diff options
70 files changed, 1866 insertions, 537 deletions
@@ -32,4 +32,5 @@ notification.mp3 /extensions /test/stdout.txt /test/stderr.txt -/cache.json +/cache.json* +/config_states/ @@ -13,9 +13,9 @@ A browser interface based on Gradio library for Stable Diffusion. - Prompt Matrix
- Stable Diffusion Upscale
- Attention, specify parts of text that the model should pay more attention to
- - a man in a ((tuxedo)) - will pay more attention to tuxedo
- - a man in a (tuxedo:1.21) - alternative syntax
- - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
+ - a man in a `((tuxedo))` - will pay more attention to tuxedo
+ - a man in a `(tuxedo:1.21)` - alternative syntax
+ - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
- Loopback, run img2img processing multiple times
- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
- Textual Inversion
@@ -28,7 +28,7 @@ A browser interface based on Gradio library for Stable Diffusion. - CodeFormer, face restoration tool as an alternative to GFPGAN
- RealESRGAN, neural network upscaler
- ESRGAN, neural network upscaler with a lot of third party models
- - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+ - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
- LDSR, Latent diffusion super resolution upscaling
- Resizing aspect ratio options
- Sampling method selection
@@ -46,7 +46,7 @@ A browser interface based on Gradio library for Stable Diffusion. - drag and drop an image/text-parameters to promptbox
- Read Generation Parameters Button, loads parameters in promptbox to UI
- Settings page
-- Running arbitrary python code from UI (must run with --allow-code to enable)
+- Running arbitrary python code from UI (must run with `--allow-code` to enable)
- Mouseover hints for most UI elements
- Possible to change defaults/mix/max/step values for UI elements via text config
- Tiling support, a checkbox to create images that can be tiled like textures
@@ -69,7 +69,7 @@ A browser interface based on Gradio library for Stable Diffusion. - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
- DeepDanbooru integration, creates danbooru style tags for anime prompts
-- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
- Generate forever option
- Training tab
@@ -78,11 +78,11 @@ A browser interface based on Gradio library for Stable Diffusion. - Clip skip
- Hypernetworks
- Loras (same as Hypernetworks but more pretty)
-- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt.
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
- Can select to load a different VAE from settings screen
- Estimated completion time in progress bar
- API
-- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
@@ -91,7 +91,6 @@ A browser interface based on Gradio library for Stable Diffusion. - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
- Now with a license!
- Reorder elements in the UI from settings screen
--
## Installation and Running
Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -101,7 +100,7 @@ Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
### Automatic Installation on Windows
-1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
+1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
2. Install [git](https://git-scm.com/download/win).
3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
@@ -116,11 +115,12 @@ sudo dnf install wget git python3 # Arch-based:
sudo pacman -S wget git python3
```
-2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
+2. Navigate to the directory you would like the webui to be installed and execute the following command:
```bash
bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
```
3. Run `webui.sh`.
+4. Check `webui-user.sh` for options.
### Installation on Apple Silicon
Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
diff --git a/environment-wsl2.yaml b/environment-wsl2.yaml index f8872750..0c4ae680 100644 --- a/environment-wsl2.yaml +++ b/environment-wsl2.yaml @@ -4,8 +4,8 @@ channels: - defaults dependencies: - python=3.10 - - pip=22.2.2 - - cudatoolkit=11.3 - - pytorch=1.12.1 - - torchvision=0.13.1 - - numpy=1.23.1
\ No newline at end of file + - pip=23.0 + - cudatoolkit=11.8 + - pytorch=2.0 + - torchvision=0.15 + - numpy=1.23 diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py index b8cff29b..da19cff1 100644 --- a/extensions-builtin/LDSR/scripts/ldsr_model.py +++ b/extensions-builtin/LDSR/scripts/ldsr_model.py @@ -25,22 +25,28 @@ class UpscalerLDSR(Upscaler): yaml_path = os.path.join(self.model_path, "project.yaml") old_model_path = os.path.join(self.model_path, "model.pth") new_model_path = os.path.join(self.model_path, "model.ckpt") - safetensors_model_path = os.path.join(self.model_path, "model.safetensors") + + local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"]) + local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None) + local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None) + local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None) + if os.path.exists(yaml_path): statinfo = os.stat(yaml_path) if statinfo.st_size >= 10485760: print("Removing invalid LDSR YAML file.") os.remove(yaml_path) + if os.path.exists(old_model_path): print("Renaming model from model.pth to model.ckpt") os.rename(old_model_path, new_model_path) - if os.path.exists(safetensors_model_path): - model = safetensors_model_path + + if local_safetensors_path is not None and os.path.exists(local_safetensors_path): + model = local_safetensors_path else: - model = load_file_from_url(url=self.model_url, model_dir=self.model_path, - file_name="model.ckpt", progress=True) - yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path, - file_name="project.yaml", progress=True) + model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True) + + yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True) try: return LDSR(model, yaml) diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index 6be6ef73..45f899fc 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -8,7 +8,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): def activate(self, p, params_list):
additional = shared.opts.sd_lora
- if additional != "" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+ if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 7c371deb..d3eb0d3b 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -2,20 +2,34 @@ import glob import os
import re
import torch
+from typing import Union
from modules import shared, devices, sd_models, errors
metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
re_digits = re.compile(r"\d+")
-re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
-re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+ "attentions": {},
+ "resnets": {
+ "conv1": "in_layers_2",
+ "conv2": "out_layers_3",
+ "time_emb_proj": "emb_layers_1",
+ "conv_shortcut": "skip_connection",
+ }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+ def match(match_list, regex_text):
+ regex = re_compiled.get(regex_text)
+ if regex is None:
+ regex = re.compile(regex_text)
+ re_compiled[regex_text] = regex
-
-def convert_diffusers_name_to_compvis(key):
- def match(match_list, regex):
r = re.match(regex, key)
if not r:
return False
@@ -26,16 +40,33 @@ def convert_diffusers_name_to_compvis(key): m = []
- if match(m, re_unet_down_blocks):
- return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
- if match(m, re_unet_mid_blocks):
- return f"diffusion_model_middle_block_1_{m[1]}"
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
- if match(m, re_unet_up_blocks):
- return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+ if is_sd2:
+ if 'mlp_fc1' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+ elif 'mlp_fc2' in m[1]:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+ else:
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
- if match(m, re_text_block):
return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
return key
@@ -101,15 +132,22 @@ def load_lora(name, filename): sd = sd_models.read_state_dict(filename)
- keys_failed_to_match = []
+ keys_failed_to_match = {}
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
for key_diffusers, weight in sd.items():
- fullkey = convert_diffusers_name_to_compvis(key_diffusers)
- key, lora_key = fullkey.split(".", 1)
+ key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
+ key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+
if sd_module is None:
- keys_failed_to_match.append(key_diffusers)
+ m = re_x_proj.match(key)
+ if m:
+ sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
+
+ if sd_module is None:
+ keys_failed_to_match[key_diffusers] = key
continue
lora_module = lora.modules.get(key, None)
@@ -123,15 +161,21 @@ def load_lora(name, filename): if type(sd_module) == torch.nn.Linear:
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+ elif type(sd_module) == torch.nn.MultiheadAttention:
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
elif type(sd_module) == torch.nn.Conv2d:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
else:
+ print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
+ continue
assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
with torch.no_grad():
module.weight.copy_(weight)
- module.to(device=devices.device, dtype=devices.dtype)
+ module.to(device=devices.cpu, dtype=devices.dtype)
if lora_key == "lora_up.weight":
lora_module.up = module
@@ -177,29 +221,120 @@ def load_loras(names, multipliers=None): loaded_loras.append(lora)
-def lora_forward(module, input, res):
- input = devices.cond_cast_unet(input)
- if len(loaded_loras) == 0:
- return res
+def lora_calc_updown(lora, module, target):
+ with torch.no_grad():
+ up = module.up.weight.to(target.device, dtype=target.dtype)
+ down = module.down.weight.to(target.device, dtype=target.dtype)
- lora_layer_name = getattr(module, 'lora_layer_name', None)
- for lora in loaded_loras:
- module = lora.modules.get(lora_layer_name, None)
- if module is not None:
- if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
- res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
+ updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+ else:
+ updown = up @ down
+
+ updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+ return updown
+
+
+def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+ """
+ Applies the currently selected set of Loras to the weights of torch layer self.
+ If weights already have this particular set of loras applied, does nothing.
+ If not, restores orginal weights from backup and alters weights according to loras.
+ """
+
+ lora_layer_name = getattr(self, 'lora_layer_name', None)
+ if lora_layer_name is None:
+ return
+
+ current_names = getattr(self, "lora_current_names", ())
+ wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
+
+ weights_backup = getattr(self, "lora_weights_backup", None)
+ if weights_backup is None:
+ 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))
+ else:
+ weights_backup = self.weight.to(devices.cpu, copy=True)
+
+ self.lora_weights_backup = weights_backup
+
+ if current_names != wanted_names:
+ 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])
else:
- res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ self.weight.copy_(weights_backup)
- return res
+ for lora in loaded_loras:
+ module = lora.modules.get(lora_layer_name, None)
+ if module is not None and hasattr(self, 'weight'):
+ self.weight += lora_calc_updown(lora, module, self.weight)
+ continue
+
+ module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
+ module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
+ module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
+ module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
+
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+ updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
+ updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
+ updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+
+ self.in_proj_weight += updown_qkv
+ self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
+ continue
+
+ if module is None:
+ continue
+
+ print(f'failed to calculate lora weights for layer {lora_layer_name}')
+
+ setattr(self, "lora_current_names", wanted_names)
+
+
+def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+ setattr(self, "lora_current_names", ())
+ setattr(self, "lora_weights_backup", None)
def lora_Linear_forward(self, input):
- return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Linear_forward_before_lora(self, input)
+
+
+def lora_Linear_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
def lora_Conv2d_forward(self, input):
- return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Conv2d_forward_before_lora(self, input)
+
+
+def lora_Conv2d_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_forward(self, *args, **kwargs):
+ lora_apply_weights(self)
+
+ return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+ lora_reset_cached_weight(self)
+
+ return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
def list_available_loras():
@@ -212,7 +347,7 @@ def list_available_loras(): glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
- for filename in sorted(candidates):
+ for filename in sorted(candidates, key=str.lower):
if os.path.isdir(filename):
continue
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 2e860160..3fc38ab9 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -9,7 +9,11 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared def unload():
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+ torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+ torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
+ torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
+ torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
def before_ui():
@@ -20,11 +24,27 @@ def before_ui(): if not hasattr(torch.nn, 'Linear_forward_before_lora'):
torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+ torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+ torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
+ torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
+ torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+
torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
@@ -32,7 +52,5 @@ script_callbacks.on_before_ui(before_ui) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
- "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
- "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
-
+ "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
}))
diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index e0fbf3a3..c7fd5739 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -5,11 +5,15 @@ import traceback import PIL.Image import numpy as np import torch +from tqdm import tqdm + from basicsr.utils.download_util import load_file_from_url import modules.upscaler from modules import devices, modelloader from scunet_model_arch import SCUNet as net +from modules.shared import opts +from modules import images class UpscalerScuNET(modules.upscaler.Upscaler): @@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers.append(scaler_data2) self.scalers = scalers - def do_upscale(self, img: PIL.Image, selected_file): + @staticmethod + @torch.no_grad() + def tiled_inference(img, model): + # test the image tile by tile + h, w = img.shape[2:] + tile = opts.SCUNET_tile + tile_overlap = opts.SCUNET_tile_overlap + if tile == 0: + return model(img) + + device = devices.get_device_for('scunet') + assert tile % 8 == 0, "tile size should be a multiple of window_size" + sf = 1 + + stride = tile - tile_overlap + h_idx_list = list(range(0, h - tile, stride)) + [h - tile] + w_idx_list = list(range(0, w - tile, stride)) + [w - tile] + E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device) + W = torch.zeros_like(E, dtype=devices.dtype, device=device) + + with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar: + for h_idx in h_idx_list: + + for w_idx in w_idx_list: + + in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile] + + out_patch = model(in_patch) + out_patch_mask = torch.ones_like(out_patch) + + E[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch) + W[ + ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf + ].add_(out_patch_mask) + pbar.update(1) + output = E.div_(W) + + return output + + def do_upscale(self, img: PIL.Image.Image, selected_file): + torch.cuda.empty_cache() model = self.load_model(selected_file) if model is None: + print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) return img device = devices.get_device_for('scunet') - img = np.array(img) - img = img[:, :, ::-1] - img = np.moveaxis(img, 2, 0) / 255 - img = torch.from_numpy(img).float() - img = img.unsqueeze(0).to(device) - - with torch.no_grad(): - output = model(img) - output = output.squeeze().float().cpu().clamp_(0, 1).numpy() - output = 255. * np.moveaxis(output, 0, 2) - output = output.astype(np.uint8) - output = output[:, :, ::-1] + tile = opts.SCUNET_tile + h, w = img.height, img.width + np_img = np.array(img) + np_img = np_img[:, :, ::-1] # RGB to BGR + np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW + torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore + + if tile > h or tile > w: + _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device) + _img[:, :, :h, :w] = torch_img # pad image + torch_img = _img + + torch_output = self.tiled_inference(torch_img, model).squeeze(0) + torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any + np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() + del torch_img, torch_output torch.cuda.empty_cache() - return PIL.Image.fromarray(output, 'RGB') + + output = np_output.transpose((1, 2, 0)) # CHW to HWC + output = output[:, :, ::-1] # BGR to RGB + return PIL.Image.fromarray((output * 255).astype(np.uint8)) def load_model(self, path: str): device = devices.get_device_for('scunet') @@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler): model = model.to(device) return model - diff --git a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js index f0918e26..5c7a836a 100644 --- a/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js +++ b/extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js @@ -1,103 +1,42 @@ // Stable Diffusion WebUI - Bracket checker -// Version 1.0 -// By Hingashi no Florin/Bwin4L +// By Hingashi no Florin/Bwin4L & @akx // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs. // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong. -function checkBrackets(evt, textArea, counterElt) { - errorStringParen = '(...) - Different number of opening and closing parentheses detected.