, format=image_format, comment=geninfo)
else:
@ -648,12 +654,12 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
txt_fullfn (`str` or None):
If a text file is saved for this image, this will be its full path. Otherwise None.
"""
namegen = FilenameGenerator(p, seed, prompt, image)
namegen = FilenameGenerator(p, seed, prompt, image, basename=basename)
# WebP and JPG formats have maximum dimension limits of 16383 and 65535 respectively. switch to PNG which has a much higher limit
if (image.height > 65535 or image.width > 65535) and extension.lower() in ("jpg", "jpeg") or (image.height > 16383 or image.width > 16383) and extension.lower() == "webp":
print('Image dimensions too large; saving as PNG')
extension = ".png"
extension = "png"
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)
@ -789,7 +795,10 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
if exif_comment:
geninfo = exif_comment
elif "comment" in items: # for gif
if isinstance(items["comment"], bytes):
geninfo = items["comment"].decode('utf8', errors="ignore")
else:
geninfo = items["comment"]
for field in IGNORED_INFO_KEYS:
items.pop(field, None)

16
modules/img2img.py
View File

@ -17,11 +17,14 @@ from modules.ui import plaintext_to_html
import modules.scripts
def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
def process_batch(p, input, output_dir, inpaint_mask_dir, args, to_scale=False, scale_by=1.0, use_png_info=False, png_info_props=None, png_info_dir=None):
output_dir = output_dir.strip()
processing.fix_seed(p)
batch_images = list(shared.walk_files(input_dir, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
if isinstance(input, str):
batch_images = list(shared.walk_files(input, allowed_extensions=(".png", ".jpg", ".jpeg", ".webp", ".tif", ".tiff")))
else:
batch_images = [os.path.abspath(x.name) for x in input]
is_inpaint_batch = False
if inpaint_mask_dir:
@ -146,7 +149,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
return batch_results
def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, *args):
def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, img2img_batch_source_type: str, img2img_batch_upload: list, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@ -221,6 +224,13 @@ def img2img(id_task: str, request: gr.Request, mode: int, prompt: str, negative_
with closing(p):
if is_batch:
if img2img_batch_source_type == "upload":
assert isinstance(img2img_batch_upload, list) and img2img_batch_upload
output_dir = ""
inpaint_mask_dir = ""
png_info_dir = img2img_batch_png_info_dir if not shared.cmd_opts.hide_ui_dir_config else ""
processed = process_batch(p, img2img_batch_upload, output_dir, inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=png_info_dir)
else: # "from dir"
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)

7
modules/infotext_utils.py
View File

@ -146,18 +146,19 @@ def connect_paste_params_buttons():
destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
if binding.source_image_component and destination_image_component:
need_send_dementions = destination_width_component and binding.tabname != 'inpaint'
if isinstance(binding.source_image_component, gr.Gallery):
func = send_image_and_dimensions if destination_width_component else image_from_url_text
func = send_image_and_dimensions if need_send_dementions else image_from_url_text
jsfunc = "extract_image_from_gallery"
else:
func = send_image_and_dimensions if destination_width_component else lambda x: x
func = send_image_and_dimensions if need_send_dementions else lambda x: x
jsfunc = None
binding.paste_button.click(
fn=func,
_js=jsfunc,
inputs=[binding.source_image_component],
outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
outputs=[destination_image_component, destination_width_component, destination_height_component] if need_send_dementions else [destination_image_component],
show_progress=False,
)

8
modules/launch_utils.py
View File

@ -9,6 +9,7 @@ import importlib.util
import importlib.metadata
import platform
import json
import shlex
from functools import lru_cache
from modules import cmd_args, errors
@ -76,7 +77,7 @@ def git_tag():
except Exception:
try:
changelog_md = os.path.join(os.path.dirname(os.path.dirname(__file__)), "CHANGELOG.md")
changelog_md = os.path.join(script_path, "CHANGELOG.md")
with open(changelog_md, "r", encoding="utf-8") as file:
line = next((line.strip() for line in file if line.strip()), "<none>")
line = line.replace("## ", "")
@ -231,7 +232,7 @@ def run_extension_installer(extension_dir):
try:
env = os.environ.copy()
env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
env['PYTHONPATH'] = f"{script_path}{os.pathsep}{env.get('PYTHONPATH', '')}"
stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env).strip()
if stdout:
@ -445,7 +446,6 @@ def prepare_environment():
exit(0)
def configure_for_tests():
if "--api" not in sys.argv:
sys.argv.append("--api")
@ -461,7 +461,7 @@ def configure_for_tests():
def start():
print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {' '.join(sys.argv[1:])}")
print(f"Launching {'API server' if '--nowebui' in sys.argv else 'Web UI'} with arguments: {shlex.join(sys.argv[1:])}")
import webui
if '--nowebui' in sys.argv:
webui.api_only()

28
modules/lowvram.py
View File

@ -1,9 +1,12 @@
from collections import namedtuple
import torch
from modules import devices, shared
module_in_gpu = None
cpu = torch.device("cpu")
ModuleWithParent = namedtuple('ModuleWithParent', ['module', 'parent'], defaults=['None'])
def send_everything_to_cpu():
global module_in_gpu
@ -75,13 +78,14 @@ def setup_for_low_vram(sd_model, use_medvram):
(sd_model, 'depth_model'),
(sd_model, 'embedder'),
(sd_model, 'model'),
(sd_model, 'embedder'),
]
is_sdxl = hasattr(sd_model, 'conditioner')
is_sd2 = not is_sdxl and hasattr(sd_model.cond_stage_model, 'model')
if is_sdxl:
if hasattr(sd_model, 'medvram_fields'):
to_remain_in_cpu = sd_model.medvram_fields()
elif is_sdxl:
to_remain_in_cpu.append((sd_model, 'conditioner'))
elif is_sd2:
to_remain_in_cpu.append((sd_model.cond_stage_model, 'model'))
@ -103,7 +107,21 @@ def setup_for_low_vram(sd_model, use_medvram):
setattr(obj, field, module)
# register hooks for those the first three models
if is_sdxl:
if hasattr(sd_model, "cond_stage_model") and hasattr(sd_model.cond_stage_model, "medvram_modules"):
for module in sd_model.cond_stage_model.medvram_modules():
if isinstance(module, ModuleWithParent):
parent = module.parent
module = module.module
else:
parent = None
if module:
module.register_forward_pre_hook(send_me_to_gpu)
if parent:
parents[module] = parent
elif is_sdxl:
sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
elif is_sd2:
sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
@ -117,9 +135,9 @@ def setup_for_low_vram(sd_model, use_medvram):
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.encode = first_stage_model_encode_wrap
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
if sd_model.depth_model:
if getattr(sd_model, 'depth_model', None) is not None:
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
if sd_model.embedder:
if getattr(sd_model, 'embedder', None) is not None:
sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
if use_medvram:

40
modules/modelloader.py
View File

@ -23,6 +23,7 @@ def load_file_from_url(
model_dir: str,
progress: bool = True,
file_name: str | None = None,
hash_prefix: str | None = None,
) -> str:
"""Download a file from `url` into `model_dir`, using the file present if possible.
@ -36,11 +37,11 @@ def load_file_from_url(
if not os.path.exists(cached_file):
print(f'Downloading: "{url}" to {cached_file}\n')
from torch.hub import download_url_to_file
download_url_to_file(url, cached_file, progress=progress)
download_url_to_file(url, cached_file, progress=progress, hash_prefix=hash_prefix)
return cached_file
def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None, hash_prefix=None) -> list:
"""
A one-and done loader to try finding the desired models in specified directories.
@ -49,6 +50,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
@param model_path: The location to store/find models in.
@param command_path: A command-line argument to search for models in first.
@param ext_filter: An optional list of filename extensions to filter by
@param hash_prefix: the expected sha256 of the model_url
@return: A list of paths containing the desired model(s)
"""
output = []
@ -78,7 +80,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
if model_url is not None and len(output) == 0:
if download_name is not None:
output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name))
output.append(load_file_from_url(model_url, model_dir=places[0], file_name=download_name, hash_prefix=hash_prefix))
else:
output.append(model_url)
@ -137,6 +139,27 @@ def load_upscalers():
key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
)
# None: not loaded, False: failed to load, True: loaded
_spandrel_extra_init_state = None
def _init_spandrel_extra_archs() -> None:
"""
Try to initialize `spandrel_extra_archs` (exactly once).
"""
global _spandrel_extra_init_state
if _spandrel_extra_init_state is not None:
return
try:
import spandrel
import spandrel_extra_arches
spandrel.MAIN_REGISTRY.add(*spandrel_extra_arches.EXTRA_REGISTRY)
_spandrel_extra_init_state = True
except Exception:
logger.warning("Failed to load spandrel_extra_arches", exc_info=True)
_spandrel_extra_init_state = False
def load_spandrel_model(
path: str | os.PathLike,
@ -146,11 +169,16 @@ def load_spandrel_model(
dtype: str | torch.dtype | None = None,
expected_architecture: str | None = None,
) -> spandrel.ModelDescriptor:
global _spandrel_extra_init_state
import spandrel
_init_spandrel_extra_archs()
model_descriptor = spandrel.ModelLoader(device=device).load_from_file(str(path))
if expected_architecture and model_descriptor.architecture != expected_architecture:
arch = model_descriptor.architecture
if expected_architecture and arch.name != expected_architecture:
logger.warning(
f"Model {path!r} is not a {expected_architecture!r} model (got {model_descriptor.architecture!r})",
f"Model {path!r} is not a {expected_architecture!r} model (got {arch.name!r})",
)
half = False
if prefer_half:
@ -164,6 +192,6 @@ def load_spandrel_model(
model_descriptor.model.eval()
logger.debug(
"Loaded %s from %s (device=%s, half=%s, dtype=%s)",
model_descriptor, path, device, half, dtype,
arch, path, device, half, dtype,
)
return model_descriptor

2
modules/models/diffusion/uni_pc/uni_pc.py
View File

@ -323,7 +323,7 @@ def model_wrapper(
def model_fn(x, t_continuous, condition, unconditional_condition):
"""
The noise predicition model function that is used for DPM-Solver.
The noise prediction model function that is used for DPM-Solver.
"""
if t_continuous.reshape((-1,)).shape[0] == 1:
t_continuous = t_continuous.expand((x.shape[0]))

622
modules/models/sd3/mmdit.py Normal file
View File

@ -0,0 +1,622 @@
### This file contains impls for MM-DiT, the core model component of SD3
import math
from typing import Dict, Optional
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange, repeat
from modules.models.sd3.other_impls import attention, Mlp
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding"""
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
flatten: bool = True,
bias: bool = True,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
dtype=None,
device=None,
):
super().__init__()
self.patch_size = (patch_size, patch_size)
if img_size is not None:
self.img_size = (img_size, img_size)
self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
self.num_patches = self.grid_size[0] * self.grid_size[1]
else:
self.img_size = None
self.grid_size = None
self.num_patches = None
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
return x
def modulate(x, shift, scale):
if shift is None:
shift = torch.zeros_like(scale)
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, scaling_factor=None, offset=None):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
if scaling_factor is not None:
grid = grid / scaling_factor
if offset is not None:
grid = grid - offset
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
return np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""Embeds scalar timesteps into vector representations."""
def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32)
/ half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
if torch.is_floating_point(t):
embedding = embedding.to(dtype=t.dtype)
return embedding
def forward(self, t, dtype, **kwargs):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
t_emb = self.mlp(t_freq)
return t_emb
class VectorEmbedder(nn.Module):
"""Embeds a flat vector of dimension input_dim"""
def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.mlp(x)
#################################################################################
# Core DiT Model #
#################################################################################
class QkvLinear(torch.nn.Linear):
pass
def split_qkv(qkv, head_dim):
qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
return qkv[0], qkv[1], qkv[2]
def optimized_attention(qkv, num_heads):
return attention(qkv[0], qkv[1], qkv[2], num_heads)
class SelfAttention(nn.Module):
ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
attn_mode: str = "xformers",
pre_only: bool = False,
qk_norm: Optional[str] = None,
rmsnorm: bool = False,
dtype=None,
device=None,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = QkvLinear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
if not pre_only:
self.proj = nn.Linear(dim, dim, dtype=dtype, device=device)
assert attn_mode in self.ATTENTION_MODES
self.attn_mode = attn_mode
self.pre_only = pre_only
if qk_norm == "rms":
self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
elif qk_norm == "ln":
self.ln_q = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
elif qk_norm is None:
self.ln_q = nn.Identity()
self.ln_k = nn.Identity()
else:
raise ValueError(qk_norm)
def pre_attention(self, x: torch.Tensor):
B, L, C = x.shape
qkv = self.qkv(x)
q, k, v = split_qkv(qkv, self.head_dim)
q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
return (q, k, v)
def post_attention(self, x: torch.Tensor) -> torch.Tensor:
assert not self.pre_only
x = self.proj(x)
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
(q, k, v) = self.pre_attention(x)
x = attention(q, k, v, self.num_heads)
x = self.post_attention(x)
return x
class RMSNorm(torch.nn.Module):
def __init__(
self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
super().__init__()
self.eps = eps
self.learnable_scale = elementwise_affine
if self.learnable_scale:
self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
else:
self.register_parameter("weight", None)
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
x = self._norm(x)
if self.learnable_scale:
return x * self.weight.to(device=x.device, dtype=x.dtype)
else:
return x
class SwiGLUFeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float] = None,
):
"""
Initialize the FeedForward module.
Args:
dim (int): Input dimension.
hidden_dim (int): Hidden dimension of the feedforward layer.
multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
Attributes:
w1 (ColumnParallelLinear): Linear transformation for the first layer.
w2 (RowParallelLinear): Linear transformation for the second layer.
w3 (ColumnParallelLinear): Linear transformation for the third layer.
"""
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
class DismantledBlock(nn.Module):
"""A DiT block with gated adaptive layer norm (adaLN) conditioning."""
ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
def __init__(
self,
hidden_size: int,
num_heads: int,
mlp_ratio: float = 4.0,
attn_mode: str = "xformers",
qkv_bias: bool = False,
pre_only: bool = False,
rmsnorm: bool = False,
scale_mod_only: bool = False,
swiglu: bool = False,
qk_norm: Optional[str] = None,
dtype=None,
device=None,
**block_kwargs,
):
super().__init__()
assert attn_mode in self.ATTENTION_MODES
if not rmsnorm:
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
else:
self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=pre_only, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device)
if not pre_only:
if not rmsnorm:
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
else:
self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
if not pre_only:
if not swiglu:
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=nn.GELU(approximate="tanh"), dtype=dtype, device=device)
else:
self.mlp = SwiGLUFeedForward(dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256)
self.scale_mod_only = scale_mod_only
if not scale_mod_only:
n_mods = 6 if not pre_only else 2
else:
n_mods = 4 if not pre_only else 1
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device))
self.pre_only = pre_only
def pre_attention(self, x: torch.Tensor, c: torch.Tensor):
assert x is not None, "pre_attention called with None input"
if not self.pre_only:
if not self.scale_mod_only:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
else:
shift_msa = None
shift_mlp = None
scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(4, dim=1)
qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
return qkv, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp)
else:
if not self.scale_mod_only:
shift_msa, scale_msa = self.adaLN_modulation(c).chunk(2, dim=1)
else:
shift_msa = None
scale_msa = self.adaLN_modulation(c)
qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
return qkv, None
def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
assert not self.pre_only
x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
assert not self.pre_only
(q, k, v), intermediates = self.pre_attention(x, c)
attn = attention(q, k, v, self.attn.num_heads)
return self.post_attention(attn, *intermediates)
def block_mixing(context, x, context_block, x_block, c):
assert context is not None, "block_mixing called with None context"
context_qkv, context_intermediates = context_block.pre_attention(context, c)
x_qkv, x_intermediates = x_block.pre_attention(x, c)
o = []
for t in range(3):
o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
q, k, v = tuple(o)
attn = attention(q, k, v, x_block.attn.num_heads)
context_attn, x_attn = (attn[:, : context_qkv[0].shape[1]], attn[:, context_qkv[0].shape[1] :])
if not context_block.pre_only:
context = context_block.post_attention(context_attn, *context_intermediates)
else:
context = None
x = x_block.post_attention(x_attn, *x_intermediates)
return context, x
class JointBlock(nn.Module):
"""just a small wrapper to serve as a fsdp unit"""
def __init__(self, *args, **kwargs):
super().__init__()
pre_only = kwargs.pop("pre_only")
qk_norm = kwargs.pop("qk_norm", None)
self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
def forward(self, *args, **kwargs):
return block_mixing(*args, context_block=self.context_block, x_block=self.x_block, **kwargs)
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size: int, patch_size: int, out_channels: int, total_out_channels: Optional[int] = None, dtype=None, device=None):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.linear = (
nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
if (total_out_channels is None)
else nn.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class MMDiT(nn.Module):
"""Diffusion model with a Transformer backbone."""
def __init__(
self,
input_size: int = 32,
patch_size: int = 2,
in_channels: int = 4,
depth: int = 28,
mlp_ratio: float = 4.0,
learn_sigma: bool = False,
adm_in_channels: Optional[int] = None,
context_embedder_config: Optional[Dict] = None,
register_length: int = 0,
attn_mode: str = "torch",
rmsnorm: bool = False,
scale_mod_only: bool = False,
swiglu: bool = False,
out_channels: Optional[int] = None,
pos_embed_scaling_factor: Optional[float] = None,
pos_embed_offset: Optional[float] = None,
pos_embed_max_size: Optional[int] = None,
num_patches = None,
qk_norm: Optional[str] = None,
qkv_bias: bool = True,
dtype = None,
device = None,
):
super().__init__()
self.dtype = dtype
self.learn_sigma = learn_sigma
self.in_channels = in_channels
default_out_channels = in_channels * 2 if learn_sigma else in_channels
self.out_channels = out_channels if out_channels is not None else default_out_channels
self.patch_size = patch_size
self.pos_embed_scaling_factor = pos_embed_scaling_factor
self.pos_embed_offset = pos_embed_offset
self.pos_embed_max_size = pos_embed_max_size
# apply magic --> this defines a head_size of 64
hidden_size = 64 * depth
num_heads = depth
self.num_heads = num_heads
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True, strict_img_size=self.pos_embed_max_size is None, dtype=dtype, device=device)
self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device)
if adm_in_channels is not None:
assert isinstance(adm_in_channels, int)
self.y_embedder = VectorEmbedder(adm_in_channels, hidden_size, dtype=dtype, device=device)
self.context_embedder = nn.Identity()
if context_embedder_config is not None:
if context_embedder_config["target"] == "torch.nn.Linear":
self.context_embedder = nn.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
self.register_length = register_length
if self.register_length > 0:
self.register = nn.Parameter(torch.randn(1, register_length, hidden_size, dtype=dtype, device=device))
# num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
# just use a buffer already
if num_patches is not None:
self.register_buffer(
"pos_embed",
torch.zeros(1, num_patches, hidden_size, dtype=dtype, device=device),
)
else:
self.pos_embed = None
self.joint_blocks = nn.ModuleList(
[
JointBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=i == depth - 1, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, qk_norm=qk_norm, dtype=dtype, device=device)
for i in range(depth)
]
)
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels, dtype=dtype, device=device)
def cropped_pos_embed(self, hw):
assert self.pos_embed_max_size is not None
p = self.x_embedder.patch_size[0]
h, w = hw
# patched size
h = h // p
w = w // p
assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
top = (self.pos_embed_max_size - h) // 2
left = (self.pos_embed_max_size - w) // 2
spatial_pos_embed = rearrange(
self.pos_embed,
"1 (h w) c -> 1 h w c",
h=self.pos_embed_max_size,
w=self.pos_embed_max_size,
)
spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
return spatial_pos_embed
def unpatchify(self, x, hw=None):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
if hw is None:
h = w = int(x.shape[1] ** 0.5)
else:
h, w = hw
h = h // p
w = w // p
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
return imgs
def forward_core_with_concat(self, x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.register_length > 0:
context = torch.cat((repeat(self.register, "1 ... -> b ...", b=x.shape[0]), context if context is not None else torch.Tensor([]).type_as(x)), 1)
# context is B, L', D
# x is B, L, D
for block in self.joint_blocks:
context, x = block(context, x, c=c_mod)
x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels)
return x
def forward(self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
hw = x.shape[-2:]
x = self.x_embedder(x) + self.cropped_pos_embed(hw)
c = self.t_embedder(t, dtype=x.dtype) # (N, D)
if y is not None:
y = self.y_embedder(y) # (N, D)
c = c + y # (N, D)
context = self.context_embedder(context)
x = self.forward_core_with_concat(x, c, context)
x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W)
return x

