Merge branch 'release_candidate'
This commit is contained in:
AUTOMATIC
commit
b08500cec8
45
CHANGELOG.md
45
CHANGELOG.md
@ -1,3 +1,48 @@
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## 1.2.0
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### Features:
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* do not wait for stable diffusion model to load at startup
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* add filename patterns: [denoising]
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* directory hiding for extra networks: dirs starting with . will hide their cards on extra network tabs unless specifically searched for
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* Lora: for the `<...>` text in prompt, use name of Lora that is in the metdata of the file, if present, instead of filename (both can be used to activate lora)
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* Lora: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
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* Lora: Fix some Loras not working (ones that have 3x3 convolution layer)
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* Lora: add an option to use old method of applying loras (producing same results as with kohya-ss)
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* add version to infotext, footer and console output when starting
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* add links to wiki for filename pattern settings
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* add extended info for quicksettings setting and use multiselect input instead of a text field
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### Minor:
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* gradio bumped to 3.29.0
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* torch bumped to 2.0.1
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* --subpath option for gradio for use with reverse proxy
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* linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
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* possible frontend optimization: do not apply localizations if there are none
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* Add extra `None` option for VAE in XYZ plot
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* print error to console when batch processing in img2img fails
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* create HTML for extra network pages only on demand
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* allow directories starting with . to still list their models for lora, checkpoints, etc
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* put infotext options into their own category in settings tab
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* do not show licenses page when user selects Show all pages in settings
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### Extensions:
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* Tooltip localization support
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* Add api method to get LoRA models with prompt
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### Bug Fixes:
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* re-add /docs endpoint
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* fix gamepad navigation
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* make the lightbox fullscreen image function properly
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* fix squished thumbnails in extras tab
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* keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
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* fix webui showing the same image if you configure the generation to always save results into same file
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* fix bug with upscalers not working properly
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* Fix MPS on PyTorch 2.0.1, Intel Macs
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* make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
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* prevent Reload UI button/link from reloading the page when it's not yet ready
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* fix prompts from file script failing to read contents from a drag/drop file
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## 1.1.1
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### Bug Fixes:
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* fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle
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@ -1,6 +1,7 @@
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from modules import extra_networks, shared
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import lora
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class ExtraNetworkLora(extra_networks.ExtraNetwork):
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def __init__(self):
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super().__init__('lora')
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@ -4,7 +4,7 @@ import re
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import torch
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from typing import Union
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from modules import shared, devices, sd_models, errors
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from modules import shared, devices, sd_models, errors, scripts
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metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
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@ -93,6 +93,7 @@ class LoraOnDisk:
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self.metadata = m
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self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text
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self.alias = self.metadata.get('ss_output_name', self.name)
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class LoraModule:
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@ -165,8 +166,10 @@ def load_lora(name, filename):
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.MultiheadAttention:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.Conv2d:
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elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
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elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
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module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
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else:
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print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
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continue
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@ -199,11 +202,11 @@ def load_loras(names, multipliers=None):
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loaded_loras.clear()
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loras_on_disk = [available_loras.get(name, None) for name in names]
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loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
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if any([x is None for x in loras_on_disk]):
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list_available_loras()
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loras_on_disk = [available_loras.get(name, None) for name in names]
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loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
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for i, name in enumerate(names):
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lora = already_loaded.get(name, None)
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@ -232,6 +235,8 @@ def lora_calc_updown(lora, module, target):
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if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
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updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
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elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
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updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
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else:
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updown = up @ down
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@ -240,6 +245,19 @@ def lora_calc_updown(lora, module, target):
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return updown
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def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
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weights_backup = getattr(self, "lora_weights_backup", None)
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if weights_backup is None:
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return
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if isinstance(self, torch.nn.MultiheadAttention):
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self.in_proj_weight.copy_(weights_backup[0])
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self.out_proj.weight.copy_(weights_backup[1])
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else:
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self.weight.copy_(weights_backup)
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def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
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"""
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Applies the currently selected set of Loras to the weights of torch layer self.
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@ -264,12 +282,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
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self.lora_weights_backup = weights_backup
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if current_names != wanted_names:
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if weights_backup is not None:
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if isinstance(self, torch.nn.MultiheadAttention):
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self.in_proj_weight.copy_(weights_backup[0])
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self.out_proj.weight.copy_(weights_backup[1])
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else:
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self.weight.copy_(weights_backup)
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lora_restore_weights_from_backup(self)
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for lora in loaded_loras:
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module = lora.modules.get(lora_layer_name, None)
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@ -300,12 +313,45 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
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setattr(self, "lora_current_names", wanted_names)
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def lora_forward(module, input, original_forward):
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"""
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Old way of applying Lora by executing operations during layer's forward.
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Stacking many loras this way results in big performance degradation.
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"""
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if len(loaded_loras) == 0:
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return original_forward(module, input)
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input = devices.cond_cast_unet(input)
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lora_restore_weights_from_backup(module)
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lora_reset_cached_weight(module)
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res = original_forward(module, input)
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lora_layer_name = getattr(module, 'lora_layer_name', None)
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for lora in loaded_loras:
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module = lora.modules.get(lora_layer_name, None)
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if module is None:
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continue
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module.up.to(device=devices.device)
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module.down.to(device=devices.device)
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res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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return res
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def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
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setattr(self, "lora_current_names", ())
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setattr(self, "lora_weights_backup", None)
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def lora_Linear_forward(self, input):
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if shared.opts.lora_functional:
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return lora_forward(self, input, torch.nn.Linear_forward_before_lora)
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lora_apply_weights(self)
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return torch.nn.Linear_forward_before_lora(self, input)
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@ -318,6 +364,9 @@ def lora_Linear_load_state_dict(self, *args, **kwargs):
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def lora_Conv2d_forward(self, input):
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if shared.opts.lora_functional:
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return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora)
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lora_apply_weights(self)
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return torch.nn.Conv2d_forward_before_lora(self, input)
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@ -343,24 +392,59 @@ def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
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def list_available_loras():
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available_loras.clear()
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available_lora_aliases.clear()
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os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
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candidates = \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.pt'), recursive=True) + \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
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glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)
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candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
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for filename in sorted(candidates, key=str.lower):
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if os.path.isdir(filename):
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continue
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name = os.path.splitext(os.path.basename(filename))[0]
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entry = LoraOnDisk(name, filename)
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available_loras[name] = LoraOnDisk(name, filename)
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available_loras[name] = entry
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available_lora_aliases[name] = entry
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available_lora_aliases[entry.alias] = entry
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re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
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def infotext_pasted(infotext, params):
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if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
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return # if the other extension is active, it will handle those fields, no need to do anything
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added = []
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for k, v in params.items():
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if not k.startswith("AddNet Model "):
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continue
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num = k[13:]
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if params.get("AddNet Module " + num) != "LoRA":
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continue
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name = params.get("AddNet Model " + num)
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if name is None:
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continue
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m = re_lora_name.match(name)
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if m:
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name = m.group(1)
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multiplier = params.get("AddNet Weight A " + num, "1.0")
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added.append(f"<lora:{name}:{multiplier}>")
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if added:
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params["Prompt"] += "\n" + "".join(added)
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available_loras = {}
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available_lora_aliases = {}
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loaded_loras = []
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list_available_loras()
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@ -1,12 +1,12 @@
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import torch
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import gradio as gr
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from fastapi import FastAPI
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import lora
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import extra_networks_lora
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import ui_extra_networks_lora
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from modules import script_callbacks, ui_extra_networks, extra_networks, shared
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def unload():
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torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
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torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
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@ -49,8 +49,33 @@ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention
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script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
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script_callbacks.on_script_unloaded(unload)
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script_callbacks.on_before_ui(before_ui)
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script_callbacks.on_infotext_pasted(lora.infotext_pasted)
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|
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shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
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"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),
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}))
|
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|
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|
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shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
|
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"lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
|
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}))
|
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|
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|
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def create_lora_json(obj: lora.LoraOnDisk):
|
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return {
|
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"name": obj.name,
|
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"alias": obj.alias,
|
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"path": obj.filename,
|
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"metadata": obj.metadata,
|
||||
}
|
||||
|
||||
|
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def api_loras(_: gr.Blocks, app: FastAPI):
|
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@app.get("/sdapi/v1/loras")
|
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async def get_loras():
|
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return [create_lora_json(obj) for obj in lora.available_loras.values()]
|
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|
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|
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script_callbacks.on_app_started(api_loras)
|
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|
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|
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@ -21,7 +21,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
|
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"preview": self.find_preview(path),
|
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"description": self.find_description(path),
|
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"search_term": self.search_terms_from_path(lora_on_disk.filename),
|
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"prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
|
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"prompt": json.dumps(f"<lora:{lora_on_disk.alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
|
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"local_preview": f"{path}.{shared.opts.samples_format}",
|
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"metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
|
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}
|
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|
||||
@ -6,7 +6,7 @@
|
||||
<ul>
|
||||
<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
|
||||
</ul>
|
||||
<span style="display:none" class='search_term'>{search_term}</span>
|
||||
<span style="display:none" class='search_term{serach_only}'>{search_term}</span>
|
||||
</div>
|
||||
<span class='name'>{name}</span>
|
||||
<span class='description'>{description}</span>
|
||||
|
||||
@ -45,29 +45,24 @@ function dimensionChange(e, is_width, is_height){
|
||||
|
||||
var viewportOffset = targetElement.getBoundingClientRect();
|
||||
|
||||
viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
|
||||
var viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
|
||||
|
||||
scaledx = targetElement.naturalWidth*viewportscale
|
||||
scaledy = targetElement.naturalHeight*viewportscale
|
||||
var scaledx = targetElement.naturalWidth*viewportscale
|
||||
var scaledy = targetElement.naturalHeight*viewportscale
|
||||
|
||||
cleintRectTop = (viewportOffset.top+window.scrollY)
|
||||
cleintRectLeft = (viewportOffset.left+window.scrollX)
|
||||
cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
|
||||
cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
|
||||
var cleintRectTop = (viewportOffset.top+window.scrollY)
|
||||
var cleintRectLeft = (viewportOffset.left+window.scrollX)
|
||||
var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
|
||||
var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
|
||||
|
||||
viewRectTop = cleintRectCentreY-(scaledy/2)
|
||||
viewRectLeft = cleintRectCentreX-(scaledx/2)
|
||||
arRectWidth = scaledx
|
||||
arRectHeight = scaledy
|
||||
var arscale = Math.min( scaledx/currentWidth, scaledy/currentHeight )
|
||||
var arscaledx = currentWidth*arscale
|
||||
var arscaledy = currentHeight*arscale
|
||||
|
||||
arscale = Math.min( arRectWidth/currentWidth, arRectHeight/currentHeight )
|
||||
arscaledx = currentWidth*arscale
|
||||
arscaledy = currentHeight*arscale
|
||||
|
||||
arRectTop = cleintRectCentreY-(arscaledy/2)
|
||||
arRectLeft = cleintRectCentreX-(arscaledx/2)
|
||||
arRectWidth = arscaledx
|
||||
arRectHeight = arscaledy
|
||||
var arRectTop = cleintRectCentreY-(arscaledy/2)
|
||||
var arRectLeft = cleintRectCentreX-(arscaledx/2)
|
||||
var arRectWidth = arscaledx
|
||||
var arRectHeight = arscaledy
|
||||
|
||||
arPreviewRect.style.top = arRectTop+'px';
|
||||
arPreviewRect.style.left = arRectLeft+'px';
|
||||
|
||||
@ -4,7 +4,7 @@ contextMenuInit = function(){
|
||||
let menuSpecs = new Map();
|
||||
|
||||
const uid = function(){
|
||||
return Date.now().toString(36) + Math.random().toString(36).substr(2);
|
||||
return Date.now().toString(36) + Math.random().toString(36).substring(2);
|
||||
}
|
||||
|
||||
function showContextMenu(event,element,menuEntries){
|
||||
@ -16,8 +16,7 @@ contextMenuInit = function(){
|
||||
oldMenu.remove()
|
||||
}
|
||||
|
||||
let tabButton = uiCurrentTab
|
||||
let baseStyle = window.getComputedStyle(tabButton)
|
||||
let baseStyle = window.getComputedStyle(uiCurrentTab)
|
||||
|
||||
const contextMenu = document.createElement('nav')
|
||||
contextMenu.id = "context-menu"
|
||||
@ -36,7 +35,7 @@ contextMenuInit = function(){
|
||||
menuEntries.forEach(function(entry){
|
||||
let contextMenuEntry = document.createElement('a')
|
||||
contextMenuEntry.innerHTML = entry['name']
|
||||
contextMenuEntry.addEventListener("click", function(e) {
|
||||
contextMenuEntry.addEventListener("click", function() {
|
||||
entry['func']();
|
||||
})
|
||||
contextMenuList.append(contextMenuEntry);
|
||||
@ -63,7 +62,7 @@ contextMenuInit = function(){
|
||||
|
||||
function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
|
||||
|
||||
currentItems = menuSpecs.get(targetElementSelector)
|
||||
var currentItems = menuSpecs.get(targetElementSelector)
|
||||
|
||||
if(!currentItems){
|
||||
currentItems = []
|
||||
@ -79,7 +78,7 @@ contextMenuInit = function(){
|
||||
}
|
||||
|
||||
function removeContextMenuOption(uid){
|
||||
menuSpecs.forEach(function(v,k) {
|
||||
menuSpecs.forEach(function(v) {
|
||||
let index = -1
|
||||
v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
|
||||
if(index>=0){
|
||||
@ -93,8 +92,7 @@ contextMenuInit = function(){
|
||||
return;
|
||||
}
|
||||
gradioApp().addEventListener("click", function(e) {
|
||||
let source = e.composedPath()[0]
|
||||
if(source.id && source.id.indexOf('check_progress')>-1){
|
||||
if(! e.isTrusted){
|
||||
return
|
||||
}
|
||||
|
||||
@ -112,7 +110,6 @@ contextMenuInit = function(){
|
||||
if(e.composedPath()[0].matches(k)){
|
||||
showContextMenu(e,e.composedPath()[0],v)
|
||||
e.preventDefault()
|
||||
return
|
||||
}
|
||||
})
|
||||
});
|
||||
|
||||
@ -69,8 +69,8 @@ function keyupEditAttention(event){
|
||||
|
||||
event.preventDefault();
|
||||
|
||||
closeCharacter = ')'
|
||||
delta = opts.keyedit_precision_attention
|
||||
var closeCharacter = ')'
|
||||
var delta = opts.keyedit_precision_attention
|
||||
|
||||
if (selectionStart > 0 && text[selectionStart - 1] == '<'){
|
||||
closeCharacter = '>'
|
||||
@ -91,8 +91,8 @@ function keyupEditAttention(event){
|
||||
selectionEnd += 1;
|
||||
}
|
||||
|
||||
end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
|
||||
weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
|
||||
var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
|
||||
var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
|
||||
if (isNaN(weight)) return;
|
||||
|
||||
weight += isPlus ? delta : -delta;
|
||||
|
||||
@ -1,14 +1,14 @@
|
||||
|
||||
function extensions_apply(_, _, disable_all){
|
||||
function extensions_apply(_disabled_list, _update_list, disable_all){
|
||||
var disable = []
|
||||
var update = []
|
||||
|
||||
gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
|
||||
if(x.name.startsWith("enable_") && ! x.checked)
|
||||
disable.push(x.name.substr(7))
|
||||
disable.push(x.name.substring(7))
|
||||
|
||||
if(x.name.startsWith("update_") && x.checked)
|
||||
update.push(x.name.substr(7))
|
||||
update.push(x.name.substring(7))
|
||||
})
|
||||
|
||||
restart_reload()
|
||||
@ -16,12 +16,12 @@ function extensions_apply(_, _, disable_all){
|
||||
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){
|
||||
if(x.name.startsWith("enable_") && ! x.checked)
|
||||
disable.push(x.name.substr(7))
|
||||
disable.push(x.name.substring(7))
|
||||
})
|
||||
|
||||
gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
|
||||
@ -41,7 +41,7 @@ function install_extension_from_index(button, url){
|
||||
button.disabled = "disabled"
|
||||
button.value = "Installing..."