\n'; - errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n'; - errorStringCurly = '{...} - Different number of opening and closing curly brackets detected.\n'; - - openBracketRegExp = /\(/g; - closeBracketRegExp = /\)/g; - - openSquareBracketRegExp = /\[/g; - closeSquareBracketRegExp = /\]/g; - - openCurlyBracketRegExp = /\{/g; - closeCurlyBracketRegExp = /\}/g; - - totalOpenBracketMatches = 0; - totalCloseBracketMatches = 0; - totalOpenSquareBracketMatches = 0; - totalCloseSquareBracketMatches = 0; - totalOpenCurlyBracketMatches = 0; - totalCloseCurlyBracketMatches = 0; - - openBracketMatches = textArea.value.match(openBracketRegExp); - if(openBracketMatches) { - totalOpenBracketMatches = openBracketMatches.length; - } - - closeBracketMatches = textArea.value.match(closeBracketRegExp); - if(closeBracketMatches) { - totalCloseBracketMatches = closeBracketMatches.length; - } - - openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp); - if(openSquareBracketMatches) { - totalOpenSquareBracketMatches = openSquareBracketMatches.length; - } - - closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp); - if(closeSquareBracketMatches) { - totalCloseSquareBracketMatches = closeSquareBracketMatches.length; - } - - openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp); - if(openCurlyBracketMatches) { - totalOpenCurlyBracketMatches = openCurlyBracketMatches.length; - } - - closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp); - if(closeCurlyBracketMatches) { - totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length; - } - - if(totalOpenBracketMatches != totalCloseBracketMatches) { - if(!counterElt.title.includes(errorStringParen)) { - counterElt.title += errorStringParen; - } - } else { - counterElt.title = counterElt.title.replace(errorStringParen, ''); - } - - if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) { - if(!counterElt.title.includes(errorStringSquare)) { - counterElt.title += errorStringSquare; - } - } else { - counterElt.title = counterElt.title.replace(errorStringSquare, ''); - } - - if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) { - if(!counterElt.title.includes(errorStringCurly)) { - counterElt.title += errorStringCurly; +function checkBrackets(textArea, counterElt) { + var counts = {}; + (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => { + counts[bracket] = (counts[bracket] || 0) + 1; + }); + var errors = []; + + function checkPair(open, close, kind) { + if (counts[open] !== counts[close]) { + errors.push( + `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.` + ); } - } else { - counterElt.title = counterElt.title.replace(errorStringCurly, ''); } - if(counterElt.title != '') { - counterElt.classList.add('error'); - } else { - counterElt.classList.remove('error'); - } + checkPair('(', ')', 'round brackets'); + checkPair('[', ']', 'square brackets'); + checkPair('{', '}', 'curly brackets'); + counterElt.title = errors.join('\n'); + counterElt.classList.toggle('error', errors.length !== 0); } -function setupBracketChecking(id_prompt, id_counter){ - var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); - var counter = gradioApp().getElementById(id_counter) +function setupBracketChecking(id_prompt, id_counter) { + var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea"); + var counter = gradioApp().getElementById(id_counter) - textarea.addEventListener("input", function(evt){ - checkBrackets(evt, textarea, counter) - }); + if (textarea && counter) { + textarea.addEventListener("input", () => checkBrackets(textarea, counter)); + } } -onUiLoaded(function(){ - setupBracketChecking('txt2img_prompt', 'txt2img_token_counter') - setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter') - setupBracketChecking('img2img_prompt', 'img2img_token_counter') - setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter') -})
\ No newline at end of file +onUiLoaded(function () { + setupBracketChecking('txt2img_prompt', 'txt2img_token_counter'); + setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter'); + setupBracketChecking('img2img_prompt', 'img2img_token_counter'); + setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter'); +}); diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index 0f164b82..a8278cca 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -12,7 +12,7 @@ function dimensionChange(e, is_width, is_height){ currentHeight = e.target.value*1.0
}
- var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
+ var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
if(!inImg2img){
return;
@@ -22,7 +22,7 @@ function dimensionChange(e, is_width, is_height){ var tabIndex = get_tab_index('mode_img2img')
if(tabIndex == 0){ // img2img
- targetElement = gradioApp().querySelector('div[data-testid=image] img');
+ targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
} else if(tabIndex == 1){ //Sketch
targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
} else if(tabIndex == 2){ // Inpaint
@@ -30,7 +30,7 @@ function dimensionChange(e, is_width, is_height){ } else if(tabIndex == 3){ // Inpaint sketch
targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
}
-
+
if(targetElement){
@@ -38,7 +38,7 @@ function dimensionChange(e, is_width, is_height){ if(!arPreviewRect){
arPreviewRect = document.createElement('div')
arPreviewRect.id = "imageARPreview";
- gradioApp().getRootNode().appendChild(arPreviewRect)
+ gradioApp().appendChild(arPreviewRect)
}
@@ -91,23 +91,26 @@ onUiUpdate(function(){ if(arPreviewRect){
arPreviewRect.style.display = 'none';
}
- var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
- if(inImg2img){
- let inputs = gradioApp().querySelectorAll('input');
- inputs.forEach(function(e){
- var is_width = e.parentElement.id == "img2img_width"
- var is_height = e.parentElement.id == "img2img_height"
-
- if((is_width || is_height) && !e.classList.contains('scrollwatch')){
- e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
- e.classList.add('scrollwatch')
- }
- if(is_width){
- currentWidth = e.value*1.0
- }
- if(is_height){
- currentHeight = e.value*1.0
- }
- })
- }
+ var tabImg2img = gradioApp().querySelector("#tab_img2img");
+ if (tabImg2img) {
+ var inImg2img = tabImg2img.style.display == "block";
+ if(inImg2img){
+ let inputs = gradioApp().querySelectorAll('input');
+ inputs.forEach(function(e){
+ var is_width = e.parentElement.id == "img2img_width"
+ var is_height = e.parentElement.id == "img2img_height"
+
+ if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+ e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+ e.classList.add('scrollwatch')
+ }
+ if(is_width){
+ currentWidth = e.value*1.0
+ }
+ if(is_height){
+ currentHeight = e.value*1.0
+ }
+ })
+ }
+ }
});
diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js index 06f505b0..9468c107 100644 --- a/javascript/contextMenus.js +++ b/javascript/contextMenus.js @@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2]; appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
- appendContextMenuOption('#roll','Roll three',
- function(){
- let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
- setTimeout(function(){rollbutton.click()},100)
- setTimeout(function(){rollbutton.click()},200)
- setTimeout(function(){rollbutton.click()},300)
- }
- )
})();
//End example Context Menu Items
diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 20a5aadf..588c7b77 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -17,7 +17,7 @@ function keyupEditAttention(event){ // Find opening parenthesis around current cursor
const before = text.substring(0, selectionStart);
let beforeParen = before.lastIndexOf(OPEN);
- if (beforeParen == -1) return false;
+ if (beforeParen == -1) return false;
let beforeParenClose = before.lastIndexOf(CLOSE);
while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
@@ -27,7 +27,7 @@ function keyupEditAttention(event){ // Find closing parenthesis around current cursor
const after = text.substring(selectionStart);
let afterParen = after.indexOf(CLOSE);
- if (afterParen == -1) return false;
+ if (afterParen == -1) return false;
let afterParenOpen = after.indexOf(OPEN);
while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
afterParen = after.indexOf(CLOSE, afterParen + 1);
@@ -43,10 +43,28 @@ function keyupEditAttention(event){ target.setSelectionRange(selectionStart, selectionEnd);
return true;
}
+
+ function selectCurrentWord(){
+ if (selectionStart !== selectionEnd) return false;
+ const delimiters = opts.keyedit_delimiters + " \r\n\t";
+
+ // seek backward until to find beggining
+ while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+ selectionStart--;
+ }
+
+ // seek forward to find end
+ while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+ selectionEnd++;
+ }
- // If the user hasn't selected anything, let's select their current parenthesis block
- if(! selectCurrentParenthesisBlock('<', '>')){
- selectCurrentParenthesisBlock('(', ')')
+ target.setSelectionRange(selectionStart, selectionEnd);
+ return true;
+ }
+
+ // If the user hasn't selected anything, let's select their current parenthesis block or word
+ if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+ selectCurrentWord();
}
event.preventDefault();
@@ -81,7 +99,13 @@ function keyupEditAttention(event){ weight = parseFloat(weight.toPrecision(12));
if(String(weight).length == 1) weight += ".0"
- text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+ if (closeCharacter == ')' && weight == 1) {
+ text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+ selectionStart--;
+ selectionEnd--;
+ } else {
+ text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+ }
target.focus();
target.value = text;
@@ -93,4 +117,4 @@ function keyupEditAttention(event){ addEventListener('keydown', (event) => {
keyupEditAttention(event);
-});
\ No newline at end of file +});
diff --git a/javascript/extensions.js b/javascript/extensions.js index c593cd2e..3c2f995a 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -1,5 +1,5 @@ -function extensions_apply(_, _){
+function extensions_apply(_, _, disable_all){
var disable = []
var update = []
@@ -13,10 +13,10 @@ function extensions_apply(_, _){ restart_reload()
- return [JSON.stringify(disable), JSON.stringify(update)]
+ return [JSON.stringify(disable), JSON.stringify(update), disable_all]
}
-function extensions_check(){
+function extensions_check(_, _){
var disable = []
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
@@ -47,3 +47,25 @@ function install_extension_from_index(button, url){ gradioApp().querySelector('#install_extension_button').click()
}
+
+function config_state_confirm_restore(_, config_state_name, config_restore_type) {
+ if (config_state_name == "Current") {
+ return [false, config_state_name, config_restore_type];
+ }
+ let restored = "";
+ if (config_restore_type == "extensions") {
+ restored = "all saved extension versions";
+ } else if (config_restore_type == "webui") {
+ restored = "the webui version";
+ } else {
+ restored = "the webui version and all saved extension versions";
+ }
+ let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
+ if (confirmed) {
+ restart_reload();
+ gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+ x.innerHTML = "Loading..."
+ })
+ }
+ return [confirmed, config_state_name, config_restore_type];
+}
diff --git a/javascript/generationParams.js b/javascript/generationParams.js index 95f05093..1266a266 100644 --- a/javascript/generationParams.js +++ b/javascript/generationParams.js @@ -16,9 +16,9 @@ onUiUpdate(function(){ let modalObserver = new MutationObserver(function(mutations) { mutations.forEach(function(mutationRecord) { - let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText - if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img') - gradioApp().getElementById(selectedTab+"_generation_info_button").click() + let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText + if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img')) + gradioApp().getElementById(selectedTab+"_generation_info_button")?.click() }); }); diff --git a/javascript/hints.js b/javascript/hints.js index b3f3d08d..fa023585 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -21,8 +21,7 @@ titles = { "\u{1f5d1}\ufe0f": "Clear prompt", "\u{1f4cb}": "Apply selected styles to current prompt", "\u{1f4d2}": "Paste available values into the field", - "\u{1f3b4}": "Show extra networks", - + "\u{1f3b4}": "Show/hide extra networks", "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt", "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back", @@ -66,8 +65,8 @@ titles = { "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.", - "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.", - "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.", + "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.", + "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg],[prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.", "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle", "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.", @@ -86,7 +85,6 @@ titles = { "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).", "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.", - "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.", "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.", @@ -112,7 +110,8 @@ titles = { "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.", "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.", - "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited." + "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.", + "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction." } diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js index 7547e771..3deffa9b 100644 --- a/javascript/imageviewer.js +++ b/javascript/imageviewer.js @@ -32,13 +32,7 @@ function negmod(n, m) { function updateOnBackgroundChange() { const modalImage = gradioApp().getElementById("modalImage") if (modalImage && modalImage.offsetParent) { - let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2") - let currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) + let currentButton = selected_gallery_button(); if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { modalImage.src = currentButton.children[0].src; @@ -50,22 +44,10 @@ function updateOnBackgroundChange() { } function modalImageSwitch(offset) { - var allgalleryButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item") - var galleryButtons = [] - allgalleryButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - galleryButtons.push(elem); - } - }) + var galleryButtons = all_gallery_buttons(); if (galleryButtons.length > 1) { - var allcurrentButtons = gradioApp().querySelectorAll(".gradio-gallery .thumbnail-item.selected") - var currentButton = null - allcurrentButtons.forEach(function(elem) { - if (elem.parentElement.offsetParent) { - currentButton = elem; - } - }) + var currentButton = selected_gallery_button(); var result = -1 galleryButtons.forEach(function(v, i) { @@ -269,8 +251,11 @@ document.addEventListener("DOMContentLoaded", function() { modal.appendChild(modalNext) - gradioApp().appendChild(modal) - + try { + gradioApp().appendChild(modal); + } catch (e) { + gradioApp().body.appendChild(modal); + } document.body.appendChild(modal); diff --git a/javascript/notification.js b/javascript/notification.js index 5ae6df24..8ddd4c5d 100644 --- a/javascript/notification.js +++ b/javascript/notification.js @@ -15,7 +15,7 @@ onUiUpdate(function(){ } } - const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] img.h-full.w-full.overflow-hidden'); + const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img'); if (galleryPreviews == null) return; diff --git a/javascript/progressbar.js b/javascript/progressbar.js index 4ac9b8db..8df3f569 100644 --- a/javascript/progressbar.js +++ b/javascript/progressbar.js @@ -138,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre return } - if(elapsedFromStart > 5 && !res.queued && !res.active){ + if(elapsedFromStart > 40 && !res.queued && !res.active){ removeProgressBar() return } diff --git a/javascript/ui.js b/javascript/ui.js index fcaf5608..dc538231 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -7,9 +7,31 @@ function set_theme(theme){ } } +function all_gallery_buttons() { + var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small'); + var visibleGalleryButtons = []; + allGalleryButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleGalleryButtons.push(elem); + } + }) + return visibleGalleryButtons; +} + +function selected_gallery_button() { + var allCurrentButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected'); + var visibleCurrentButton = null; + allCurrentButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleCurrentButton = elem; + } + }) + return visibleCurrentButton; +} + function selected_gallery_index(){ - var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item') - var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2') + var buttons = all_gallery_buttons(); + var button = selected_gallery_button(); var result = -1 buttons.forEach(function(v, i){ if(v==button) { result = i } }) @@ -18,14 +40,18 @@ function selected_gallery_index(){ } function extract_image_from_gallery(gallery){ - if(gallery.length == 1){ - return [gallery[0]] + if (gallery.length == 0){ + return [null]; + } + if (gallery.length == 1){ + return [gallery[0]]; } index = selected_gallery_index() if (index < 0 || index >= gallery.length){ - return [null] + // Use the first image in the gallery as the default + index = 0; } return [gallery[index]]; @@ -335,3 +361,8 @@ function selectCheckpoint(name){ desiredCheckpointName = name; gradioApp().getElementById('change_checkpoint').click() } + +function currentImg2imgSourceResolution(_, _, scaleBy){ + var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img') + return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy] +} @@ -19,7 +19,6 @@ python = sys.executable git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
stored_commit_hash = None
-skip_install = False
dir_repos = "repositories"
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
@@ -49,7 +48,7 @@ or any other error regarding unsuccessful package (library) installation, please downgrade (or upgrade) to the latest version of 3.10 Python
and delete current Python and "venv" folder in WebUI's directory.