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### This file contains impls for underlying related models (CLIP, T5, etc)
import torch
import math
from torch import nn
from transformers import CLIPTokenizer, T5TokenizerFast
from modules import sd_hijack
#################################################################################################
### Core/Utility
#################################################################################################
class AutocastLinear(nn.Linear):
"""Same as usual linear layer, but casts its weights to whatever the parameter type is.
This is different from torch.autocast in a way that float16 layer processing float32 input
will return float16 with autocast on, and float32 with this. T5 seems to be fucked
if you do it in full float16 (returning almost all zeros in the final output).
"""
def forward(self, x):
return torch.nn.functional.linear(x, self.weight.to(x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
def attention(q, k, v, heads, mask=None):
"""Convenience wrapper around a basic attention operation"""
b, _, dim_head = q.shape
dim_head //= heads
q, k, v = [t.view(b, -1, heads, dim_head).transpose(1, 2) for t in (q, k, v)]
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, dtype=None, device=None):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
self.act = act_layer
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
#################################################################################################
### CLIP
#################################################################################################
class CLIPAttention(torch.nn.Module):
def __init__(self, embed_dim, heads, dtype, device):
super().__init__()
self.heads = heads
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
def forward(self, x, mask=None):
q = self.q_proj(x)
k = self.k_proj(x)
v = self.v_proj(x)
out = attention(q, k, v, self.heads, mask)
return self.out_proj(out)
ACTIVATIONS = {
"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a),
"gelu": torch.nn.functional.gelu,
}
class CLIPLayer(torch.nn.Module):
def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device):
super().__init__()
self.layer_norm1 = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
self.self_attn = CLIPAttention(embed_dim, heads, dtype, device)
self.layer_norm2 = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
#self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device)
self.mlp = Mlp(embed_dim, intermediate_size, embed_dim, act_layer=ACTIVATIONS[intermediate_activation], dtype=dtype, device=device)
def forward(self, x, mask=None):
x += self.self_attn(self.layer_norm1(x), mask)
x += self.mlp(self.layer_norm2(x))
return x
class CLIPEncoder(torch.nn.Module):
def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device):
super().__init__()
self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device) for i in range(num_layers)])
def forward(self, x, mask=None, intermediate_output=None):
if intermediate_output is not None:
if intermediate_output < 0:
intermediate_output = len(self.layers) + intermediate_output
intermediate = None
for i, layer in enumerate(self.layers):
x = layer(x, mask)
if i == intermediate_output:
intermediate = x.clone()
return x, intermediate
class CLIPEmbeddings(torch.nn.Module):
def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, textual_inversion_key="clip_l"):
super().__init__()
self.token_embedding = sd_hijack.TextualInversionEmbeddings(vocab_size, embed_dim, dtype=dtype, device=device, textual_inversion_key=textual_inversion_key)
self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens):
return self.token_embedding(input_tokens) + self.position_embedding.weight
class CLIPTextModel_(torch.nn.Module):
def __init__(self, config_dict, dtype, device):
num_layers = config_dict["num_hidden_layers"]
embed_dim = config_dict["hidden_size"]
heads = config_dict["num_attention_heads"]
intermediate_size = config_dict["intermediate_size"]
intermediate_activation = config_dict["hidden_act"]
super().__init__()
self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device, textual_inversion_key=config_dict.get('textual_inversion_key', 'clip_l'))
self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device)
self.final_layer_norm = nn.LayerNorm(embed_dim, dtype=dtype, device=device)
def forward(self, input_tokens, intermediate_output=None, final_layer_norm_intermediate=True):
x = self.embeddings(input_tokens)
causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
x, i = self.encoder(x, mask=causal_mask, intermediate_output=intermediate_output)
x = self.final_layer_norm(x)
if i is not None and final_layer_norm_intermediate:
i = self.final_layer_norm(i)
pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
return x, i, pooled_output
class CLIPTextModel(torch.nn.Module):
def __init__(self, config_dict, dtype, device):
super().__init__()
self.num_layers = config_dict["num_hidden_layers"]
self.text_model = CLIPTextModel_(config_dict, dtype, device)
embed_dim = config_dict["hidden_size"]
self.text_projection = nn.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
self.text_projection.weight.copy_(torch.eye(embed_dim))
self.dtype = dtype
def get_input_embeddings(self):
return self.text_model.embeddings.token_embedding
def set_input_embeddings(self, embeddings):
self.text_model.embeddings.token_embedding = embeddings
def forward(self, *args, **kwargs):
x = self.text_model(*args, **kwargs)
out = self.text_projection(x[2])
return (x[0], x[1], out, x[2])
class SDTokenizer:
def __init__(self, max_length=77, pad_with_end=True, tokenizer=None, has_start_token=True, pad_to_max_length=True, min_length=None):
self.tokenizer = tokenizer
self.max_length = max_length
self.min_length = min_length
empty = self.tokenizer('')["input_ids"]
if has_start_token:
self.tokens_start = 1
self.start_token = empty[0]
self.end_token = empty[1]
else:
self.tokens_start = 0
self.start_token = None
self.end_token = empty[0]
self.pad_with_end = pad_with_end
self.pad_to_max_length = pad_to_max_length
vocab = self.tokenizer.get_vocab()
self.inv_vocab = {v: k for k, v in vocab.items()}
self.max_word_length = 8
def tokenize_with_weights(self, text:str):
"""Tokenize the text, with weight values - presume 1.0 for all and ignore other features here. The details aren't relevant for a reference impl, and weights themselves has weak effect on SD3."""
if self.pad_with_end:
pad_token = self.end_token
else:
pad_token = 0
batch = []
if self.start_token is not None:
batch.append((self.start_token, 1.0))
to_tokenize = text.replace("\n", " ").split(' ')
to_tokenize = [x for x in to_tokenize if x != ""]
for word in to_tokenize:
batch.extend([(t, 1) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]])
batch.append((self.end_token, 1.0))
if self.pad_to_max_length:
batch.extend([(pad_token, 1.0)] * (self.max_length - len(batch)))
if self.min_length is not None and len(batch) < self.min_length:
batch.extend([(pad_token, 1.0)] * (self.min_length - len(batch)))
return [batch]
class SDXLClipGTokenizer(SDTokenizer):
def __init__(self, tokenizer):
super().__init__(pad_with_end=False, tokenizer=tokenizer)
class SD3Tokenizer:
def __init__(self):
clip_tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
self.clip_l = SDTokenizer(tokenizer=clip_tokenizer)
self.clip_g = SDXLClipGTokenizer(clip_tokenizer)
self.t5xxl = T5XXLTokenizer()
def tokenize_with_weights(self, text:str):
out = {}
out["g"] = self.clip_g.tokenize_with_weights(text)
out["l"] = self.clip_l.tokenize_with_weights(text)
out["t5xxl"] = self.t5xxl.tokenize_with_weights(text)
return out
class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
tokens = [a[0] for a in token_weight_pairs[0]]
out, pooled = self([tokens])
if pooled is not None:
first_pooled = pooled[0:1].cpu()
else:
first_pooled = pooled
output = [out[0:1]]
return torch.cat(output, dim=-2).cpu(), first_pooled
class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = ["last", "pooled", "hidden"]
def __init__(self, device="cpu", max_length=77, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=CLIPTextModel,
special_tokens=None, layer_norm_hidden_state=True, return_projected_pooled=True):
super().__init__()
assert layer in self.LAYERS
self.transformer = model_class(textmodel_json_config, dtype, device)
self.num_layers = self.transformer.num_layers
self.max_length = max_length
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
self.layer = layer
self.layer_idx = None
self.special_tokens = special_tokens if special_tokens is not None else {"start": 49406, "end": 49407, "pad": 49407}
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
self.layer_norm_hidden_state = layer_norm_hidden_state
self.return_projected_pooled = return_projected_pooled
if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) < self.num_layers
self.set_clip_options({"layer": layer_idx})
self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
def set_clip_options(self, options):
layer_idx = options.get("layer", self.layer_idx)
self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
if layer_idx is None or abs(layer_idx) > self.num_layers:
self.layer = "last"
else:
self.layer = "hidden"
self.layer_idx = layer_idx
def forward(self, tokens):
backup_embeds = self.transformer.get_input_embeddings()
tokens = torch.asarray(tokens, dtype=torch.int64, device=backup_embeds.weight.device)
outputs = self.transformer(tokens, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
self.transformer.set_input_embeddings(backup_embeds)
if self.layer == "last":
z = outputs[0]
else:
z = outputs[1]
pooled_output = None
if len(outputs) >= 3:
if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
pooled_output = outputs[3].float()
elif outputs[2] is not None:
pooled_output = outputs[2].float()
return z.float(), pooled_output
class SDXLClipG(SDClipModel):
"""Wraps the CLIP-G model into the SD-CLIP-Model interface"""
def __init__(self, config, device="cpu", layer="penultimate", layer_idx=None, dtype=None):
if layer == "penultimate":
layer="hidden"
layer_idx=-2
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False)
class T5XXLModel(SDClipModel):
"""Wraps the T5-XXL model into the SD-CLIP-Model interface for convenience"""
def __init__(self, config, device="cpu", layer="last", layer_idx=None, dtype=None):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=T5)
#################################################################################################
### T5 implementation, for the T5-XXL text encoder portion, largely pulled from upstream impl
#################################################################################################
class T5XXLTokenizer(SDTokenizer):
"""Wraps the T5 Tokenizer from HF into the SDTokenizer interface"""
def __init__(self):
super().__init__(pad_with_end=False, tokenizer=T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl"), has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
class T5LayerNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None):
super().__init__()
self.weight = torch.nn.Parameter(torch.ones(hidden_size, dtype=dtype, device=device))
self.variance_epsilon = eps
def forward(self, x):
variance = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(variance + self.variance_epsilon)
return self.weight.to(device=x.device, dtype=x.dtype) * x
class T5DenseGatedActDense(torch.nn.Module):
def __init__(self, model_dim, ff_dim, dtype, device):
super().__init__()
self.wi_0 = AutocastLinear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wi_1 = AutocastLinear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wo = AutocastLinear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
def forward(self, x):
hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
hidden_linear = self.wi_1(x)
x = hidden_gelu * hidden_linear
x = self.wo(x)
return x
class T5LayerFF(torch.nn.Module):
def __init__(self, model_dim, ff_dim, dtype, device):
super().__init__()
self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device)
self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
def forward(self, x):
forwarded_states = self.layer_norm(x)
forwarded_states = self.DenseReluDense(forwarded_states)
x += forwarded_states
return x
class T5Attention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device):
super().__init__()
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = AutocastLinear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.k = AutocastLinear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.v = AutocastLinear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.o = AutocastLinear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
self.num_heads = num_heads
self.relative_attention_bias = None
if relative_attention_bias:
self.relative_attention_num_buckets = 32
self.relative_attention_max_distance = 128
self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1))
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length, device):
"""Compute binned relative position bias"""
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=True,
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values
def forward(self, x, past_bias=None):
q = self.q(x)
k = self.k(x)
v = self.v(x)
if self.relative_attention_bias is not None:
past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
if past_bias is not None:
mask = past_bias
else:
mask = None
out = attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask.to(x.dtype) if mask is not None else None)
return self.o(out), past_bias
class T5LayerSelfAttention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device):
super().__init__()
self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device)
self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
def forward(self, x, past_bias=None):
output, past_bias = self.SelfAttention(self.layer_norm(x), past_bias=past_bias)
x += output
return x, past_bias
class T5Block(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device):
super().__init__()
self.layer = torch.nn.ModuleList()
self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device))
self.layer.append(T5LayerFF(model_dim, ff_dim, dtype, device))
def forward(self, x, past_bias=None):
x, past_bias = self.layer[0](x, past_bias)
x = self.layer[-1](x)
return x, past_bias
class T5Stack(torch.nn.Module):
def __init__(self, num_layers, model_dim, inner_dim, ff_dim, num_heads, vocab_size, dtype, device):
super().__init__()
self.embed_tokens = torch.nn.Embedding(vocab_size, model_dim, device=device)
self.block = torch.nn.ModuleList([T5Block(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device) for i in range(num_layers)])
self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device)
def forward(self, input_ids, intermediate_output=None, final_layer_norm_intermediate=True):
intermediate = None
x = self.embed_tokens(input_ids).to(torch.float32) # needs float32 or else T5 returns all zeroes
past_bias = None
for i, layer in enumerate(self.block):
x, past_bias = layer(x, past_bias)
if i == intermediate_output:
intermediate = x.clone()
x = self.final_layer_norm(x)
if intermediate is not None and final_layer_norm_intermediate:
intermediate = self.final_layer_norm(intermediate)
return x, intermediate
class T5(torch.nn.Module):
def __init__(self, config_dict, dtype, device):
super().__init__()
self.num_layers = config_dict["num_layers"]
self.encoder = T5Stack(self.num_layers, config_dict["d_model"], config_dict["d_model"], config_dict["d_ff"], config_dict["num_heads"], config_dict["vocab_size"], dtype, device)
self.dtype = dtype
def get_input_embeddings(self):
return self.encoder.embed_tokens
def set_input_embeddings(self, embeddings):
self.encoder.embed_tokens = embeddings
def forward(self, *args, **kwargs):
return self.encoder(*args, **kwargs)