|
||||
|
||||
textarea = gradioApp().querySelector('#extension_to_install textarea')
|
||||
var textarea = gradioApp().querySelector('#extension_to_install textarea')
|
||||
textarea.value = url
|
||||
updateInput(textarea)
|
||||
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
|
||||
function setupExtraNetworksForTab(tabname){
|
||||
gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
|
||||
|
||||
@ -10,16 +9,34 @@ function setupExtraNetworksForTab(tabname){
|
||||
tabs.appendChild(search)
|
||||
tabs.appendChild(refresh)
|
||||
|
||||
search.addEventListener("input", function(evt){
|
||||
searchTerm = search.value.toLowerCase()
|
||||
var applyFilter = function(){
|
||||
var searchTerm = search.value.toLowerCase()
|
||||
|
||||
gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
|
||||
text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
|
||||
elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
|
||||
var searchOnly = elem.querySelector('.search_only')
|
||||
var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
|
||||
|
||||
var visible = text.indexOf(searchTerm) != -1
|
||||
|
||||
if(searchOnly && searchTerm.length < 4){
|
||||
visible = false
|
||||
}
|
||||
|
||||
elem.style.display = visible ? "" : "none"
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
search.addEventListener("input", applyFilter);
|
||||
applyFilter();
|
||||
|
||||
extraNetworksApplyFilter[tabname] = applyFilter;
|
||||
}
|
||||
|
||||
function applyExtraNetworkFilter(tabname){
|
||||
setTimeout(extraNetworksApplyFilter[tabname], 1);
|
||||
}
|
||||
|
||||
var extraNetworksApplyFilter = {}
|
||||
var activePromptTextarea = {};
|
||||
|
||||
function setupExtraNetworks(){
|
||||
@ -55,7 +72,7 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text){
|
||||
|
||||
var partToSearch = m[1]
|
||||
var replaced = false
|
||||
var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
|
||||
var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
|
||||
m = found.match(re_extranet);
|
||||
if(m[1] == partToSearch){
|
||||
replaced = true;
|
||||
@ -96,9 +113,9 @@ function saveCardPreview(event, tabname, filename){
|
||||
}
|
||||
|
||||
function extraNetworksSearchButton(tabs_id, event){
|
||||
searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
|
||||
button = event.target
|
||||
text = button.classList.contains("search-all") ? "" : button.textContent.trim()
|
||||
var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
|
||||
var button = event.target
|
||||
var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
|
||||
|
||||
searchTextarea.value = text
|
||||
updateInput(searchTextarea)
|
||||
@ -133,7 +150,7 @@ function popup(contents){
|
||||
}
|
||||
|
||||
function extraNetworksShowMetadata(text){
|
||||
elem = document.createElement('pre')
|
||||
var elem = document.createElement('pre')
|
||||
elem.classList.add('popup-metadata');
|
||||
elem.textContent = text;
|
||||
|
||||
@ -165,7 +182,7 @@ function requestGet(url, data, handler, errorHandler){
|
||||
}
|
||||
|
||||
function extraNetworksRequestMetadata(event, extraPage, cardName){
|
||||
showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
|
||||
var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
|
||||
|
||||
requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
|
||||
if(data && data.metadata){
|
||||
|
||||
@ -23,7 +23,7 @@ let modalObserver = new MutationObserver(function(mutations) {
|
||||
});
|
||||
|
||||
function attachGalleryListeners(tab_name) {
|
||||
gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
|
||||
var gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
|
||||
gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
|
||||
gallery?.addEventListener('keydown', (e) => {
|
||||
if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
|
||||
|
||||
@ -66,8 +66,8 @@ 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], [clip_skip], [batch_number], [generation_number], [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], [clip_skip], [batch_number], [generation_number], [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.",
|
||||
"Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [denoising], [clip_skip], [batch_number], [generation_number], [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], [denoising], [clip_skip], [batch_number], [generation_number], [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.",
|
||||
@ -118,16 +118,18 @@ titles = {
|
||||
|
||||
onUiUpdate(function(){
|
||||
gradioApp().querySelectorAll('span, button, select, p').forEach(function(span){
|
||||
tooltip = titles[span.textContent];
|
||||
if (span.title) return; // already has a title
|
||||
|
||||
if(!tooltip){
|
||||
tooltip = titles[span.value];
|
||||
let tooltip = localization[titles[span.textContent]] || titles[span.textContent];
|
||||
|
||||
if(!tooltip){
|
||||
tooltip = localization[titles[span.value]] || titles[span.value];
|
||||
}
|
||||
|
||||
if(!tooltip){
|
||||
for (const c of span.classList) {
|
||||
if (c in titles) {
|
||||
tooltip = titles[c];
|
||||
tooltip = localization[titles[c]] || titles[c];
|
||||
break;
|
||||
}
|
||||
}
|
||||
@ -142,7 +144,7 @@ onUiUpdate(function(){
|
||||
if (select.onchange != null) return;
|
||||
|
||||
select.onchange = function(){
|
||||
select.title = titles[select.value] || "";
|
||||
select.title = localization[titles[select.value]] || titles[select.value] || "";
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
@ -1,16 +1,12 @@
|
||||
|
||||
function setInactive(elem, inactive){
|
||||
if(inactive){
|
||||
elem.classList.add('inactive')
|
||||
} else{
|
||||
elem.classList.remove('inactive')
|
||||
}
|
||||
}
|
||||
|
||||
function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
|
||||
hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
|
||||
hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
|
||||
hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
|
||||
function setInactive(elem, inactive){
|
||||
elem.classList.toggle('inactive', !!inactive)
|
||||
}
|
||||
|
||||
var hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
|
||||
var hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
|
||||
var hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
|
||||
|
||||
gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
|
||||
|
||||
|
||||
@ -2,11 +2,10 @@
|
||||
* temporary fix for https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/668
|
||||
* @see https://github.com/gradio-app/gradio/issues/1721
|
||||
*/
|
||||
window.addEventListener( 'resize', () => imageMaskResize());
|
||||
function imageMaskResize() {
|
||||
const canvases = gradioApp().querySelectorAll('#img2maskimg .touch-none canvas');
|
||||
if ( ! canvases.length ) {
|
||||
canvases_fixed = false;
|
||||
canvases_fixed = false; // TODO: this is unused..?
|
||||
window.removeEventListener( 'resize', imageMaskResize );
|
||||
return;
|
||||
}
|
||||
@ -15,7 +14,7 @@ function imageMaskResize() {
|
||||
const previewImage = wrapper.previousElementSibling;
|
||||
|
||||
if ( ! previewImage.complete ) {
|
||||
previewImage.addEventListener( 'load', () => imageMaskResize());
|
||||
previewImage.addEventListener( 'load', imageMaskResize);
|
||||
return;
|
||||
}
|
||||
|
||||
@ -24,7 +23,6 @@ function imageMaskResize() {
|
||||
const nw = previewImage.naturalWidth;
|
||||
const nh = previewImage.naturalHeight;
|
||||
const portrait = nh > nw;
|
||||
const factor = portrait;
|
||||
|
||||
const wW = Math.min(w, portrait ? h/nh*nw : w/nw*nw);
|
||||
const wH = Math.min(h, portrait ? h/nh*nh : w/nw*nh);
|
||||
@ -40,6 +38,7 @@ function imageMaskResize() {
|
||||
c.style.maxHeight = '100%';
|
||||
c.style.objectFit = 'contain';
|
||||
});
|
||||
}
|
||||
|
||||
onUiUpdate(() => imageMaskResize());
|
||||
}
|
||||
|
||||
onUiUpdate(imageMaskResize);
|
||||
window.addEventListener( 'resize', imageMaskResize);
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
window.onload = (function(){
|
||||
window.addEventListener('drop', e => {
|
||||
const target = e.composedPath()[0];
|
||||
const idx = selected_gallery_index();
|
||||
if (target.placeholder.indexOf("Prompt") == -1) return;
|
||||
|
||||
let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
|
||||
|
||||
@ -57,7 +57,7 @@ function modalImageSwitch(offset) {
|
||||
})
|
||||
|
||||
if (result != -1) {
|
||||
nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
|
||||
var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]
|
||||
nextButton.click()
|
||||
const modalImage = gradioApp().getElementById("modalImage");
|
||||
const modal = gradioApp().getElementById("lightboxModal");
|
||||
@ -144,15 +144,11 @@ function setupImageForLightbox(e) {
|
||||
}
|
||||
|
||||
function modalZoomSet(modalImage, enable) {
|
||||
if (enable) {
|
||||
modalImage.classList.add('modalImageFullscreen');
|
||||
} else {
|
||||
modalImage.classList.remove('modalImageFullscreen');
|
||||
}
|
||||
if(modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable);
|
||||
}
|
||||
|
||||
function modalZoomToggle(event) {
|
||||
modalImage = gradioApp().getElementById("modalImage");
|
||||
var modalImage = gradioApp().getElementById("modalImage");
|
||||
modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen'))
|
||||
event.stopPropagation()
|
||||
}
|
||||
@ -179,7 +175,7 @@ function galleryImageHandler(e) {
|
||||
}
|
||||
|
||||
onUiUpdate(function() {
|
||||
fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
|
||||
var fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
|
||||
if (fullImg_preview != null) {
|
||||
fullImg_preview.forEach(setupImageForLightbox);
|
||||
}
|
||||
|
||||
@ -1,36 +1,57 @@
|
||||
let delay = 350//ms
|
||||
window.addEventListener('gamepadconnected', (e) => {
|
||||
console.log("Gamepad connected!")
|
||||
const gamepad = e.gamepad;
|
||||
setInterval(() => {
|
||||
const xValue = gamepad.axes[0].toFixed(2);
|
||||
if (xValue < -0.3) {
|
||||
modalPrevImage(e);
|
||||
} else if (xValue > 0.3) {
|
||||
modalNextImage(e);
|
||||
}
|
||||
|
||||
}, delay);
|
||||
});
|
||||
|
||||
|
||||
/*
|
||||
Primarily for vr controller type pointer devices.
|
||||
I use the wheel event because there's currently no way to do it properly with web xr.