-You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3109/
+You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
@@ -121,12 +120,12 @@ def run_python(code, desc=None, errdesc=None): return run(f'"{python}" -c "{code}"', desc, errdesc)
-def run_pip(args, desc=None):
- if skip_install:
+def run_pip(command, desc=None, live=False):
+ if args.skip_install:
return
index_url_line = f' --index-url {index_url}' if index_url != '' else ''
- return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+ return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)
def check_run_python(code):
@@ -206,6 +205,10 @@ def list_extensions(settings_file): print(e, file=sys.stderr)
disabled_extensions = set(settings.get('disabled_extensions', []))
+ disable_all_extensions = settings.get('disable_all_extensions', 'none')
+
+ if disable_all_extensions != 'none':
+ return []
return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
@@ -219,12 +222,10 @@ def run_extensions_installers(settings_file): def prepare_environment():
- global skip_install
-
- torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
+ torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
- xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.16rc425')
+ xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -235,7 +236,7 @@ def prepare_environment(): codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
- stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
+ stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
@@ -267,7 +268,7 @@ def prepare_environment(): if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
if platform.system() == "Windows":
if platform.python_version().startswith("3.10"):
- run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+ run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
else:
print("Installation of xformers is not supported in this version of Python.")
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
@@ -292,7 +293,7 @@ def prepare_environment(): if not os.path.isfile(requirements_file):
requirements_file = os.path.join(script_path, requirements_file)
- run_pip(f"install -r \"{requirements_file}\"", "requirements for Web UI")
+ run_pip(f"install -r \"{requirements_file}\"", "requirements")
run_extensions_installers(settings_file=args.ui_settings_file)
diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th Binary files differnew file mode 100644 index 00000000..a6a06e94 --- /dev/null +++ b/models/karlo/ViT-L-14_stats.th diff --git a/modules/api/api.py b/modules/api/api.py index 13af9ed6..9ffcbd5f 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -3,9 +3,9 @@ import io import time import datetime import uvicorn +import gradio as gr from threading import Lock from io import BytesIO -from gradio.processing_utils import decode_base64_to_file from fastapi import APIRouter, Depends, FastAPI, Request, Response from fastapi.security import HTTPBasic, HTTPBasicCredentials from fastapi.exceptions import HTTPException @@ -197,6 +197,9 @@ class Api: self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"]) self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList) + self.default_script_arg_txt2img = [] + self.default_script_arg_img2img = [] + def add_api_route(self, path: str, endpoint, **kwargs): if shared.cmd_opts.api_auth: return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) @@ -230,7 +233,7 @@ class Api: script_idx = script_name_to_index(script_name, script_runner.scripts) return script_runner.scripts[script_idx] - def init_script_args(self, request, selectable_scripts, selectable_idx, script_runner): + def init_default_script_args(self, script_runner): #find max idx from the scripts in runner and generate a none array to init script_args last_arg_index = 1 for script in script_runner.scripts: @@ -238,13 +241,24 @@ class Api: last_arg_index = script.args_to # None everywhere except position 0 to initialize script args script_args = [None]*last_arg_index + script_args[0] = 0 + + # get default values + with gr.Blocks(): # will throw errors calling ui function without this + for script in script_runner.scripts: + if script.ui(script.is_img2img): + ui_default_values = [] + for elem in script.ui(script.is_img2img): + ui_default_values.append(elem.value) + script_args[script.args_from:script.args_to] = ui_default_values + return script_args + + def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner): + script_args = default_script_args.copy() # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run() if selectable_scripts: script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args script_args[0] = selectable_idx + 1 - else: - # when [0] = 0 no selectable script to run - script_args[0] = 0 # Now check for always on scripts if request.alwayson_scripts and (len(request.alwayson_scripts) > 0): @@ -257,7 +271,9 @@ class Api: raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params") # always on script with no arg should always run so you don't really need to add them to the requests if "args" in request.alwayson_scripts[alwayson_script_name]: - script_args[alwayson_script.args_from:alwayson_script.args_to] = request.alwayson_scripts[alwayson_script_name]["args"] + # min between arg length in scriptrunner and arg length in the request + for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))): + script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx] return script_args def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): @@ -265,6 +281,8 @@ class Api: if not script_runner.scripts: script_runner.initialize_scripts(False) ui.create_ui() + if not self.default_script_arg_txt2img: + self.default_script_arg_txt2img = self.init_default_script_args(script_runner) selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner) populate = txt2imgreq.copy(update={ # Override __init__ params @@ -280,7 +298,7 @@ class Api: args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) - script_args = self.init_script_args(txt2imgreq, selectable_scripts, selectable_script_idx, script_runner) + script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner) send_images = args.pop('send_images', True) args.pop('save_images', None) @@ -317,6 +335,8 @@ class Api: if not script_runner.scripts: script_runner.initialize_scripts(True) ui.create_ui() + if not self.default_script_arg_img2img: + self.default_script_arg_img2img = self.init_default_script_args(script_runner) selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner) populate = img2imgreq.copy(update={ # Override __init__ params @@ -334,7 +354,7 @@ class Api: args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them args.pop('alwayson_scripts', None) - script_args = self.init_script_args(img2imgreq, selectable_scripts, selectable_script_idx, script_runner) + script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner) send_images = args.pop('send_images', True) args.pop('save_images', None) @@ -376,16 +396,11 @@ class Api: def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): reqDict = setUpscalers(req) - def prepareFiles(file): - file = decode_base64_to_file(file.data, file_path=file.name) - file.orig_name = file.name - return file - - reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList'])) - reqDict.pop('imageList') + image_list = reqDict.pop('imageList', []) + image_folder = [decode_base64_to_image(x.data) for x in image_list] with self.queue_lock: - result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict) + result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict) return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) diff --git a/modules/cmd_args.py b/modules/cmd_args.py index 0af87251..81c0b82a 100644 --- a/modules/cmd_args.py +++ b/modules/cmd_args.py @@ -4,6 +4,7 @@ from modules.paths_internal import models_path, script_path, data_path, extensio parser = argparse.ArgumentParser()
+parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS) # allows running as root; implemented outside of webui
parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program")
parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version")
parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
diff --git a/modules/config_states.py b/modules/config_states.py new file mode 100644 index 00000000..2ea00929 --- /dev/null +++ b/modules/config_states.py @@ -0,0 +1,200 @@ +""" +Supports saving and restoring webui and extensions from a known working set of commits +""" + +import os +import sys +import traceback +import json +import time +import tqdm + +from datetime import datetime +from collections import OrderedDict +import git + +from modules import shared, extensions +from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir + + +all_config_states = OrderedDict() + + +def list_config_states(): + global all_config_states + + all_config_states.clear() + os.makedirs(config_states_dir, exist_ok=True) + + config_states = [] + for filename in os.listdir(config_states_dir): + if filename.endswith(".json"): + path = os.path.join(config_states_dir, filename) + with open(path, "r", encoding="utf-8") as f: + j = json.load(f) + j["filepath"] = path + config_states.append(j) + + config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)) + + for cs in config_states: + timestamp = time.asctime(time.gmtime(cs["created_at"])) + name = cs.get("name", "Config") + full_name = f"{name}: {timestamp}" + all_config_states[full_name] = cs + + return all_config_states + + +def get_webui_config(): + webui_repo = None + + try: + if os.path.exists(os.path.join(script_path, ".git")): + webui_repo = git.Repo(script_path) + except Exception: + print(f"Error reading webui git info from {script_path}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + webui_remote = None + webui_commit_hash = None + webui_commit_date = None + webui_branch = None + if webui_repo and not webui_repo.bare: + try: + webui_remote = next(webui_repo.remote().urls, None) + head = webui_repo.head.commit + webui_commit_date = webui_repo.head.commit.committed_date + webui_commit_hash = head.hexsha + webui_branch = webui_repo.active_branch.name + + except Exception: + webui_remote = None + + return { + "remote": webui_remote, + "commit_hash": webui_commit_hash, + "commit_date": webui_commit_date, + "branch": webui_branch, + } + + +def get_extension_config(): + ext_config = {} + + for ext in extensions.extensions: + entry = { + "name": ext.name, + "path": ext.path, + "enabled": ext.enabled, + "is_builtin": ext.is_builtin, + "remote": ext.remote, + "commit_hash": ext.commit_hash, + "commit_date": ext.commit_date, + "branch": ext.branch, + "have_info_from_repo": ext.have_info_from_repo + } + + ext_config[ext.name] = entry + + return ext_config + + +def get_config(): + creation_time = datetime.now().timestamp() + webui_config = get_webui_config() + ext_config = get_extension_config() + + return { + "created_at": creation_time, + "webui": webui_config, + "extensions": ext_config + } + + +def restore_webui_config(config): + print("* Restoring webui state...") + + if "webui" not in config: + print("Error: No webui data saved to config") + return + + webui_config = config["webui"] + + if "commit_hash" not in webui_config: + print("Error: No commit saved to webui config") + return + + webui_commit_hash = webui_config.get("commit_hash", None) + webui_repo = None + + try: + if os.path.exists(os.path.join(script_path, ".git")): + webui_repo = git.Repo(script_path) + except Exception: + print(f"Error reading webui git info from {script_path}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + return + + try: + webui_repo.git.fetch(all=True) + webui_repo.git.reset(webui_commit_hash, hard=True) + print(f"* Restored webui to commit {webui_commit_hash}.") + except Exception: + print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + + +def restore_extension_config(config): + print("* Restoring extension state...") + + if "extensions" not in config: + print("Error: No extension data saved to config") + return + + ext_config = config["extensions"] + + results = [] + disabled = [] + + for ext in tqdm.tqdm(extensions.extensions): + if ext.is_builtin: + continue + + ext.read_info_from_repo() + current_commit = ext.commit_hash + + if ext.name not in ext_config: + ext.disabled = True + disabled.append(ext.name) + results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled")) + continue + + entry = ext_config[ext.name] + + if "commit_hash" in entry and entry["commit_hash"]: + try: + ext.fetch_and_reset_hard(entry["commit_hash"]) + ext.read_info_from_repo() + if current_commit != entry["commit_hash"]: + results.append((ext, current_commit[:8], True, entry["commit_hash"][:8])) + except Exception as ex: + results.append((ext, current_commit[:8], False, ex)) + else: + results.append((ext, current_commit[:8], False, "No commit hash found in config")) + + if not entry.get("enabled", False): + ext.disabled = True + disabled.append(ext.name) + else: + ext.disabled = False + + shared.opts.disabled_extensions = disabled + shared.opts.save(shared.config_filename) + + print("* Finished restoring extensions. Results:") + for ext, prev_commit, success, result in results: + if success: + print(f" + {ext.name}: {prev_commit} -> {result}") + else: + print(f" ! {ext.name}: FAILURE ({result})") diff --git a/modules/devices.py b/modules/devices.py index 52c3e7cd..c705a3cb 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -92,14 +92,18 @@ def cond_cast_float(input): def randn(seed, shape): + from modules.shared import opts + torch.manual_seed(seed) - if device.type == 'mps': + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): - if device.type == 'mps': + from modules.shared import opts + + if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) diff --git a/modules/extensions.py b/modules/extensions.py index a14ffbf0..34d9d654 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -3,18 +3,25 @@ import sys import traceback
import time
+from datetime import datetime
import git
-from modules import paths, shared
-from modules.paths_internal import extensions_dir, extensions_builtin_dir
+from modules import shared
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path
extensions = []
-if not os.path.exists(paths.extensions_dir):
- os.makedirs(paths.extensions_dir)
+if not os.path.exists(extensions_dir):
+ os.makedirs(extensions_dir)
+
def active():
- return [x for x in extensions if x.enabled]
+ if shared.opts.disable_all_extensions == "all":
+ return []
+ elif shared.opts.disable_all_extensions == "extra":
+ return [x for x in extensions if x.enabled and x.is_builtin]
+ else:
+ return [x for x in extensions if x.enabled]
class Extension:
@@ -25,27 +32,43 @@ class Extension: self.status = ''
self.can_update = False
self.is_builtin = is_builtin
+ self.commit_hash = ''
+ self.commit_date = None
self.version = ''
+ self.branch = None
+ self.remote = None
+ self.have_info_from_repo = False
+
+ def read_info_from_repo(self):
+ if self.is_builtin or self.have_info_from_repo:
+ return
+
+ self.have_info_from_repo = True
repo = None
try:
- if os.path.exists(os.path.join(path, ".git")):
- repo = git.Repo(path)
+ if os.path.exists(os.path.join(self.path, ".git")):
+ repo = git.Repo(self.path)
except Exception:
- print(f"Error reading github repository info from {path}:", file=sys.stderr)
+ print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
if repo is None or repo.bare:
self.remote = None
else:
try:
- self.remote = next(repo.remote().urls, None)
self.status = 'unknown'
+ self.remote = next(repo.remote().urls, None)
head = repo.head.commit
- ts = time.asctime(time.gmtime(repo.head.commit.committed_date))
- self.version = f'{head.hexsha[:8]} ({ts})'
-
- except Exception:
+ self.commit_date = repo.head.commit.committed_date
+ ts = time.asctime(time.gmtime(self.commit_date))
+ if repo.active_branch:
+ self.branch = repo.active_branch.name
+ self.commit_hash = head.hexsha
+ self.version = f'{self.commit_hash[:8]} ({ts})'
+
+ except Exception as ex:
+ print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
self.remote = None
def list_files(self, subdir, extension):
@@ -68,28 +91,45 @@ class Extension: for fetch in repo.remote().fetch(dry_run=True):
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
- self.status = "behind"
+ self.status = "new commits"
return
+ try:
+ origin = repo.rev_parse('origin')
+ if repo.head.commit != origin:
+ self.can_update = True
+ self.status = "behind HEAD"
+ return
+ except Exception:
+ self.can_update = False
+ self.status = "unknown (remote error)"
+ return
+
self.can_update = False
self.status = "latest"
- def fetch_and_reset_hard(self):
+ def fetch_and_reset_hard(self, commit='origin'):
repo = git.Repo(self.path)
# 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)
- repo.git.reset('origin', hard=True)
+ repo.git.reset(commit, hard=True)
+ self.have_info_from_repo = False
def list_extensions():
extensions.clear()
- if not os.path.isdir(paths.extensions_dir):
+ if not os.path.isdir(extensions_dir):
return
+ if shared.opts.disable_all_extensions == "all":
+ print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+ elif shared.opts.disable_all_extensions == "extra":
+ print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
+
extension_paths = []
- for dirname in [paths.extensions_dir, paths.extensions_builtin_dir]:
+ for dirname in [extensions_dir, extensions_builtin_dir]:
if not os.path.isdir(dirname):
return
@@ -98,9 +138,8 @@ def list_extensions(): if not os.path.isdir(path):
continue
- extension_paths.append((extension_dirname, path, dirname == paths.extensions_builtin_dir))
+ extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
for dirname, path, is_builtin in extension_paths:
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
extensions.append(extension)
-
diff --git a/modules/extra_networks_hypernet.py b/modules/extra_networks_hypernet.py index d3a4d7ad..33d100dd 100644 --- a/modules/extra_networks_hypernet.py +++ b/modules/extra_networks_hypernet.py @@ -9,7 +9,7 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork): def activate(self, p, params_list):
additional = shared.opts.sd_hypernetwork
- if additional != "" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+ if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
diff --git a/modules/extras.py b/modules/extras.py index d8ece955..ff4e9c4e 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -1,6 +1,7 @@ import os
import re
import shutil
+import json
import torch
@@ -71,7 +72,7 @@ def to_half(tensor, enable): return tensor
-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
shared.state.begin()
shared.state.job = 'model-merge'
@@ -241,13 +242,54 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_ shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
+ metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
+
+ if save_metadata:
+ merge_recipe = {
+ "type": "webui", # indicate this model was merged with webui's built-in merger
+ "primary_model_hash": primary_model_info.sha256,
+ "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
+ "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
+ "interp_method": interp_method,
+ "multiplier": multiplier,
+ "save_as_half": save_as_half,
+ "custom_name": custom_name,
+ "config_source": config_source,
+ "bake_in_vae": bake_in_vae,
+ "discard_weights": discard_weights,
+ "is_inpainting": result_is_inpainting_model,
+ "is_instruct_pix2pix": result_is_instruct_pix2pix_model
+ }
+ metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
+
+ def add_model_metadata(checkpoint_info):
+ checkpoint_info.calculate_shorthash()
+ metadata["sd_merge_models"][checkpoint_info.sha256] = {
+ "name": checkpoint_info.name,
+ "legacy_hash": checkpoint_info.hash,
+ "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
+ }
+
+ metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+
+ add_model_metadata(primary_model_info)
+ if secondary_model_info:
+ add_model_metadata(secondary_model_info)
+ if tertiary_model_info:
+ add_model_metadata(tertiary_model_info)
+
+ metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+
_, extension = os.path.splitext(output_modelname)
if extension.lower() == ".safetensors":
- safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
+ safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
else:
torch.save(theta_0, output_modelname)
sd_models.list_models()
+ created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
+ if created_model:
+ created_model.calculate_shorthash()
create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 6df76858..99f1a0d3 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -284,6 +284,10 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model restore_old_hires_fix_params(res)
+ # Missing RNG means the default was set, which is GPU RNG
+ if "RNG" not in res:
+ res["RNG"] = "GPU"
+
return res
@@ -304,6 +308,8 @@ infotext_to_setting_name_mapping = [ ('UniPC skip type', 'uni_pc_skip_type'),
('UniPC order', 'uni_pc_order'),
('UniPC lower order final', 'uni_pc_lower_order_final'),
+ ('RNG', 'randn_source'),
+ ('NGMS', 's_min_uncond'),
]
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f6ef42d5..1fc49537 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -312,7 +312,7 @@ class Hypernetwork: def list_hypernetworks(path):
res = {}
- for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
+ for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
name = os.path.splitext(os.path.basename(filename))[0]