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import os
import safetensors
import torch
import typing
from transformers import CLIPTokenizer, T5TokenizerFast
from modules import shared, devices, modelloader, sd_hijack_clip, prompt_parser
from modules.models.sd3.other_impls import SDClipModel, SDXLClipG, T5XXLModel, SD3Tokenizer
class SafetensorsMapping(typing.Mapping):
def __init__(self, file):
self.file = file
def __len__(self):
return len(self.file.keys())
def __iter__(self):
for key in self.file.keys():
yield key
def __getitem__(self, key):
return self.file.get_tensor(key)
CLIPL_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
CLIPL_CONFIG = {
"hidden_act": "quick_gelu",
"hidden_size": 768,
"intermediate_size": 3072,
"num_attention_heads": 12,
"num_hidden_layers": 12,
}
CLIPG_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
CLIPG_CONFIG = {
"hidden_act": "gelu",
"hidden_size": 1280,
"intermediate_size": 5120,
"num_attention_heads": 20,
"num_hidden_layers": 32,
"textual_inversion_key": "clip_g",
}
T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
T5_CONFIG = {
"d_ff": 10240,
"d_model": 4096,
"num_heads": 64,
"num_layers": 24,
"vocab_size": 32128,
}
class Sd3ClipLG(sd_hijack_clip.TextConditionalModel):
def __init__(self, clip_l, clip_g):
super().__init__()
self.clip_l = clip_l
self.clip_g = clip_g
self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
empty = self.tokenizer('')["input_ids"]
self.id_start = empty[0]
self.id_end = empty[1]
self.id_pad = empty[1]
self.return_pooled = True
def tokenize(self, texts):
return self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
def encode_with_transformers(self, tokens):
tokens_g = tokens.clone()
for batch_pos in range(tokens_g.shape[0]):
index = tokens_g[batch_pos].cpu().tolist().index(self.id_end)
tokens_g[batch_pos, index+1:tokens_g.shape[1]] = 0
l_out, l_pooled = self.clip_l(tokens)
g_out, g_pooled = self.clip_g(tokens_g)
lg_out = torch.cat([l_out, g_out], dim=-1)
lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
vector_out = torch.cat((l_pooled, g_pooled), dim=-1)
lg_out.pooled = vector_out
return lg_out
def encode_embedding_init_text(self, init_text, nvpt):
return torch.zeros((nvpt, 768+1280), device=devices.device) # XXX
class Sd3T5(torch.nn.Module):
def __init__(self, t5xxl):
super().__init__()
self.t5xxl = t5xxl
self.tokenizer = T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl")
empty = self.tokenizer('', padding='max_length', max_length=2)["input_ids"]
self.id_end = empty[0]
self.id_pad = empty[1]
def tokenize(self, texts):
return self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
def tokenize_line(self, line, *, target_token_count=None):
if shared.opts.emphasis != "None":
parsed = prompt_parser.parse_prompt_attention(line)
else:
parsed = [[line, 1.0]]
tokenized = self.tokenize([text for text, _ in parsed])
tokens = []
multipliers = []
for text_tokens, (text, weight) in zip(tokenized, parsed):
if text == 'BREAK' and weight == -1:
continue
tokens += text_tokens
multipliers += [weight] * len(text_tokens)
tokens += [self.id_end]
multipliers += [1.0]
if target_token_count is not None:
if len(tokens) < target_token_count:
tokens += [self.id_pad] * (target_token_count - len(tokens))
multipliers += [1.0] * (target_token_count - len(tokens))
else:
tokens = tokens[0:target_token_count]
multipliers = multipliers[0:target_token_count]
return tokens, multipliers
def forward(self, texts, *, token_count):
if not self.t5xxl or not shared.opts.sd3_enable_t5:
return torch.zeros((len(texts), token_count, 4096), device=devices.device, dtype=devices.dtype)
tokens_batch = []
for text in texts:
tokens, multipliers = self.tokenize_line(text, target_token_count=token_count)
tokens_batch.append(tokens)
t5_out, t5_pooled = self.t5xxl(tokens_batch)
return t5_out
def encode_embedding_init_text(self, init_text, nvpt):
return torch.zeros((nvpt, 4096), device=devices.device) # XXX
class SD3Cond(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = SD3Tokenizer()
with torch.no_grad():
self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
if shared.opts.sd3_enable_t5:
self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
else:
self.t5xxl = None
self.model_lg = Sd3ClipLG(self.clip_l, self.clip_g)
self.model_t5 = Sd3T5(self.t5xxl)
def forward(self, prompts: list[str]):
with devices.without_autocast():
lg_out, vector_out = self.model_lg(prompts)
t5_out = self.model_t5(prompts, token_count=lg_out.shape[1])
lgt_out = torch.cat([lg_out, t5_out], dim=-2)
return {
'crossattn': lgt_out,
'vector': vector_out,
}
def before_load_weights(self, state_dict):
clip_path = os.path.join(shared.models_path, "CLIP")
if 'text_encoders.clip_g.transformer.text_model.embeddings.position_embedding.weight' not in state_dict:
clip_g_file = modelloader.load_file_from_url(CLIPG_URL, model_dir=clip_path, file_name="clip_g.safetensors")
with safetensors.safe_open(clip_g_file, framework="pt") as file:
self.clip_g.transformer.load_state_dict(SafetensorsMapping(file))
if 'text_encoders.clip_l.transformer.text_model.embeddings.position_embedding.weight' not in state_dict:
clip_l_file = modelloader.load_file_from_url(CLIPL_URL, model_dir=clip_path, file_name="clip_l.safetensors")
with safetensors.safe_open(clip_l_file, framework="pt") as file:
self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
if self.t5xxl and 'text_encoders.t5xxl.transformer.encoder.embed_tokens.weight' not in state_dict:
t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
with safetensors.safe_open(t5_file, framework="pt") as file:
self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
def encode_embedding_init_text(self, init_text, nvpt):
return self.model_lg.encode_embedding_init_text(init_text, nvpt)
def tokenize(self, texts):
return self.model_lg.tokenize(texts)
def medvram_modules(self):
return [self.clip_g, self.clip_l, self.t5xxl]
def get_token_count(self, text):
_, token_count = self.model_lg.process_texts([text])
return token_count
def get_target_prompt_token_count(self, token_count):
return self.model_lg.get_target_prompt_token_count(token_count)

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### Impls of the SD3 core diffusion model and VAE
import torch
import math
import einops
from modules.models.sd3.mmdit import MMDiT
from PIL import Image
#################################################################################################
### MMDiT Model Wrapping
#################################################################################################
class ModelSamplingDiscreteFlow(torch.nn.Module):
"""Helper for sampler scheduling (ie timestep/sigma calculations) for Discrete Flow models"""
def __init__(self, shift=1.0):
super().__init__()
self.shift = shift
timesteps = 1000
ts = self.sigma(torch.arange(1, timesteps + 1, 1))
self.register_buffer('sigmas', ts)
@property
def sigma_min(self):
return self.sigmas[0]
@property
def sigma_max(self):
return self.sigmas[-1]
def timestep(self, sigma):
return sigma * 1000
def sigma(self, timestep: torch.Tensor):
timestep = timestep / 1000.0
if self.shift == 1.0:
return timestep
return self.shift * timestep / (1 + (self.shift - 1) * timestep)
def calculate_denoised(self, sigma, model_output, model_input):
sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
return model_input - model_output * sigma
def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
return sigma * noise + (1.0 - sigma) * latent_image
class BaseModel(torch.nn.Module):
"""Wrapper around the core MM-DiT model"""
def __init__(self, shift=1.0, device=None, dtype=torch.float32, state_dict=None, prefix=""):
super().__init__()
# Important configuration values can be quickly determined by checking shapes in the source file
# Some of these will vary between models (eg 2B vs 8B primarily differ in their depth, but also other details change)
patch_size = state_dict[f"{prefix}x_embedder.proj.weight"].shape[2]
depth = state_dict[f"{prefix}x_embedder.proj.weight"].shape[0] // 64
num_patches = state_dict[f"{prefix}pos_embed"].shape[1]
pos_embed_max_size = round(math.sqrt(num_patches))
adm_in_channels = state_dict[f"{prefix}y_embedder.mlp.0.weight"].shape[1]
context_shape = state_dict[f"{prefix}context_embedder.weight"].shape
context_embedder_config = {
"target": "torch.nn.Linear",
"params": {
"in_features": context_shape[1],
"out_features": context_shape[0]
}
}
self.diffusion_model = MMDiT(input_size=None, pos_embed_scaling_factor=None, pos_embed_offset=None, pos_embed_max_size=pos_embed_max_size, patch_size=patch_size, in_channels=16, depth=depth, num_patches=num_patches, adm_in_channels=adm_in_channels, context_embedder_config=context_embedder_config, device=device, dtype=dtype)
self.model_sampling = ModelSamplingDiscreteFlow(shift=shift)
self.depth = depth
def apply_model(self, x, sigma, c_crossattn=None, y=None):
dtype = self.get_dtype()
timestep = self.model_sampling.timestep(sigma).float()
model_output = self.diffusion_model(x.to(dtype), timestep, context=c_crossattn.to(dtype), y=y.to(dtype)).float()
return self.model_sampling.calculate_denoised(sigma, model_output, x)
def forward(self, *args, **kwargs):
return self.apply_model(*args, **kwargs)
def get_dtype(self):
return self.diffusion_model.dtype
class CFGDenoiser(torch.nn.Module):
"""Helper for applying CFG Scaling to diffusion outputs"""
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, x, timestep, cond, uncond, cond_scale):
# Run cond and uncond in a batch together
batched = self.model.apply_model(torch.cat([x, x]), torch.cat([timestep, timestep]), c_crossattn=torch.cat([cond["c_crossattn"], uncond["c_crossattn"]]), y=torch.cat([cond["y"], uncond["y"]]))
# Then split and apply CFG Scaling
pos_out, neg_out = batched.chunk(2)
scaled = neg_out + (pos_out - neg_out) * cond_scale
return scaled
class SD3LatentFormat:
"""Latents are slightly shifted from center - this class must be called after VAE Decode to correct for the shift"""
def __init__(self):
self.scale_factor = 1.5305
self.shift_factor = 0.0609
def process_in(self, latent):
return (latent - self.shift_factor) * self.scale_factor
def process_out(self, latent):
return (latent / self.scale_factor) + self.shift_factor
def decode_latent_to_preview(self, x0):
"""Quick RGB approximate preview of sd3 latents"""
factors = torch.tensor([
[-0.0645, 0.0177, 0.1052], [ 0.0028, 0.0312, 0.0650],
[ 0.1848, 0.0762, 0.0360], [ 0.0944, 0.0360, 0.0889],
[ 0.0897, 0.0506, -0.0364], [-0.0020, 0.1203, 0.0284],
[ 0.0855, 0.0118, 0.0283], [-0.0539, 0.0658, 0.1047],
[-0.0057, 0.0116, 0.0700], [-0.0412, 0.0281, -0.0039],
[ 0.1106, 0.1171, 0.1220], [-0.0248, 0.0682, -0.0481],
[ 0.0815, 0.0846, 0.1207], [-0.0120, -0.0055, -0.0867],
[-0.0749, -0.0634, -0.0456], [-0.1418, -0.1457, -0.1259]
], device="cpu")
latent_image = x0[0].permute(1, 2, 0).cpu() @ factors
latents_ubyte = (((latent_image + 1) / 2)
.clamp(0, 1) # change scale from -1..1 to 0..1
.mul(0xFF) # to 0..255
.byte()).cpu()
return Image.fromarray(latents_ubyte.numpy())
#################################################################################################
### K-Diffusion Sampling
#################################################################################################
def append_dims(x, target_dims):
"""Appends dimensions to the end of a tensor until it has target_dims dimensions."""
dims_to_append = target_dims - x.ndim
return x[(...,) + (None,) * dims_to_append]
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / append_dims(sigma, x.ndim)
@torch.no_grad()
@torch.autocast("cuda", dtype=torch.float16)
def sample_euler(model, x, sigmas, extra_args=None):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
for i in range(len(sigmas) - 1):
sigma_hat = sigmas[i]
denoised = model(x, sigma_hat * s_in, **extra_args)
d = to_d(x, sigma_hat, denoised)
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = x + d * dt
return x
#################################################################################################
### VAE
#################################################################################################
def Normalize(in_channels, num_groups=32, dtype=torch.float32, device=None):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
class ResnetBlock(torch.nn.Module):
def __init__(self, *, in_channels, out_channels=None, dtype=torch.float32, device=None):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.norm1 = Normalize(in_channels, dtype=dtype, device=device)
self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
self.norm2 = Normalize(out_channels, dtype=dtype, device=device)
self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
if self.in_channels != self.out_channels:
self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
else:
self.nin_shortcut = None
self.swish = torch.nn.SiLU(inplace=True)
def forward(self, x):
hidden = x
hidden = self.norm1(hidden)
hidden = self.swish(hidden)
hidden = self.conv1(hidden)
hidden = self.norm2(hidden)
hidden = self.swish(hidden)
hidden = self.conv2(hidden)
if self.in_channels != self.out_channels:
x = self.nin_shortcut(x)
return x + hidden
class AttnBlock(torch.nn.Module):
def __init__(self, in_channels, dtype=torch.float32, device=None):
super().__init__()
self.norm = Normalize(in_channels, dtype=dtype, device=device)
self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0, dtype=dtype, device=device)
def forward(self, x):
hidden = self.norm(x)
q = self.q(hidden)
k = self.k(hidden)
v = self.v(hidden)
b, c, h, w = q.shape
q, k, v = [einops.rearrange(x, "b c h w -> b 1 (h w) c").contiguous() for x in (q, k, v)]
hidden = torch.nn.functional.scaled_dot_product_attention(q, k, v) # scale is dim ** -0.5 per default
hidden = einops.rearrange(hidden, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
hidden = self.proj_out(hidden)
return x + hidden
class Downsample(torch.nn.Module):
def __init__(self, in_channels, dtype=torch.float32, device=None):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0, dtype=dtype, device=device)
def forward(self, x):
pad = (0,1,0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
return x
class Upsample(torch.nn.Module):
def __init__(self, in_channels, dtype=torch.float32, device=None):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
x = self.conv(x)
return x
class VAEEncoder(torch.nn.Module):
def __init__(self, ch=128, ch_mult=(1,2,4,4), num_res_blocks=2, in_channels=3, z_channels=16, dtype=torch.float32, device=None):
super().__init__()
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
# downsampling
self.conv_in = torch.nn.Conv2d(in_channels, ch, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
in_ch_mult = (1,) + tuple(ch_mult)
self.in_ch_mult = in_ch_mult
self.down = torch.nn.ModuleList()
for i_level in range(self.num_resolutions):
block = torch.nn.ModuleList()
attn = torch.nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for _ in range(num_res_blocks):
block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device))
block_in = block_out
down = torch.nn.Module()
down.block = block
down.attn = attn
if i_level != self.num_resolutions - 1:
down.downsample = Downsample(block_in, dtype=dtype, device=device)
self.down.append(down)
# middle
self.mid = torch.nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device)
self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
# end
self.norm_out = Normalize(block_in, dtype=dtype, device=device)
self.conv_out = torch.nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
self.swish = torch.nn.SiLU(inplace=True)
def forward(self, x):
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_resolutions):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1])
hs.append(h)
if i_level != self.num_resolutions-1:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h)
h = self.mid.attn_1(h)
h = self.mid.block_2(h)
# end
h = self.norm_out(h)
h = self.swish(h)
h = self.conv_out(h)
return h
class VAEDecoder(torch.nn.Module):
def __init__(self, ch=128, out_ch=3, ch_mult=(1, 2, 4, 4), num_res_blocks=2, resolution=256, z_channels=16, dtype=torch.float32, device=None):
super().__init__()
self.num_resolutions = len(ch_mult)
self.num_res_blocks = num_res_blocks
block_in = ch * ch_mult[self.num_resolutions - 1]
curr_res = resolution // 2 ** (self.num_resolutions - 1)
# z to block_in
self.conv_in = torch.nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
# middle
self.mid = torch.nn.Module()
self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
self.mid.attn_1 = AttnBlock(block_in, dtype=dtype, device=device)
self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in, dtype=dtype, device=device)
# upsampling
self.up = torch.nn.ModuleList()
for i_level in reversed(range(self.num_resolutions)):
block = torch.nn.ModuleList()
block_out = ch * ch_mult[i_level]
for _ in range(self.num_res_blocks + 1):
block.append(ResnetBlock(in_channels=block_in, out_channels=block_out, dtype=dtype, device=device))
block_in = block_out
up = torch.nn.Module()
up.block = block
if i_level != 0:
up.upsample = Upsample(block_in, dtype=dtype, device=device)
curr_res = curr_res * 2
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in, dtype=dtype, device=device)
self.conv_out = torch.nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device)
self.swish = torch.nn.SiLU(inplace=True)
def forward(self, z):
# z to block_in
hidden = self.conv_in(z)
# middle
hidden = self.mid.block_1(hidden)
hidden = self.mid.attn_1(hidden)
hidden = self.mid.block_2(hidden)
# upsampling
for i_level in reversed(range(self.num_resolutions)):
for i_block in range(self.num_res_blocks + 1):
hidden = self.up[i_level].block[i_block](hidden)
if i_level != 0:
hidden = self.up[i_level].upsample(hidden)
# end
hidden = self.norm_out(hidden)
hidden = self.swish(hidden)
hidden = self.conv_out(hidden)
return hidden
class SDVAE(torch.nn.Module):
def __init__(self, dtype=torch.float32, device=None):
super().__init__()
self.encoder = VAEEncoder(dtype=dtype, device=device)
self.decoder = VAEDecoder(dtype=dtype, device=device)
@torch.autocast("cuda", dtype=torch.float16)
def decode(self, latent):
return self.decoder(latent)
@torch.autocast("cuda", dtype=torch.float16)
def encode(self, image):
hidden = self.encoder(image)
mean, logvar = torch.chunk(hidden, 2, dim=1)
logvar = torch.clamp(logvar, -30.0, 20.0)
std = torch.exp(0.5 * logvar)
return mean + std * torch.randn_like(mean)