|
||||
*/
|
||||
|
||||
let isScrolling = false;
|
||||
window.addEventListener('wheel', (e) => {
|
||||
if (isScrolling) return;
|
||||
isScrolling = true;
|
||||
|
||||
if (e.deltaX <= -0.6) {
|
||||
window.addEventListener('gamepadconnected', (e) => {
|
||||
const index = e.gamepad.index;
|
||||
let isWaiting = false;
|
||||
setInterval(async () => {
|
||||
if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
|
||||
const gamepad = navigator.getGamepads()[index];
|
||||
const xValue = gamepad.axes[0];
|
||||
if (xValue <= -0.3) {
|
||||
modalPrevImage(e);
|
||||
} else if (e.deltaX >= 0.6) {
|
||||
isWaiting = true;
|
||||
} else if (xValue >= 0.3) {
|
||||
modalNextImage(e);
|
||||
isWaiting = true;
|
||||
}
|
||||
if (isWaiting) {
|
||||
await sleepUntil(() => {
|
||||
const xValue = navigator.getGamepads()[index].axes[0]
|
||||
if (xValue < 0.3 && xValue > -0.3) {
|
||||
return true;
|
||||
}
|
||||
}, opts.js_modal_lightbox_gamepad_repeat);
|
||||
isWaiting = false;
|
||||
}
|
||||
}, 10);
|
||||
});
|
||||
|
||||
setTimeout(() => {
|
||||
isScrolling = false;
|
||||
}, delay);
|
||||
});
|
||||
/*
|
||||
Primarily for vr controller type pointer devices.
|
||||
I use the wheel event because there's currently no way to do it properly with web xr.
|
||||
*/
|
||||
let isScrolling = false;
|
||||
window.addEventListener('wheel', (e) => {
|
||||
if (!opts.js_modal_lightbox_gamepad || isScrolling) return;
|
||||
isScrolling = true;
|
||||
|
||||
if (e.deltaX <= -0.6) {
|
||||
modalPrevImage(e);
|
||||
} else if (e.deltaX >= 0.6) {
|
||||
modalNextImage(e);
|
||||
}
|
||||
|
||||
setTimeout(() => {
|
||||
isScrolling = false;
|
||||
}, opts.js_modal_lightbox_gamepad_repeat);
|
||||
});
|
||||
|
||||
function sleepUntil(f, timeout) {
|
||||
return new Promise((resolve) => {
|
||||
const timeStart = new Date();
|
||||
const wait = setInterval(function() {
|
||||
if (f() || new Date() - timeStart > timeout) {
|
||||
clearInterval(wait);
|
||||
resolve();
|
||||
}
|
||||
}, 20);
|
||||
});
|
||||
}
|
||||
|
||||
@ -25,6 +25,10 @@ re_emoji = /[\p{Extended_Pictographic}\u{1F3FB}-\u{1F3FF}\u{1F9B0}-\u{1F9B3}]/u
|
||||
original_lines = {}
|
||||
translated_lines = {}
|
||||
|
||||
function hasLocalization() {
|
||||
return window.localization && Object.keys(window.localization).length > 0;
|
||||
}
|
||||
|
||||
function textNodesUnder(el){
|
||||
var n, a=[], walk=document.createTreeWalker(el,NodeFilter.SHOW_TEXT,null,false);
|
||||
while(n=walk.nextNode()) a.push(n);
|
||||
@ -35,11 +39,11 @@ function canBeTranslated(node, text){
|
||||
if(! text) return false;
|
||||
if(! node.parentElement) return false;
|
||||
|
||||
parentType = node.parentElement.nodeName
|
||||
var parentType = node.parentElement.nodeName
|
||||
if(parentType=='SCRIPT' || parentType=='STYLE' || parentType=='TEXTAREA') return false;
|
||||
|
||||
if (parentType=='OPTION' || parentType=='SPAN'){
|
||||
pnode = node
|
||||
var pnode = node
|
||||
for(var level=0; level<4; level++){
|
||||
pnode = pnode.parentElement
|
||||
if(! pnode) break;
|
||||
@ -69,7 +73,7 @@ function getTranslation(text){
|
||||
}
|
||||
|
||||
function processTextNode(node){
|
||||
text = node.textContent.trim()
|
||||
var text = node.textContent.trim()
|
||||
|
||||
if(! canBeTranslated(node, text)) return
|
||||
|
||||
@ -105,7 +109,7 @@ function processNode(node){
|
||||
}
|
||||
|
||||
function dumpTranslations(){
|
||||
dumped = {}
|
||||
var dumped = {}
|
||||
if (localization.rtl) {
|
||||
dumped.rtl = true
|
||||
}
|
||||
@ -119,39 +123,8 @@ function dumpTranslations(){
|
||||
return dumped
|
||||
}
|
||||
|
||||
onUiUpdate(function(m){
|
||||
m.forEach(function(mutation){
|
||||
mutation.addedNodes.forEach(function(node){
|
||||
processNode(node)
|
||||
})
|
||||
});
|
||||
})
|
||||
|
||||
|
||||
document.addEventListener("DOMContentLoaded", function() {
|
||||
processNode(gradioApp())
|
||||
|
||||
if (localization.rtl) { // if the language is from right to left,
|
||||
(new MutationObserver((mutations, observer) => { // wait for the style to load
|
||||
mutations.forEach(mutation => {
|
||||
mutation.addedNodes.forEach(node => {
|
||||
if (node.tagName === 'STYLE') {
|
||||
observer.disconnect();
|
||||
|
||||
for (const x of node.sheet.rules) { // find all rtl media rules
|
||||
if (Array.from(x.media || []).includes('rtl')) {
|
||||
x.media.appendMedium('all'); // enable them
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
});
|
||||
})).observe(gradioApp(), { childList: true });
|
||||
}
|
||||
})
|
||||
|
||||
function download_localization() {
|
||||
text = JSON.stringify(dumpTranslations(), null, 4)
|
||||
var text = JSON.stringify(dumpTranslations(), null, 4)
|
||||
|
||||
var element = document.createElement('a');
|
||||
element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
|
||||
@ -163,3 +136,36 @@ function download_localization() {
|
||||
|
||||
document.body.removeChild(element);
|
||||
}
|
||||
|
||||
if(hasLocalization()) {
|
||||
onUiUpdate(function (m) {
|
||||
m.forEach(function (mutation) {
|
||||
mutation.addedNodes.forEach(function (node) {
|
||||
processNode(node)
|
||||
})
|
||||
});
|
||||
})
|
||||
|
||||
|
||||
document.addEventListener("DOMContentLoaded", function () {
|
||||
processNode(gradioApp())
|
||||
|
||||
if (localization.rtl) { // if the language is from right to left,
|
||||
(new MutationObserver((mutations, observer) => { // wait for the style to load
|
||||
mutations.forEach(mutation => {
|
||||
mutation.addedNodes.forEach(node => {
|
||||
if (node.tagName === 'STYLE') {
|
||||
observer.disconnect();
|
||||
|
||||
for (const x of node.sheet.rules) { // find all rtl media rules
|
||||
if (Array.from(x.media || []).includes('rtl')) {
|
||||
x.media.appendMedium('all'); // enable them
|
||||
}
|
||||
}
|
||||
}
|
||||
})
|
||||
});
|
||||
})).observe(gradioApp(), { childList: true });
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
@ -2,15 +2,15 @@
|
||||
|
||||
let lastHeadImg = null;
|
||||
|
||||
notificationButton = null
|
||||
let notificationButton = null;
|
||||
|
||||
onUiUpdate(function(){
|
||||
if(notificationButton == null){
|
||||
notificationButton = gradioApp().getElementById('request_notifications')
|
||||
|
||||
if(notificationButton != null){
|
||||
notificationButton.addEventListener('click', function (evt) {
|
||||
Notification.requestPermission();
|
||||
notificationButton.addEventListener('click', () => {
|
||||
void Notification.requestPermission();
|
||||
},true);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1,16 +1,15 @@
|
||||
// code related to showing and updating progressbar shown as the image is being made
|
||||
|
||||
function rememberGallerySelection(id_gallery){
|
||||
function rememberGallerySelection(){
|
||||
|
||||
}
|
||||
|
||||
function getGallerySelectedIndex(id_gallery){
|
||||
function getGallerySelectedIndex(){
|
||||
|
||||
}
|
||||
|
||||
function request(url, data, handler, errorHandler){
|
||||
var xhr = new XMLHttpRequest();
|
||||
var url = url;
|
||||
xhr.open("POST", url, true);
|
||||
xhr.setRequestHeader("Content-Type", "application/json");
|
||||
xhr.onreadystatechange = function () {
|
||||
@ -107,7 +106,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
|
||||
divProgress.style.width = rect.width + "px";
|
||||
}
|
||||
|
||||
progressText = ""
|
||||
let progressText = ""
|
||||
|
||||
divInner.style.width = ((res.progress || 0) * 100.0) + '%'
|
||||
divInner.style.background = res.progress ? "" : "transparent"
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
// various functions for interaction with ui.py not large enough to warrant putting them in separate files
|
||||
|
||||
function set_theme(theme){
|
||||
gradioURL = window.location.href
|
||||
var gradioURL = window.location.href
|
||||
if (!gradioURL.includes('?__theme=')) {
|
||||
window.location.replace(gradioURL + '?__theme=' + theme);
|
||||
}
|
||||
@ -47,7 +47,7 @@ function extract_image_from_gallery(gallery){
|
||||
return [gallery[0]];
|
||||
}
|
||||
|
||||
index = selected_gallery_index()
|
||||
var index = selected_gallery_index()
|
||||
|
||||
if (index < 0 || index >= gallery.length){
|
||||
// Use the first image in the gallery as the default
|
||||
@ -58,7 +58,7 @@ function extract_image_from_gallery(gallery){
|
||||
}
|
||||
|
||||
function args_to_array(args){
|
||||
res = []
|
||||
var res = []
|
||||
for(var i=0;i<args.length;i++){
|
||||
res.push(args[i])
|
||||
}
|
||||
@ -138,7 +138,7 @@ function get_img2img_tab_index() {
|
||||
}
|
||||
|
||||
function create_submit_args(args){
|
||||
res = []
|
||||
var res = []
|
||||
for(var i=0;i<args.length;i++){
|
||||
res.push(args[i])
|
||||
}
|
||||
@ -160,7 +160,7 @@ function showSubmitButtons(tabname, show){
|
||||
}
|
||||
|
||||
function showRestoreProgressButton(tabname, show){
|
||||
button = gradioApp().getElementById(tabname + "_restore_progress")
|
||||
var button = gradioApp().getElementById(tabname + "_restore_progress")
|
||||
if(! button) return
|
||||
|
||||
button.style.display = show ? "flex" : "none"
|
||||
@ -207,8 +207,9 @@ function submit_img2img(){
|
||||
return res
|
||||
}
|
||||
|
||||
function restoreProgressTxt2img(x){
|
||||
function restoreProgressTxt2img(){
|
||||
showRestoreProgressButton("txt2img", false)
|
||||
var id = localStorage.getItem("txt2img_task_id")
|
||||
|
||||
id = localStorage.getItem("txt2img_task_id")
|
||||
|
||||
@ -220,10 +221,11 @@ function restoreProgressTxt2img(x){
|
||||
|
||||
return id
|
||||
}
|
||||
function restoreProgressImg2img(x){
|
||||
showRestoreProgressButton("img2img", false)
|
||||
|
||||
id = localStorage.getItem("img2img_task_id")
|
||||
function restoreProgressImg2img(){
|
||||
showRestoreProgressButton("img2img", false)
|
||||
|
||||
var id = localStorage.getItem("img2img_task_id")
|
||||
|
||||
if(id) {
|
||||
requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
|
||||
@ -252,7 +254,7 @@ function modelmerger(){
|
||||
|
||||
|
||||
function ask_for_style_name(_, prompt_text, negative_prompt_text) {
|
||||
name_ = prompt('Style name:')
|
||||
var name_ = prompt('Style name:')
|
||||
return [name_, prompt_text, negative_prompt_text]
|
||||
}
|
||||
|
||||
@ -287,11 +289,11 @@ function recalculate_prompts_img2img(){
|
||||
}
|
||||
|
||||
|
||||
opts = {}
|
||||
var opts = {}
|
||||
onUiUpdate(function(){
|
||||
if(Object.keys(opts).length != 0) return;
|
||||
|
||||
json_elem = gradioApp().getElementById('settings_json')
|
||||
var json_elem = gradioApp().getElementById('settings_json')
|
||||
if(json_elem == null) return;
|
||||
|
||||
var textarea = json_elem.querySelector('textarea')
|
||||
@ -340,12 +342,15 @@ onUiUpdate(function(){
|
||||
registerTextarea('img2img_prompt', 'img2img_token_counter', 'img2img_token_button')
|
||||
registerTextarea('img2img_neg_prompt', 'img2img_negative_token_counter', 'img2img_negative_token_button')
|
||||
|
||||
show_all_pages = gradioApp().getElementById('settings_show_all_pages')
|
||||
settings_tabs = gradioApp().querySelector('#settings div')
|
||||
var show_all_pages = gradioApp().getElementById('settings_show_all_pages')
|
||||
var settings_tabs = gradioApp().querySelector('#settings div')
|
||||
if(show_all_pages && settings_tabs){
|
||||
settings_tabs.appendChild(show_all_pages)
|
||||
show_all_pages.onclick = function(){
|
||||
gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
|
||||
if(elem.id == "settings_tab_licenses")
|
||||
return;
|
||||
|
||||
elem.style.display = "block";
|
||||
})
|
||||
}
|
||||
@ -353,9 +358,9 @@ onUiUpdate(function(){
|
||||
})
|
||||
|
||||
onOptionsChanged(function(){
|
||||
elem = gradioApp().getElementById('sd_checkpoint_hash')
|
||||
sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
|
||||
shorthash = sd_checkpoint_hash.substr(0,10)
|
||||
var elem = gradioApp().getElementById('sd_checkpoint_hash')
|
||||
var sd_checkpoint_hash = opts.sd_checkpoint_hash || ""
|
||||
var shorthash = sd_checkpoint_hash.substring(0,10)
|
||||
|
||||
if(elem && elem.textContent != shorthash){
|
||||
elem.textContent = shorthash
|
||||
@ -390,7 +395,16 @@ function update_token_counter(button_id) {
|
||||
|
||||
function restart_reload(){
|
||||
document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
|
||||
setTimeout(function(){location.reload()},2000)
|
||||
|
||||
var requestPing = function(){
|
||||
requestGet("./internal/ping", {}, function(data){
|
||||
location.reload();
|
||||
}, function(){
|
||||
setTimeout(requestPing, 500);
|
||||
})
|
||||
}
|
||||
|
||||
setTimeout(requestPing, 2000);
|
||||
|
||||
return []
|
||||
}
|
||||
|
||||
41
javascript/ui_settings_hints.js
Normal file
41
javascript/ui_settings_hints.js
Normal file
@ -0,0 +1,41 @@
|
||||
// various hints and extra info for the settings tab
|
||||
|
||||
onUiLoaded(function(){
|
||||
createLink = function(elem_id, text, href){
|
||||
var a = document.createElement('A')
|
||||
a.textContent = text
|
||||
a.target = '_blank';
|
||||
|
||||
elem = gradioApp().querySelector('#'+elem_id)
|
||||
elem.insertBefore(a, elem.querySelector('label'))
|
||||
|
||||
return a
|
||||
}
|
||||
|
||||
createLink("setting_samples_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
|
||||
createLink("setting_directories_filename_pattern", "[wiki] ").href = "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"
|
||||
|
||||
createLink("setting_quicksettings_list", "[info] ").addEventListener("click", function(event){
|
||||
requestGet("./internal/quicksettings-hint", {}, function(data){
|
||||
var table = document.createElement('table')
|
||||
table.className = 'settings-value-table'
|
||||
|
||||
data.forEach(function(obj){
|
||||
var tr = document.createElement('tr')
|
||||
var td = document.createElement('td')
|
||||
td.textContent = obj.name
|
||||
tr.appendChild(td)
|
||||
|
||||
var td = document.createElement('td')
|
||||
td.textContent = obj.label
|
||||
tr.appendChild(td)
|
||||
|
||||