# Prevent a hypothetical "None.pt" from being listed.
if name != "None":
diff --git a/modules/images.py b/modules/images.py index 7030aaaa..fd173829 100644 --- a/modules/images.py +++ b/modules/images.py @@ -261,9 +261,12 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None): if scale > 1.0:
upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
- assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
+ if len(upscalers) == 0:
+ upscaler = shared.sd_upscalers[0]
+ print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
+ else:
+ upscaler = upscalers[0]
- upscaler = upscalers[0]
im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
if im.width != w or im.height != h:
@@ -315,6 +318,7 @@ re_nonletters = re.compile(r'[\s' + string.punctuation + ']+') re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
max_filename_part_length = 128
+NOTHING_AND_SKIP_PREVIOUS_TEXT = object()
def sanitize_filename_part(text, replace_spaces=True):
@@ -349,6 +353,10 @@ class FilenameGenerator: 'prompt_no_styles': lambda self: self.prompt_no_style(),
'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
'prompt_words': lambda self: self.prompt_words(),
+ 'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
+ 'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+ 'hasprompt': lambda self, *args: self.hasprompt(*args), # accepts formats:[hasprompt<prompt1|default><prompt2>..]
+ 'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
}
default_time_format = '%Y%m%d%H%M%S'
@@ -357,6 +365,22 @@ class FilenameGenerator: self.seed = seed
self.prompt = prompt
self.image = image
+
+ def hasprompt(self, *args):
+ lower = self.prompt.lower()
+ if self.p is None or self.prompt is None:
+ return None
+ outres = ""
+ for arg in args:
+ if arg != "":
+ division = arg.split("|")
+ expected = division[0].lower()
+ default = division[1] if len(division) > 1 else ""
+ if lower.find(expected) >= 0:
+ outres = f'{outres}{expected}'
+ else:
+ outres = outres if default == "" else f'{outres}{default}'
+ return sanitize_filename_part(outres)
def prompt_no_style(self):
if self.p is None or self.prompt is None:
@@ -400,9 +424,9 @@ class FilenameGenerator: for m in re_pattern.finditer(x):
text, pattern = m.groups()
- res += text
if pattern is None:
+ res += text
continue
pattern_args = []
@@ -423,11 +447,13 @@ class FilenameGenerator: print(f"Error adding [{pattern}] to filename", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
- if replacement is not None:
- res += str(replacement)
+ if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
+ continue
+ elif replacement is not None:
+ res += text + str(replacement)
continue
- res += f'[{pattern}]'
+ res += f'{text}[{pattern}]'
return res
diff --git a/modules/img2img.py b/modules/img2img.py index c973b770..56c846d6 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -4,7 +4,7 @@ import sys import traceback
import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError
from modules import devices, sd_samplers
from modules.generation_parameters_copypaste import create_override_settings_dict
@@ -46,7 +46,10 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): if state.interrupted:
break
- img = Image.open(image)
+ try:
+ img = Image.open(image)
+ except UnidentifiedImageError:
+ continue
# Use the EXIF orientation of photos taken by smartphones.
img = ImageOps.exif_transpose(img)
p.init_images = [img] * p.batch_size
@@ -78,7 +81,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename))
-def img2img(id_task: str, 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, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, 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, *args):
+def img2img(id_task: str, 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, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, 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, *args):
override_settings = create_override_settings_dict(override_settings_texts)
is_batch = mode == 5
@@ -114,6 +117,12 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s if image is not None:
image = ImageOps.exif_transpose(image)
+ if selected_scale_tab == 1:
+ assert image, "Can't scale by because no image is selected"
+
+ width = int(image.width * scale_by)
+ height = int(image.height * scale_by)
+
assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'
p = StableDiffusionProcessingImg2Img(
@@ -151,13 +160,14 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s override_settings=override_settings,
)
- p.scripts = modules.scripts.scripts_txt2img
+ p.scripts = modules.scripts.scripts_img2img
p.script_args = args
if shared.cmd_opts.enable_console_prompts:
print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
- p.extra_generation_params["Mask blur"] = mask_blur
+ if mask:
+ p.extra_generation_params["Mask blur"] = mask_blur
if is_batch:
assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
diff --git a/modules/interrogate.py b/modules/interrogate.py index cbb80683..e1665708 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir): category_types = ["artists", "flavors", "mediums", "movements"]
try:
- os.makedirs(tmpdir)
+ os.makedirs(tmpdir, exist_ok=True)
for category_type in category_types:
torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
os.rename(tmpdir, content_dir)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir): errors.display(e, "downloading default CLIP interrogate categories")
finally:
if os.path.exists(tmpdir):
- os.remove(tmpdir)
+ os.removedirs(tmpdir)
class InterrogateModels:
diff --git a/modules/lowvram.py b/modules/lowvram.py index 042a0254..e254cc13 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram): if hasattr(sd_model.cond_stage_model, 'model'):
sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model
- # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
+ # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
- stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
+ stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
sd_model.to(devices.device)
- sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
+ sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored
# register hooks for those the first three models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
@@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram): sd_model.first_stage_model.decode = first_stage_model_decode_wrap
if sd_model.depth_model:
sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+ if sd_model.embedder:
+ sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if hasattr(sd_model.cond_stage_model, 'model'):
diff --git a/modules/ngrok.py b/modules/ngrok.py index 3df2c06b..1ad7989b 100644 --- a/modules/ngrok.py +++ b/modules/ngrok.py @@ -13,6 +13,18 @@ def connect(token, port, region): config = conf.PyngrokConfig( auth_token=token, region=region ) + + # Guard for existing tunnels + existing = ngrok.get_tunnels(pyngrok_config=config) + if existing: + for established in existing: + # Extra configuration in the case that the user is also using ngrok for other tunnels + if established.config['addr'][-4:] == str(port): + public_url = existing[0].public_url + print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n' + 'You can use this link after the launch is complete.') + return + try: if account is None: public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url diff --git a/modules/paths_internal.py b/modules/paths_internal.py index 926ec3bb..6765bafe 100644 --- a/modules/paths_internal.py +++ b/modules/paths_internal.py @@ -20,3 +20,4 @@ data_path = cmd_opts_pre.data_dir models_path = 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")
diff --git a/modules/postprocessing.py b/modules/postprocessing.py index 09d8e605..736315e2 100644 --- a/modules/postprocessing.py +++ b/modules/postprocessing.py @@ -18,9 +18,14 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, if extras_mode == 1:
for img in image_folder:
- image = Image.open(img)
+ if isinstance(img, Image.Image):
+ image = img
+ fn = ''
+ else:
+ image = Image.open(os.path.abspath(img.name))
+ fn = os.path.splitext(img.orig_name)[0]
image_data.append(image)
- image_names.append(os.path.splitext(img.orig_name)[0])
+ image_names.append(fn)
elif extras_mode == 2:
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
assert input_dir, 'input directory not selected'
diff --git a/modules/processing.py b/modules/processing.py index 2e5a363f..a48fff99 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -3,6 +3,7 @@ import math import os
import sys
import warnings
+import hashlib
import torch
import numpy as np
@@ -78,28 +79,34 @@ def apply_overlay(image, paste_loc, index, overlays): def txt2img_image_conditioning(sd_model, x, width, height):
- if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
- # Dummy zero conditioning if we're not using inpainting model.
- # Still takes up a bit of memory, but no encoder call.
- # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
- return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+ if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+
+ # The "masked-image" in this case will just be all zeros since the entire image is masked.
+ image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+ image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+ # Add the fake full 1s mask to the first dimension.
+ image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+ image_conditioning = image_conditioning.to(x.dtype)
- # The "masked-image" in this case will just be all zeros since the entire image is masked.
- image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+ return image_conditioning
- # Add the fake full 1s mask to the first dimension.
- image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
- image_conditioning = image_conditioning.to(x.dtype)
+ elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
- return image_conditioning
+ return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+ else:
+ # Dummy zero conditioning if we're not using inpainting or unclip models.
+ # Still takes up a bit of memory, but no encoder call.
+ # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+ return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
class StableDiffusionProcessing:
"""
The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
"""
- def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+ def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
if sampler_index is not None:
print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
@@ -134,6 +141,7 @@ class StableDiffusionProcessing: self.denoising_strength: float = denoising_strength
self.sampler_noise_scheduler_override = None
self.ddim_discretize = ddim_discretize or opts.ddim_discretize
+ self.s_min_uncond = s_min_uncond or opts.s_min_uncond
self.s_churn = s_churn or opts.s_churn
self.s_tmin = s_tmin or opts.s_tmin
self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
@@ -156,6 +164,8 @@ class StableDiffusionProcessing: self.all_seeds = None
self.all_subseeds = None
self.iteration = 0
+ self.is_hr_pass = False
+
@property
def sd_model(self):
@@ -190,6 +200,14 @@ class StableDiffusionProcessing: return conditioning_image
+ def unclip_image_conditioning(self, source_image):
+ c_adm = self.sd_model.embedder(source_image)
+ if self.sd_model.noise_augmentor is not None:
+ noise_level = 0 # TODO: Allow other noise levels?
+ c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+ c_adm = torch.cat((c_adm, noise_level_emb), 1)
+ return c_adm
+
def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
self.is_using_inpainting_conditioning = True
@@ -241,6 +259,9 @@ class StableDiffusionProcessing: if self.sampler.conditioning_key in {'hybrid', 'concat'}:
return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+ if self.sampler.conditioning_key == "crossattn-adm":
+ return self.unclip_image_conditioning(source_image)
+
# Dummy zero conditioning if we're not using inpainting or depth model.
return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
@@ -459,6 +480,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
"Clip skip": None if clip_skip <= 1 else clip_skip,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+ "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,
}
generation_params.update(p.extra_generation_params)
@@ -622,8 +646,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
- uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
- c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+ step_multiplier = 1
+ if not shared.opts.dont_fix_second_order_samplers_schedule:
+ try:
+ step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
+ except:
+ pass
+ uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
+ c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)
if len(model_hijack.comments) > 0:
for comment in model_hijack.comments:
@@ -653,6 +683,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
for i, x_sample in enumerate(x_samples_ddim):
+ p.batch_index = i
+
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
@@ -689,9 +721,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image.info["parameters"] = text
output_images.append(image)
- if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+ if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
image_mask = p.mask_for_overlay.convert('RGB')
- image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA')
+ image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
if opts.save_mask:
images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
@@ -701,7 +733,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: if opts.return_mask:
output_images.append(image_mask)
-
+
if opts.return_mask_composite:
output_images.append(image_mask_composite)
@@ -854,6 +886,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not self.enable_hr:
return samples
+ self.is_hr_pass = True
+
target_width = self.hr_upscale_to_x
target_height = self.hr_upscale_to_y
@@ -923,6 +957,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
+ self.is_hr_pass = False
+
return samples
@@ -990,6 +1026,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.color_corrections = []
imgs = []
for img in self.init_images:
+
+ # Save init image
+ if opts.save_init_img:
+ self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+ images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
image = images.flatten(img, opts.img2img_background_color)
if crop_region is None and self.resize_mode != 3:
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py index aad4a629..d6079433 100644 --- a/modules/realesrgan_model.py +++ b/modules/realesrgan_model.py @@ -9,7 +9,7 @@ from realesrgan import RealESRGANer from modules.upscaler import Upscaler, UpscalerData
from modules.shared import cmd_opts, opts
-
+from modules import modelloader
class UpscalerRealESRGAN(Upscaler):
def __init__(self, path):
@@ -23,7 +23,15 @@ class UpscalerRealESRGAN(Upscaler): self.enable = True
self.scalers = []
scalers = self.load_models(path)
+
+ local_model_paths = self.find_models(ext_filter=[".pth"])
for scaler in scalers:
+ if scaler.local_data_path.startswith("http"):
+ filename = modelloader.friendly_name(scaler.local_data_path)
+ local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
+ if local:
+ scaler.local_data_path = local
+
if scaler.name in opts.realesrgan_enabled_models:
self.scalers.append(scaler)
@@ -64,7 +72,9 @@ class UpscalerRealESRGAN(Upscaler): print(f"Unable to find model info: {path}")
return None
- info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+ if info.local_data_path.startswith("http"):
+ info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+
return info
except Exception as e:
print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
diff --git a/modules/safe.py b/modules/safe.py index 82d44be3..dadf319c 100644 --- a/modules/safe.py +++ b/modules/safe.py @@ -1,6 +1,5 @@ # this code is adapted from the script contributed by anon from /h/
-import io
import pickle
import collections
import sys
@@ -12,11 +11,9 @@ import _codecs import zipfile
import re
-
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
-
def encode(*args):
out = _codecs.encode(*args)
return out
@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler): def persistent_load(self, saved_id):
assert saved_id[0] == 'storage'
- return TypedStorage()
+ return TypedStorage(_internal=True)
def find_class(self, module, name):
if self.extra_handler is not None:
diff --git a/modules/scripts.py b/modules/scripts.py index d661be4f..4d0bbd66 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -553,3 +553,15 @@ def IOComponent_init(self, *args, **kwargs): original_IOComponent_init = gr.components.IOComponent.__init__
gr.components.IOComponent.__init__ = IOComponent_init
+
+
+def BlockContext_init(self, *args, **kwargs):
+ res = original_BlockContext_init(self, *args, **kwargs)
+
+ add_classes_to_gradio_component(self)
+
+ return res
+
+
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+gr.blocks.BlockContext.__init__ = BlockContext_init
diff --git a/modules/sd_models.py b/modules/sd_models.py index 86218c08..4f7613a1 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -52,6 +52,15 @@ class CheckpointInfo: self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
+ self.metadata = {}
+
+ _, ext = os.path.splitext(self.filename)
+ if ext.lower() == ".safetensors":
+ try:
+ self.metadata = read_metadata_from_safetensors(filename)
+ except Exception as e:
+ errors.display(e, f"reading checkpoint metadata: {filename}")
+
def register(self):
checkpoints_list[self.title] = self
for id in self.ids:
@@ -122,7 +131,7 @@ def list_models(): elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
- for filename in model_list:
+ for filename in sorted(model_list, key=str.lower):
checkpoint_info = CheckpointInfo(filename)
checkpoint_info.register()
@@ -383,6 +392,14 @@ def repair_config(sd_config): elif shared.cmd_opts.upcast_sampling:
sd_config.model.params.unet_config.params.use_fp16 = True
+ 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"
+
+ # For UnCLIP-L, override the hardcoded karlo directory
+ if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+ 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)
+
sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
@@ -536,4 +553,4 @@ def unload_model_weights(sd_model=None, info=None): print(f"Unloaded weights {timer.summary()}.")