96
modules/models/sd3/sd3_model.py Normal file
View File

@ -0,0 +1,96 @@
import contextlib
import torch
import k_diffusion
from modules.models.sd3.sd3_impls import BaseModel, SDVAE, SD3LatentFormat
from modules.models.sd3.sd3_cond import SD3Cond
from modules import shared, devices
class SD3Denoiser(k_diffusion.external.DiscreteSchedule):
def __init__(self, inner_model, sigmas):
super().__init__(sigmas, quantize=shared.opts.enable_quantization)
self.inner_model = inner_model
def forward(self, input, sigma, **kwargs):
return self.inner_model.apply_model(input, sigma, **kwargs)
class SD3Inferencer(torch.nn.Module):
def __init__(self, state_dict, shift=3, use_ema=False):
super().__init__()
self.shift = shift
with torch.no_grad():
self.model = BaseModel(shift=shift, state_dict=state_dict, prefix="model.diffusion_model.", device="cpu", dtype=devices.dtype)
self.first_stage_model = SDVAE(device="cpu", dtype=devices.dtype_vae)
self.first_stage_model.dtype = self.model.diffusion_model.dtype
self.alphas_cumprod = 1 / (self.model.model_sampling.sigmas ** 2 + 1)
self.text_encoders = SD3Cond()
self.cond_stage_key = 'txt'
self.parameterization = "eps"
self.model.conditioning_key = "crossattn"
self.latent_format = SD3LatentFormat()
self.latent_channels = 16
@property
def cond_stage_model(self):
return self.text_encoders
def before_load_weights(self, state_dict):
self.cond_stage_model.before_load_weights(state_dict)
def ema_scope(self):
return contextlib.nullcontext()
def get_learned_conditioning(self, batch: list[str]):
return self.cond_stage_model(batch)
def apply_model(self, x, t, cond):
return self.model(x, t, c_crossattn=cond['crossattn'], y=cond['vector'])
def decode_first_stage(self, latent):
latent = self.latent_format.process_out(latent)
return self.first_stage_model.decode(latent)
def encode_first_stage(self, image):
latent = self.first_stage_model.encode(image)
return self.latent_format.process_in(latent)
def get_first_stage_encoding(self, x):
return x
def create_denoiser(self):
return SD3Denoiser(self, self.model.model_sampling.sigmas)
def medvram_fields(self):
return [
(self, 'first_stage_model'),
(self, 'text_encoders'),
(self, 'model'),
]
def add_noise_to_latent(self, x, noise, amount):
return x * (1 - amount) + noise * amount
def fix_dimensions(self, width, height):
return width // 16 * 16, height // 16 * 16
def diffusers_weight_mapping(self):
for i in range(self.model.depth):
yield f"transformer.transformer_blocks.{i}.attn.to_q", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_q_proj"
yield f"transformer.transformer_blocks.{i}.attn.to_k", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_k_proj"
yield f"transformer.transformer_blocks.{i}.attn.to_v", f"diffusion_model_joint_blocks_{i}_x_block_attn_qkv_v_proj"
yield f"transformer.transformer_blocks.{i}.attn.to_out.0", f"diffusion_model_joint_blocks_{i}_x_block_attn_proj"
yield f"transformer.transformer_blocks.{i}.attn.add_q_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_q_proj"
yield f"transformer.transformer_blocks.{i}.attn.add_k_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_k_proj"
yield f"transformer.transformer_blocks.{i}.attn.add_v_proj", f"diffusion_model_joint_blocks_{i}_context_block.attn_qkv_v_proj"
yield f"transformer.transformer_blocks.{i}.attn.add_out_proj.0", f"diffusion_model_joint_blocks_{i}_context_block_attn_proj"

3
modules/paths_internal.py
View File

@ -24,11 +24,12 @@ default_sd_model_file = sd_model_file
# Parse the --data-dir flag first so we can use it as a base for our other argument default values
parser_pre = argparse.ArgumentParser(add_help=False)
parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(modules_path), help="base path where all user data is stored", )
parser_pre.add_argument("--models-dir", type=str, default=None, help="base path where models are stored; overrides --data-dir", )
cmd_opts_pre = parser_pre.parse_known_args()[0]
data_path = cmd_opts_pre.data_dir
models_path = os.path.join(data_path, "models")
models_path = cmd_opts_pre.models_dir if cmd_opts_pre.models_dir else os.path.join(data_path, "models")
extensions_dir = os.path.join(data_path, "extensions")
extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
config_states_dir = os.path.join(script_path, "config_states")

6
modules/postprocessing.py
View File

@ -51,7 +51,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
shared.state.textinfo = name
shared.state.skipped = False
if shared.state.interrupted:
if shared.state.interrupted or shared.state.stopping_generation:
break
if isinstance(image_placeholder, str):
@ -62,11 +62,13 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
else:
image_data = image_placeholder
image_data = image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB")
parameters, existing_pnginfo = images.read_info_from_image(image_data)
if parameters:
existing_pnginfo["parameters"] = parameters
initial_pp = scripts_postprocessing.PostprocessedImage(image_data if image_data.mode in ("RGBA", "RGB") else image_data.convert("RGB"))
initial_pp = scripts_postprocessing.PostprocessedImage(image_data)
scripts.scripts_postproc.run(initial_pp, args)

73
modules/processing.py
View File

@ -16,7 +16,7 @@ from skimage import exposure
from typing import Any
import modules.sd_hijack
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng, profiling
from modules.rng import slerp # noqa: F401
from modules.sd_hijack import model_hijack
from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@ -115,10 +115,7 @@ def txt2img_image_conditioning(sd_model, x, width, height):
return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
else:
sd = sd_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
if sd_model.is_sdxl_inpaint:
# The "masked-image" in this case will just be all 0.5 since the entire image is masked.
image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
image_conditioning = images_tensor_to_samples(image_conditioning,
@ -238,11 +235,6 @@ class StableDiffusionProcessing:
self.styles = []
self.sampler_noise_scheduler_override = None
self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
self.extra_generation_params = self.extra_generation_params or {}
self.override_settings = self.override_settings or {}
@ -259,6 +251,13 @@ class StableDiffusionProcessing:
self.cached_uc = StableDiffusionProcessing.cached_uc
self.cached_c = StableDiffusionProcessing.cached_c
def fill_fields_from_opts(self):
self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
@property
def sd_model(self):
return shared.sd_model
@ -390,10 +389,7 @@ class StableDiffusionProcessing:
if self.sampler.conditioning_key == "crossattn-adm":
return self.unclip_image_conditioning(source_image)
sd = self.sampler.model_wrap.inner_model.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
if self.sampler.model_wrap.inner_model.is_sdxl_inpaint:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
# Dummy zero conditioning if we're not using inpainting or depth model.
@ -569,7 +565,7 @@ class Processed:
self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
self.all_seeds = all_seeds or p.all_seeds or [self.seed]
self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
self.infotexts = infotexts or [info]
self.infotexts = infotexts or [info] * len(images_list)
self.version = program_version()
def js(self):
@ -629,6 +625,9 @@ class DecodedSamples(list):
def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
samples = DecodedSamples()
if check_for_nans:
devices.test_for_nans(batch, "unet")
for i in range(batch.shape[0]):
sample = decode_first_stage(model, batch[i:i + 1])[0]
@ -794,7 +793,6 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
"Init image hash": getattr(p, 'init_img_hash', None),
"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
"Tiling": "True" if p.tiling else None,
**p.extra_generation_params,
"Version": program_version() if opts.add_version_to_infotext else None,
@ -842,6 +840,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio())
# backwards compatibility, fix sampler and scheduler if invalid
sd_samplers.fix_p_invalid_sampler_and_scheduler(p)
with profiling.Profiler():
res = process_images_inner(p)
finally:
@ -882,6 +884,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.refiner_checkpoint_info is None:
raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
if hasattr(shared.sd_model, 'fix_dimensions'):
p.width, p.height = shared.sd_model.fix_dimensions(p.width, p.height)
p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
p.sd_model_hash = shared.sd_model.sd_model_hash
p.sd_vae_name = sd_vae.get_loaded_vae_name()
@ -890,6 +895,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
modules.sd_hijack.model_hijack.clear_comments()
p.fill_fields_from_opts()
p.setup_prompts()
if isinstance(seed, list):
@ -939,7 +945,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
p.subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
p.rng = rng.ImageRNG((opt_C, p.height // opt_f, p.width // opt_f), p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
latent_channels = getattr(shared.sd_model, 'latent_channels', opt_C)
p.rng = rng.ImageRNG((latent_channels, p.height // opt_f, p.width // opt_f), p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w)
if p.scripts is not None:
p.scripts.before_process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
@ -988,6 +995,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim
else:
devices.test_for_nans(samples_ddim, "unet")
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
@ -1325,6 +1334,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
# here we generate an image normally
x = self.rng.next()
if self.scripts is not None:
self.scripts.process_before_every_sampling(
p=self,
x=x,
noise=x,
c=conditioning,
uc=unconditional_conditioning
)
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
del x
@ -1425,6 +1443,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.scripts is not None:
self.scripts.before_hr(self)
self.scripts.process_before_every_sampling(
p=self,
x=samples,
noise=noise,
c=self.hr_c,
uc=self.hr_uc,
)
samples = self.sampler.sample_img2img(self, samples, noise, self.hr_c, self.hr_uc, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
@ -1715,10 +1740,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
latmask = latmask[0]
if self.mask_round:
latmask = np.around(latmask)
latmask = np.tile(latmask[None], (4, 1, 1))
latmask = np.tile(latmask[None], (self.init_latent.shape[1], 1, 1))
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(devices.dtype)
self.nmask = torch.asarray(latmask).to(shared.device).type(devices.dtype)
# this needs to be fixed to be done in sample() using actual seeds for batches
if self.inpainting_fill == 2:
@ -1738,6 +1763,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
x *= self.initial_noise_multiplier
if self.scripts is not None:
self.scripts.process_before_every_sampling(
p=self,
x=self.init_latent,
noise=x,
c=conditioning,
uc=unconditional_conditioning
)
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
if self.mask is not None:

46
modules/profiling.py Normal file
View File

@ -0,0 +1,46 @@
import torch
from modules import shared, ui_gradio_extensions
class Profiler:
def __init__(self):
if not shared.opts.profiling_enable:
self.profiler = None
return
activities = []
if "CPU" in shared.opts.profiling_activities:
activities.append(torch.profiler.ProfilerActivity.CPU)
if "CUDA" in shared.opts.profiling_activities:
activities.append(torch.profiler.ProfilerActivity.CUDA)
if not activities:
self.profiler = None
return
self.profiler = torch.profiler.profile(
activities=activities,
record_shapes=shared.opts.profiling_record_shapes,
profile_memory=shared.opts.profiling_profile_memory,
with_stack=shared.opts.profiling_with_stack
)
def __enter__(self):
if self.profiler:
self.profiler.__enter__()
return self
def __exit__(self, exc_type, exc, exc_tb):
if self.profiler:
shared.state.textinfo = "Finishing profile..."
self.profiler.__exit__(exc_type, exc, exc_tb)
self.profiler.export_chrome_trace(shared.opts.profiling_filename)
def webpath():
return ui_gradio_extensions.webpath(shared.opts.profiling_filename)

2
modules/prompt_parser.py
View File

@ -268,7 +268,7 @@ def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None,
class DictWithShape(dict):
def __init__(self, x, shape):
def __init__(self, x, shape=None):
super().__init__()
self.update(x)

4
modules/safe.py
View File

@ -64,8 +64,8 @@ class RestrictedUnpickler(pickle.Unpickler):
raise Exception(f"global '{module}/{name}' is forbidden")
# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/<number>'
allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$")
# Regular expression that accepts 'dirname/version', 'dirname/byteorder', 'dirname/data.pkl', '.data/serialization_id', and 'dirname/data/<number>'
allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|byteorder|.data/serialization_id|(data\.pkl))$")
data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$")
def check_zip_filenames(filename, names):

15
modules/scripts.py
View File

@ -187,6 +187,13 @@ class Script:
"""
pass
def process_before_every_sampling(self, p, *args, **kwargs):
"""
Similar to process(), called before every sampling.
If you use high-res fix, this will be called two times.
"""
pass
def process_batch(self, p, *args, **kwargs):
"""
Same as process(), but called for every batch.
@ -826,6 +833,14 @@ class ScriptRunner:
except Exception:
errors.report(f"Error running process: {script.filename}", exc_info=True)
def process_before_every_sampling(self, p, **kwargs):
for script in self.ordered_scripts('process_before_every_sampling'):
try:
script_args = p.script_args[script.args_from:script.args_to]
script.process_before_every_sampling(p, *script_args, **kwargs)
except Exception:
errors.report(f"Error running process_before_every_sampling: {script.filename}", exc_info=True)
def before_process_batch(self, p, **kwargs):
for script in self.ordered_scripts('before_process_batch'):
try:

20
modules/sd_hijack.py
View File

@ -325,6 +325,9 @@ class StableDiffusionModelHijack:
if self.clip is None:
return "-", "-"
if hasattr(self.clip, 'get_token_count'):
token_count = self.clip.get_token_count(text)
else:
_, token_count = self.clip.process_texts([text])
return token_count, self.clip.get_target_prompt_token_count(token_count)
@ -356,13 +359,28 @@ class EmbeddingsWithFixes(torch.nn.Module):
vec = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
emb = devices.cond_cast_unet(vec)
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]).to(dtype=inputs_embeds.dtype)
vecs.append(tensor)
return torch.stack(vecs)
class TextualInversionEmbeddings(torch.nn.Embedding):
def __init__(self, num_embeddings: int, embedding_dim: int, textual_inversion_key='clip_l', **kwargs):
super().__init__(num_embeddings, embedding_dim, **kwargs)
self.embeddings = model_hijack
self.textual_inversion_key = textual_inversion_key
@property
def wrapped(self):
return super().forward
def forward(self, input_ids):
return EmbeddingsWithFixes.forward(self, input_ids)
def add_circular_option_to_conv_2d():
conv2d_constructor = torch.nn.Conv2d.__init__

63
modules/sd_hijack_clip.py
View File

@ -27,24 +27,21 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack):
class TextConditionalModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.wrapped = wrapped
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
self.hijack = sd_hijack.model_hijack
self.chunk_length = 75
self.is_trainable = getattr(wrapped, 'is_trainable', False)
self.input_key = getattr(wrapped, 'input_key', 'txt')
self.legacy_ucg_val = None
self.is_trainable = False
self.input_key = 'txt'
self.return_pooled = False
self.comma_token = None
self.id_start = None
self.id_end = None
self.id_pad = None
def empty_chunk(self):
"""creates an empty PromptChunk and returns it"""
@ -210,10 +207,6 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
"""
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
batch_chunks, token_count = self.process_texts(texts)
used_embeddings = {}
@ -252,7 +245,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
if getattr(self.wrapped, 'return_pooled', False):
if self.return_pooled:
return torch.hstack(zs), zs[0].pooled
else:
return torch.hstack(zs)
@ -292,6 +285,34 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
return z
class FrozenCLIPEmbedderWithCustomWordsBase(TextConditionalModel):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack):
super().__init__()
self.hijack = hijack
self.wrapped = wrapped
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.is_trainable = getattr(wrapped, 'is_trainable', False)
self.input_key = getattr(wrapped, 'input_key', 'txt')
self.return_pooled = getattr(self.wrapped, 'return_pooled', False)
self.legacy_ucg_val = None # for sgm codebase
def forward(self, texts):
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
return super().forward(texts)
class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
def __init__(self, wrapped, hijack):
super().__init__(wrapped, hijack)
@ -353,7 +374,9 @@ class FrozenCLIPEmbedderForSDXLWithCustomWords(FrozenCLIPEmbedderWithCustomWords
def encode_with_transformers(self, tokens):
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=self.wrapped.layer == "hidden")
if self.wrapped.layer == "last":
if opts.sdxl_clip_l_skip is True:
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
elif self.wrapped.layer == "last":
z = outputs.last_hidden_state
else:
z = outputs.hidden_states[self.wrapped.layer_idx]

6
modules/sd_hijack_optimizations.py
View File

@ -486,7 +486,8 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
k_in = self.to_k(context_k)
v_in = self.to_v(context_v)
q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
q, k, v = (t.reshape(t.shape[0], t.shape[1], h, -1) for t in (q_in, k_in, v_in))
del q_in, k_in, v_in
dtype = q.dtype
@ -497,7 +498,8 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
out = out.to(dtype)
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
b, n, h, d = out.shape
out = out.reshape(b, n, h * d)
return self.to_out(out)

79
modules/sd_hijack_unet.py
View File

@ -1,5 +1,7 @@
import torch
from packaging import version
from einops import repeat
import math
from modules import devices
from modules.sd_hijack_utils import CondFunc
@ -36,7 +38,7 @@ th = TorchHijackForUnet()
# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
"""Always make sure inputs to unet are in correct dtype."""
if isinstance(cond, dict):
for y in cond.keys():
if isinstance(cond[y], list):
@ -45,7 +47,59 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
with devices.autocast():
return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
if devices.unet_needs_upcast:
return result.float()
else:
return result
# Monkey patch to create timestep embed tensor on device, avoiding a block.
def timestep_embedding(_, timesteps, dim, max_period=10000, repeat_only=False):
"""
Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an [N x dim] Tensor of positional embeddings.
"""
if not repeat_only:
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half
)
args = timesteps[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
else:
embedding = repeat(timesteps, 'b -> b d', d=dim)
return embedding
# Monkey patch to SpatialTransformer removing unnecessary contiguous calls.
# Prevents a lot of unnecessary aten::copy_ calls
def spatial_transformer_forward(_, self, x: torch.Tensor, context=None):
# note: if no context is given, cross-attention defaults to self-attention
if not isinstance(context, list):
context = [context]
b, c, h, w = x.shape
x_in = x
x = self.norm(x)
if not self.use_linear:
x = self.proj_in(x)
x = x.permute(0, 2, 3, 1).reshape(b, h * w, c)
if self.use_linear:
x = self.proj_in(x)
for i, block in enumerate(self.transformer_blocks):
x = block(x, context=context[i])
if self.use_linear:
x = self.proj_out(x)
x = x.view(b, h, w, c).permute(0, 3, 1, 2)
if not self.use_linear:
x = self.proj_out(x)
return x + x_in
class GELUHijack(torch.nn.GELU, torch.nn.Module):
@ -64,12 +118,15 @@ def hijack_ddpm_edit():
if not ddpm_edit_hijack:
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model)
unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding)
CondFunc('ldm.modules.attention.SpatialTransformer.forward', spatial_transformer_forward)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
@ -81,5 +138,17 @@ CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_s
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
if devices.unet_needs_upcast and timesteps.dtype == torch.int64:
dtype = torch.float32
else:
dtype = devices.dtype_unet
return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)

6
modules/sd_hijack_utils.py
View File

@ -1,7 +1,11 @@
import importlib
always_true_func = lambda *args, **kwargs: True
class CondFunc:
def __new__(cls, orig_func, sub_func, cond_func):
def __new__(cls, orig_func, sub_func, cond_func=always_true_func):
self = super(CondFunc, cls).__new__(cls)
if isinstance(orig_func, str):
func_path = orig_func.split('.')