table.appendChild(tr)
|
||||
})
|
||||
|
||||
popup(table);
|
||||
})
|
||||
});
|
||||
})
|
||||
|
||||
|
||||
19
launch.py
19
launch.py
@ -19,6 +19,7 @@ python = sys.executable
|
||||
git = os.environ.get('GIT', "git")
|
||||
index_url = os.environ.get('INDEX_URL', "")
|
||||
stored_commit_hash = None
|
||||
stored_git_tag = None
|
||||
dir_repos = "repositories"
|
||||
|
||||
if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
|
||||
@ -70,6 +71,20 @@ def commit_hash():
|
||||
return stored_commit_hash
|
||||
|
||||
|
||||
def git_tag():
|
||||
global stored_git_tag
|
||||
|
||||
if stored_git_tag is not None:
|
||||
return stored_git_tag
|
||||
|
||||
try:
|
||||
stored_git_tag = run(f"{git} describe --tags").strip()
|
||||
except Exception:
|
||||
stored_git_tag = "<none>"
|
||||
|
||||
return stored_git_tag
|
||||
|
||||
|
||||
def run(command, desc=None, errdesc=None, custom_env=None, live=False):
|
||||
if desc is not None:
|
||||
print(desc)
|
||||
@ -222,7 +237,7 @@ def run_extensions_installers(settings_file):
|
||||
|
||||
|
||||
def prepare_environment():
|
||||
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --extra-index-url https://download.pytorch.org/whl/cu118")
|
||||
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.1 torchvision==0.15.2 --extra-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==0.0.17')
|
||||
@ -246,8 +261,10 @@ def prepare_environment():
|
||||
check_python_version()
|
||||
|
||||
commit = commit_hash()
|
||||
tag = git_tag()
|
||||
|
||||
print(f"Python {sys.version}")
|
||||
print(f"Version: {tag}")
|
||||
print(f"Commit hash: {commit}")
|
||||
|
||||
if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
|
||||
|
||||
@ -570,20 +570,20 @@ class Api:
|
||||
filename = create_embedding(**args) # create empty embedding
|
||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used
|
||||
shared.state.end()
|
||||
return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename))
|
||||
return CreateResponse(info=f"create embedding filename: {filename}")
|
||||
except AssertionError as e:
|
||||
shared.state.end()
|
||||
return TrainResponse(info = "create embedding error: {error}".format(error = e))
|
||||
return TrainResponse(info=f"create embedding error: {e}")
|
||||
|
||||
def create_hypernetwork(self, args: dict):
|
||||
try:
|
||||
shared.state.begin()
|
||||
filename = create_hypernetwork(**args) # create empty embedding
|
||||
shared.state.end()
|
||||
return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename))
|
||||
return CreateResponse(info=f"create hypernetwork filename: {filename}")
|
||||
except AssertionError as e:
|
||||
shared.state.end()
|
||||
return TrainResponse(info = "create hypernetwork error: {error}".format(error = e))
|
||||
return TrainResponse(info=f"create hypernetwork error: {e}")
|
||||
|
||||
def preprocess(self, args: dict):
|
||||
try:
|
||||
@ -593,13 +593,13 @@ class Api:
|
||||
return PreprocessResponse(info = 'preprocess complete')
|
||||
except KeyError as e:
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e))
|
||||
return PreprocessResponse(info=f"preprocess error: invalid token: {e}")
|
||||
except AssertionError as e:
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info = "preprocess error: {error}".format(error = e))
|
||||
return PreprocessResponse(info=f"preprocess error: {e}")
|
||||
except FileNotFoundError as e:
|
||||
shared.state.end()
|
||||
return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e))
|
||||
return PreprocessResponse(info=f'preprocess error: {e}')
|
||||
|
||||
def train_embedding(self, args: dict):
|
||||
try:
|
||||
@ -617,10 +617,10 @@ class Api:
|
||||
if not apply_optimizations:
|
||||
sd_hijack.apply_optimizations()
|
||||
shared.state.end()
|
||||
return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error))
|
||||
return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||
except AssertionError as msg:
|
||||
shared.state.end()
|
||||
return TrainResponse(info = "train embedding error: {msg}".format(msg = msg))
|
||||
return TrainResponse(info=f"train embedding error: {msg}")
|
||||
|
||||
def train_hypernetwork(self, args: dict):
|
||||
try:
|
||||
@ -641,10 +641,10 @@ class Api:
|
||||
if not apply_optimizations:
|
||||
sd_hijack.apply_optimizations()
|
||||
shared.state.end()
|
||||
return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error))
|
||||
return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
|
||||
except AssertionError as msg:
|
||||
shared.state.end()
|
||||
return TrainResponse(info="train embedding error: {error}".format(error=error))
|
||||
return TrainResponse(info=f"train embedding error: {error}")
|
||||
|
||||
def get_memory(self):
|
||||
try:
|
||||
|
||||
@ -60,7 +60,7 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
||||
max_debug_str_len = 131072 # (1024*1024)/8
|
||||
|
||||
print("Error completing request", file=sys.stderr)
|
||||
argStr = f"Arguments: {str(args)} {str(kwargs)}"
|
||||
argStr = f"Arguments: {args} {kwargs}"
|
||||
print(argStr[:max_debug_str_len], file=sys.stderr)
|
||||
if len(argStr) > max_debug_str_len:
|
||||
print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
|
||||
@ -73,7 +73,8 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
|
||||
if extra_outputs_array is None:
|
||||
extra_outputs_array = [None, '']
|
||||
|
||||
res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
|
||||
error_message = f'{type(e).__name__}: {e}'
|
||||
res = extra_outputs_array + [f"<div class='error'>{html.escape(error_message)}</div>"]
|
||||
|
||||
shared.state.skipped = False
|
||||
shared.state.interrupted = False
|
||||
|
||||
@ -102,3 +102,4 @@ parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gra
|
||||
parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
|
||||
parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
|
||||
parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
|
||||
parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
|
||||
@ -156,13 +156,16 @@ class UpscalerESRGAN(Upscaler):
|
||||
|
||||
def load_model(self, path: str):
|
||||
if "http" in path:
|
||||
filename = load_file_from_url(url=self.model_url, model_dir=self.model_path,
|
||||
file_name="%s.pth" % self.model_name,
|
||||
progress=True)
|
||||
filename = load_file_from_url(
|
||||
url=self.model_url,
|
||||
model_dir=self.model_path,
|
||||
file_name=f"{self.model_name}.pth",
|
||||
progress=True,
|
||||
)
|
||||
else:
|
||||
filename = path
|
||||
if not os.path.exists(filename) or filename is None:
|
||||
print("Unable to load %s from %s" % (self.model_path, filename))
|
||||
print(f"Unable to load {self.model_path} from {filename}")
|
||||
return None
|
||||
|
||||
state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
|
||||
|
||||
@ -38,7 +38,7 @@ class RRDBNet(nn.Module):
|
||||
elif upsample_mode == 'pixelshuffle':
|
||||
upsample_block = pixelshuffle_block
|
||||
else:
|
||||
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
|
||||
raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
|
||||
if upscale == 3:
|
||||
upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
|
||||
else:
|
||||
@ -261,10 +261,10 @@ class Upsample(nn.Module):
|
||||
|
||||
def extra_repr(self):
|
||||
if self.scale_factor is not None:
|
||||
info = 'scale_factor=' + str(self.scale_factor)
|
||||
info = f'scale_factor={self.scale_factor}'
|
||||
else:
|
||||
info = 'size=' + str(self.size)
|
||||
info += ', mode=' + self.mode
|
||||
info = f'size={self.size}'
|
||||
info += f', mode={self.mode}'
|
||||
return info
|
||||
|
||||
|
||||
@ -350,7 +350,7 @@ def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
|
||||
elif act_type == 'sigmoid': # [0, 1] range output
|
||||
layer = nn.Sigmoid()
|
||||
else:
|
||||
raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
|
||||
raise NotImplementedError(f'activation layer [{act_type}] is not found')
|
||||
return layer
|
||||
|
||||
|
||||
@ -372,7 +372,7 @@ def norm(norm_type, nc):
|
||||
elif norm_type == 'none':
|
||||
def norm_layer(x): return Identity()
|
||||
else:
|
||||
raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
|
||||
raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
|
||||
return layer
|
||||
|
||||
|
||||
@ -388,7 +388,7 @@ def pad(pad_type, padding):
|
||||
elif pad_type == 'zero':
|
||||
layer = nn.ZeroPad2d(padding)
|
||||
else:
|
||||
raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
|
||||
raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
|
||||
return layer
|
||||
|
||||
|
||||
@ -432,7 +432,7 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
|
||||
pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
|
||||
spectral_norm=False):
|
||||
""" Conv layer with padding, normalization, activation """
|
||||
assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
|
||||
assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
|
||||
padding = get_valid_padding(kernel_size, dilation)
|
||||
p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
|
||||
padding = padding if pad_type == 'zero' else 0
|
||||
|
||||
@ -10,7 +10,8 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
|
||||
additional = shared.opts.sd_hypernetwork
|
||||
|
||||
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]
|
||||
hypernet_prompt_text = f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>"
|
||||
p.all_prompts = [f"{prompt}{hypernet_prompt_text}" for prompt in p.all_prompts]
|
||||
params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
|
||||
|
||||
names = []
|
||||
|
||||
@ -59,6 +59,7 @@ def image_from_url_text(filedata):
|
||||
is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
|
||||
assert is_in_right_dir, 'trying to open image file outside of allowed directories'
|
||||
|
||||
filename = filename.rsplit('?', 1)[0]
|
||||
return Image.open(filename)
|
||||
|
||||
if type(filedata) == list:
|
||||
@ -129,6 +130,7 @@ def connect_paste_params_buttons():
|
||||
_js=jsfunc,
|
||||
inputs=[binding.source_image_component],
|
||||
outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
if binding.source_text_component is not None and fields is not None:
|
||||
@ -140,6 +142,7 @@ def connect_paste_params_buttons():
|
||||
fn=lambda *x: x,
|
||||
inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
|
||||
outputs=[field for field, name in fields if name in paste_field_names],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
binding.paste_button.click(
|
||||
@ -147,6 +150,7 @@ def connect_paste_params_buttons():
|
||||
_js=f"switch_to_{binding.tabname}",
|
||||
inputs=None,
|
||||
outputs=None,
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
|
||||
@ -265,8 +269,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
|
||||
v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
|
||||
m = re_imagesize.match(v)
|
||||
if m is not None:
|
||||
res[k+"-1"] = m.group(1)
|
||||
res[k+"-2"] = m.group(2)
|
||||
res[f"{k}-1"] = m.group(1)
|
||||
res[f"{k}-2"] = m.group(2)
|
||||
else:
|
||||
res[k] = v
|
||||
|
||||
@ -409,12 +413,14 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
|
||||
fn=paste_func,
|
||||
inputs=[input_comp],
|
||||
outputs=[x[0] for x in paste_fields],
|
||||
show_progress=False,
|
||||
)
|
||||
button.click(
|
||||
fn=None,
|
||||
_js=f"recalculate_prompts_{tabname}",
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
|
||||
|
||||
@ -13,7 +13,7 @@ cache_data = None
|
||||
|
||||
|
||||
def dump_cache():
|
||||
with filelock.FileLock(cache_filename+".lock"):
|
||||
with filelock.FileLock(f"{cache_filename}.lock"):
|
||||
with open(cache_filename, "w", encoding="utf8") as file:
|
||||
json.dump(cache_data, file, indent=4)
|
||||
|
||||
@ -22,7 +22,7 @@ def cache(subsection):
|
||||
global cache_data
|
||||
|
||||
if cache_data is None:
|
||||
with filelock.FileLock(cache_filename+".lock"):
|
||||
with filelock.FileLock(f"{cache_filename}.lock"):
|
||||
if not os.path.isfile(cache_filename):
|
||||
cache_data = {}
|
||||
else:
|
||||
|
||||
@ -357,6 +357,7 @@ class FilenameGenerator:
|
||||
'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"],
|
||||
'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
|
||||
}
|
||||
default_time_format = '%Y%m%d%H%M%S'
|
||||
|
||||
@ -466,7 +467,7 @@ def get_next_sequence_number(path, basename):
|
||||
"""
|
||||
result = -1
|
||||
if basename != '':
|
||||
basename = basename + "-"
|
||||
basename = f"{basename}-"
|
||||
|
||||
prefix_length = len(basename)
|
||||
for p in os.listdir(path):
|
||||
@ -535,7 +536,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
||||
add_number = opts.save_images_add_number or file_decoration == ''
|
||||
|
||||
if file_decoration != "" and add_number:
|
||||
file_decoration = "-" + file_decoration
|
||||
file_decoration = f"-{file_decoration}"
|
||||
|
||||
file_decoration = namegen.apply(file_decoration) + suffix
|
||||
|
||||
@ -565,7 +566,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
||||
|
||||
def _atomically_save_image(image_to_save, filename_without_extension, extension):
|
||||
# save image with .tmp extension to avoid race condition when another process detects new image in the directory
|
||||
temp_file_path = filename_without_extension + ".tmp"
|
||||
temp_file_path = f"{filename_without_extension}.tmp"
|
||||
image_format = Image.registered_extensions()[extension]
|
||||
|
||||
if extension.lower() == '.png':
|
||||
@ -625,7 +626,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
|
||||
if opts.save_txt and info is not None:
|
||||
txt_fullfn = f"{fullfn_without_extension}.txt"
|
||||
with open(txt_fullfn, "w", encoding="utf8") as file:
|
||||
file.write(info + "\n")
|
||||
file.write(f"{info}\n")
|
||||
else:
|
||||
txt_fullfn = None
|
||||
|
||||
|
||||
@ -48,7 +48,8 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
|
||||
|
||||
try:
|
||||
img = Image.open(image)
|
||||
except UnidentifiedImageError:
|
||||
except UnidentifiedImageError as e:
|
||||
print(e)