- return sd_model
\ No newline at end of file + return sd_model
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 91c21700..9398f528 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml") config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
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")
@@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict): def guess_model_config_from_state_dict(sd, filename):
sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
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 sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
return config_depth_model
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+ return config_unclip
+ elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+ return config_unopenclip
if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
if diffusion_model_input.shape[1] == 9:
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py index a1aac7cf..bc074238 100644 --- a/modules/sd_samplers_common.py +++ b/modules/sd_samplers_common.py @@ -60,3 +60,13 @@ def store_latent(decoded): class InterruptedException(BaseException):
pass
+
+
+if opts.randn_source == "CPU":
+ import torchsde._brownian.brownian_interval
+
+ def torchsde_randn(size, dtype, device, seed):
+ generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+ return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+ torchsde._brownian.brownian_interval._randn = torchsde_randn
diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index 083da18c..bfcc5574 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler: # Have to unwrap the inpainting conditioning here to perform pre-processing
image_conditioning = None
+ uc_image_conditioning = None
if isinstance(cond, dict):
- image_conditioning = cond["c_concat"][0]
+ if self.conditioning_key == "crossattn-adm":
+ image_conditioning = cond["c_adm"]
+ uc_image_conditioning = unconditional_conditioning["c_adm"]
+ else:
+ image_conditioning = cond["c_concat"][0]
cond = cond["c_crossattn"][0]
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
@@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler: # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
# Note that they need to be lists because it just concatenates them later.
if image_conditioning is not None:
- cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
+ else:
+ cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
return x, ts, cond, unconditional_conditioning
@@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler: # Wrap the conditioning models with additional image conditioning for inpainting model
if image_conditioning is not None:
- conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
- unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
+ else:
+ conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+ unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
@@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler: # Wrap the conditioning models with additional image conditioning for inpainting model
# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
if image_conditioning is not None:
- conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
- unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+ if self.conditioning_key == "crossattn-adm":
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
+ else:
+ conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+ unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 93f0e55a..eb98e599 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -76,7 +76,7 @@ class CFGDenoiser(torch.nn.Module): return denoised
- def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
+ def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
@@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module): batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
+ if shared.sd_model.model.conditioning_key == "crossattn-adm":
+ image_uncond = torch.zeros_like(image_cond)
+ make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm}
+ else:
+ image_uncond = image_cond
+ make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]}
+
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
else:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
- image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)])
+ image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
cfg_denoiser_callback(denoiser_params)
@@ -108,21 +115,30 @@ class CFGDenoiser(torch.nn.Module): sigma_in = denoiser_params.sigma
tensor = denoiser_params.text_cond
uncond = denoiser_params.text_uncond
+ skip_uncond = False
- if tensor.shape[1] == uncond.shape[1]:
- if not is_edit_model:
- cond_in = torch.cat([tensor, uncond])
- else:
+ # 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:
+ skip_uncond = True
+ x_in = x_in[:-batch_size]
+ sigma_in = sigma_in[:-batch_size]
+
+ if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+ if is_edit_model:
cond_in = torch.cat([tensor, uncond, uncond])
+ elif skip_uncond:
+ cond_in = tensor
+ else:
+ cond_in = torch.cat([tensor, uncond])
if shared.batch_cond_uncond:
- x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
+ x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
@@ -135,9 +151,15 @@ class CFGDenoiser(torch.nn.Module): else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
- x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]})
+ x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+ if not skip_uncond:
+ x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
- x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
+ denoised_image_indexes = [x[0][0] for x in conds_list]
+ if skip_uncond:
+ fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+ x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
cfg_denoised_callback(denoised_params)
@@ -145,20 +167,21 @@ class CFGDenoiser(torch.nn.Module): devices.test_for_nans(x_out, "unet")
if opts.live_preview_content == "Prompt":
- sd_samplers_common.store_latent(x_out[0:uncond.shape[0]])
+ sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
elif opts.live_preview_content == "Negative prompt":
sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
- if not is_edit_model:
- denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
- else:
+ if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+ 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)
if self.mask is not None:
denoised = self.init_latent * self.mask + self.nmask * denoised
self.step += 1
-
return denoised
@@ -183,7 +206,7 @@ class TorchHijack: if noise.shape == x.shape:
return noise
- if x.device.type == 'mps':
+ if opts.randn_source == "CPU" or x.device.type == 'mps':
return torch.randn_like(x, device=devices.cpu).to(x.device)
else:
return torch.randn_like(x)
@@ -203,6 +226,7 @@ class KDiffusionSampler: self.eta = None
self.config = None
self.last_latent = None
+ self.s_min_uncond = None
self.conditioning_key = sd_model.model.conditioning_key
@@ -237,6 +261,7 @@ class KDiffusionSampler: self.model_wrap_cfg.step = 0
self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+ self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
@@ -319,6 +344,7 @@ class KDiffusionSampler: 'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
}
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
@@ -352,7 +378,8 @@ class KDiffusionSampler: 'cond': conditioning,
'image_cond': image_conditioning,
'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale
+ 'cond_scale': p.cfg_scale,
+ 's_min_uncond': self.s_min_uncond
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
return samples
diff --git a/modules/shared.py b/modules/shared.py index 11be3985..6a2b3c2b 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -4,6 +4,7 @@ import json import os
import sys
import time
+import requests
from PIL import Image
import gradio as gr
@@ -39,6 +40,7 @@ restricted_opts = { "outdir_grids",
"outdir_txt2img_grids",
"outdir_save",
+ "outdir_init_images"
}
ui_reorder_categories = [
@@ -54,6 +56,21 @@ ui_reorder_categories = [ "scripts",
]
+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+ "gradio/glass",
+ "gradio/monochrome",
+ "gradio/seafoam",
+ "gradio/soft",
+ "freddyaboulton/dracula_revamped",
+ "gradio/dracula_test",
+ "abidlabs/dracula_test",
+ "abidlabs/pakistan",
+ "dawood/microsoft_windows",
+ "ysharma/steampunk"
+]
+
+
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -252,7 +269,7 @@ 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"),
- "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+ "save_init_img": OptionInfo(False, "Save init images when using img2img"),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
@@ -268,6 +285,7 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), { "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+ "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
@@ -283,6 +301,8 @@ options_templates.update(options_section(('upscaling', "Upscaling"), { "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+ "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+ "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -331,6 +351,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+ "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
@@ -338,6 +359,7 @@ options_templates.update(options_section(('compatibility', "Compatibility"), { "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."),
}))
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -361,7 +383,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), { "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
- "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+ "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
@@ -382,11 +404,13 @@ options_templates.update(options_section(('ui', "User interface"), { "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+ "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
"hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+ "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
}))
options_templates.update(options_section(('ui', "Live previews"), {
@@ -405,6 +429,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+ 's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
@@ -422,7 +447,9 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), { }))
options_templates.update(options_section((None, "Hidden options"), {
- "disabled_extensions": OptionInfo([], "Disable those extensions"),
+ "disabled_extensions": OptionInfo([], "Disable these extensions"),
+ "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
+ "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))
@@ -599,6 +626,24 @@ clip_model = None progress_print_out = sys.stdout
+gradio_theme = gr.themes.Base()
+
+
+def reload_gradio_theme(theme_name=None):
+ global gradio_theme
+ if not theme_name:
+ theme_name = opts.gradio_theme
+
+ if theme_name == "Default":
+ gradio_theme = gr.themes.Default()
+ else:
+ try:
+ gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+ except requests.exceptions.ConnectionError:
+ print("Can't access HuggingFace Hub, falling back to default Gradio theme")
+ gradio_theme = gr.themes.Default()
+
+
class TotalTQDM:
def __init__(self):
@@ -640,7 +685,7 @@ mem_mon.start() def listfiles(dirname):
- filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+ filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
return [file for file in filenames if os.path.isfile(file)]
diff --git a/modules/styles.py b/modules/styles.py index 990d5623..9ed85991 100644 --- a/modules/styles.py +++ b/modules/styles.py @@ -72,16 +72,14 @@ class StyleDatabase: return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
def save_styles(self, path: str) -> None:
- # Write to temporary file first, so we don't nuke the file if something goes wrong
- fd, temp_path = tempfile.mkstemp(".csv")
+ # Always keep a backup file around
+ if os.path.exists(path):
+ shutil.copy(path, path + ".bak")
+
+ fd = os.open(path, os.O_RDWR|os.O_CREAT)
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
# _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
# and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
writer.writeheader()
writer.writerows(style._asdict() for k, style in self.styles.items())
-
- # Always keep a backup file around
- if os.path.exists(path):
- shutil.move(path, path + ".bak")
- shutil.move(temp_path, path)
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 9ad1d3f4..4a29151d 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -161,7 +161,9 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre params.subindex = 0
filename = os.path.join(src, imagefile)
try:
- img = Image.open(filename).convert("RGB")
+ img = Image.open(filename)
+ img = ImageOps.exif_transpose(img)
+ img = img.convert("RGB")
except Exception:
continue
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index d2e62e58..379df243 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -233,6 +233,12 @@ class EmbeddingDatabase: self.load_from_dir(embdir)
embdir.update()
+ # re-sort word_embeddings because load_from_dir may not load in alphabetic order.
+ # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
+ sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
+ self.word_embeddings.clear()
+ self.word_embeddings.update(sorted_word_embeddings)
+
displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
if self.previously_displayed_embeddings != displayed_embeddings:
self.previously_displayed_embeddings = displayed_embeddings
diff --git a/modules/ui.py b/modules/ui.py index 974f2a30..1130345c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -70,17 +70,6 @@ def gr_show(visible=True): sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.wrap.cover-bg .z-20::before { content:"" }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