134
modules/sd_models.py
View File

@ -1,7 +1,9 @@
import collections
import importlib
import os
import sys
import threading
import enum
import torch
import re
@ -10,8 +12,6 @@ from omegaconf import OmegaConf, ListConfig
from urllib import request
import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
from modules.timer import Timer
from modules.shared import opts
@ -27,6 +27,14 @@ checkpoint_alisases = checkpoint_aliases # for compatibility with old name
checkpoints_loaded = collections.OrderedDict()
class ModelType(enum.Enum):
SD1 = 1
SD2 = 2
SDXL = 3
SSD = 4
SD3 = 5
def replace_key(d, key, new_key, value):
keys = list(d.keys())
@ -149,10 +157,12 @@ def list_models():
cmd_ckpt = shared.cmd_opts.ckpt
if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
model_url = None
expected_sha256 = None
else:
model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
expected_sha256 = '6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa'
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"], hash_prefix=expected_sha256)
if os.path.exists(cmd_ckpt):
checkpoint_info = CheckpointInfo(cmd_ckpt)
@ -280,10 +290,12 @@ def read_metadata_from_safetensors(filename):
json_start = file.read(2)
assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
json_data = json_start + file.read(metadata_len-2)
json_obj = json.loads(json_data)
res = {}
try:
json_data = json_start + file.read(metadata_len-2)
json_obj = json.loads(json_data)
for k, v in json_obj.get("__metadata__", {}).items():
res[k] = v
if isinstance(v, str) and v[0:1] == '{':
@ -291,6 +303,8 @@ def read_metadata_from_safetensors(filename):
res[k] = json.loads(v)
except Exception:
pass
except Exception:
errors.report(f"Error reading metadata from file: {filename}", exc_info=True)
return res
@ -362,6 +376,37 @@ def check_fp8(model):
return enable_fp8
def set_model_type(model, state_dict):
model.is_sd1 = False
model.is_sd2 = False
model.is_sdxl = False
model.is_ssd = False
model.is_sd3 = False
if "model.diffusion_model.x_embedder.proj.weight" in state_dict:
model.is_sd3 = True
model.model_type = ModelType.SD3
elif hasattr(model, 'conditioner'):
model.is_sdxl = True
if 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys():
model.is_ssd = True
model.model_type = ModelType.SSD
else:
model.model_type = ModelType.SDXL
elif hasattr(model.cond_stage_model, 'model'):
model.is_sd2 = True
model.model_type = ModelType.SD2
else:
model.is_sd1 = True
model.model_type = ModelType.SD1
def set_model_fields(model):
if not hasattr(model, 'latent_channels'):
model.latent_channels = 4
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash")
@ -376,10 +421,9 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
if state_dict is None:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
model.is_sdxl = hasattr(model, 'conditioner')
model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
model.is_sd1 = not model.is_sdxl and not model.is_sd2
model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
set_model_type(model, state_dict)
set_model_fields(model)
if model.is_sdxl:
sd_models_xl.extend_sdxl(model)
@ -390,11 +434,30 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
# cache newly loaded model
checkpoints_loaded[checkpoint_info] = state_dict.copy()
if hasattr(model, "before_load_weights"):
model.before_load_weights(state_dict)
model.load_state_dict(state_dict, strict=False)
timer.record("apply weights to model")
if hasattr(model, "after_load_weights"):
model.after_load_weights(state_dict)
del state_dict
# Set is_sdxl_inpaint flag.
# Checks Unet structure to detect inpaint model. The inpaint model's
# checkpoint state_dict does not contain the key
# 'diffusion_model.input_blocks.0.0.weight'.
diffusion_model_input = model.model.state_dict().get(
'diffusion_model.input_blocks.0.0.weight'
)
model.is_sdxl_inpaint = (
model.is_sdxl and
diffusion_model_input is not None and
diffusion_model_input.shape[1] == 9
)
if shared.cmd_opts.opt_channelslast:
model.to(memory_format=torch.channels_last)
timer.record("apply channels_last")
@ -403,6 +466,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
model.float()
model.alphas_cumprod_original = model.alphas_cumprod
devices.dtype_unet = torch.float32
assert shared.cmd_opts.precision != "half", "Cannot use --precision half with --no-half"
timer.record("apply float()")
else:
vae = model.first_stage_model
@ -532,17 +596,17 @@ def patch_given_betas():
original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
def repair_config(sd_config):
def repair_config(sd_config, state_dict=None):
if not hasattr(sd_config.model.params, "use_ema"):
sd_config.model.params.use_ema = False
if hasattr(sd_config.model.params, 'unet_config'):
if shared.cmd_opts.no_half:
sd_config.model.params.unet_config.params.use_fp16 = False
elif shared.cmd_opts.upcast_sampling:
elif shared.cmd_opts.upcast_sampling or shared.cmd_opts.precision == "half":
sd_config.model.params.unet_config.params.use_fp16 = True
if hasattr(sd_config.model.params, 'first_stage_config'):
if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
@ -551,6 +615,15 @@ def repair_config(sd_config):
karlo_path = os.path.join(paths.models_path, 'karlo')
sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
# Do not use checkpoint for inference.
# This helps prevent extra performance overhead on checking parameters.
# The perf overhead is about 100ms/it on 4090 for SDXL.
if hasattr(sd_config.model.params, "network_config"):
sd_config.model.params.network_config.params.use_checkpoint = False
if hasattr(sd_config.model.params, "unet_config"):
sd_config.model.params.unet_config.params.use_checkpoint = False
def rescale_zero_terminal_snr_abar(alphas_cumprod):
alphas_bar_sqrt = alphas_cumprod.sqrt()
@ -651,14 +724,19 @@ def get_empty_cond(sd_model):
p = processing.StableDiffusionProcessingTxt2Img()
extra_networks.activate(p, {})
if hasattr(sd_model, 'conditioner'):
if hasattr(sd_model, 'get_learned_conditioning'):
d = sd_model.get_learned_conditioning([""])
return d['crossattn']
else:
return sd_model.cond_stage_model([""])
d = sd_model.cond_stage_model([""])
if isinstance(d, dict):
d = d['crossattn']
return d
def send_model_to_cpu(m):
if m is not None:
if m.lowvram:
lowvram.send_everything_to_cpu()
else:
@ -686,6 +764,25 @@ def send_model_to_trash(m):
devices.torch_gc()
def instantiate_from_config(config, state_dict=None):
constructor = get_obj_from_str(config["target"])
params = {**config.get("params", {})}
if state_dict and "state_dict" in params and params["state_dict"] is None:
params["state_dict"] = state_dict
return constructor(**params)
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
from modules import sd_hijack
checkpoint_info = checkpoint_info or select_checkpoint()
@ -710,7 +807,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
timer.record("find config")
sd_config = OmegaConf.load(checkpoint_config)
repair_config(sd_config)
repair_config(sd_config, state_dict)
timer.record("load config")
@ -720,7 +817,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
try:
with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd or shared.cmd_opts.do_not_download_clip):
with sd_disable_initialization.InitializeOnMeta():
sd_model = instantiate_from_config(sd_config.model)
sd_model = instantiate_from_config(sd_config.model, state_dict)
except Exception as e:
errors.display(e, "creating model quickly", full_traceback=True)
@ -729,7 +826,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
with sd_disable_initialization.InitializeOnMeta():
sd_model = instantiate_from_config(sd_config.model)
sd_model = instantiate_from_config(sd_config.model, state_dict)
sd_model.used_config = checkpoint_config
@ -746,6 +843,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
timer.record("load weights from state dict")
send_model_to_device(sd_model)

16
modules/sd_models_config.py
View File

@ -23,6 +23,8 @@ config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml"
config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
config_sd3 = os.path.join(sd_configs_path, "sd3-inference.yaml")
def is_using_v_parameterization_for_sd2(state_dict):
"""
@ -31,11 +33,11 @@ def is_using_v_parameterization_for_sd2(state_dict):
import ldm.modules.diffusionmodules.openaimodel
device = devices.cpu
device = devices.device
with sd_disable_initialization.DisableInitialization():
unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
use_checkpoint=True,
use_checkpoint=False,
use_fp16=False,
image_size=32,
in_channels=4,
@ -56,12 +58,13 @@ def is_using_v_parameterization_for_sd2(state_dict):
with torch.no_grad():
unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
unet.load_state_dict(unet_sd, strict=True)
unet.to(device=device, dtype=torch.float)
unet.to(device=device, dtype=devices.dtype_unet)
test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
with devices.autocast():
out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().cpu().item()
return out < -1
@ -71,11 +74,15 @@ def guess_model_config_from_state_dict(sd, filename):
diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
if "model.diffusion_model.x_embedder.proj.weight" in sd:
return config_sd3
if sd.get('conditioner.embedders.1.model.ln_final.weight', None) is not None:
if diffusion_model_input.shape[1] == 9:
return config_sdxl_inpainting
else:
return config_sdxl
if sd.get('conditioner.embedders.0.model.ln_final.weight', None) is not None:
return config_sdxl_refiner
elif sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
@ -99,7 +106,6 @@ def guess_model_config_from_state_dict(sd, filename):
if diffusion_model_input.shape[1] == 8:
return config_instruct_pix2pix
if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
return config_alt_diffusion_m18

6
modules/sd_models_types.py
View File

@ -32,3 +32,9 @@ class WebuiSdModel(LatentDiffusion):
is_sd1: bool
"""True if the model's architecture is SD 1.x"""
is_sd3: bool
"""True if the model's architecture is SD 3"""
latent_channels: int
"""number of layer in latent image representation; will be 16 in SD3 and 4 in other version"""

7
modules/sd_models_xl.py
View File

@ -35,10 +35,9 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
sd = self.model.state_dict()
diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
if diffusion_model_input is not None:
if diffusion_model_input.shape[1] == 9:
"""WARNING: This function is called once per denoising iteration. DO NOT add
expensive functionc calls such as `model.state_dict`. """
if self.is_sdxl_inpaint:
x = torch.cat([x] + cond['c_concat'], dim=1)
return self.model(x, t, cond)

13
modules/sd_samplers.py
View File

@ -1,7 +1,7 @@
from __future__ import annotations
import functools
import logging
from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared, sd_samplers_common, sd_schedulers
# imports for functions that previously were here and are used by other modules
@ -98,7 +98,7 @@ def get_hr_scheduler_from_infotext(d: dict):
@functools.cache
def get_sampler_and_scheduler(sampler_name, scheduler_name):
def get_sampler_and_scheduler(sampler_name, scheduler_name, *, convert_automatic=True):
default_sampler = samplers[0]
found_scheduler = sd_schedulers.schedulers_map.get(scheduler_name, sd_schedulers.schedulers[0])
@ -116,10 +116,17 @@ def get_sampler_and_scheduler(sampler_name, scheduler_name):
sampler = all_samplers_map.get(name, default_sampler)
# revert back to Automatic if it's the default scheduler for the selected sampler
if sampler.options.get('scheduler', None) == found_scheduler.name:
if convert_automatic and sampler.options.get('scheduler', None) == found_scheduler.name:
found_scheduler = sd_schedulers.schedulers[0]
return sampler.name, found_scheduler.label
def fix_p_invalid_sampler_and_scheduler(p):
i_sampler_name, i_scheduler = p.sampler_name, p.scheduler
p.sampler_name, p.scheduler = get_sampler_and_scheduler(p.sampler_name, p.scheduler, convert_automatic=False)
if p.sampler_name != i_sampler_name or i_scheduler != p.scheduler:
logging.warning(f'Sampler Scheduler autocorrection: "{i_sampler_name}" -> "{p.sampler_name}", "{i_scheduler}" -> "{p.scheduler}"')
set_samplers()

25
modules/sd_samplers_cfg_denoiser.py
View File

@ -1,5 +1,5 @@
import torch
from modules import prompt_parser, devices, sd_samplers_common
from modules import prompt_parser, sd_samplers_common
from modules.shared import opts, state
import modules.shared as shared
@ -58,6 +58,11 @@ class CFGDenoiser(torch.nn.Module):
self.model_wrap = None
self.p = None
self.cond_scale_miltiplier = 1.0
self.need_last_noise_uncond = False
self.last_noise_uncond = None
# NOTE: masking before denoising can cause the original latents to be oversmoothed
# as the original latents do not have noise
self.mask_before_denoising = False
@ -212,9 +217,16 @@ class CFGDenoiser(torch.nn.Module):
uncond = denoiser_params.text_uncond
skip_uncond = False
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
if shared.opts.skip_early_cond != 0. and self.step / self.total_steps <= shared.opts.skip_early_cond:
skip_uncond = True
self.p.extra_generation_params["Skip Early CFG"] = shared.opts.skip_early_cond
elif (self.step % 2 or shared.opts.s_min_uncond_all) and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
skip_uncond = True
self.p.extra_generation_params["NGMS"] = s_min_uncond
if shared.opts.s_min_uncond_all:
self.p.extra_generation_params["NGMS all steps"] = shared.opts.s_min_uncond_all
if skip_uncond:
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
@ -266,14 +278,15 @@ class CFGDenoiser(torch.nn.Module):
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
cfg_denoised_callback(denoised_params)
devices.test_for_nans(x_out, "unet")
if self.need_last_noise_uncond:
self.last_noise_uncond = torch.clone(x_out[-uncond.shape[0]:])
if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale * self.cond_scale_miltiplier)
elif skip_uncond:
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale * self.cond_scale_miltiplier)
# Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None:

6
modules/sd_samplers_common.py
View File

@ -54,7 +54,7 @@ def samples_to_images_tensor(sample, approximation=None, model=None):
else:
if model is None:
model = shared.sd_model
with devices.without_autocast(): # fixes an issue with unstable VAEs that are flaky even in fp32
with torch.no_grad(), devices.without_autocast(): # fixes an issue with unstable VAEs that are flaky even in fp32
x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
return x_sample
@ -163,7 +163,7 @@ def apply_refiner(cfg_denoiser, sigma=None):
else:
# torch.max(sigma) only to handle rare case where we might have different sigmas in the same batch
try:
timestep = torch.argmin(torch.abs(cfg_denoiser.inner_model.sigmas - torch.max(sigma)))
timestep = torch.argmin(torch.abs(cfg_denoiser.inner_model.sigmas.to(sigma.device) - torch.max(sigma)))
except AttributeError: # for samplers that don't use sigmas (DDIM) sigma is actually the timestep
timestep = torch.max(sigma).to(dtype=int)
completed_ratio = (999 - timestep) / 1000
@ -246,7 +246,7 @@ class Sampler:
self.eta_infotext_field = 'Eta'
self.eta_default = 1.0
self.conditioning_key = shared.sd_model.model.conditioning_key
self.conditioning_key = getattr(shared.sd_model.model, 'conditioning_key', 'crossattn')
self.p = None
self.model_wrap_cfg = None