|
||||
continue
|
||||
# Use the EXIF orientation of photos taken by smartphones.
|
||||
img = ImageOps.exif_transpose(img)
|
||||
|
||||
@ -28,7 +28,7 @@ def category_types():
|
||||
def download_default_clip_interrogate_categories(content_dir):
|
||||
print("Downloading CLIP categories...")
|
||||
|
||||
tmpdir = content_dir + "_tmp"
|
||||
tmpdir = f"{content_dir}_tmp"
|
||||
category_types = ["artists", "flavors", "mediums", "movements"]
|
||||
|
||||
try:
|
||||
@ -214,7 +214,7 @@ class InterrogateModels:
|
||||
if shared.opts.interrogate_return_ranks:
|
||||
res += f", ({match}:{score/100:.3f})"
|
||||
else:
|
||||
res += ", " + match
|
||||
res += f", {match}"
|
||||
|
||||
except Exception:
|
||||
print("Error interrogating", file=sys.stderr)
|
||||
|
||||
@ -54,6 +54,11 @@ if has_mps:
|
||||
CondFunc('torch.cumsum', cumsum_fix_func, None)
|
||||
CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
|
||||
CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
|
||||
if version.parse(torch.__version__) == version.parse("2.0"):
|
||||
|
||||
# MPS workaround for https://github.com/pytorch/pytorch/issues/96113
|
||||
CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)
|
||||
CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda _, input, *args, **kwargs: len(args) == 4 and input.device.type == 'mps')
|
||||
|
||||
# MPS workaround for https://github.com/pytorch/pytorch/issues/92311
|
||||
if platform.processor() == 'i386':
|
||||
for funcName in ['torch.argmax', 'torch.Tensor.argmax']:
|
||||
CondFunc(funcName, lambda _, input, *args, **kwargs: torch.max(input.float() if input.dtype == torch.int64 else input, *args, **kwargs)[1], lambda _, input, *args, **kwargs: input.device.type == 'mps')
|
||||
@ -22,9 +22,6 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
|
||||
"""
|
||||
output = []
|
||||
|
||||
if ext_filter is None:
|
||||
ext_filter = []
|
||||
|
||||
try:
|
||||
places = []
|
||||
|
||||
@ -39,22 +36,14 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
|
||||
places.append(model_path)
|
||||
|
||||
for place in places:
|
||||
if os.path.exists(place):
|
||||
for file in glob.iglob(place + '**/**', recursive=True):
|
||||
full_path = file
|
||||
if os.path.isdir(full_path):
|
||||
continue
|
||||
if os.path.islink(full_path) and not os.path.exists(full_path):
|
||||
print(f"Skipping broken symlink: {full_path}")
|
||||
continue
|
||||
if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
|
||||
continue
|
||||
if len(ext_filter) != 0:
|
||||
model_name, extension = os.path.splitext(file)
|
||||
if extension not in ext_filter:
|
||||
continue
|
||||
if file not in output:
|
||||
output.append(full_path)
|
||||
for full_path in shared.walk_files(place, allowed_extensions=ext_filter):
|
||||
if os.path.islink(full_path) and not os.path.exists(full_path):
|
||||
print(f"Skipping broken symlink: {full_path}")
|
||||
continue
|
||||
if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
|
||||
continue
|
||||
if full_path not in output:
|
||||
output.append(full_path)
|
||||
|
||||
if model_url is not None and len(output) == 0:
|
||||
if download_name is not None:
|
||||
@ -133,12 +122,9 @@ forbidden_upscaler_classes = set()
|
||||
|
||||
|
||||
def list_builtin_upscalers():
|
||||
load_upscalers()
|
||||
|
||||
builtin_upscaler_classes.clear()
|
||||
builtin_upscaler_classes.extend(Upscaler.__subclasses__())
|
||||
|
||||
|
||||
def forbid_loaded_nonbuiltin_upscalers():
|
||||
for cls in Upscaler.__subclasses__():
|
||||
if cls not in builtin_upscaler_classes:
|
||||
|
||||
@ -223,7 +223,7 @@ class DDPM(pl.LightningModule):
|
||||
for k in keys:
|
||||
for ik in ignore_keys:
|
||||
if k.startswith(ik):
|
||||
print("Deleting key {} from state_dict.".format(k))
|
||||
print(f"Deleting key {k} from state_dict.")
|
||||
del sd[k]
|
||||
missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
|
||||
sd, strict=False)
|
||||
@ -386,7 +386,7 @@ class DDPM(pl.LightningModule):
|
||||
_, loss_dict_no_ema = self.shared_step(batch)
|
||||
with self.ema_scope():
|
||||
_, loss_dict_ema = self.shared_step(batch)
|
||||
loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
|
||||
loss_dict_ema = {f"{key}_ema": loss_dict_ema[key] for key in loss_dict_ema}
|
||||
self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
|
||||
self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
|
||||
|
||||
|
||||
@ -94,7 +94,7 @@ class NoiseScheduleVP:
|
||||
"""
|
||||
|
||||
if schedule not in ['discrete', 'linear', 'cosine']:
|
||||
raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
|
||||
raise ValueError(f"Unsupported noise schedule {schedule}. The schedule needs to be 'discrete' or 'linear' or 'cosine'")
|
||||
|
||||
self.schedule = schedule
|
||||
if schedule == 'discrete':
|
||||
@ -469,7 +469,7 @@ class UniPC:
|
||||
t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
|
||||
return t
|
||||
else:
|
||||
raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
|
||||
raise ValueError(f"Unsupported skip_type {skip_type}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'")
|
||||
|
||||
def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
|
||||
"""
|
||||
|
||||
@ -7,8 +7,8 @@ def connect(token, port, region):
|
||||
else:
|
||||
if ':' in token:
|
||||
# token = authtoken:username:password
|
||||
account = token.split(':')[1] + ':' + token.split(':')[-1]
|
||||
token = token.split(':')[0]
|
||||
token, username, password = token.split(':', 2)
|
||||
account = f"{username}:{password}"
|
||||
|
||||
config = conf.PyngrokConfig(
|
||||
auth_token=token, region=region
|
||||
|
||||
@ -16,7 +16,7 @@ for possible_sd_path in possible_sd_paths:
|
||||
sd_path = os.path.abspath(possible_sd_path)
|
||||
break
|
||||
|
||||
assert sd_path is not None, "Couldn't find Stable Diffusion in any of: " + str(possible_sd_paths)
|
||||
assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possible_sd_paths}"
|
||||
|
||||
path_dirs = [
|
||||
(sd_path, 'ldm', 'Stable Diffusion', []),
|
||||
|
||||
@ -458,6 +458,16 @@ def fix_seed(p):
|
||||
p.subseed = get_fixed_seed(p.subseed)
|
||||
|
||||
|
||||
def program_version():
|
||||
import launch
|
||||
|
||||
res = launch.git_tag()
|
||||
if res == "<none>":
|
||||
res = None
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
|
||||
index = position_in_batch + iteration * p.batch_size
|
||||
|
||||
@ -483,13 +493,14 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
|
||||
"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,
|
||||
"Version": program_version() if opts.add_version_to_infotext else None,
|
||||
}
|
||||
|
||||
generation_params.update(p.extra_generation_params)
|
||||
|
||||
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
|
||||
|
||||
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
|
||||
negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
|
||||
|
||||
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
|
||||
|
||||
@ -769,7 +780,16 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
res = Processed(
|
||||
p,
|
||||
images_list=output_images,
|
||||
seed=p.all_seeds[0],
|
||||
info=infotext(),
|
||||
comments="".join(f"\n\n{comment}" for comment in comments),
|
||||
subseed=p.all_subseeds[0],
|
||||
index_of_first_image=index_of_first_image,
|
||||
infotexts=infotexts,
|
||||
)
|
||||
|
||||
if p.scripts is not None:
|
||||
p.scripts.postprocess(p, res)
|
||||
|
||||
@ -96,7 +96,8 @@ def progressapi(req: ProgressRequest):
|
||||
if image is not None:
|
||||
buffered = io.BytesIO()
|
||||
image.save(buffered, format="png")
|
||||
live_preview = 'data:image/png;base64,' + base64.b64encode(buffered.getvalue()).decode("ascii")
|
||||
base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
|
||||
live_preview = f"data:image/png;base64,{base64_image}"
|
||||
id_live_preview = shared.state.id_live_preview
|
||||
else:
|
||||
live_preview = None
|
||||
|
||||
@ -28,9 +28,9 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
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
|
||||
local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
|
||||
if local_model_candidates:
|
||||
scaler.local_data_path = local_model_candidates[0]
|
||||
|
||||
if scaler.name in opts.realesrgan_enabled_models:
|
||||
self.scalers.append(scaler)
|
||||
@ -47,7 +47,7 @@ class UpscalerRealESRGAN(Upscaler):
|
||||
|
||||
info = self.load_model(path)
|
||||
if not os.path.exists(info.local_data_path):
|
||||
print("Unable to load RealESRGAN model: %s" % info.name)
|
||||
print(f"Unable to load RealESRGAN model: {info.name}")
|
||||
return img
|
||||
|
||||
upsampler = RealESRGANer(
|
||||
|
||||
@ -163,7 +163,8 @@ class Script:
|
||||
"""helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
|
||||
|
||||
need_tabname = self.show(True) == self.show(False)
|
||||
tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
|
||||
tabkind = 'img2img' if self.is_img2img else 'txt2txt'
|
||||
tabname = f"{tabkind}_" if need_tabname else ""
|
||||
title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
|
||||
|
||||
return f'script_{tabname}{title}_{item_id}'
|
||||
@ -526,7 +527,7 @@ def add_classes_to_gradio_component(comp):
|
||||
this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
|
||||
"""
|
||||
|
||||
comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
|
||||
comp.elem_classes = [f"gradio-{comp.get_block_name()}", *(comp.elem_classes or [])]
|
||||
|
||||
if getattr(comp, 'multiselect', False):
|
||||
comp.elem_classes.append('multiselect')
|
||||
|
||||
@ -75,7 +75,8 @@ def forward_old(self: sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase, text
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
embedding_names = ", ".join(f"{word} [{checksum}]" for word, checksum in used_custom_terms)
|
||||
self.hijack.comments.append(f"Used embeddings: {embedding_names}")
|
||||
|
||||
self.hijack.fixes = hijack_fixes
|
||||
return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
|
||||
@ -256,6 +256,9 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
|
||||
k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
|
||||
|
||||
if q.device.type == 'mps':
|
||||
q, k, v = q.contiguous(), k.contiguous(), v.contiguous()
|
||||
|
||||
dtype = q.dtype
|
||||
if shared.opts.upcast_attn:
|
||||
q, k = q.float(), k.float()
|
||||
|
||||
@ -18,7 +18,7 @@ class TorchHijackForUnet:
|
||||
if hasattr(torch, item):
|
||||
return getattr(torch, item)
|
||||
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
|
||||
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
|
||||
|
||||
def cat(self, tensors, *args, **kwargs):
|
||||
if len(tensors) == 2:
|
||||
|
||||
@ -2,6 +2,8 @@ import collections
|
||||
import os.path
|
||||
import sys
|
||||
import gc
|
||||
import threading
|
||||
|
||||
import torch
|
||||
import re
|
||||
import safetensors.torch
|
||||
@ -45,7 +47,7 @@ class CheckpointInfo:
|
||||
self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
|
||||
self.hash = model_hash(filename)
|
||||
|
||||
self.sha256 = hashes.sha256_from_cache(self.filename, "checkpoint/" + name)
|
||||
self.sha256 = hashes.sha256_from_cache(self.filename, f"checkpoint/{name}")
|
||||
self.shorthash = self.sha256[0:10] if self.sha256 else None
|
||||
|
||||
self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
|
||||
@ -67,7 +69,7 @@ class CheckpointInfo:
|
||||
checkpoint_alisases[id] = self
|
||||
|
||||
def calculate_shorthash(self):
|
||||
self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
|
||||
self.sha256 = hashes.sha256(self.filename, f"checkpoint/{self.name}")
|
||||
if self.sha256 is None:
|
||||
return
|
||||
|
||||
@ -404,13 +406,39 @@ def repair_config(sd_config):
|
||||
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'
|
||||
|
||||
def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
|
||||
|
||||
class SdModelData:
|
||||
def __init__(self):
|
||||
self.sd_model = None
|
||||
self.lock = threading.Lock()
|
||||
|
||||
def get_sd_model(self):
|
||||
if self.sd_model is None:
|
||||
with self.lock:
|
||||
try:
|
||||
load_model()