# Using constants for these since the variation selector isn't visible.
# Important that they exactly match script.js for tooltip to work.
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
@@ -105,6 +94,9 @@ def send_gradio_gallery_to_image(x): def visit(x, func, path=""):
if hasattr(x, 'children'):
+ if isinstance(x, gr.Tabs) and x.elem_id is not None:
+ # Tabs element can't have a label, have to use elem_id instead
+ func(f"{path}/Tabs@{x.elem_id}", x)
for c in x.children:
visit(c, func, path)
elif x.label is not None:
@@ -138,6 +130,16 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
+def resize_from_to_html(width, height, scale_by):
+ target_width = int(width * scale_by)
+ target_height = int(height * scale_by)
+
+ if not target_width or not target_height:
+ return "no image selected"
+
+ return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+
+
def apply_styles(prompt, prompt_neg, styles):
prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
@@ -182,8 +184,8 @@ def create_seed_inputs(target_interface): with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
seed.style(container=False)
- random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed')
- reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+ random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
+ reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')
seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
@@ -479,7 +481,7 @@ def create_ui(): height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn")
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="txt2img_column_batch"):
@@ -684,6 +686,8 @@ def create_ui(): copy_image_buttons.append((button, name, elem))
with gr.Tabs(elem_id="mode_img2img"):
+ img2img_selected_tab = gr.State(0)
+
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
add_copy_image_controls('img2img', init_img)
@@ -726,6 +730,12 @@ def create_ui(): 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")
+ img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+ img2img_image_inputs = [init_img, sketch, init_img_with_mask, inpaint_color_sketch]
+
+ for i, tab in enumerate(img2img_tabs):
+ tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
+
def copy_image(img):
if isinstance(img, dict) and 'image' in img:
return img['image']
@@ -755,11 +765,34 @@ def create_ui(): elif category == "dimensions":
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")
- height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
-
- with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
- res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+ selected_scale_tab = gr.State(value=0)
+
+ with gr.Tabs():
+ with gr.Tab(label="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")
+ height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+ with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+
+ with gr.Tab(label="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():
+ scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+ gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+
+ scale_by.change(
+ fn=resize_from_to_html,
+ _js="currentImg2imgSourceResolution",
+ inputs=[dummy_component, dummy_component, scale_by],
+ outputs=scale_by_html,
+ show_progress=False,
+ )
+
+ tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+ tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
if opts.dimensions_and_batch_together:
with gr.Column(elem_id="img2img_column_batch"):
@@ -817,7 +850,7 @@ def create_ui(): def select_img2img_tab(tab):
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
- for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
+ for i, elem in enumerate(img2img_tabs):
elem.select(
fn=lambda tab=i: select_img2img_tab(tab),
inputs=[],
@@ -870,8 +903,10 @@ def create_ui(): denoising_strength,
seed,
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+ selected_scale_tab,
height,
width,
+ scale_by,
resize_mode,
inpaint_full_res,
inpaint_full_res_padding,
@@ -1030,8 +1065,9 @@ def create_ui(): interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
with FormRow():
- checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+ checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+ save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")
with FormRow():
with gr.Column():
@@ -1059,7 +1095,7 @@ def create_ui(): with gr.Row(variant="compact").style(equal_height=False):
with gr.Tabs(elem_id="train_tabs"):
- with gr.Tab(label="Create embedding"):
+ with gr.Tab(label="Create embedding", id="create_embedding"):
new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
@@ -1072,7 +1108,7 @@ def create_ui(): with gr.Column():
create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
- with gr.Tab(label="Create hypernetwork"):
+ with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
@@ -1090,7 +1126,7 @@ def create_ui(): with gr.Column():
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
- with gr.Tab(label="Preprocess images"):
+ with gr.Tab(label="Preprocess images", id="preprocess_images"):
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
@@ -1158,7 +1194,7 @@ def create_ui(): def get_textual_inversion_template_names():
return sorted([x for x in textual_inversion.textual_inversion_templates])
- with gr.Tab(label="Train"):
+ with gr.Tab(label="Train", id="train"):
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
with FormRow():
train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
@@ -1216,7 +1252,7 @@ def create_ui(): with gr.Column(elem_id='ti_gallery_container'):
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
- ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+ ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
ti_progress = gr.HTML(elem_id="ti_progress", value="")
ti_outcome = gr.HTML(elem_id="ti_error", value="")
@@ -1492,7 +1528,7 @@ def create_ui(): current_row.__exit__()
current_tab.__exit__()
- with gr.TabItem("Actions"):
+ with gr.TabItem("Actions", id="actions"):
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
@@ -1500,7 +1536,7 @@ def create_ui(): unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")
- with gr.TabItem("Licenses"):
+ with gr.TabItem("Licenses", id="licenses"):
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
@@ -1568,22 +1604,6 @@ def create_ui(): (train_interface, "Train", "ti"),
]
- css = ""
-
- for cssfile in modules.scripts.list_files_with_name("style.css"):
- if not os.path.isfile(cssfile):
- continue
-
- with open(cssfile, "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if os.path.exists(os.path.join(data_path, "user.css")):
- with open(os.path.join(data_path, "user.css"), "r", encoding="utf8") as file:
- css += file.read() + "\n"
-
- if not cmd_opts.no_progressbar_hiding:
- css += css_hide_progressbar
-
interfaces += script_callbacks.ui_tabs_callback()
interfaces += [(settings_interface, "Settings", "settings")]
@@ -1594,7 +1614,7 @@ def create_ui(): for _interface, label, _ifid in interfaces:
shared.tab_names.append(label)
- with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+ with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
with gr.Row(elem_id="quicksettings", variant="compact"):
for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
component = create_setting_component(k, is_quicksettings=True)
@@ -1657,6 +1677,7 @@ def create_ui(): fn=get_settings_values,
inputs=[],
outputs=[component_dict[k] for k in component_keys],
+ queue=False,
)
def modelmerger(*args):
@@ -1686,6 +1707,7 @@ def create_ui(): config_source,
bake_in_vae,
discard_weights,
+ save_metadata,
],
outputs=[
primary_model_name,
@@ -1733,7 +1755,7 @@ def create_ui(): if init_field is not None:
init_field(saved_value)
- if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
+ if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
apply_field(x, 'visible')
if type(x) == gr.Slider:
@@ -1763,12 +1785,27 @@ def create_ui(): apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
+ def check_tab_id(tab_id):
+ tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
+ if type(tab_id) == str:
+ tab_ids = [t.id for t in tab_items]
+ return tab_id in tab_ids
+ elif type(tab_id) == int:
+ return tab_id >= 0 and tab_id < len(tab_items)
+ else:
+ return False
+
+ if type(x) == gr.Tabs:
+ apply_field(x, 'selected', check_tab_id)
+
visit(txt2img_interface, loadsave, "txt2img")
visit(img2img_interface, loadsave, "img2img")
visit(extras_interface, loadsave, "extras")
visit(modelmerger_interface, loadsave, "modelmerger")
visit(train_interface, loadsave, "train")
+ loadsave(f"webui/Tabs@{tabs.elem_id}", tabs)
+
if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
with open(ui_config_file, "w", encoding="utf8") as file:
json.dump(ui_settings, file, indent=4)
@@ -1779,25 +1816,60 @@ def create_ui(): return demo
-def reload_javascript():
+def webpath(fn):
+ if fn.startswith(script_path):
+ web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+ else:
+ web_path = os.path.abspath(fn)
+
+ return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
script_js = os.path.join(script_path, "script.js")
- head = f'<script type="text/javascript" src="file={os.path.abspath(script_js)}?{os.path.getmtime(script_js)}"></script>\n'
+ head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'
inline = f"{localization.localization_js(shared.opts.localization)};"
if cmd_opts.theme is not None:
inline += f"set_theme('{cmd_opts.theme}');"
for script in modules.scripts.list_scripts("javascript", ".js"):
- head += f'<script type="text/javascript" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
+ head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
for script in modules.scripts.list_scripts("javascript", ".mjs"):
- head += f'<script type="module" src="file={script.path}?{os.path.getmtime(script.path)}"></script>\n'
+ head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
head += f'<script type="text/javascript">{inline}</script>\n'
+ return head
+
+
+def css_html():
+ head = ""
+
+ def stylesheet(fn):
+ return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
+
+ for cssfile in modules.scripts.list_files_with_name("style.css"):
+ if not os.path.isfile(cssfile):
+ continue
+
+ head += stylesheet(cssfile)
+
+ if os.path.exists(os.path.join(data_path, "user.css")):
+ head += stylesheet(os.path.join(data_path, "user.css"))
+
+ return head
+
+
+def reload_javascript():
+ js = javascript_html()
+ css = css_html()
+
def template_response(*args, **kwargs):
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
- res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
+ res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+ res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
res.init_headers()
return res
diff --git a/modules/ui_common.py b/modules/ui_common.py index 0f3427c8..27ab3ebb 100644 --- a/modules/ui_common.py +++ b/modules/ui_common.py @@ -125,7 +125,7 @@ Requested path was: {f} with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
with gr.Group(elem_id=f"{tabname}_gallery_container"):
- result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)
generation_info = None
with gr.Column():
@@ -145,8 +145,7 @@ Requested path was: {f} )
if tabname != "extras":
- with gr.Row():
- download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
+ download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
with gr.Group():
html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
diff --git a/modules/ui_components.py b/modules/ui_components.py index 2b1da2cb..64451df7 100644 --- a/modules/ui_components.py +++ b/modules/ui_components.py @@ -62,3 +62,13 @@ class DropdownMulti(FormComponent, gr.Dropdown): def get_block_name(self):
return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+ """Same as gr.Dropdown but allows editing value"""
+ def __init__(self, **kwargs):
+ super().__init__(allow_custom_value=True, **kwargs)
+
+ def get_block_name(self):
+ return "dropdown"
+
diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py index da7e79f0..99ac8756 100644 --- a/modules/ui_extensions.py +++ b/modules/ui_extensions.py @@ -2,6 +2,7 @@ import json import os.path
import sys
import time
+from datetime import datetime
import traceback
import git
@@ -11,17 +12,19 @@ import html import shutil
import errno
-from modules import extensions, shared, paths
+from modules import extensions, shared, paths, config_states
+from modules.paths_internal import config_states_dir
from modules.call_queue import wrap_gradio_gpu_call
available_extensions = {"extensions": []}
+STYLE_PRIMARY = ' style="color: var(--primary-400)"'
def check_access():
assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"
-def apply_and_restart(disable_list, update_list):
+def apply_and_restart(disable_list, update_list, disable_all):
check_access()
disabled = json.loads(disable_list)
@@ -30,6 +33,9 @@ def apply_and_restart(disable_list, update_list): update = json.loads(update_list)
assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"
+ if update:
+ save_config_state("Backup (pre-update)")
+
update = set(update)
for ext in extensions.extensions:
@@ -43,12 +49,53 @@ def apply_and_restart(disable_list, update_list): print(traceback.format_exc(), file=sys.stderr)
shared.opts.disabled_extensions = disabled
+ shared.opts.disable_all_extensions = disable_all
shared.opts.save(shared.config_filename)
shared.state.interrupt()
shared.state.need_restart = True
+def save_config_state(name):
+ current_config_state = config_states.get_config()
+ if not name:
+ name = "Config"
+ current_config_state["name"] = name
+ filename = os.path.join(config_states_dir, datetime.now().strftime("%Y_%m_%d-%H_%M_%S") + "_" + name + ".json")
+ print(f"Saving backup of webui/extension state to {filename}.")
+ with open(filename, "w", encoding="utf-8") as f:
+ json.dump(current_config_state, f)
+ config_states.list_config_states()
+ new_value = next(iter(config_states.all_config_states.keys()), "Current")
+ new_choices = ["Current"] + list(config_states.all_config_states.keys())
+ return gr.Dropdown.update(value=new_value, choices=new_choices), f"<span>Saved current webui/extension state to \"{filename}\"</span>"
+
+
+def restore_config_state(confirmed, config_state_name, restore_type):
+ if config_state_name == "Current":
+ return "<span>Select a config to restore from.</span>"
+ if not confirmed:
+ return "<span>Cancelled.</span>"
+
+ check_access()
+
+ config_state = config_states.all_config_states[config_state_name]
+
+ print(f"*** Restoring webui state from backup: {restore_type} ***")
+
+ if restore_type == "extensions" or restore_type == "both":
+ shared.opts.restore_config_state_file = config_state["filepath"]
+ shared.opts.save(shared.config_filename)
+
+ if restore_type == "webui" or restore_type == "both":
+ config_states.restore_webui_config(config_state)
+
+ shared.state.interrupt()
+ shared.state.need_restart = True
+
+ return ""
+
+
def check_updates(id_task, disable_list):
check_access()
@@ -63,6 +110,9 @@ def check_updates(id_task, disable_list): try:
ext.check_updates()
+ except FileNotFoundError as e:
+ if 'FETCH_HEAD' not in str(e):
+ raise
except Exception:
print(f"Error checking updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
@@ -72,6 +122,16 @@ def check_updates(id_task, disable_list): return extension_table(), ""
+def make_commit_link(commit_hash, remote, text=None):
+ if text is None:
+ text = commit_hash[:8]
+ if remote.startswith("https://github.com/"):
+ href = os.path.join(remote, "commit", commit_hash)
+ return f'<a href="{href}" target="_blank">{text}</a>'
+ else:
+ return text
+
+
def extension_table():
code = f"""<!-- {time.time()} -->
<table id="extensions">
@@ -87,6 +147,8 @@ def extension_table(): """
for ext in extensions.extensions:
+ ext.read_info_from_repo()
+
remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
if ext.can_update:
@@ -94,11 +156,19 @@ def extension_table(): else:
ext_status = ext.status
+ style = ""
+ if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+ style = STYLE_PRIMARY
+
+ version_link = ext.version
+ if ext.commit_hash and ext.remote:
+ version_link = make_commit_link(ext.commit_hash, ext.remote, ext.version)
+
code += f"""
<tr>
- <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+ <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
<td>{remote}</td>
- <td>{ext.version}</td>
+ <td>{version_link}</td>
<td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
</tr>
"""
@@ -111,6 +181,133 @@ def extension_table(): return code
+def update_config_states_table(state_name):
+ if state_name == "Current":
+ config_state = config_states.get_config()
+ else:
+ config_state = config_states.all_config_states[state_name]
+
+ config_name = config_state.get("name", "Config")
+ created_date = time.asctime(time.gmtime(config_state["created_at"]))
+ filepath = config_state.get("filepath", "<unknown>")
+
+ code = f"""<!-- {time.time()} -->"""
+
+ webui_remote = config_state["webui"]["remote"] or ""
+ webui_branch = config_state["webui"]["branch"]
+ webui_commit_hash = config_state["webui"]["commit_hash"] or "<unknown>"
+ webui_commit_date = config_state["webui"]["commit_date"]
+ if webui_commit_date:
+ webui_commit_date = time.asctime(time.gmtime(webui_commit_date))