18
modules/sd_samplers_kdiffusion.py
View File

@ -1,7 +1,7 @@
import torch
import inspect
import k_diffusion.sampling
from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers
from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser, sd_schedulers, devices
from modules.sd_samplers_cfg_denoiser import CFGDenoiser # noqa: F401
from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
@ -53,6 +53,11 @@ class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
@property
def inner_model(self):
if self.model_wrap is None:
denoiser_constructor = getattr(shared.sd_model, 'create_denoiser', None)
if denoiser_constructor is not None:
self.model_wrap = denoiser_constructor()
else:
denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
@ -115,12 +120,16 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
if scheduler.need_inner_model:
sigmas_kwargs['inner_model'] = self.model_wrap
sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=shared.device)
if scheduler.label == 'Beta':
p.extra_generation_params["Beta schedule alpha"] = opts.beta_dist_alpha
p.extra_generation_params["Beta schedule beta"] = opts.beta_dist_beta
sigmas = scheduler.function(n=steps, **sigmas_kwargs, device=devices.cpu)
if discard_next_to_last_sigma:
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
return sigmas
return sigmas.cpu()
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
@ -128,6 +137,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
sigmas = self.get_sigmas(p, steps)
sigma_sched = sigmas[steps - t_enc - 1:]
if hasattr(shared.sd_model, 'add_noise_to_latent'):
xi = shared.sd_model.add_noise_to_latent(x, noise, sigma_sched[0])
else:
xi = x + noise * sigma_sched[0]
if opts.img2img_extra_noise > 0:

1
modules/sd_samplers_timesteps.py
View File

@ -10,6 +10,7 @@ import modules.shared as shared
samplers_timesteps = [
('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
('DDIM CFG++', sd_samplers_timesteps_impl.ddim_cfgpp, ['ddim_cfgpp'], {}),
('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
]

45
modules/sd_samplers_timesteps_impl.py
View File

@ -5,13 +5,14 @@ import numpy as np
from modules import shared
from modules.models.diffusion.uni_pc import uni_pc
from modules.torch_utils import float64
@torch.no_grad()
def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
@ -39,11 +40,51 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
return x
@torch.no_grad()
def ddim_cfgpp(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
""" Implements CFG++: Manifold-constrained Classifier Free Guidance For Diffusion Models (2024).
Uses the unconditional noise prediction instead of the conditional noise to guide the denoising direction.
The CFG scale is divided by 12.5 to map CFG from [0.0, 12.5] to [0, 1.0].
"""
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
model.cond_scale_miltiplier = 1 / 12.5
model.need_last_noise_uncond = True
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones((x.shape[0]))
s_x = x.new_ones((x.shape[0], 1, 1, 1))
for i in tqdm.trange(len(timesteps) - 1, disable=disable):
index = len(timesteps) - 1 - i
e_t = model(x, timesteps[index].item() * s_in, **extra_args)
last_noise_uncond = model.last_noise_uncond
a_t = alphas[index].item() * s_x
a_prev = alphas_prev[index].item() * s_x
sigma_t = sigmas[index].item() * s_x
sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * last_noise_uncond
noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
x = a_prev.sqrt() * pred_x0 + dir_xt + noise
if callback is not None:
callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
return x
@torch.no_grad()
def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
alphas = alphas_cumprod[timesteps]
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' and x.device.type != 'xpu' else torch.float32)
alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(float64(x))
sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
extra_args = {} if extra_args is None else extra_args

108
modules/sd_schedulers.py
View File

@ -1,8 +1,18 @@
import dataclasses
import torch
import k_diffusion
import numpy as np
from scipy import stats
from modules import shared
def to_d(x, sigma, denoised):
"""Converts a denoiser output to a Karras ODE derivative."""
return (x - denoised) / sigma
k_diffusion.sampling.to_d = to_d
@dataclasses.dataclass
@ -17,7 +27,7 @@ class Scheduler:
def uniform(n, sigma_min, sigma_max, inner_model, device):
return inner_model.get_sigmas(n)
return inner_model.get_sigmas(n).to(device)
def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
@ -31,6 +41,92 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
return torch.FloatTensor(sigs).to(device)
def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device):
# https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
def loglinear_interp(t_steps, num_steps):
"""
Performs log-linear interpolation of a given array of decreasing numbers.
"""
xs = np.linspace(0, 1, len(t_steps))
ys = np.log(t_steps[::-1])
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
interped_ys = np.exp(new_ys)[::-1].copy()
return interped_ys
if shared.sd_model.is_sdxl:
sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
else:
# Default to SD 1.5 sigmas.
sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
if n != len(sigmas):
sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
else:
sigmas.append(0.0)
return torch.FloatTensor(sigmas).to(device)
def kl_optimal(n, sigma_min, sigma_max, device):
alpha_min = torch.arctan(torch.tensor(sigma_min, device=device))
alpha_max = torch.arctan(torch.tensor(sigma_max, device=device))
step_indices = torch.arange(n + 1, device=device)
sigmas = torch.tan(step_indices / n * alpha_min + (1.0 - step_indices / n) * alpha_max)
return sigmas
def simple_scheduler(n, sigma_min, sigma_max, inner_model, device):
sigs = []
ss = len(inner_model.sigmas) / n
for x in range(n):
sigs += [float(inner_model.sigmas[-(1 + int(x * ss))])]
sigs += [0.0]
return torch.FloatTensor(sigs).to(device)
def normal_scheduler(n, sigma_min, sigma_max, inner_model, device, sgm=False, floor=False):
start = inner_model.sigma_to_t(torch.tensor(sigma_max))
end = inner_model.sigma_to_t(torch.tensor(sigma_min))
if sgm:
timesteps = torch.linspace(start, end, n + 1)[:-1]
else:
timesteps = torch.linspace(start, end, n)
sigs = []
for x in range(len(timesteps)):
ts = timesteps[x]
sigs.append(inner_model.t_to_sigma(ts))
sigs += [0.0]
return torch.FloatTensor(sigs).to(device)
def ddim_scheduler(n, sigma_min, sigma_max, inner_model, device):
sigs = []
ss = max(len(inner_model.sigmas) // n, 1)
x = 1
while x < len(inner_model.sigmas):
sigs += [float(inner_model.sigmas[x])]
x += ss
sigs = sigs[::-1]
sigs += [0.0]
return torch.FloatTensor(sigs).to(device)
def beta_scheduler(n, sigma_min, sigma_max, inner_model, device):
# From "Beta Sampling is All You Need" [arXiv:2407.12173] (Lee et. al, 2024) """
alpha = shared.opts.beta_dist_alpha
beta = shared.opts.beta_dist_beta
timesteps = 1 - np.linspace(0, 1, n)
timesteps = [stats.beta.ppf(x, alpha, beta) for x in timesteps]
sigmas = [sigma_min + (x * (sigma_max-sigma_min)) for x in timesteps]
sigmas += [0.0]
return torch.FloatTensor(sigmas).to(device)
schedulers = [
Scheduler('automatic', 'Automatic', None),
Scheduler('uniform', 'Uniform', uniform, need_inner_model=True),
@ -38,6 +134,12 @@ schedulers = [
Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
Scheduler('kl_optimal', 'KL Optimal', kl_optimal),
Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
Scheduler('simple', 'Simple', simple_scheduler, need_inner_model=True),
Scheduler('normal', 'Normal', normal_scheduler, need_inner_model=True),
Scheduler('ddim', 'DDIM', ddim_scheduler, need_inner_model=True),
Scheduler('beta', 'Beta', beta_scheduler, need_inner_model=True),
]
schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}

27
modules/sd_vae_approx.py
View File

@ -8,9 +8,9 @@ sd_vae_approx_models = {}
class VAEApprox(nn.Module):
def __init__(self):
def __init__(self, latent_channels=4):
super(VAEApprox, self).__init__()
self.conv1 = nn.Conv2d(4, 8, (7, 7))
self.conv1 = nn.Conv2d(latent_channels, 8, (7, 7))
self.conv2 = nn.Conv2d(8, 16, (5, 5))
self.conv3 = nn.Conv2d(16, 32, (3, 3))
self.conv4 = nn.Conv2d(32, 64, (3, 3))
@ -40,7 +40,13 @@ def download_model(model_path, model_url):
def model():
model_name = "vaeapprox-sdxl.pt" if getattr(shared.sd_model, 'is_sdxl', False) else "model.pt"
if shared.sd_model.is_sd3:
model_name = "vaeapprox-sd3.pt"
elif shared.sd_model.is_sdxl:
model_name = "vaeapprox-sdxl.pt"
else:
model_name = "model.pt"
loaded_model = sd_vae_approx_models.get(model_name)
if loaded_model is None:
@ -52,7 +58,7 @@ def model():
model_path = os.path.join(paths.models_path, "VAE-approx", model_name)
download_model(model_path, 'https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/download/v1.0.0-pre/' + model_name)
loaded_model = VAEApprox()
loaded_model = VAEApprox(latent_channels=shared.sd_model.latent_channels)
loaded_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
loaded_model.eval()
loaded_model.to(devices.device, devices.dtype)
@ -64,7 +70,18 @@ def model():
def cheap_approximation(sample):
# https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/2
if shared.sd_model.is_sdxl:
if shared.sd_model.is_sd3:
coeffs = [
[-0.0645, 0.0177, 0.1052], [ 0.0028, 0.0312, 0.0650],
[ 0.1848, 0.0762, 0.0360], [ 0.0944, 0.0360, 0.0889],
[ 0.0897, 0.0506, -0.0364], [-0.0020, 0.1203, 0.0284],
[ 0.0855, 0.0118, 0.0283], [-0.0539, 0.0658, 0.1047],
[-0.0057, 0.0116, 0.0700], [-0.0412, 0.0281, -0.0039],
[ 0.1106, 0.1171, 0.1220], [-0.0248, 0.0682, -0.0481],
[ 0.0815, 0.0846, 0.1207], [-0.0120, -0.0055, -0.0867],
[-0.0749, -0.0634, -0.0456], [-0.1418, -0.1457, -0.1259],
]
elif shared.sd_model.is_sdxl:
coeffs = [
[ 0.3448, 0.4168, 0.4395],
[-0.1953, -0.0290, 0.0250],

40
modules/sd_vae_taesd.py
View File

@ -34,9 +34,9 @@ class Block(nn.Module):
return self.fuse(self.conv(x) + self.skip(x))
def decoder():
def decoder(latent_channels=4):
return nn.Sequential(
Clamp(), conv(4, 64), nn.ReLU(),
Clamp(), conv(latent_channels, 64), nn.ReLU(),
Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
@ -44,13 +44,13 @@ def decoder():
)
def encoder():
def encoder(latent_channels=4):
return nn.Sequential(
conv(3, 64), Block(64, 64),
conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
conv(64, 4),
conv(64, latent_channels),
)
@ -58,10 +58,14 @@ class TAESDDecoder(nn.Module):
latent_magnitude = 3
latent_shift = 0.5
def __init__(self, decoder_path="taesd_decoder.pth"):
def __init__(self, decoder_path="taesd_decoder.pth", latent_channels=None):
"""Initialize pretrained TAESD on the given device from the given checkpoints."""
super().__init__()
self.decoder = decoder()
if latent_channels is None:
latent_channels = 16 if "taesd3" in str(decoder_path) else 4
self.decoder = decoder(latent_channels)
self.decoder.load_state_dict(
torch.load(decoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
@ -70,10 +74,14 @@ class TAESDEncoder(nn.Module):
latent_magnitude = 3
latent_shift = 0.5
def __init__(self, encoder_path="taesd_encoder.pth"):
def __init__(self, encoder_path="taesd_encoder.pth", latent_channels=None):
"""Initialize pretrained TAESD on the given device from the given checkpoints."""
super().__init__()
self.encoder = encoder()
if latent_channels is None:
latent_channels = 16 if "taesd3" in str(encoder_path) else 4
self.encoder = encoder(latent_channels)
self.encoder.load_state_dict(
torch.load(encoder_path, map_location='cpu' if devices.device.type != 'cuda' else None))
@ -87,7 +95,13 @@ def download_model(model_path, model_url):
def decoder_model():
model_name = "taesdxl_decoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_decoder.pth"
if shared.sd_model.is_sd3:
model_name = "taesd3_decoder.pth"
elif shared.sd_model.is_sdxl:
model_name = "taesdxl_decoder.pth"
else:
model_name = "taesd_decoder.pth"
loaded_model = sd_vae_taesd_models.get(model_name)
if loaded_model is None:
@ -106,7 +120,13 @@ def decoder_model():
def encoder_model():
model_name = "taesdxl_encoder.pth" if getattr(shared.sd_model, 'is_sdxl', False) else "taesd_encoder.pth"
if shared.sd_model.is_sd3:
model_name = "taesd3_encoder.pth"
elif shared.sd_model.is_sdxl:
model_name = "taesdxl_encoder.pth"
else:
model_name = "taesd_encoder.pth"
loaded_model = sd_vae_taesd_models.get(model_name)
if loaded_model is None:

2
modules/shared.py
View File

@ -47,7 +47,7 @@ restricted_opts: set[str] = None
sd_model: sd_models_types.WebuiSdModel = None
settings_components: dict = None
"""assigned from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
"""assigned from ui.py, a mapping on setting names to gradio components responsible for those settings"""
tab_names = []

41
modules/shared_gradio_themes.py
View File

@ -69,3 +69,44 @@ def reload_gradio_theme(theme_name=None):
# append additional values gradio_theme
shared.gradio_theme.sd_webui_modal_lightbox_toolbar_opacity = shared.opts.sd_webui_modal_lightbox_toolbar_opacity
shared.gradio_theme.sd_webui_modal_lightbox_icon_opacity = shared.opts.sd_webui_modal_lightbox_icon_opacity
def resolve_var(name: str, gradio_theme=None, history=None):
"""
Attempt to resolve a theme variable name to its value
Parameters:
name (str): The name of the theme variable
ie "background_fill_primary", "background_fill_primary_dark"
spaces and asterisk (*) prefix is removed from name before lookup
gradio_theme (gradio.themes.ThemeClass): The theme object to resolve the variable from
blank to use the webui default shared.gradio_theme
history (list): A list of previously resolved variables to prevent circular references
for regular use leave blank
Returns:
str: The resolved value
Error handling:
return either #000000 or #ffffff depending on initial name ending with "_dark"
"""
try:
if history is None:
history = []
if gradio_theme is None:
gradio_theme = shared.gradio_theme
name = name.strip()
name = name[1:] if name.startswith("*") else name
if name in history:
raise ValueError(f'Circular references: name "{name}" in {history}')
if value := getattr(gradio_theme, name, None):
return resolve_var(value, gradio_theme, history + [name])
else:
return name
except Exception:
name = history[0] if history else name
errors.report(f'resolve_color({name})', exc_info=True)
return '#000000' if name.endswith("_dark") else '#ffffff'

8
modules/shared_init.py
View File

@ -31,6 +31,14 @@ def initialize():
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
if cmd_opts.precision == "half":
msg = "--no-half and --no-half-vae conflict with --precision half"
assert devices.dtype == torch.float16, msg
assert devices.dtype_vae == torch.float16, msg
assert devices.dtype_inference == torch.float16, msg
devices.force_fp16 = True
devices.force_model_fp16()
shared.device = devices.device
shared.weight_load_location = None if cmd_opts.lowram else "cpu"