|
||||
except Exception as e:
|
||||
errors.display(e, "loading stable diffusion model")
|
||||
print("", file=sys.stderr)
|
||||
print("Stable diffusion model failed to load", file=sys.stderr)
|
||||
self.sd_model = None
|
||||
|
||||
return self.sd_model
|
||||
|
||||
def set_sd_model(self, v):
|
||||
self.sd_model = v
|
||||
|
||||
|
||||
model_data = SdModelData()
|
||||
|
||||
|
||||
def load_model(checkpoint_info=None, already_loaded_state_dict=None):
|
||||
from modules import lowvram, sd_hijack
|
||||
checkpoint_info = checkpoint_info or select_checkpoint()
|
||||
|
||||
if shared.sd_model:
|
||||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||||
shared.sd_model = None
|
||||
if model_data.sd_model:
|
||||
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
|
||||
model_data.sd_model = None
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
@ -464,7 +492,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_
|
||||
timer.record("hijack")
|
||||
|
||||
sd_model.eval()
|
||||
shared.sd_model = sd_model
|
||||
model_data.sd_model = sd_model
|
||||
|
||||
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
|
||||
|
||||
@ -484,7 +512,7 @@ def reload_model_weights(sd_model=None, info=None):
|
||||
checkpoint_info = info or select_checkpoint()
|
||||
|
||||
if not sd_model:
|
||||
sd_model = shared.sd_model
|
||||
sd_model = model_data.sd_model
|
||||
|
||||
if sd_model is None: # previous model load failed
|
||||
current_checkpoint_info = None
|
||||
@ -512,7 +540,7 @@ def reload_model_weights(sd_model=None, info=None):
|
||||
del sd_model
|
||||
checkpoints_loaded.clear()
|
||||
load_model(checkpoint_info, already_loaded_state_dict=state_dict)
|
||||
return shared.sd_model
|
||||
return model_data.sd_model
|
||||
|
||||
try:
|
||||
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
|
||||
@ -535,17 +563,15 @@ def reload_model_weights(sd_model=None, info=None):
|
||||
|
||||
return sd_model
|
||||
|
||||
|
||||
def unload_model_weights(sd_model=None, info=None):
|
||||
from modules import lowvram, devices, sd_hijack
|
||||
timer = Timer()
|
||||
|
||||
if shared.sd_model:
|
||||
|
||||
# shared.sd_model.cond_stage_model.to(devices.cpu)
|
||||
# shared.sd_model.first_stage_model.to(devices.cpu)
|
||||
shared.sd_model.to(devices.cpu)
|
||||
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
|
||||
shared.sd_model = None
|
||||
if model_data.sd_model:
|
||||
model_data.sd_model.to(devices.cpu)
|
||||
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
|
||||
model_data.sd_model = None
|
||||
sd_model = None
|
||||
gc.collect()
|
||||
devices.torch_gc()
|
||||
|
||||
@ -111,7 +111,7 @@ def find_checkpoint_config_near_filename(info):
|
||||
if info is None:
|
||||
return None
|
||||
|
||||
config = os.path.splitext(info.filename)[0] + ".yaml"
|
||||
config = f"{os.path.splitext(info.filename)[0]}.yaml"
|
||||
if os.path.exists(config):
|
||||
return config
|
||||
|
||||
|
||||
@ -198,7 +198,7 @@ class TorchHijack:
|
||||
if hasattr(torch, item):
|
||||
return getattr(torch, item)
|
||||
|
||||
raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
|
||||
raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
|
||||
|
||||
def randn_like(self, x):
|
||||
if self.sampler_noises:
|
||||
|
||||
@ -89,7 +89,7 @@ def refresh_vae_list():
|
||||
|
||||
def find_vae_near_checkpoint(checkpoint_file):
|
||||
checkpoint_path = os.path.splitext(checkpoint_file)[0]
|
||||
for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
|
||||
for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
|
||||
if os.path.isfile(vae_location):
|
||||
return vae_location
|
||||
|
||||
|
||||
@ -16,6 +16,7 @@ import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
|
||||
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir
|
||||
from ldm.models.diffusion.ddpm import LatentDiffusion
|
||||
|
||||
demo = None
|
||||
|
||||
@ -391,21 +392,20 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
|
||||
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
|
||||
"disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
|
||||
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
|
||||
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
|
||||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"js_modal_lightbox_gamepad": OptionInfo(True, "Navigate image viewer with gamepad"),
|
||||
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
|
||||
"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"),
|
||||
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
|
||||
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
|
||||
"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"),
|
||||
@ -413,6 +413,13 @@ options_templates.update(options_section(('ui', "User interface"), {
|
||||
"gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('infotext', "Infotext"), {
|
||||
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
|
||||
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
|
||||
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
|
||||
"disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
"show_progressbar": OptionInfo(True, "Show progressbar"),
|
||||
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
|
||||
@ -542,6 +549,10 @@ class Options:
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
self.data = json.load(file)
|
||||
|
||||
# 1.1.1 quicksettings list migration
|
||||
if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
|
||||
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
|
||||
|
||||
bad_settings = 0
|
||||
for k, v in self.data.items():
|
||||
info = self.data_labels.get(k, None)
|
||||
@ -600,13 +611,37 @@ class Options:
|
||||
return value
|
||||
|
||||
|
||||
|
||||
opts = Options()
|
||||
if os.path.exists(config_filename):
|
||||
opts.load(config_filename)
|
||||
|
||||
|
||||
class Shared(sys.modules[__name__].__class__):
|
||||
"""
|
||||
this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
|
||||
at program startup.
|
||||
"""
|
||||
|
||||
sd_model_val = None
|
||||
|
||||
@property
|
||||
def sd_model(self):
|
||||
import modules.sd_models
|
||||
|
||||
return modules.sd_models.model_data.get_sd_model()
|
||||
|
||||
@sd_model.setter
|
||||
def sd_model(self, value):
|
||||
import modules.sd_models
|
||||
|
||||
modules.sd_models.model_data.set_sd_model(value)
|
||||
|
||||
|
||||
sd_model: LatentDiffusion = None # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
|
||||
sys.modules[__name__].__class__ = Shared
|
||||
|
||||
settings_components = None
|
||||
"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
|
||||
"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
|
||||
|
||||
latent_upscale_default_mode = "Latent"
|
||||
latent_upscale_modes = {
|
||||
@ -620,8 +655,6 @@ latent_upscale_modes = {
|
||||
|
||||
sd_upscalers = []
|
||||
|
||||
sd_model = None
|
||||
|
||||
clip_model = None
|
||||
|
||||
progress_print_out = sys.stdout
|
||||
@ -639,8 +672,8 @@ def reload_gradio_theme(theme_name=None):
|
||||
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")
|
||||
except Exception as e:
|
||||
errors.display(e, "changing gradio theme")
|
||||
gradio_theme = gr.themes.Default()
|
||||
|
||||
|
||||
@ -701,3 +734,20 @@ def html(filename):
|
||||
return file.read()
|
||||
|
||||
return ""
|
||||
|
||||
|
||||
def walk_files(path, allowed_extensions=None):
|
||||
if not os.path.exists(path):
|
||||
return
|
||||
|
||||
if allowed_extensions is not None:
|
||||
allowed_extensions = set(allowed_extensions)
|
||||
|
||||
for root, dirs, files in os.walk(path):
|
||||
for filename in files:
|
||||
if allowed_extensions is not None:
|
||||
_, ext = os.path.splitext(filename)
|
||||
if ext not in allowed_extensions:
|
||||
continue
|
||||
|
||||
yield os.path.join(root, filename)
|
||||
|
||||
@ -74,7 +74,7 @@ class StyleDatabase:
|
||||
def save_styles(self, path: str) -> None:
|
||||
# Always keep a backup file around
|
||||
if os.path.exists(path):
|
||||
shutil.copy(path, path + ".bak")
|
||||
shutil.copy(path, f"{path}.bak")
|
||||
|
||||
fd = os.open(path, os.O_RDWR|os.O_CREAT)
|
||||
with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
|
||||
|
||||
@ -111,7 +111,7 @@ def focal_point(im, settings):
|
||||
if corner_centroid is not None:
|
||||
color = BLUE
|
||||
box = corner_centroid.bounding(max_size * corner_centroid.weight)
|
||||
d.text((box[0], box[1]-15), "Edge: %.02f" % corner_centroid.weight, fill=color)
|
||||
d.text((box[0], box[1]-15), f"Edge: {corner_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(corner_points) > 1:
|
||||
for f in corner_points:
|
||||
@ -119,7 +119,7 @@ def focal_point(im, settings):
|
||||
if entropy_centroid is not None:
|
||||
color = "#ff0"
|
||||
box = entropy_centroid.bounding(max_size * entropy_centroid.weight)
|
||||
d.text((box[0], box[1]-15), "Entropy: %.02f" % entropy_centroid.weight, fill=color)
|
||||
d.text((box[0], box[1]-15), f"Entropy: {entropy_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(entropy_points) > 1:
|
||||
for f in entropy_points:
|
||||
@ -127,7 +127,7 @@ def focal_point(im, settings):
|
||||
if face_centroid is not None:
|
||||
color = RED
|
||||
box = face_centroid.bounding(max_size * face_centroid.weight)
|
||||
d.text((box[0], box[1]-15), "Face: %.02f" % face_centroid.weight, fill=color)
|
||||
d.text((box[0], box[1]-15), f"Face: {face_centroid.weight:.02f}", fill=color)
|
||||
d.ellipse(box, outline=color)
|
||||
if len(face_points) > 1:
|
||||
for f in face_points:
|
||||
|
||||
@ -72,7 +72,7 @@ class PersonalizedBase(Dataset):
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
text_filename = os.path.splitext(path)[0] + ".txt"
|
||||
text_filename = f"{os.path.splitext(path)[0]}.txt"
|
||||
filename = os.path.basename(path)
|
||||
|
||||
if os.path.exists(text_filename):
|
||||
|
||||
@ -63,9 +63,9 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
|
||||
image.save(os.path.join(params.dstdir, f"{basename}.png"))
|
||||
|
||||
if params.preprocess_txt_action == 'prepend' and existing_caption:
|
||||
caption = existing_caption + ' ' + caption
|
||||
caption = f"{existing_caption} {caption}"
|
||||
elif params.preprocess_txt_action == 'append' and existing_caption:
|
||||
caption = caption + ' ' + existing_caption
|
||||
caption = f"{caption} {existing_caption}"
|
||||
elif params.preprocess_txt_action == 'copy' and existing_caption:
|
||||
caption = existing_caption
|
||||
|
||||
@ -174,7 +174,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
|
||||
params.src = filename
|
||||
|
||||
existing_caption = None
|
||||
existing_caption_filename = os.path.splitext(filename)[0] + '.txt'
|
||||
existing_caption_filename = f"{os.path.splitext(filename)[0]}.txt"
|
||||
if os.path.exists(existing_caption_filename):
|
||||
with open(existing_caption_filename, 'r', encoding="utf8") as file:
|
||||
existing_caption = file.read()
|
||||
|
||||
@ -69,7 +69,7 @@ class Embedding:
|
||||
'hash': self.checksum(),
|
||||
'optimizer_state_dict': self.optimizer_state_dict,
|
||||
}
|
||||
torch.save(optimizer_saved_dict, filename + '.optim')
|
||||
torch.save(optimizer_saved_dict, f"{filename}.optim")
|
||||
|
||||
def checksum(self):
|
||||
if self.cached_checksum is not None:
|
||||
@ -437,8 +437,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
||||
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
|
||||
if shared.opts.save_optimizer_state:
|
||||
optimizer_state_dict = None
|
||||
if os.path.exists(filename + '.optim'):
|
||||
optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')
|
||||
if os.path.exists(f"{filename}.optim"):
|
||||
optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
|
||||
if embedding.checksum() == optimizer_saved_dict.get('hash', None):
|
||||
optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
|
||||
|
||||
@ -599,7 +599,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
||||
data = torch.load(last_saved_file)
|
||||
info.add_text("sd-ti-embedding", embedding_to_b64(data))
|
||||
|
||||
title = "<{}>".format(data.get('name', '???'))