+ else:
+ webui_commit_date = "<unknown>"
+
+ remote = f"""<a href="{html.escape(webui_remote)}" target="_blank">{html.escape(webui_remote or '')}</a>"""
+ commit_link = make_commit_link(webui_commit_hash, webui_remote)
+ date_link = make_commit_link(webui_commit_hash, webui_remote, webui_commit_date)
+
+ current_webui = config_states.get_webui_config()
+
+ style_remote = ""
+ style_branch = ""
+ style_commit = ""
+ if current_webui["remote"] != webui_remote:
+ style_remote = STYLE_PRIMARY
+ if current_webui["branch"] != webui_branch:
+ style_branch = STYLE_PRIMARY
+ if current_webui["commit_hash"] != webui_commit_hash:
+ style_commit = STYLE_PRIMARY
+
+ code += f"""<h2>Config Backup: {config_name}</h2>
+ <div><b>Filepath:</b> {filepath}</div>
+ <div><b>Created at:</b> {created_date}</div>"""
+
+ code += f"""<h2>WebUI State</h2>
+ <table id="config_state_webui">
+ <thead>
+ <tr>
+ <th>URL</th>
+ <th>Branch</th>
+ <th>Commit</th>
+ <th>Date</th>
+ </tr>
+ </thead>
+ <tbody>
+ <tr>
+ <td><label{style_remote}>{remote}</label></td>
+ <td><label{style_branch}>{webui_branch}</label></td>
+ <td><label{style_commit}>{commit_link}</label></td>
+ <td><label{style_commit}>{date_link}</label></td>
+ </tr>
+ </tbody>
+ </table>
+ """
+
+ code += """<h2>Extension State</h2>
+ <table id="config_state_extensions">
+ <thead>
+ <tr>
+ <th>Extension</th>
+ <th>URL</th>
+ <th>Branch</th>
+ <th>Commit</th>
+ <th>Date</th>
+ </tr>
+ </thead>
+ <tbody>
+ """
+
+ ext_map = {ext.name: ext for ext in extensions.extensions}
+
+ for ext_name, ext_conf in config_state["extensions"].items():
+ ext_remote = ext_conf["remote"] or ""
+ ext_branch = ext_conf["branch"] or "<unknown>"
+ ext_enabled = ext_conf["enabled"]
+ ext_commit_hash = ext_conf["commit_hash"] or "<unknown>"
+ ext_commit_date = ext_conf["commit_date"]
+ if ext_commit_date:
+ ext_commit_date = time.asctime(time.gmtime(ext_commit_date))
+ else:
+ ext_commit_date = "<unknown>"
+
+ remote = f"""<a href="{html.escape(ext_remote)}" target="_blank">{html.escape(ext_remote or '')}</a>"""
+ commit_link = make_commit_link(ext_commit_hash, ext_remote)
+ date_link = make_commit_link(ext_commit_hash, ext_remote, ext_commit_date)
+
+ style_enabled = ""
+ style_remote = ""
+ style_branch = ""
+ style_commit = ""
+ if ext_name in ext_map:
+ current_ext = ext_map[ext_name]
+ current_ext.read_info_from_repo()
+ if current_ext.enabled != ext_enabled:
+ style_enabled = STYLE_PRIMARY
+ if current_ext.remote != ext_remote:
+ style_remote = STYLE_PRIMARY
+ if current_ext.branch != ext_branch:
+ style_branch = STYLE_PRIMARY
+ if current_ext.commit_hash != ext_commit_hash:
+ style_commit = STYLE_PRIMARY
+
+ code += f"""
+ <tr>
+ <td><label{style_enabled}><input class="gr-check-radio gr-checkbox" type="checkbox" disabled="true" {'checked="checked"' if ext_enabled else ''}>{html.escape(ext_name)}</label></td>
+ <td><label{style_remote}>{remote}</label></td>
+ <td><label{style_branch}>{ext_branch}</label></td>
+ <td><label{style_commit}>{commit_link}</label></td>
+ <td><label{style_commit}>{date_link}</label></td>
+ </tr>
+ """
+
+ code += """
+ </tbody>
+ </table>
+ """
+
+ return code
+
+
def normalize_git_url(url):
if url is None:
return ""
@@ -119,7 +316,7 @@ def normalize_git_url(url): return url
-def install_extension_from_url(dirname, url):
+def install_extension_from_url(dirname, url, branch_name=None):
check_access()
assert url, 'No URL specified'
@@ -140,10 +337,17 @@ def install_extension_from_url(dirname, url): try:
shutil.rmtree(tmpdir, True)
- with git.Repo.clone_from(url, tmpdir) as repo:
- repo.remote().fetch()
- for submodule in repo.submodules:
- submodule.update()
+ if not branch_name:
+ # if no branch is specified, use the default branch
+ with git.Repo.clone_from(url, tmpdir) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
+ else:
+ with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
+ repo.remote().fetch()
+ for submodule in repo.submodules:
+ submodule.update()
try:
os.rename(tmpdir, target_dir)
except OSError as err:
@@ -282,23 +486,33 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=" def create_ui():
import modules.ui
+ config_states.list_config_states()
+
with gr.Blocks(analytics_enabled=False) as ui:
with gr.Tabs(elem_id="tabs_extensions") as tabs:
- with gr.TabItem("Installed"):
+ with gr.TabItem("Installed", id="installed"):
with gr.Row(elem_id="extensions_installed_top"):
apply = gr.Button(value="Apply and restart UI", variant="primary")
check = gr.Button(value="Check for updates")
+ extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
- info = gr.HTML()
+ html = ""
+ if shared.opts.disable_all_extensions != "none":
+ html = """
+<span style="color: var(--primary-400);">
+ "Disable all extensions" was set, change it to "none" to load all extensions again
+</span>
+ """
+ info = gr.HTML(html)
extensions_table = gr.HTML(lambda: extension_table())
apply.click(
fn=apply_and_restart,
_js="extensions_apply",
- inputs=[extensions_disabled_list, extensions_update_list],
+ inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
outputs=[],
)
@@ -309,7 +523,7 @@ def create_ui(): outputs=[extensions_table, info],
)
- with gr.TabItem("Available"):
+ with gr.TabItem("Available", id="available"):
with gr.Row():
refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json", label="Extension index URL").style(container=False)
@@ -356,16 +570,41 @@ def create_ui(): outputs=[available_extensions_table, install_result]
)
- with gr.TabItem("Install from URL"):
+ with gr.TabItem("Install from URL", id="install_from_url"):
install_url = gr.Text(label="URL for extension's git repository")
+ install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")
install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
install_button = gr.Button(value="Install", variant="primary")
install_result = gr.HTML(elem_id="extension_install_result")
install_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
- inputs=[install_dirname, install_url],
+ inputs=[install_dirname, install_url, install_branch],
outputs=[extensions_table, install_result],
)
+ with gr.TabItem("Backup/Restore"):
+ with gr.Row(elem_id="extensions_backup_top_row"):
+ config_states_list = gr.Dropdown(label="Saved Configs", elem_id="extension_backup_saved_configs", value="Current", choices=["Current"] + list(config_states.all_config_states.keys()))
+ modules.ui.create_refresh_button(config_states_list, config_states.list_config_states, lambda: {"choices": ["Current"] + list(config_states.all_config_states.keys())}, "refresh_config_states")
+ config_restore_type = gr.Radio(label="State to restore", choices=["extensions", "webui", "both"], value="extensions", elem_id="extension_backup_restore_type")
+ config_restore_button = gr.Button(value="Restore Selected Config", variant="primary", elem_id="extension_backup_restore")
+ with gr.Row(elem_id="extensions_backup_top_row2"):
+ config_save_name = gr.Textbox("", placeholder="Config Name", show_label=False)
+ config_save_button = gr.Button(value="Save Current Config")
+
+ config_states_info = gr.HTML("")
+ config_states_table = gr.HTML(lambda: update_config_states_table("Current"))
+
+ config_save_button.click(fn=save_config_state, inputs=[config_save_name], outputs=[config_states_list, config_states_info])
+
+ dummy_component = gr.Label(visible=False)
+ config_restore_button.click(fn=restore_config_state, _js="config_state_confirm_restore", inputs=[dummy_component, config_states_list, config_restore_type], outputs=[config_states_info])
+
+ config_states_list.change(
+ fn=update_config_states_table,
+ inputs=[config_states_list],
+ outputs=[config_states_table],
+ )
+
return ui
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py index daea03d6..aa2f5d1b 100644 --- a/modules/ui_extra_networks.py +++ b/modules/ui_extra_networks.py @@ -2,8 +2,10 @@ import glob import os.path
import urllib.parse
from pathlib import Path
+from PIL import PngImagePlugin
from modules import shared
+from modules.images import read_info_from_image
import gradio as gr
import json
import html
@@ -239,7 +241,7 @@ def create_ui(container, button, tabname): with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
for page in ui.stored_extra_pages:
- with gr.Tab(page.title):
+ with gr.Tab(page.title, id=page.title.lower().replace(" ", "_")):
page_elem = gr.HTML(page.create_html(ui.tabname))
ui.pages.append(page_elem)
@@ -252,10 +254,10 @@ def create_ui(container, button, tabname): def toggle_visibility(is_visible):
is_visible = not is_visible
- return is_visible, gr.update(visible=is_visible)
+ return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
state_visible = gr.State(value=False)
- button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container])
+ button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
def refresh():
res = []
@@ -290,6 +292,7 @@ def setup_ui(ui, gallery): img_info = images[index if index >= 0 else 0]
image = image_from_url_text(img_info)
+ geninfo, items = read_info_from_image(image)
is_allowed = False
for extra_page in ui.stored_extra_pages:
@@ -299,7 +302,12 @@ def setup_ui(ui, gallery): assert is_allowed, f'writing to {filename} is not allowed'
- image.save(filename)
+ if geninfo:
+ pnginfo_data = PngImagePlugin.PngInfo()
+ pnginfo_data.add_text('parameters', geninfo)
+ image.save(filename, pnginfo=pnginfo_data)
+ else:
+ image.save(filename)
return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py index b418d955..f25639e5 100644 --- a/modules/ui_postprocessing.py +++ b/modules/ui_postprocessing.py @@ -9,13 +9,13 @@ def create_ui(): with gr.Row().style(equal_height=False, variant='compact'):
with gr.Column(variant='compact'):
with gr.Tabs(elem_id="mode_extras"):
- with gr.TabItem('Single Image', elem_id="extras_single_tab") as tab_single:
+ with gr.TabItem('Single Image', id="single_image", elem_id="extras_single_tab") as tab_single:
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
- with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
- image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
+ with gr.TabItem('Batch Process', id="batch_process", elem_id="extras_batch_process_tab") as tab_batch:
+ image_batch = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
- with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
+ with gr.TabItem('Batch from Directory', id="batch_from_directory", elem_id="extras_batch_directory_tab") as tab_batch_dir:
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
diff --git a/requirements.txt b/requirements.txt index c72b2927..44e44608 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,11 @@ +astunparse
blendmodes
accelerate
basicsr
fonts
font-roboto
gfpgan
-gradio==3.23
-invisible-watermark
+gradio==3.27
numpy
omegaconf
opencv-contrib-python
diff --git a/requirements_versions.txt b/requirements_versions.txt index df65431a..94d32d3d 100644 --- a/requirements_versions.txt +++ b/requirements_versions.txt @@ -1,10 +1,10 @@ blendmodes==2022
transformers==4.25.1
-accelerate==0.12.0
+accelerate==0.18.0
basicsr==1.4.2
gfpgan==1.3.8
-gradio==3.23
-numpy==1.23.3
+gradio==3.27
+numpy==1.23.5
Pillow==9.4.0
realesrgan==0.3.0
torch
@@ -25,6 +25,6 @@ lark==1.1.2 inflection==0.5.1
GitPython==3.1.30
torchsde==0.2.5
-safetensors==0.3.0
+safetensors==0.3.1
httpcore<=0.15
fastapi==0.94.0
@@ -7,7 +7,7 @@ function gradioApp() { } function get_uiCurrentTab() { - return gradioApp().querySelector('#tabs button:not(.border-transparent)') + return gradioApp().querySelector('#tabs button.selected') } function get_uiCurrentTabContent() { diff --git a/scripts/custom_code.py b/scripts/custom_code.py index d29113e6..4071d86d 100644 --- a/scripts/custom_code.py +++ b/scripts/custom_code.py @@ -1,9 +1,40 @@ import modules.scripts as scripts
import gradio as gr
+import ast
+import copy
from modules.processing import Processed
from modules.shared import opts, cmd_opts, state
+
+def convertExpr2Expression(expr):
+ expr.lineno = 0
+ expr.col_offset = 0
+ result = ast.Expression(expr.value, lineno=0, col_offset = 0)
+
+ return result
+
+
+def exec_with_return(code, module):
+ """
+ like exec() but can return values
+ https://stackoverflow.com/a/52361938/5862977
+ """
+ code_ast = ast.parse(code)
+
+ init_ast = copy.deepcopy(code_ast)
+ init_ast.body = code_ast.body[:-1]
+
+ last_ast = copy.deepcopy(code_ast)
+ last_ast.body = code_ast.body[-1:]
+
+ exec(compile(init_ast, "<ast>", "exec"), module.__dict__)
+ if type(last_ast.body[0]) == ast.Expr:
+ return eval(compile(convertExpr2Expression(last_ast.body[0]), "<ast>", "eval"), module.__dict__)
+ else:
+ exec(compile(last_ast, "<ast>", "exec"), module.__dict__)
+
+
class Script(scripts.Script):
def title(self):
@@ -13,12 +44,23 @@ class Script(scripts.Script): return cmd_opts.allow_code
def ui(self, is_img2img):
- code = gr.Textbox(label="Python code", lines=1, elem_id=self.elem_id("code"))
+ example = """from modules.processing import process_images
+
+p.width = 768
+p.height = 768
+p.batch_size = 2
+p.steps = 10
+
+return process_images(p)
+"""
+
- return [code]
+ code = gr.Code(value=example, language="python", label="Python code", elem_id=self.elem_id("code"))
+ indent_level = gr.Number(label='Indent level', value=2, precision=0, elem_id=self.elem_id("indent_level"))
+ return [code, indent_level]
- def run(self, p, code):
+ def run(self, p, code, indent_level):
assert cmd_opts.allow_code, '--allow-code option must be enabled'
display_result_data = [[], -1, ""]
@@ -29,13 +71,20 @@ class Script(scripts.Script): display_result_data[2] = i
from types import ModuleType
- compiled = compile(code, '', 'exec')
module = ModuleType("testmodule")
module.__dict__.update(globals())
module.p = p
module.display = display
- exec(compiled, module.__dict__)
+
+ indent = " " * indent_level
+ indented = code.replace('\n', '\n' + indent)
+ body = f"""def __webuitemp__():
+{indent}{indented}
+__webuitemp__()"""
+
+ result = exec_with_return(body, module)
+
+ if isinstance(result, Processed):
+ return result
return Processed(p, *display_result_data)
-
-
\ No newline at end of file diff --git a/scripts/loopback.py b/scripts/loopback.py index 9c388aa8..d3065fe6 100644 --- a/scripts/loopback.py +++ b/scripts/loopback.py @@ -54,15 +54,12 @@ class Script(scripts.Script): return strength
progress = loop / (loops - 1)
- match denoising_curve:
- case "Aggressive":
- strength = math.sin((progress) * math.pi * 0.5)
-
- case "Lazy":
- strength = 1 - math.cos((progress) * math.pi * 0.5)
-
- case _:
- strength = progress
+ if denoising_curve == "Aggressive":
+ strength = math.sin((progress) * math.pi * 0.5)
+ elif denoising_curve == "Lazy":
+ strength = 1 - math.cos((progress) * math.pi * 0.5)
+ else:
+ strength = progress
change = (final_denoising_strength - initial_denoising_strength) * strength
return initial_denoising_strength + change
diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index 0906da6a..670bb8ac 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -275,7 +275,7 @@ class Script(scripts.Script): if opts.samples_save:
for img in all_processed_images:
- images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)
+ images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.samples_format, info=res.info, p=p)
if opts.grid_save and not unwanted_grid_because_of_img_count:
images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)
diff --git a/scripts/poor_mans_outpainting.py b/scripts/poor_mans_outpainting.py index d8feda00..ddcbd2d3 100644 --- a/scripts/poor_mans_outpainting.py +++ b/scripts/poor_mans_outpainting.py @@ -138,7 +138,7 @@ class Script(scripts.Script): combined_image = images.combine_grid(grid)
if opts.samples_save:
- images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.grid_format, info=initial_info, p=p)
+ images.save_image(combined_image, p.outpath_samples, "", initial_seed, p.prompt, opts.samples_format, info=initial_info, p=p)
processed = Processed(p, [combined_image], initial_seed, initial_info)
diff --git a/scripts/postprocessing_upscale.py b/scripts/postprocessing_upscale.py index 11eab31a..ef1186ac 100644 --- a/scripts/postprocessing_upscale.py +++ b/scripts/postprocessing_upscale.py @@ -4,8 +4,8 @@ import numpy as np from modules import scripts_postprocessing, shared
import gradio as gr
-from modules.ui_components import FormRow
-
+from modules.ui_components import FormRow, ToolButton
+from modules.ui import switch_values_symbol
upscale_cache = {}
@@ -25,9 +25,12 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
with FormRow():
- upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
- upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
- upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
+ with gr.Column(elem_id="upscaling_column_size", scale=4):
+ upscaling_resize_w = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="extras_upscaling_resize_w")
+ upscaling_resize_h = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="extras_upscaling_resize_h")
+ with gr.Column(elem_id="upscaling_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+ upscaling_res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="upscaling_res_switch_btn")
+ upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