34
modules/shared_options.py
View File

@ -54,7 +54,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg and avif images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
"export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
@ -64,6 +64,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"save_write_log_csv": OptionInfo(True, "Write log.csv when saving images using 'Save' button"),
"save_init_img": OptionInfo(False, "Save init images when using img2img"),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
@ -129,6 +130,22 @@ options_templates.update(options_section(('system', "System", "system"), {
"dump_stacks_on_signal": OptionInfo(False, "Print stack traces before exiting the program with ctrl+c."),
}))
options_templates.update(options_section(('profiler', "Profiler", "system"), {
"profiling_explanation": OptionHTML("""
Those settings allow you to enable torch profiler when generating pictures.
Profiling allows you to see which code uses how much of computer's resources during generation.
Each generation writes its own profile to one file, overwriting previous.
The file can be viewed in <a href="chrome:tracing">Chrome</a>, or on a <a href="https://ui.perfetto.dev/">Perfetto</a> web site.
Warning: writing profile can take a lot of time, up to 30 seconds, and the file itelf can be around 500MB in size.
"""),
"profiling_enable": OptionInfo(False, "Enable profiling"),
"profiling_activities": OptionInfo(["CPU"], "Activities", gr.CheckboxGroup, {"choices": ["CPU", "CUDA"]}),
"profiling_record_shapes": OptionInfo(True, "Record shapes"),
"profiling_profile_memory": OptionInfo(True, "Profile memory"),
"profiling_with_stack": OptionInfo(True, "Include python stack"),
"profiling_filename": OptionInfo("trace.json", "Profile filename"),
}))
options_templates.update(options_section(('API', "API", "system"), {
"api_enable_requests": OptionInfo(True, "Allow http:// and https:// URLs for input images in API", restrict_api=True),
"api_forbid_local_requests": OptionInfo(True, "Forbid URLs to local resources", restrict_api=True),
@ -160,6 +177,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion", "sd"), {
"emphasis": OptionInfo("Original", "Emphasis mode", gr.Radio, lambda: {"choices": [x.name for x in sd_emphasis.options]}, infotext="Emphasis").info("makes it possible to make model to pay (more:1.1) or (less:0.9) attention to text when you use the syntax in prompt; " + sd_emphasis.get_options_descriptions()),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"sdxl_clip_l_skip": OptionInfo(False, "Clip skip SDXL", gr.Checkbox).info("Enable Clip skip for the secondary clip model in sdxl. Has no effect on SD 1.5 or SD 2.0/2.1."),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}, infotext="Clip skip").link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}, infotext="RNG").info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
@ -174,6 +192,10 @@ options_templates.update(options_section(('sdxl', "Stable Diffusion XL", "sd"),
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))
options_templates.update(options_section(('sd3', "Stable Diffusion 3", "sd"), {
"sd3_enable_t5": OptionInfo(False, "Enable T5").info("load T5 text encoder; increases VRAM use by a lot, potentially improving quality of generation; requires model reload to apply"),
}))
options_templates.update(options_section(('vae', "VAE", "sd"), {
"sd_vae_explanation": OptionHTML("""
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
@ -209,7 +231,8 @@ options_templates.update(options_section(('img2img', "img2img", "sd"), {
options_templates.update(options_section(('optimizations', "Optimizations", "sd"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}, infotext='NGMS').link("PR", "https://github.com/AUTOMATIC1111/stablediffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"s_min_uncond_all": OptionInfo(False, "Negative Guidance minimum sigma all steps", infotext='NGMS all steps').info("By default, NGMS above skips every other step; this makes it skip all steps"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio').link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}, infotext='Token merging ratio hr').info("only applies if non-zero and overrides above"),
@ -227,7 +250,6 @@ options_templates.update(options_section(('compatibility', "Compatibility", "sd"
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
"use_old_scheduling": OptionInfo(False, "Use old prompt editing timelines.", infotext="Old prompt editing timelines").info("For [red:green:N]; old: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"),
"use_downcasted_alpha_bar": OptionInfo(False, "Downcast model alphas_cumprod to fp16 before sampling. For reproducing old seeds.", infotext="Downcast alphas_cumprod"),
@ -359,6 +381,7 @@ options_templates.update(options_section(('ui', "Live previews", "ui"), {
"live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
"live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
"js_live_preview_in_modal_lightbox": OptionInfo(False, "Show Live preview in full page image viewer"),
"prevent_screen_sleep_during_generation": OptionInfo(True, "Prevent screen sleep during generation"),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters", "sd"), {
@ -380,7 +403,10 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}, infotext='UniPC skip type'),
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}, infotext='UniPC order').info("must be < sampling steps"),
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final", infotext='UniPC lower order final'),
'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models")
'sd_noise_schedule': OptionInfo("Default", "Noise schedule for sampling", gr.Radio, {"choices": ["Default", "Zero Terminal SNR"]}, infotext="Noise Schedule").info("for use with zero terminal SNR trained models"),
'skip_early_cond': OptionInfo(0.0, "Ignore negative prompt during early sampling", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext="Skip Early CFG").info("disables CFG on a proportion of steps at the beginning of generation; 0=skip none; 1=skip all; can both improve sample diversity/quality and speed up sampling"),
'beta_dist_alpha': OptionInfo(0.6, "Beta scheduler - alpha", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler alpha').info('Default = 0.6; the alpha parameter of the beta distribution used in Beta sampling'),
'beta_dist_beta': OptionInfo(0.6, "Beta scheduler - beta", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Beta scheduler beta').info('Default = 0.6; the beta parameter of the beta distribution used in Beta sampling'),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing", "postprocessing"), {

2
modules/shared_state.py
View File

@ -162,7 +162,7 @@ class State:
errors.record_exception()
def assign_current_image(self, image):
if shared.opts.live_previews_image_format == 'jpeg' and image.mode == 'RGBA':
if shared.opts.live_previews_image_format == 'jpeg' and image.mode in ('RGBA', 'P'):
image = image.convert('RGB')
self.current_image = image
self.id_live_preview += 1

109
modules/sysinfo.py
View File

@ -1,15 +1,13 @@
import json
import os
import sys
import subprocess
import platform
import hashlib
import pkg_resources
import psutil
import re
from pathlib import Path
import launch
from modules import paths_internal, timer, shared, extensions, errors
from modules import paths_internal, timer, shared_cmd_options, errors, launch_utils
checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
environment_whitelist = {
@ -69,14 +67,46 @@ def check(x):
return h.hexdigest() == m.group(1)
def get_dict():
ram = psutil.virtual_memory()
def get_cpu_info():
cpu_info = {"model": platform.processor()}
try:
import psutil
cpu_info["count logical"] = psutil.cpu_count(logical=True)
cpu_info["count physical"] = psutil.cpu_count(logical=False)
except Exception as e:
cpu_info["error"] = str(e)
return cpu_info
def get_ram_info():
try:
import psutil
ram = psutil.virtual_memory()
return {x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0}
except Exception as e:
return str(e)
def get_packages():
try:
return subprocess.check_output([sys.executable, '-m', 'pip', 'freeze', '--all']).decode("utf8").splitlines()
except Exception as pip_error:
try:
import importlib.metadata
packages = importlib.metadata.distributions()
return sorted([f"{package.metadata['Name']}=={package.version}" for package in packages])
except Exception as e2:
return {'error pip': pip_error, 'error importlib': str(e2)}
def get_dict():
config = get_config()
res = {
"Platform": platform.platform(),
"Python": platform.python_version(),
"Version": launch.git_tag(),
"Commit": launch.commit_hash(),
"Version": launch_utils.git_tag(),
"Commit": launch_utils.commit_hash(),
"Git status": git_status(paths_internal.script_path),
"Script path": paths_internal.script_path,
"Data path": paths_internal.data_path,
"Extensions dir": paths_internal.extensions_dir,
@ -84,20 +114,14 @@ def get_dict():
"Commandline": get_argv(),
"Torch env info": get_torch_sysinfo(),
"Exceptions": errors.get_exceptions(),
"CPU": {
"model": platform.processor(),
"count logical": psutil.cpu_count(logical=True),
"count physical": psutil.cpu_count(logical=False),
},
"RAM": {
x: pretty_bytes(getattr(ram, x, 0)) for x in ["total", "used", "free", "active", "inactive", "buffers", "cached", "shared"] if getattr(ram, x, 0) != 0
},
"Extensions": get_extensions(enabled=True),
"Inactive extensions": get_extensions(enabled=False),
"CPU": get_cpu_info(),
"RAM": get_ram_info(),
"Extensions": get_extensions(enabled=True, fallback_disabled_extensions=config.get('disabled_extensions', [])),
"Inactive extensions": get_extensions(enabled=False, fallback_disabled_extensions=config.get('disabled_extensions', [])),
"Environment": get_environment(),
"Config": get_config(),
"Config": config,
"Startup": timer.startup_record,
"Packages": sorted([f"{pkg.key}=={pkg.version}" for pkg in pkg_resources.working_set]),
"Packages": get_packages(),
}
return res
@ -111,11 +135,11 @@ def get_argv():
res = []
for v in sys.argv:
if shared.cmd_opts.gradio_auth and shared.cmd_opts.gradio_auth == v:
if shared_cmd_options.cmd_opts.gradio_auth and shared_cmd_options.cmd_opts.gradio_auth == v:
res.append("<hidden>")
continue
if shared.cmd_opts.api_auth and shared.cmd_opts.api_auth == v:
if shared_cmd_options.cmd_opts.api_auth and shared_cmd_options.cmd_opts.api_auth == v:
res.append("<hidden>")
continue
@ -123,6 +147,7 @@ def get_argv():
return res
re_newline = re.compile(r"\r*\n")
@ -136,25 +161,55 @@ def get_torch_sysinfo():
return str(e)
def get_extensions(*, enabled):
def run_git(path, *args):
try:
return subprocess.check_output([launch_utils.git, '-C', path, *args], shell=False, encoding='utf8').strip()
except Exception as e:
return str(e)
def git_status(path):
if (Path(path) / '.git').is_dir():
return run_git(paths_internal.script_path, 'status')
def get_info_from_repo_path(path: Path):
is_repo = (path / '.git').is_dir()
return {
'name': path.name,
'path': str(path),
'commit': run_git(path, 'rev-parse', 'HEAD') if is_repo else None,
'branch': run_git(path, 'branch', '--show-current') if is_repo else None,
'remote': run_git(path, 'remote', 'get-url', 'origin') if is_repo else None,
}
def get_extensions(*, enabled, fallback_disabled_extensions=None):
try:
from modules import extensions
if extensions.extensions:
def to_json(x: extensions.Extension):
return {
"name": x.name,
"path": x.path,
"version": x.version,
"commit": x.commit_hash,
"branch": x.branch,
"remote": x.remote,
}
return [to_json(x) for x in extensions.extensions if not x.is_builtin and x.enabled == enabled]
else:
return [get_info_from_repo_path(d) for d in Path(paths_internal.extensions_dir).iterdir() if d.is_dir() and enabled != (str(d.name) in fallback_disabled_extensions)]
except Exception as e:
return str(e)
def get_config():
try:
from modules import shared
return shared.opts.data
except Exception as _:
try:
with open(shared_cmd_options.cmd_opts.ui_settings_file, 'r') as f:
return json.load(f)
except Exception as e:
return str(e)

4
modules/textual_inversion/textual_inversion.py
View File

@ -181,12 +181,16 @@ class EmbeddingDatabase:
else:
return
if data is not None:
embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
if self.expected_shape == -1 or self.expected_shape == embedding.shape:
self.register_embedding(embedding, shared.sd_model)
else:
self.skipped_embeddings[name] = embedding
else:
print(f"Unable to load Textual inversion embedding due to data issue: '{name}'.")
def load_from_dir(self, embdir):
if not os.path.isdir(embdir.path):

8
modules/torch_utils.py
View File

@ -1,6 +1,7 @@
from __future__ import annotations
import torch.nn
import torch
def get_param(model) -> torch.nn.Parameter:
@ -15,3 +16,10 @@ def get_param(model) -> torch.nn.Parameter:
return param
raise ValueError(f"No parameters found in model {model!r}")
def float64(t: torch.Tensor):
"""return torch.float64 if device is not mps or xpu, else return torch.float32"""
if t.device.type in ['mps', 'xpu']:
return torch.float32
return torch.float64

23
modules/ui.py
View File

@ -10,7 +10,7 @@ import gradio as gr
import gradio.utils
import numpy as np
from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call, wrap_gradio_call_no_job # noqa: F401
from modules import gradio_extensons, sd_schedulers # noqa: F401
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
@ -38,9 +38,11 @@ warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
mimetypes.init()
mimetypes.add_type('application/javascript', '.js')
mimetypes.add_type('application/javascript', '.mjs')
# Likewise, add explicit content-type header for certain missing image types
mimetypes.add_type('image/webp', '.webp')
mimetypes.add_type('image/avif', '.avif')
if not cmd_opts.share and not cmd_opts.listen:
# fix gradio phoning home
@ -566,6 +568,11 @@ def create_ui():
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
with gr.Tabs(elem_id="img2img_batch_source"):
img2img_batch_source_type = gr.Textbox(visible=False, value="upload")
with gr.TabItem('Upload', id='batch_upload', elem_id="img2img_batch_upload_tab") as tab_batch_upload:
img2img_batch_upload = gr.Files(label="Files", interactive=True, elem_id="img2img_batch_upload")
with gr.TabItem('From directory', id='batch_from_dir', elem_id="img2img_batch_from_dir_tab") as tab_batch_from_dir:
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
gr.HTML(
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
@ -576,8 +583,10 @@ def create_ui():
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
tab_batch_upload.select(fn=lambda: "upload", inputs=[], outputs=[img2img_batch_source_type])
tab_batch_from_dir.select(fn=lambda: "from dir", inputs=[], outputs=[img2img_batch_source_type])
with gr.Accordion("PNG info", open=False):
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", **shared.hide_dirs, elem_id="img2img_batch_use_png_info")
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", elem_id="img2img_batch_use_png_info")
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
@ -613,8 +622,8 @@ def create_ui():
with gr.Column(elem_id="img2img_column_size", scale=4):
selected_scale_tab = gr.Number(value=0, visible=False)
with gr.Tabs():
with gr.Tab(label="Resize to", elem_id="img2img_tab_resize_to") as tab_scale_to:
with gr.Tabs(elem_id="img2img_tabs_resize"):
with gr.Tab(label="Resize to", id="to", elem_id="img2img_tab_resize_to") as tab_scale_to:
with FormRow():
with gr.Column(elem_id="img2img_column_size", scale=4):
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
@ -623,7 +632,7 @@ def create_ui():
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
with gr.Tab(label="Resize by", elem_id="img2img_tab_resize_by") as tab_scale_by:
with gr.Tab(label="Resize by", id="by", elem_id="img2img_tab_resize_by") as tab_scale_by:
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
with FormRow():
@ -759,6 +768,8 @@ def create_ui():
img2img_batch_use_png_info,
img2img_batch_png_info_props,
img2img_batch_png_info_dir,
img2img_batch_source_type,
img2img_batch_upload,
] + custom_inputs,
outputs=[
output_panel.gallery,
@ -878,7 +889,7 @@ def create_ui():
))
image.change(
fn=wrap_gradio_call(modules.extras.run_pnginfo),
fn=wrap_gradio_call_no_job(modules.extras.run_pnginfo),
inputs=[image],
outputs=[html, generation_info, html2],
)

11
modules/ui_common.py
View File

@ -3,6 +3,7 @@ import dataclasses
import json
import html
import os
from contextlib import nullcontext
import gradio as gr
@ -103,10 +104,11 @@ def save_files(js_data, images, do_make_zip, index):
# NOTE: ensure csv integrity when fields are added by
# updating headers and padding with delimiters where needed
if os.path.exists(logfile_path):
if shared.opts.save_write_log_csv and os.path.exists(logfile_path):
update_logfile(logfile_path, fields)
with open(logfile_path, "a", encoding="utf8", newline='') as file:
with (open(logfile_path, "a", encoding="utf8", newline='') if shared.opts.save_write_log_csv else nullcontext()) as file:
if file:
at_start = file.tell() == 0
writer = csv.writer(file)
if at_start:
@ -130,6 +132,7 @@ def save_files(js_data, images, do_make_zip, index):
filenames.append(os.path.basename(txt_fullfn))
fullfns.append(txt_fullfn)
if file:
writer.writerow([parsed_infotexts[0]['Prompt'], parsed_infotexts[0]['Seed'], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], parsed_infotexts[0]['Negative prompt'], data["sd_model_name"], data["sd_model_hash"]])
# Make Zip
@ -228,7 +231,7 @@ def create_output_panel(tabname, outdir, toprow=None):
)
save.click(
fn=call_queue.wrap_gradio_call(save_files),
fn=call_queue.wrap_gradio_call_no_job(save_files),
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
inputs=[
res.generation_info,
@ -244,7 +247,7 @@ def create_output_panel(tabname, outdir, toprow=None):
)
save_zip.click(
fn=call_queue.wrap_gradio_call(save_files),
fn=call_queue.wrap_gradio_call_no_job(save_files),
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
inputs=[
res.generation_info,