|
||||
title = f"<{data.get('name', '???')}>"
|
||||
|
||||
try:
|
||||
vectorSize = list(data['string_to_param'].values())[0].shape[0]
|
||||
@ -608,8 +608,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
|
||||
|
||||
checkpoint = sd_models.select_checkpoint()
|
||||
footer_left = checkpoint.model_name
|
||||
footer_mid = '[{}]'.format(checkpoint.shorthash)
|
||||
footer_right = '{}v {}s'.format(vectorSize, steps_done)
|
||||
footer_mid = f'[{checkpoint.shorthash}]'
|
||||
footer_right = f'{vectorSize}v {steps_done}s'
|
||||
|
||||
captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
|
||||
captioned_image = insert_image_data_embed(captioned_image, data)
|
||||
|
||||
@ -101,7 +101,7 @@ def visit(x, func, path=""):
|
||||
for c in x.children:
|
||||
visit(c, func, path)
|
||||
elif x.label is not None:
|
||||
func(path + "/" + str(x.label), x)
|
||||
func(f"{path}/{x.label}", x)
|
||||
|
||||
|
||||
def add_style(name: str, prompt: str, negative_prompt: str):
|
||||
@ -166,7 +166,7 @@ def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_di
|
||||
img = Image.open(image)
|
||||
filename = os.path.basename(image)
|
||||
left, _ = os.path.splitext(filename)
|
||||
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
|
||||
print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a'))
|
||||
|
||||
return [gr.update(), None]
|
||||
|
||||
@ -182,29 +182,29 @@ def interrogate_deepbooru(image):
|
||||
|
||||
|
||||
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')
|
||||
with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
|
||||
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=f"{target_interface}_seed")
|
||||
seed.style(container=False)
|
||||
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')
|
||||
random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
|
||||
reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
|
||||
|
||||
seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
|
||||
seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
|
||||
|
||||
# Components to show/hide based on the 'Extra' checkbox
|
||||
seed_extras = []
|
||||
|
||||
with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
|
||||
with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
|
||||
seed_extras.append(seed_extra_row_1)
|
||||
subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
|
||||
subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed")
|
||||
subseed.style(container=False)
|
||||
random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
|
||||
reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
|
||||
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
|
||||
random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
|
||||
reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
|
||||
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
|
||||
|
||||
with FormRow(visible=False) as seed_extra_row_2:
|
||||
seed_extras.append(seed_extra_row_2)
|
||||
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
|
||||
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
|
||||
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
|
||||
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
|
||||
|
||||
random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
|
||||
random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
|
||||
@ -765,7 +765,7 @@ def create_ui():
|
||||
)
|
||||
button.click(
|
||||
fn=lambda: None,
|
||||
_js="switch_to_"+name.replace(" ", "_"),
|
||||
_js=f"switch_to_{name.replace(' ', '_')}",
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
@ -828,7 +828,7 @@ def create_ui():
|
||||
with FormGroup():
|
||||
with FormRow():
|
||||
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
|
||||
image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
|
||||
image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
|
||||
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
|
||||
|
||||
elif category == "seed":
|
||||
@ -1462,18 +1462,18 @@ def create_ui():
|
||||
elif t == bool:
|
||||
comp = gr.Checkbox
|
||||
else:
|
||||
raise Exception(f'bad options item type: {str(t)} for key {key}')
|
||||
raise Exception(f'bad options item type: {t} for key {key}')
|
||||
|
||||
elem_id = "setting_"+key
|
||||
elem_id = f"setting_{key}"
|
||||
|
||||
if info.refresh is not None:
|
||||
if is_quicksettings:
|
||||
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
|
||||
create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
|
||||
create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
|
||||
else:
|
||||
with FormRow():
|
||||
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
|
||||
create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
|
||||
create_refresh_button(res, info.refresh, info.component_args, f"refresh_{key}")
|
||||
else:
|
||||
res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))
|
||||
|
||||
@ -1525,7 +1525,7 @@ def create_ui():
|
||||
|
||||
result = gr.HTML(elem_id="settings_result")
|
||||
|
||||
quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
|
||||
quicksettings_names = opts.quicksettings_list
|
||||
quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
|
||||
|
||||
quicksettings_list = []
|
||||
@ -1545,7 +1545,7 @@ def create_ui():
|
||||
current_tab.__exit__()
|
||||
|
||||
gr.Group()
|
||||
current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
|
||||
current_tab = gr.TabItem(elem_id=f"settings_{elem_id}", label=text)
|
||||
current_tab.__enter__()
|
||||
current_row = gr.Column(variant='compact')
|
||||
current_row.__enter__()
|
||||
@ -1566,7 +1566,7 @@ def create_ui():
|
||||
current_row.__exit__()
|
||||
current_tab.__exit__()
|
||||
|
||||
with gr.TabItem("Actions", id="actions"):
|
||||
with gr.TabItem("Actions", id="actions", elem_id="settings_tab_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")
|
||||
@ -1574,7 +1574,7 @@ 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", id="licenses"):
|
||||
with gr.TabItem("Licenses", id="licenses", elem_id="settings_tab_licenses"):
|
||||
gr.HTML(shared.html("licenses.html"), elem_id="licenses")
|
||||
|
||||
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
|
||||
@ -1664,7 +1664,7 @@ def create_ui():
|
||||
for interface, label, ifid in interfaces:
|
||||
if label in shared.opts.hidden_tabs:
|
||||
continue
|
||||
with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
|
||||
with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
|
||||
interface.render()
|
||||
|
||||
if os.path.exists(os.path.join(script_path, "notification.mp3")):
|
||||
@ -1693,11 +1693,9 @@ def create_ui():
|
||||
show_progress=info.refresh is not None,
|
||||
)
|
||||
|
||||
text_settings.change(
|
||||
fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
|
||||
inputs=[],
|
||||
outputs=[image_cfg_scale],
|
||||
)
|
||||
update_image_cfg_scale_visibility = lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
|
||||
text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
|
||||
demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
|
||||
|
||||
button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
|
||||
button_set_checkpoint.click(
|
||||
@ -1773,10 +1771,10 @@ def create_ui():
|
||||
|
||||
def loadsave(path, x):
|
||||
def apply_field(obj, field, condition=None, init_field=None):
|
||||
key = path + "/" + field
|
||||
key = f"{path}/{field}"
|
||||
|
||||
if getattr(obj, 'custom_script_source', None) is not None:
|
||||
key = 'customscript/' + obj.custom_script_source + '/' + key
|
||||
key = f"customscript/{obj.custom_script_source}/{key}"
|
||||
|
||||
if getattr(obj, 'do_not_save_to_config', False):
|
||||
return
|
||||
@ -1925,7 +1923,7 @@ def versions_html():
|
||||
|
||||
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
|
||||
commit = launch.commit_hash()
|
||||
short_commit = commit[0:8]
|
||||
tag = launch.git_tag()
|
||||
|
||||
if shared.xformers_available:
|
||||
import xformers
|
||||
@ -1934,6 +1932,8 @@ def versions_html():
|
||||
xformers_version = "N/A"
|
||||
|
||||
return f"""
|
||||
version: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{tag}</a>
|
||||
•
|
||||
python: <span title="{sys.version}">{python_version}</span>
|
||||
•
|
||||
torch: {getattr(torch, '__long_version__',torch.__version__)}
|
||||
@ -1942,7 +1942,21 @@ xformers: {xformers_version}
|
||||
•
|
||||
gradio: {gr.__version__}
|
||||
•
|
||||
commit: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{short_commit}</a>
|
||||
•
|
||||
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
|
||||
"""
|
||||
|
||||
|
||||
def setup_ui_api(app):
|
||||
from pydantic import BaseModel, Field
|
||||
from typing import List
|
||||
|
||||
class QuicksettingsHint(BaseModel):
|
||||
name: str = Field(title="Name of the quicksettings field")
|
||||
label: str = Field(title="Label of the quicksettings field")
|
||||
|
||||
def quicksettings_hint():
|
||||
return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
|
||||
|
||||
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=List[QuicksettingsHint])
|
||||
|
||||
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
|
||||
|
||||
@ -61,7 +61,8 @@ def save_config_state(name):
|
||||
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")
|
||||
timestamp = datetime.now().strftime('%Y_%m_%d-%H_%M_%S')
|
||||
filename = os.path.join(config_states_dir, f"{timestamp}_{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)
|
||||
|
||||
@ -69,7 +69,9 @@ class ExtraNetworksPage:
|
||||
pass
|
||||
|
||||
def link_preview(self, filename):
|
||||
return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
|
||||
quoted_filename = urllib.parse.quote(filename.replace('\\', '/'))
|
||||
mtime = os.path.getmtime(filename)
|
||||
return f"./sd_extra_networks/thumb?filename={quoted_filename}&mtime={mtime}"
|
||||
|
||||
def search_terms_from_path(self, filename, possible_directories=None):
|
||||
abspath = os.path.abspath(filename)
|
||||
@ -89,19 +91,22 @@ class ExtraNetworksPage:
|
||||
|
||||
subdirs = {}
|
||||
for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
|
||||
for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
|
||||
if not os.path.isdir(x):
|
||||
continue
|
||||
for root, dirs, files in os.walk(parentdir):
|
||||
for dirname in dirs:
|
||||
x = os.path.join(root, dirname)
|
||||
|
||||
subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
|
||||
while subdir.startswith("/"):
|
||||
subdir = subdir[1:]
|
||||
if not os.path.isdir(x):
|
||||
continue
|
||||
|
||||
is_empty = len(os.listdir(x)) == 0
|
||||
if not is_empty and not subdir.endswith("/"):
|
||||
subdir = subdir + "/"
|
||||
subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
|
||||
while subdir.startswith("/"):
|
||||
subdir = subdir[1:]
|
||||
|
||||
subdirs[subdir] = 1
|
||||
is_empty = len(os.listdir(x)) == 0
|
||||
if not is_empty and not subdir.endswith("/"):
|
||||
subdir = subdir + "/"
|
||||
|
||||
subdirs[subdir] = 1
|
||||
|
||||
if subdirs:
|
||||
subdirs = {"": 1, **subdirs}
|
||||
@ -157,8 +162,20 @@ class ExtraNetworksPage:
|
||||
if metadata:
|
||||
metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
|
||||
|
||||
local_path = ""
|
||||
filename = item.get("filename", "")
|
||||
for reldir in self.allowed_directories_for_previews():
|
||||
absdir = os.path.abspath(reldir)
|
||||
|
||||
if filename.startswith(absdir):
|
||||
local_path = filename[len(absdir):]
|
||||
|
||||
# if this is true, the item must not be show in the default view, and must instead only be
|
||||
# shown when searching for it
|
||||
serach_only = "/." in local_path or "\\." in local_path
|
||||
|
||||
args = {
|
||||
"style": f"'{height}{width}{background_image}'",
|
||||
"style": f"'display: none; {height}{width}{background_image}'",
|
||||
"prompt": item.get("prompt", None),
|
||||
"tabname": json.dumps(tabname),
|
||||
"local_preview": json.dumps(item["local_preview"]),
|
||||
@ -168,6 +185,7 @@ class ExtraNetworksPage:
|
||||
"save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
|
||||
"search_term": item.get("search_term", ""),
|
||||
"metadata_button": metadata_button,
|
||||
"serach_only": " search_only" if serach_only else "",
|
||||
}
|
||||
|
||||
return self.card_page.format(**args)
|
||||
@ -209,6 +227,11 @@ def intialize():
|
||||
class ExtraNetworksUi:
|
||||
def __init__(self):
|
||||
self.pages = None
|
||||
"""gradio HTML components related to extra networks' pages"""
|
||||
|
||||
self.page_contents = None
|
||||
"""HTML content of the above; empty initially, filled when extra pages have to be shown"""
|
||||
|
||||
self.stored_extra_pages = None
|
||||
|
||||
self.button_save_preview = None
|
||||
@ -236,17 +259,22 @@ def pages_in_preferred_order(pages):
|
||||
def create_ui(container, button, tabname):
|
||||
ui = ExtraNetworksUi()
|
||||
ui.pages = []
|
||||
ui.pages_contents = []
|
||||
ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
|