with FormRow():
extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
@@ -36,6 +39,7 @@ class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing): extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
+ upscaling_res_switch_btn.click(lambda w, h: (h, w), inputs=[upscaling_resize_w, upscaling_resize_h], outputs=[upscaling_resize_w, upscaling_resize_h], show_progress=False)
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 3895a795..398065d9 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -211,7 +211,8 @@ axis_options = [ AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
- AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
+ AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: sorted(sd_models.checkpoints_list, key=str.casefold)),
+ AxisOption("Negative Guidance minimum sigma", float, apply_field("s_min_uncond")),
AxisOption("Sigma Churn", float, apply_field("s_churn")),
AxisOption("Sigma min", float, apply_field("s_tmin")),
AxisOption("Sigma max", float, apply_field("s_tmax")),
@@ -374,16 +375,19 @@ class Script(scripts.Script): with gr.Row():
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
+ x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
with gr.Row():
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
+ y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
with gr.Row():
z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
+ z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
with gr.Row(variant="compact", elem_id="axis_options"):
@@ -401,54 +405,74 @@ class Script(scripts.Script): swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
swap_xz_axes_button = gr.Button(value="Swap X/Z axes", elem_id="xz_grid_swap_axes_button")
- def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):
- return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values
+ def swap_axes(axis1_type, axis1_values, axis1_values_dropdown, axis2_type, axis2_values, axis2_values_dropdown):
+ return self.current_axis_options[axis2_type].label, axis2_values, axis2_values_dropdown, self.current_axis_options[axis1_type].label, axis1_values, axis1_values_dropdown
- xy_swap_args = [x_type, x_values, y_type, y_values]
+ xy_swap_args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown]
swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)
- yz_swap_args = [y_type, y_values, z_type, z_values]
+ yz_swap_args = [y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown]
swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)
- xz_swap_args = [x_type, x_values, z_type, z_values]
+ xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
def fill(x_type):
axis = self.current_axis_options[x_type]
- return ", ".join(axis.choices()) if axis.choices else gr.update()
-
- fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
- fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
- fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])
-
- def select_axis(x_type):
- return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
-
- x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
- y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
- z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])
+ return axis.choices() if axis.choices else gr.update()
+
+ fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
+ fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
+ fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
+
+ def select_axis(axis_type,axis_values_dropdown):
+ choices = self.current_axis_options[axis_type].choices
+ has_choices = choices is not None
+ current_values = axis_values_dropdown
+ if has_choices:
+ choices = choices()
+ if isinstance(current_values,str):
+ current_values = current_values.split(",")
+ current_values = list(filter(lambda x: x in choices, current_values))
+ return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
+
+ x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
+ y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
+ z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
+
+ def get_dropdown_update_from_params(axis,params):
+ val_key = axis + " Values"
+ vals = params.get(val_key,"")
+ valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+ return gr.update(value = valslist)
self.infotext_fields = (
(x_type, "X Type"),
(x_values, "X Values"),
+ (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
(y_type, "Y Type"),
(y_values, "Y Values"),
+ (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
(z_type, "Z Type"),
(z_values, "Z Values"),
+ (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
)
- return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+ return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
- def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
+ def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
if not opts.return_grid:
p.batch_size = 1
- def process_axis(opt, vals):
+ def process_axis(opt, vals, vals_dropdown):
if opt.label == 'Nothing':
return [0]
- valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
+ if opt.choices is not None:
+ valslist = vals_dropdown
+ else:
+ valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
if opt.type == int:
valslist_ext = []
@@ -506,13 +530,19 @@ class Script(scripts.Script): return valslist
x_opt = self.current_axis_options[x_type]
- xs = process_axis(x_opt, x_values)
+ if x_opt.choices is not None:
+ x_values = ",".join(x_values_dropdown)
+ xs = process_axis(x_opt, x_values, x_values_dropdown)
y_opt = self.current_axis_options[y_type]
- ys = process_axis(y_opt, y_values)
+ if y_opt.choices is not None:
+ y_values = ",".join(y_values_dropdown)
+ ys = process_axis(y_opt, y_values, y_values_dropdown)
z_opt = self.current_axis_options[z_type]
- zs = process_axis(z_opt, z_values)
+ if z_opt.choices is not None:
+ z_values = ",".join(z_values_dropdown)
+ zs = process_axis(z_opt, z_values, z_values_dropdown)
# this could be moved to common code, but unlikely to be ever triggered anywhere else
Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
@@ -7,7 +7,7 @@ --block-background-fill: transparent;
}
-.block.padded{
+.block.padded:not(.gradio-accordion) {
padding: 0 !important;
}
@@ -54,10 +54,6 @@ div.compact{ gap: 1em;
}
-.gradio-dropdown ul.options{
- z-index: 3000;
-}
-
.gradio-dropdown label span:not(.has-info),
.gradio-textbox label span:not(.has-info),
.gradio-number label span:not(.has-info)
@@ -65,11 +61,30 @@ div.compact{ margin-bottom: 0;
}
+.gradio-dropdown ul.options{
+ z-index: 3000;
+ min-width: fit-content;
+ max-width: inherit;
+ white-space: nowrap;
+}
+
+.gradio-dropdown ul.options li.item {
+ padding: 0.05em 0;
+}
+
+.gradio-dropdown ul.options li.item.selected {
+ background-color: var(--neutral-100);
+}
+
+.dark .gradio-dropdown ul.options li.item.selected {
+ background-color: var(--neutral-900);
+}
+
.gradio-dropdown div.wrap.wrap.wrap.wrap{
box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
}
-.gradio-dropdown .wrap-inner.wrap-inner.wrap-inner{
+.gradio-dropdown:not(.multiselect) .wrap-inner.wrap-inner.wrap-inner{
flex-wrap: unset;
}
@@ -123,6 +138,18 @@ div.gradio-html.min{ border-radius: 0.5em;
}
+.gradio-button.secondary-down{
+ background: var(--button-secondary-background-fill);
+ color: var(--button-secondary-text-color);
+}
+.gradio-button.secondary-down, .gradio-button.secondary-down:hover{
+ box-shadow: 1px 1px 1px rgba(0,0,0,0.25) inset, 0px 0px 3px rgba(0,0,0,0.15) inset;
+}
+.gradio-button.secondary-down:hover{
+ background: var(--button-secondary-background-fill-hover);
+ color: var(--button-secondary-text-color-hover);
+}
+
.checkboxes-row{
margin-bottom: 0.5em;
margin-left: 0em;
@@ -266,7 +293,12 @@ button.custom-button{ margin-left: -0.75em
}
-#txtimg_hr_finalres .resolution{
+#img2img_scale_resolution_preview.block{
+ display: flex;
+ align-items: end;
+}
+
+#txtimg_hr_finalres .resolution, #img2img_scale_resolution_preview .resolution{
font-weight: bold;
}
@@ -285,12 +317,23 @@ div.dimensions-tools{ align-content: center;
}
+div#extras_scale_to_tab div.form{
+ flex-direction: row;
+}
+
#mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{
height: 480px !important;
max-height: 480px !important;
min-height: 480px !important;
}
+#img2img_sketch, #img2maskimg, #inpaint_sketch {
+ overflow: overlay !important;
+ resize: auto;
+ background: var(--panel-background-fill);
+ z-index: 5;
+}
+
.image-buttons button{
min-width: auto;
}
@@ -299,9 +342,22 @@ div.dimensions-tools{ overflow-wrap: break-word;
}
+#img2img_column_batch{
+ align-self: end;
+ margin-bottom: 0.9em;
+}
+
+#img2img_unused_scale_by_slider{
+ visibility: hidden;
+ width: 0.5em;
+ max-width: 0.5em;
+ min-width: 0.5em;
+}
+
/* settings */
#quicksettings {
width: fit-content;
+ align-items: end;
}
#quicksettings > div, #quicksettings > fieldset{
@@ -507,6 +563,17 @@ div.dimensions-tools{ background-color: rgba(0, 0, 0, 0.8);
}
+#imageARPreview {
+ position: absolute;
+ top: 0px;
+ left: 0px;
+ border: 2px solid red;
+ background: rgba(255, 0, 0, 0.3);
+ z-index: 900;
+ pointer-events: none;
+ display: none;
+}
+
/* context menu (ie for the generate button) */
#context-menu{
@@ -596,6 +663,12 @@ footer { /* extra networks UI */
+.extra-network-cards{
+ height: 400px;
+ overflow: scroll;
+ resize: vertical;
+}
+
.extra-networks > div > [id *= '_extra_']{
margin: 0.3em;
}
diff --git a/webui-macos-env.sh b/webui-macos-env.sh index 37cac4fb..10ab81c9 100644 --- a/webui-macos-env.sh +++ b/webui-macos-env.sh @@ -11,7 +11,7 @@ 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==1.12.1 torchvision==0.13.1" +export TORCH_COMMAND="pip install torch torchvision" export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git" export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71" export PYTORCH_ENABLE_MPS_FALLBACK=1 diff --git a/webui-user.sh b/webui-user.sh index bfa53cb7..49a426ff 100644 --- a/webui-user.sh +++ b/webui-user.sh @@ -43,4 +43,7 @@ # Uncomment to enable accelerated launch #export ACCELERATE="True" +# Uncomment to disable TCMalloc +#export NO_TCMALLOC="True" + ########################################### @@ -5,6 +5,7 @@ import importlib import signal
import re
import warnings
+import json
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
@@ -20,6 +21,9 @@ startup_timer = timer.Timer() import torch
import pytorch_lightning # pytorch_lightning should be imported after torch, but it re-enables warnings on import so import once to disable them
warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
+warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
+
+
startup_timer.record("import torch")
import gradio
@@ -37,7 +41,7 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__: torch.__long_version__ = torch.__version__
torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
-from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks
+from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks, config_states
import modules.codeformer_model as codeformer
import modules.face_restoration
import modules.gfpgan_model as gfpgan
@@ -67,11 +71,51 @@ else: server_name = "0.0.0.0" if cmd_opts.listen else None
+def fix_asyncio_event_loop_policy():
+ """
+ The default `asyncio` event loop policy only automatically creates
+ event loops in the main threads. Other threads must create event
+ loops explicitly or `asyncio.get_event_loop` (and therefore
+ `.IOLoop.current`) will fail. Installing this policy allows event
+ loops to be created automatically on any thread, matching the
+ behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
+ """
+
+ import asyncio
+
+ if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
+ # "Any thread" and "selector" should be orthogonal, but there's not a clean
+ # interface for composing policies so pick the right base.
+ _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy # type: ignore
+ else:
+ _BasePolicy = asyncio.DefaultEventLoopPolicy
+
+ class AnyThreadEventLoopPolicy(_BasePolicy): # type: ignore
+ """Event loop policy that allows loop creation on any thread.
+ Usage::
+
+ asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+ """
+
+ def get_event_loop(self) -> asyncio.AbstractEventLoop:
+ try:
+ return super().get_event_loop()
+ except (RuntimeError, AssertionError):
+ # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
+ # and changed to a RuntimeError in 3.4.3.
+ # "There is no current event loop in thread %r"
+ loop = self.new_event_loop()
+ self.set_event_loop(loop)
+ return loop
+
+ asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
+
+
def check_versions():
if shared.cmd_opts.skip_version_check:
return
- expected_torch_version = "1.13.1"
+ expected_torch_version = "2.0.0"
if version.parse(torch.__version__) < version.parse(expected_torch_version):
errors.print_error_explanation(f"""
@@ -84,7 +128,7 @@ there are reports of issues with training tab on the latest version. Use --skip-version-check commandline argument to disable this check.
""".strip())
- expected_xformers_version = "0.0.16rc425"
+ expected_xformers_version = "0.0.17"
if shared.xformers_available:
import xformers
@@ -99,12 +143,27 @@ Use --skip-version-check commandline argument to disable this check. def initialize():
+ fix_asyncio_event_loop_policy()
+
check_versions()
extensions.list_extensions()
localization.list_localizations(cmd_opts.localizations_dir)
startup_timer.record("list extensions")
+ config_state_file = shared.opts.restore_config_state_file
+ shared.opts.restore_config_state_file = ""
+ shared.opts.save(shared.config_filename)
+
+ if os.path.isfile(config_state_file):
+ print(f"*** About to restore extension state from file: {config_state_file}")
+ with open(config_state_file, "r", encoding="utf-8") as f:
+ config_state = json.load(f)
+ config_states.restore_extension_config(config_state)
+ startup_timer.record("restore extension config")
+ elif config_state_file:
+ print(f"!!! Config state backup not found: {config_state_file}")
+
if cmd_opts.ui_debug_mode:
shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
modules.scripts.load_scripts()
@@ -126,9 +185,6 @@ def initialize(): modules.scripts.load_scripts()
startup_timer.record("load scripts")
- modelloader.load_upscalers()
- startup_timer.record("load upscalers")
-
modules.sd_vae.refresh_vae_list()
startup_timer.record("refresh VAE")
@@ -150,6 +206,7 @@ def initialize(): shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
+ shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
startup_timer.record("opts onchange")
shared.reload_hypernetworks()
@@ -265,9 +322,6 @@ def webui(): inbrowser=cmd_opts.autolaunch,
prevent_thread_lock=True
)
- for dep in shared.demo.dependencies:
- dep['show_progress'] = False # disable gradio css animation on component update
-
# after initial launch, disable --autolaunch for subsequent restarts
cmd_opts.autolaunch = False
@@ -304,6 +358,19 @@ def webui(): extensions.list_extensions()
startup_timer.record("list extensions")
+ config_state_file = shared.opts.restore_config_state_file
+ shared.opts.restore_config_state_file = ""
+ shared.opts.save(shared.config_filename)
+
+ if os.path.isfile(config_state_file):
+ print(f"*** About to restore extension state from file: {config_state_file}")
+ with open(config_state_file, "r", encoding="utf-8") as f:
+ config_state = json.load(f)
+ config_states.restore_extension_config(config_state)
+ startup_timer.record("restore extension config")
+ elif config_state_file:
+ print(f"!!! Config state backup not found: {config_state_file}")
+
localization.list_localizations(cmd_opts.localizations_dir)
modelloader.forbid_loaded_nonbuiltin_upscalers()
@@ -23,7 +23,7 @@ fi # Install directory without trailing slash if [[ -z "${install_dir}" ]] then - install_dir="/home/$(whoami)" + install_dir="$(pwd)" fi # Name of the subdirectory (defaults to stable-diffusion-webui) @@ -113,12 +113,13 @@ case "$gpu_info" in printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half" printf "\n%s\n" "${delimiter}" ;; - *) + *) ;; esac if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]] then - export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2" + # AMD users will still use torch 1.13 because 2.0 does not seem to work. + export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2" fi for preq in "${GIT}" "${python_cmd}" @@ -172,15 +173,30 @@ else exit 1 fi +# Try using TCMalloc on Linux +prepare_tcmalloc() { + if [[ "${OSTYPE}" == "linux"* ]] && [[ -z "${NO_TCMALLOC}" ]] && [[ -z "${LD_PRELOAD}" ]]; then + TCMALLOC="$(ldconfig -p | grep -Po "libtcmalloc.so.\d" | head -n 1)" + if [[ ! -z "${TCMALLOC}" ]]; then + echo "Using TCMalloc: ${TCMALLOC}" + export LD_PRELOAD="${TCMALLOC}" + else + printf "\e[1m\e[31mCannot locate TCMalloc (improves CPU memory usage)\e[0m\n" + fi + fi +} + if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ] then printf "\n%s\n" "${delimiter}" printf "Accelerating launch.py..." printf "\n%s\n" "${delimiter}" + prepare_tcmalloc exec accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@" else printf "\n%s\n" "${delimiter}" printf "Launching launch.py..." - printf "\n%s\n" "${delimiter}" + printf "\n%s\n" "${delimiter}" + prepare_tcmalloc exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@" fi |