76
modules/ui_extensions.py
View File

@ -396,15 +396,15 @@ def install_extension_from_url(dirname, url, branch_name=None):
shutil.rmtree(tmpdir, True)
def install_extension_from_index(url, hide_tags, sort_column, filter_text):
def install_extension_from_index(url, selected_tags, showing_type, filtering_type, sort_column, filter_text):
ext_table, message = install_extension_from_url(None, url)
code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
code, _ = refresh_available_extensions_from_data(selected_tags, showing_type, filtering_type, sort_column, filter_text)
return code, ext_table, message, ''
def refresh_available_extensions(url, hide_tags, sort_column):
def refresh_available_extensions(url, selected_tags, showing_type, filtering_type, sort_column):
global available_extensions
import urllib.request
@ -413,19 +413,19 @@ def refresh_available_extensions(url, hide_tags, sort_column):
available_extensions = json.loads(text)
code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)
code, tags = refresh_available_extensions_from_data(selected_tags, showing_type, filtering_type, sort_column)
return url, code, gr.CheckboxGroup.update(choices=tags), '', ''
def refresh_available_extensions_for_tags(hide_tags, sort_column, filter_text):
code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
def refresh_available_extensions_for_tags(selected_tags, showing_type, filtering_type, sort_column, filter_text):
code, _ = refresh_available_extensions_from_data(selected_tags, showing_type, filtering_type, sort_column, filter_text)
return code, ''
def search_extensions(filter_text, hide_tags, sort_column):
code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
def search_extensions(filter_text, selected_tags, showing_type, filtering_type, sort_column):
code, _ = refresh_available_extensions_from_data(selected_tags, showing_type, filtering_type, sort_column, filter_text)
return code, ''
@ -450,13 +450,13 @@ def get_date(info: dict, key):
return ''
def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
def refresh_available_extensions_from_data(selected_tags, showing_type, filtering_type, sort_column, filter_text=""):
extlist = available_extensions["extensions"]
installed_extensions = {extension.name for extension in extensions.extensions}
installed_extension_urls = {normalize_git_url(extension.remote) for extension in extensions.extensions if extension.remote is not None}
tags = available_extensions.get("tags", {})
tags_to_hide = set(hide_tags)
selected_tags = set(selected_tags)
hidden = 0
code = f"""<!-- {time.time()} -->
@ -489,7 +489,17 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
existing = get_extension_dirname_from_url(url) in installed_extensions or normalize_git_url(url) in installed_extension_urls
extension_tags = extension_tags + ["installed"] if existing else extension_tags
if any(x for x in extension_tags if x in tags_to_hide):
if len(selected_tags) > 0:
matched_tags = [x for x in extension_tags if x in selected_tags]
if filtering_type == 'or':
need_hide = len(matched_tags) > 0
else:
need_hide = len(matched_tags) == len(selected_tags)
if showing_type == 'show':
need_hide = not need_hide
if need_hide:
hidden += 1
continue
@ -594,8 +604,12 @@ def create_ui():
install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
with gr.Row():
hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index")
selected_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Extension tags", choices=["script", "ads", "localization", "installed"], elem_classes=['compact-checkbox-group'])
sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order",'update time', 'create time', "stars"], type="index", elem_classes=['compact-checkbox-group'])
with gr.Row():
showing_type = gr.Radio(value="hide", label="Showing type", choices=["hide", "show"], elem_classes=['compact-checkbox-group'])
filtering_type = gr.Radio(value="or", label="Filtering type", choices=["or", "and"], elem_classes=['compact-checkbox-group'])
with gr.Row():
search_extensions_text = gr.Text(label="Search", container=False)
@ -605,31 +619,43 @@ def create_ui():
refresh_available_extensions_button.click(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update(), gr.update()]),
inputs=[available_extensions_index, hide_tags, sort_column],
outputs=[available_extensions_index, available_extensions_table, hide_tags, search_extensions_text, install_result],
inputs=[available_extensions_index, selected_tags, showing_type, filtering_type, sort_column],
outputs=[available_extensions_index, available_extensions_table, selected_tags, search_extensions_text, install_result],
)
install_extension_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
inputs=[extension_to_install, hide_tags, sort_column, search_extensions_text],
fn=modules.ui.wrap_gradio_call_no_job(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
inputs=[extension_to_install, selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
outputs=[available_extensions_table, extensions_table, install_result],
)
search_extensions_text.change(
fn=modules.ui.wrap_gradio_call(search_extensions, extra_outputs=[gr.update()]),
inputs=[search_extensions_text, hide_tags, sort_column],
fn=modules.ui.wrap_gradio_call_no_job(search_extensions, extra_outputs=[gr.update()]),
inputs=[search_extensions_text, selected_tags, showing_type, filtering_type, sort_column],
outputs=[available_extensions_table, install_result],
)
hide_tags.change(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
inputs=[hide_tags, sort_column, search_extensions_text],
selected_tags.change(
fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
outputs=[available_extensions_table, install_result]
)
showing_type.change(
fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
outputs=[available_extensions_table, install_result]
)
filtering_type.change(
fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
outputs=[available_extensions_table, install_result]
)
sort_column.change(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
inputs=[hide_tags, sort_column, search_extensions_text],
fn=modules.ui.wrap_gradio_call_no_job(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
inputs=[selected_tags, showing_type, filtering_type, sort_column, search_extensions_text],
outputs=[available_extensions_table, install_result]
)
@ -641,7 +667,7 @@ def create_ui():
install_result = gr.HTML(elem_id="extension_install_result")
install_button.click(
fn=modules.ui.wrap_gradio_call(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
fn=modules.ui.wrap_gradio_call_no_job(lambda *args: [gr.update(), *install_extension_from_url(*args)], extra_outputs=[gr.update(), gr.update()]),
inputs=[install_dirname, install_url, install_branch],
outputs=[install_url, extensions_table, install_result],
)

2
modules/ui_extra_networks_user_metadata.py
View File

@ -194,7 +194,7 @@ class UserMetadataEditor:
def setup_ui(self, gallery):
self.button_replace_preview.click(
fn=self.save_preview,
_js="function(x, y, z){return [selected_gallery_index(), y, z]}",
_js=f"function(x, y, z){{return [selected_gallery_index_id('{self.tabname + '_gallery_container'}'), y, z]}}",
inputs=[self.edit_name_input, gallery, self.edit_name_input],
outputs=[self.html_preview, self.html_status]
).then(

7
modules/ui_gradio_extensions.py
View File

@ -41,6 +41,11 @@ def css_html():
if os.path.exists(user_css):
head += stylesheet(user_css)
from modules.shared_gradio_themes import resolve_var
light = resolve_var('background_fill_primary')
dark = resolve_var('background_fill_primary_dark')
head += f'<style>html {{ background-color: {light}; }} @media (prefers-color-scheme: dark) {{ html {{background-color: {dark}; }} }}</style>'
return head
@ -50,7 +55,7 @@ def reload_javascript():
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
res.body = res.body.replace(b'</head>', f'{js}<meta name="referrer" content="no-referrer"/></head>'.encode("utf8"))
res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
res.init_headers()
return res

4
modules/ui_settings.py
View File

@ -1,7 +1,7 @@
import gradio as gr
from modules import ui_common, shared, script_callbacks, scripts, sd_models, sysinfo, timer, shared_items
from modules.call_queue import wrap_gradio_call
from modules.call_queue import wrap_gradio_call_no_job
from modules.options import options_section
from modules.shared import opts
from modules.ui_components import FormRow
@ -295,7 +295,7 @@ class UiSettings:
def add_functionality(self, demo):
self.submit.click(
fn=wrap_gradio_call(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
fn=wrap_gradio_call_no_job(lambda *args: self.run_settings(*args), extra_outputs=[gr.update()]),
inputs=self.components,
outputs=[self.text_settings, self.result],
)

4
modules/upscaler.py
View File

@ -56,8 +56,8 @@ class Upscaler:
dest_w = int((img.width * scale) // 8 * 8)
dest_h = int((img.height * scale) // 8 * 8)
for _ in range(3):
if img.width >= dest_w and img.height >= dest_h and scale != 1:
for i in range(3):
if img.width >= dest_w and img.height >= dest_h and (i > 0 or scale != 1):
break
if shared.state.interrupted:

4
modules/util.py
View File

@ -156,7 +156,7 @@ class MassFileLister:
def topological_sort(dependencies):
"""Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
Ignores errors relating to missing dependeencies or circular dependencies
Ignores errors relating to missing dependencies or circular dependencies
"""
visited = {}
@ -208,6 +208,6 @@ Requested path was: {path}
elif platform.system() == "Darwin":
subprocess.Popen(["open", path])
elif "microsoft-standard-WSL2" in platform.uname().release:
subprocess.Popen(["wsl-open", path])
subprocess.Popen(["explorer.exe", subprocess.check_output(["wslpath", "-w", path])])
else:
subprocess.Popen(["xdg-open", path])

1
requirements.txt
View File

@ -18,6 +18,7 @@ omegaconf
open-clip-torch
piexif
protobuf==3.20.0
psutil
pytorch_lightning
requests

4
requirements_versions.txt
View File

@ -18,12 +18,14 @@ numpy==1.26.2
omegaconf==2.2.3
open-clip-torch==2.20.0
piexif==1.1.3
protobuf==3.20.0
psutil==5.9.5
pytorch_lightning==1.9.4
resize-right==0.0.2
safetensors==0.4.2
scikit-image==0.21.0
spandrel==0.1.6
spandrel==0.3.4
spandrel-extra-arches==0.1.1
tomesd==0.1.3
torch
torchdiffeq==0.2.3

15
script.js
View File

@ -29,6 +29,7 @@ var uiAfterUpdateCallbacks = [];
var uiLoadedCallbacks = [];
var uiTabChangeCallbacks = [];
var optionsChangedCallbacks = [];
var optionsAvailableCallbacks = [];
var uiAfterUpdateTimeout = null;
var uiCurrentTab = null;
@ -77,6 +78,20 @@ function onOptionsChanged(callback) {
optionsChangedCallbacks.push(callback);
}
/**
* Register callback to be called when the options (in opts global variable) are available.
* The callback receives no arguments.
* If you register the callback after the options are available, it's just immediately called.
*/
function onOptionsAvailable(callback) {
if (Object.keys(opts).length != 0) {
callback();
return;
}
optionsAvailableCallbacks.push(callback);
}
function executeCallbacks(queue, arg) {
for (const callback of queue) {
try {

59
scripts/xyz_grid.py
View File

@ -95,15 +95,15 @@ def confirm_checkpoints_or_none(p, xs):
raise RuntimeError(f"Unknown checkpoint: {x}")
def apply_clip_skip(p, x, xs):
opts.data["CLIP_stop_at_last_layers"] = x
def confirm_range(min_val, max_val, axis_label):
"""Generates a AxisOption.confirm() function that checks all values are within the specified range."""
def confirm_range_fun(p, xs):
for x in xs:
if not (max_val >= x >= min_val):
raise ValueError(f'{axis_label} value "{x}" out of range [{min_val}, {max_val}]')
def apply_upscale_latent_space(p, x, xs):
if x.lower().strip() != '0':
opts.data["use_scale_latent_for_hires_fix"] = True
else:
opts.data["use_scale_latent_for_hires_fix"] = False
return confirm_range_fun
def apply_size(p, x: str, xs) -> None:
@ -118,21 +118,15 @@ def apply_size(p, x: str, xs) -> None:
def find_vae(name: str):
if name.lower() in ['auto', 'automatic']:
return modules.sd_vae.unspecified
if name.lower() == 'none':
return None
else:
choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]
if len(choices) == 0:
print(f"No VAE found for {name}; using automatic")
return modules.sd_vae.unspecified
else:
return modules.sd_vae.vae_dict[choices[0]]
if (name := name.strip().lower()) in ('auto', 'automatic'):
return 'Automatic'
elif name == 'none':
return 'None'
return next((k for k in modules.sd_vae.vae_dict if k.lower() == name), print(f'No VAE found for {name}; using Automatic') or 'Automatic')
def apply_vae(p, x, xs):
modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))
p.override_settings['sd_vae'] = find_vae(x)
def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
@ -140,7 +134,7 @@ def apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):
def apply_uni_pc_order(p, x, xs):
opts.data["uni_pc_order"] = min(x, p.steps - 1)
p.override_settings['uni_pc_order'] = min(x, p.steps - 1)
def apply_face_restore(p, opt, x):
@ -162,12 +156,14 @@ def apply_override(field, boolean: bool = False):
if boolean:
x = True if x.lower() == "true" else False
p.override_settings[field] = x
return fun
def boolean_choice(reverse: bool = False):
def choice():
return ["False", "True"] if reverse else ["True", "False"]
return choice
@ -212,7 +208,7 @@ def list_to_csv_string(data_list):
def csv_string_to_list_strip(data_str):
return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str)))))
return list(map(str.strip, chain.from_iterable(csv.reader(StringIO(data_str), skipinitialspace=True))))
class AxisOption:
@ -263,14 +259,16 @@ axis_options = [
AxisOption("Schedule min sigma", float, apply_override("sigma_min")),
AxisOption("Schedule max sigma", float, apply_override("sigma_max")),
AxisOption("Schedule rho", float, apply_override("rho")),
AxisOption("Beta schedule alpha", float, apply_override("beta_dist_alpha")),
AxisOption("Beta schedule beta", float, apply_override("beta_dist_beta")),
AxisOption("Eta", float, apply_field("eta")),
AxisOption("Clip skip", int, apply_clip_skip),
AxisOption("Clip skip", int, apply_override('CLIP_stop_at_last_layers')),
AxisOption("Denoising", float, apply_field("denoising_strength")),
AxisOption("Initial noise multiplier", float, apply_field("initial_noise_multiplier")),
AxisOption("Extra noise", float, apply_override("img2img_extra_noise")),
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['Automatic', 'None'] + list(sd_vae.vae_dict)),
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
@ -378,16 +376,17 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
end_index = start_index + len(xs) * len(ys)
grid = images.image_grid(processed_result.images[start_index:end_index], rows=len(ys))
if draw_legend:
grid = images.draw_grid_annotations(grid, processed_result.images[start_index].size[0], processed_result.images[start_index].size[1], hor_texts, ver_texts, margin_size)
grid_max_w, grid_max_h = map(max, zip(*(img.size for img in processed_result.images[start_index:end_index])))
grid = images.draw_grid_annotations(grid, grid_max_w, grid_max_h, hor_texts, ver_texts, margin_size)
processed_result.images.insert(i, grid)
processed_result.all_prompts.insert(i, processed_result.all_prompts[start_index])
processed_result.all_seeds.insert(i, processed_result.all_seeds[start_index])
processed_result.infotexts.insert(i, processed_result.infotexts[start_index])
sub_grid_size = processed_result.images[0].size
z_grid = images.image_grid(processed_result.images[:z_count], rows=1)
z_sub_grid_max_w, z_sub_grid_max_h = map(max, zip(*(img.size for img in processed_result.images[:z_count])))
if draw_legend:
z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
z_grid = images.draw_grid_annotations(z_grid, z_sub_grid_max_w, z_sub_grid_max_h, title_texts, [[images.GridAnnotation()]])
processed_result.images.insert(0, z_grid)
# TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
# processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
@ -399,18 +398,12 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
class SharedSettingsStackHelper(object):
def __enter__(self):
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
self.vae = opts.sd_vae
self.uni_pc_order = opts.uni_pc_order
pass
def __exit__(self, exc_type, exc_value, tb):
opts.data["sd_vae"] = self.vae
opts.data["uni_pc_order"] = self.uni_pc_order
modules.sd_models.reload_model_weights()
modules.sd_vae.reload_vae_weights()
opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")

20
style.css
View File

@ -1,6 +1,6 @@
/* temporary fix to load default gradio font in frontend instead of backend */
@import url('/webui-assets/css/sourcesanspro.css');
@import url('webui-assets/css/sourcesanspro.css');
/* temporary fix to hide gradio crop tool until it's fixed https://github.com/gradio-app/gradio/issues/3810 */
@ -279,7 +279,7 @@ input[type="checkbox"].input-accordion-checkbox{
display: inline-block;
}
.html-log .performance p.time, .performance p.vram, .performance p.time abbr, .performance p.vram abbr {
.html-log .performance p.time, .performance p.vram, .performance p.profile, .performance p.time abbr, .performance p.vram abbr {
margin-bottom: 0;
color: var(--block-title-text-color);
}
@ -291,6 +291,10 @@ input[type="checkbox"].input-accordion-checkbox{
margin-left: auto;
}
.html-log .performance p.profile {
margin-left: 0.5em;
}
.html-log .performance .measurement{
color: var(--body-text-color);
font-weight: bold;
@ -780,9 +784,9 @@ table.popup-table .link{
position:absolute;
display:block;
padding:0px 0;
border:2px solid #a55000;
border:2px solid var(--primary-800);
border-radius:8px;
box-shadow:1px 1px 2px #CE6400;
box-shadow:1px 1px 2px var(--primary-500);
width: 200px;
}
@ -799,7 +803,7 @@ table.popup-table .link{
}
.context-menu-items a:hover{
background: #a55000;
background: var(--primary-700);
}
@ -807,6 +811,8 @@ table.popup-table .link{
#tab_extensions table{
border-collapse: collapse;
overflow-x: auto;
display: block;
}
#tab_extensions table td, #tab_extensions table th{
@ -854,6 +860,10 @@ table.popup-table .link{
display: inline-block;
}
.compact-checkbox-group div label {
padding: 0.1em 0.3em !important;
}
/* extensions tab table row hover highlight */
#extensions tr:hover td,

12
webui-macos-env.sh
View File

@ -4,14 +4,14 @@
# Please modify webui-user.sh to change these instead of this file #
####################################################################
if [[ -x "$(command -v python3.10)" ]]
then
python_cmd="python3.10"
fi
export install_dir="$HOME"
export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
export TORCH_COMMAND="pip install torch==2.1.0 torchvision==0.16.0"
export PYTORCH_ENABLE_MPS_FALLBACK=1
if [[ "$(sysctl -n machdep.cpu.brand_string)" =~ ^.*"Intel".*$ ]]; then
export TORCH_COMMAND="pip install torch==2.1.2 torchvision==0.16.2"
else
export TORCH_COMMAND="pip install torch==2.3.1 torchvision==0.18.1"
fi
####################################################################

8
webui.bat
View File

@ -37,12 +37,18 @@ if %ERRORLEVEL% == 0 goto :activate_venv
for /f "delims=" %%i in ('CALL %PYTHON% -c "import sys; print(sys.executable)"') do set PYTHON_FULLNAME="%%i"
echo Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%
%PYTHON_FULLNAME% -m venv "%VENV_DIR%" >tmp/stdout.txt 2>tmp/stderr.txt
if %ERRORLEVEL% == 0 goto :activate_venv
if %ERRORLEVEL% == 0 goto :upgrade_pip
echo Unable to create venv in directory "%VENV_DIR%"
goto :show_stdout_stderr
:upgrade_pip
"%VENV_DIR%\Scripts\Python.exe" -m pip install --upgrade pip
if %ERRORLEVEL% == 0 goto :activate_venv
echo Warning: Failed to upgrade PIP version
:activate_venv
set PYTHON="%VENV_DIR%\Scripts\Python.exe"
call "%VENV_DIR%\Scripts\activate.bat"
echo venv %PYTHON%
:skip_venv

7
webui.sh
View File

@ -43,6 +43,10 @@ fi
# python3 executable
if [[ -z "${python_cmd}" ]]
then
python_cmd="python3.10"
fi
if [[ ! -x "$(command -v "${python_cmd}")" ]]
then
python_cmd="python3"
fi
@ -210,12 +214,15 @@ then
if [[ ! -d "${venv_dir}" ]]
then
"${python_cmd}" -m venv "${venv_dir}"
"${venv_dir}"/bin/python -m pip install --upgrade pip
first_launch=1
fi
# shellcheck source=/dev/null
if [[ -f "${venv_dir}"/bin/activate ]]
then
source "${venv_dir}"/bin/activate
# ensure use of python from venv
python_cmd="${venv_dir}"/bin/python
else
printf "\n%s\n" "${delimiter}"
printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"