||||
ui.tabname = tabname
|
||||
|
||||
with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
|
||||
for page in ui.stored_extra_pages:
|
||||
with gr.Tab(page.title, id=page.title.lower().replace(" ", "_")):
|
||||
page_id = page.title.lower().replace(" ", "_")
|
||||
|
||||
page_elem = gr.HTML(page.create_html(ui.tabname))
|
||||
with gr.Tab(page.title, id=page_id):
|
||||
elem_id = f"{tabname}_{page_id}_cards_html"
|
||||
page_elem = gr.HTML('', elem_id=elem_id)
|
||||
ui.pages.append(page_elem)
|
||||
|
||||
filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
|
||||
page_elem.change(fn=lambda: None, _js='function(){applyExtraNetworkFilter(' + json.dumps(tabname) + '); return []}', inputs=[], outputs=[])
|
||||
|
||||
gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
|
||||
button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
|
||||
|
||||
ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
|
||||
@ -254,19 +282,22 @@ def create_ui(container, button, tabname):
|
||||
|
||||
def toggle_visibility(is_visible):
|
||||
is_visible = not is_visible
|
||||
return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
|
||||
|
||||
if is_visible and not ui.pages_contents:
|
||||
refresh()
|
||||
|
||||
return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary")), *ui.pages_contents
|
||||
|
||||
state_visible = gr.State(value=False)
|
||||
button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])
|
||||
button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button, *ui.pages])
|
||||
|
||||
def refresh():
|
||||
res = []
|
||||
|
||||
for pg in ui.stored_extra_pages:
|
||||
pg.refresh()
|
||||
res.append(pg.create_html(ui.tabname))
|
||||
|
||||
return res
|
||||
ui.pages_contents = [pg.create_html(ui.tabname) for pg in ui.stored_extra_pages]
|
||||
|
||||
return ui.pages_contents
|
||||
|
||||
button_refresh.click(fn=refresh, inputs=[], outputs=ui.pages)
|
||||
|
||||
|
||||
@ -36,7 +36,7 @@ def save_pil_to_file(pil_image, dir=None):
|
||||
if already_saved_as and os.path.isfile(already_saved_as):
|
||||
register_tmp_file(shared.demo, already_saved_as)
|
||||
|
||||
file_obj = Savedfile(already_saved_as)
|
||||
file_obj = Savedfile(f'{already_saved_as}?{os.path.getmtime(already_saved_as)}')
|
||||
return file_obj
|
||||
|
||||
if shared.opts.temp_dir != "":
|
||||
|
||||
@ -5,7 +5,7 @@ basicsr
|
||||
fonts
|
||||
font-roboto
|
||||
gfpgan
|
||||
gradio==3.28.1
|
||||
gradio==3.29.0
|
||||
numpy
|
||||
omegaconf
|
||||
opencv-contrib-python
|
||||
|
||||
@ -3,7 +3,7 @@ transformers==4.25.1
|
||||
accelerate==0.18.0
|
||||
basicsr==1.4.2
|
||||
gfpgan==1.3.8
|
||||
gradio==3.28.1
|
||||
gradio==3.29.0
|
||||
numpy==1.23.5
|
||||
Pillow==9.4.0
|
||||
realesrgan==0.3.0
|
||||
|
||||
@ -77,7 +77,7 @@ return process_images(p)
|
||||
module.display = display
|
||||
|
||||
indent = " " * indent_level
|
||||
indented = code.replace('\n', '\n' + indent)
|
||||
indented = code.replace('\n', f"\n{indent}")
|
||||
body = f"""def __webuitemp__():
|
||||
{indent}{indented}
|
||||
__webuitemp__()"""
|
||||
|
||||
@ -84,7 +84,7 @@ class Script(scripts.Script):
|
||||
p.color_corrections = initial_color_corrections
|
||||
|
||||
if append_interrogation != "None":
|
||||
p.prompt = original_prompt + ", " if original_prompt != "" else ""
|
||||
p.prompt = f"{original_prompt}, " if original_prompt else ""
|
||||
if append_interrogation == "CLIP":
|
||||
p.prompt += shared.interrogator.interrogate(p.init_images[0])
|
||||
elif append_interrogation == "DeepBooru":
|
||||
|
||||
@ -100,30 +100,29 @@ def cmdargs(line):
|
||||
|
||||
def load_prompt_file(file):
|
||||
if file is None:
|
||||
lines = []
|
||||
return None, gr.update(), gr.update(lines=7)
|
||||
else:
|
||||
lines = [x.strip() for x in file.decode('utf8', errors='ignore').split("\n")]
|
||||
|
||||
return None, "\n".join(lines), gr.update(lines=7)
|
||||
return None, "\n".join(lines), gr.update(lines=7)
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "Prompts from file or textbox"
|
||||
|
||||
def ui(self, is_img2img):
|
||||
def ui(self, is_img2img):
|
||||
checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
|
||||
checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
|
||||
|
||||
prompt_txt = gr.Textbox(label="List of prompt inputs", lines=1, elem_id=self.elem_id("prompt_txt"))
|
||||
file = gr.File(label="Upload prompt inputs", type='binary', elem_id=self.elem_id("file"))
|
||||
|
||||
file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt])
|
||||
file.change(fn=load_prompt_file, inputs=[file], outputs=[file, prompt_txt, prompt_txt], show_progress=False)
|
||||
|
||||
# We start at one line. When the text changes, we jump to seven lines, or two lines if no \n.
|
||||
# We don't shrink back to 1, because that causes the control to ignore [enter], and it may
|
||||
# be unclear to the user that shift-enter is needed.
|
||||
prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt])
|
||||
prompt_txt.change(lambda tb: gr.update(lines=7) if ("\n" in tb) else gr.update(lines=2), inputs=[prompt_txt], outputs=[prompt_txt], show_progress=False)
|
||||
return [checkbox_iterate, checkbox_iterate_batch, prompt_txt]
|
||||
|
||||
def run(self, p, checkbox_iterate, checkbox_iterate_batch, prompt_txt: str):
|
||||
|
||||
@ -222,7 +222,7 @@ axis_options = [
|
||||
AxisOption("Denoising", float, apply_field("denoising_strength")),
|
||||
AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),
|
||||
AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),
|
||||
AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: ['None'] + list(sd_vae.vae_dict)),
|
||||
AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
|
||||
AxisOption("UniPC Order", int, apply_uni_pc_order, cost=0.5),
|
||||
AxisOption("Face restore", str, apply_face_restore, format_value=format_value),
|
||||
@ -346,7 +346,7 @@ class SharedSettingsStackHelper(object):
|
||||
self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
|
||||
self.vae = opts.sd_vae
|
||||
self.uni_pc_order = opts.uni_pc_order
|
||||
|
||||
|
||||
def __exit__(self, exc_type, exc_value, tb):
|
||||
opts.data["sd_vae"] = self.vae
|
||||
opts.data["uni_pc_order"] = self.uni_pc_order
|
||||
@ -399,7 +399,7 @@ class Script(scripts.Script):
|
||||
include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
|
||||
with gr.Column():
|
||||
margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
|
||||
|
||||
|
||||
with gr.Row(variant="compact", elem_id="swap_axes"):
|
||||
swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
|
||||
swap_yz_axes_button = gr.Button(value="Swap Y/Z axes", elem_id="yz_grid_swap_axes_button")
|
||||
@ -439,7 +439,7 @@ class Script(scripts.Script):
|
||||
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"
|
||||
val_key = f"{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)
|
||||
@ -490,7 +490,7 @@ class Script(scripts.Script):
|
||||
start = int(mc.group(1))
|
||||
end = int(mc.group(2))
|
||||
num = int(mc.group(3)) if mc.group(3) is not None else 1
|
||||
|
||||
|
||||
valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
|
||||
else:
|
||||
valslist_ext.append(val)
|
||||
@ -512,7 +512,7 @@ class Script(scripts.Script):
|
||||
start = float(mc.group(1))
|
||||
end = float(mc.group(2))
|
||||
num = int(mc.group(3)) if mc.group(3) is not None else 1
|
||||
|
||||
|
||||
valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
|
||||
else:
|
||||
valslist_ext.append(val)
|
||||
|
||||
20
style.css
20
style.css
@ -125,6 +125,10 @@ div.gradio-html.min{
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
a{
|
||||
font-weight: bold;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
|
||||
/* general styled components */
|
||||
@ -246,7 +250,7 @@ button.custom-button{
|
||||
}
|
||||
}
|
||||
|
||||
#txt2img_gallery img, #img2img_gallery img{
|
||||
#txt2img_gallery img, #img2img_gallery img, #extras_gallery img{
|
||||
object-fit: scale-down;
|
||||
}
|
||||
#txt2img_actions_column, #img2img_actions_column {
|
||||
@ -397,6 +401,18 @@ div#extras_scale_to_tab div.form{
|
||||
margin: 0 1.2em;
|
||||
}
|
||||
|
||||
table.settings-value-table{
|
||||
background: white;
|
||||
border-collapse: collapse;
|
||||
margin: 1em;
|
||||
border: 4px solid white;
|
||||
}
|
||||
|
||||
table.settings-value-table td{
|
||||
padding: 0.4em;
|
||||
border: 1px solid #ccc;
|
||||
max-width: 36em;
|
||||
}
|
||||
|
||||
/* live preview */
|
||||
.progressDiv{
|
||||
@ -534,6 +550,8 @@ div#extras_scale_to_tab div.form{
|
||||
#lightboxModal > img.modalImageFullscreen{
|
||||
object-fit: contain;
|
||||
height: 100%;
|
||||
width: 100%;
|
||||
min-height: 0;
|
||||
}
|
||||
|
||||
.modalPrev,
|
||||
|
||||
36
webui.py
36
webui.py
@ -6,6 +6,8 @@ import signal
|
||||
import re
|
||||
import warnings
|
||||
import json
|
||||
from threading import Thread
|
||||
|
||||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
@ -185,24 +187,19 @@ def initialize():
|
||||
modules.scripts.load_scripts()
|
||||
startup_timer.record("load scripts")
|
||||
|
||||
modelloader.load_upscalers()
|
||||
#startup_timer.record("load upscalers") #Is this necessary? I don't know.
|
||||
|
||||
modules.sd_vae.refresh_vae_list()
|
||||
startup_timer.record("refresh VAE")
|
||||
|
||||
modules.textual_inversion.textual_inversion.list_textual_inversion_templates()
|
||||
startup_timer.record("refresh textual inversion templates")
|
||||
|
||||
try:
|
||||
modules.sd_models.load_model()
|
||||
except Exception as e:
|
||||
errors.display(e, "loading stable diffusion model")
|
||||
print("", file=sys.stderr)
|
||||
print("Stable diffusion model failed to load, exiting", file=sys.stderr)
|
||||
exit(1)
|
||||
startup_timer.record("load SD checkpoint")
|
||||
# load model in parallel to other startup stuff
|
||||
Thread(target=lambda: shared.sd_model).start()
|
||||
|
||||
shared.opts.data["sd_model_checkpoint"] = shared.sd_model.sd_checkpoint_info.title
|
||||
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
|
||||
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()), call=False)
|
||||
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)
|
||||
@ -286,7 +283,6 @@ def api_only():
|
||||
print(f"Startup time: {startup_timer.summary()}.")
|
||||
api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861)
|
||||
|
||||
|
||||
def webui():
|
||||
launch_api = cmd_opts.api
|
||||
initialize()
|
||||
@ -313,6 +309,16 @@ def webui():
|
||||
for line in file.readlines():
|
||||
gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()]
|
||||
|
||||
# this restores the missing /docs endpoint
|
||||
if launch_api and not hasattr(FastAPI, 'original_setup'):
|
||||
def fastapi_setup(self):
|
||||
self.docs_url = "/docs"
|
||||
self.redoc_url = "/redoc"
|
||||
self.original_setup()
|
||||
|
||||
FastAPI.original_setup = FastAPI.setup
|
||||
FastAPI.setup = fastapi_setup
|
||||
|
||||
app, local_url, share_url = shared.demo.launch(
|
||||
share=cmd_opts.share,
|
||||
server_name=server_name,
|
||||
@ -339,6 +345,7 @@ def webui():
|
||||
setup_middleware(app)
|
||||
|
||||
modules.progress.setup_progress_api(app)
|
||||
modules.ui.setup_ui_api(app)
|
||||
|
||||
if launch_api:
|
||||
create_api(app)
|
||||
@ -350,6 +357,11 @@ def webui():
|
||||
|
||||
print(f"Startup time: {startup_timer.summary()}.")
|
||||
|
||||
if cmd_opts.subpath:
|
||||
redirector = FastAPI()
|
||||
redirector.get("/")
|
||||
mounted_app = gradio.mount_gradio_app(redirector, shared.demo, path=f"/{cmd_opts.subpath}")
|
||||
|
||||
wait_on_server(shared.demo)
|
||||
print('Restarting UI...')
|
||||
|
||||
|
||||
35
webui.sh
35
webui.sh
@ -153,24 +153,31 @@ else
|
||||
cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
|
||||
fi
|
||||
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Create and activate python venv"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
|
||||
if [[ ! -d "${venv_dir}" ]]
|
||||
if [[ -z "${VIRTUAL_ENV}" ]];
|
||||
then
|
||||
"${python_cmd}" -m venv "${venv_dir}"
|
||||
first_launch=1
|
||||
fi
|
||||
# shellcheck source=/dev/null
|
||||
if [[ -f "${venv_dir}"/bin/activate ]]
|
||||
then
|
||||
source "${venv_dir}"/bin/activate
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Create and activate python venv"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
|
||||
if [[ ! -d "${venv_dir}" ]]
|
||||
then
|
||||
"${python_cmd}" -m venv "${venv_dir}"
|
||||
first_launch=1
|
||||
fi
|
||||
# shellcheck source=/dev/null
|
||||
if [[ -f "${venv_dir}"/bin/activate ]]
|
||||
then
|
||||
source "${venv_dir}"/bin/activate
|
||||
else
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
exit 1
|
||||
fi
|
||||
else
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m"
|
||||
printf "python venv already activate: ${VIRTUAL_ENV}"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Try using TCMalloc on Linux
|
||||
|
||||
Loading…
Reference in New Issue
Block a user