Merge branch 'AUTOMATIC1111:master' into master
This commit is contained in:
InvincibleDude
GitHub
commit
44c0e6b993
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@ -57,6 +57,7 @@ class LoraUpDownModule:
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def __init__(self):
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self.up = None
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self.down = None
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self.alpha = None
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def assign_lora_names_to_compvis_modules(sd_model):
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@ -92,6 +93,15 @@ def load_lora(name, filename):
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keys_failed_to_match.append(key_diffusers)
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continue
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lora_module = lora.modules.get(key, None)
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if lora_module is None:
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lora_module = LoraUpDownModule()
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lora.modules[key] = lora_module
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if lora_key == "alpha":
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lora_module.alpha = weight.item()
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continue
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if type(sd_module) == torch.nn.Linear:
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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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@ -104,17 +114,12 @@ def load_lora(name, filename):
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module.to(device=devices.device, dtype=devices.dtype)
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lora_module = lora.modules.get(key, None)
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if lora_module is None:
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lora_module = LoraUpDownModule()
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lora.modules[key] = lora_module
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if lora_key == "lora_up.weight":
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lora_module.up = module
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elif lora_key == "lora_down.weight":
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lora_module.down = module
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else:
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assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight or lora_down.weight'
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assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
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if len(keys_failed_to_match) > 0:
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print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
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@ -161,7 +166,7 @@ def lora_forward(module, input, res):
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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 not None:
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res = res + module.up(module.down(input)) * lora.multiplier
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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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@ -177,12 +182,12 @@ def lora_Conv2d_forward(self, input):
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def list_available_loras():
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available_loras.clear()
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os.makedirs(lora_dir, exist_ok=True)
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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(lora_dir, '**/*.pt'), recursive=True) + \
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glob.glob(os.path.join(lora_dir, '**/*.safetensors'), recursive=True) + \
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glob.glob(os.path.join(lora_dir, '**/*.ckpt'), recursive=True)
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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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for filename in sorted(candidates):
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if os.path.isdir(filename):
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@ -193,7 +198,6 @@ def list_available_loras():
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available_loras[name] = LoraOnDisk(name, filename)
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lora_dir = os.path.join(shared.models_path, "Lora")
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available_loras = {}
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loaded_loras = []
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@ -0,0 +1,6 @@
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import os
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from modules import paths
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def preload(parser):
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parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
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@ -1,3 +1,4 @@
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import json
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import os
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import lora
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@ -26,10 +27,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
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"name": name,
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"filename": path,
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"preview": preview,
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"prompt": f"<lora:{name}:1.0>",
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"prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
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"local_preview": path + ".png",
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}
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def allowed_directories_for_previews(self):
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return [lora.lora_dir]
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return [shared.cmd_opts.lora_dir]
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@ -8,7 +8,7 @@ from basicsr.utils.download_util import load_file_from_url
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from tqdm import tqdm
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from modules import modelloader, devices, script_callbacks, shared
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from modules.shared import cmd_opts, opts
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from modules.shared import cmd_opts, opts, state
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from swinir_model_arch import SwinIR as net
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from swinir_model_arch_v2 import Swin2SR as net2
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from modules.upscaler import Upscaler, UpscalerData
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@ -145,7 +145,13 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
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with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
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for h_idx in h_idx_list:
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if state.interrupted or state.skipped:
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break
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for w_idx in w_idx_list:
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if state.interrupted or state.skipped:
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break
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in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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@ -1,8 +1,8 @@
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<div class='card' {preview_html} onclick='return cardClicked({tabname}, {prompt}, {allow_negative_prompt})'>
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<div class='card' {preview_html} onclick={card_clicked}>
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<div class='actions'>
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<div class='additional'>
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<ul>
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<a href="#" title="replace preview image with currently selected in gallery" onclick='return saveCardPreview(event, {tabname}, {local_preview})'>replace preview</a>
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<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
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</ul>
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</div>
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<span class='name'>{name}</span>
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@ -0,0 +1,7 @@
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
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<filter id='shadow' color-interpolation-filters="sRGB">
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<feDropShadow flood-color="black" dx="0" dy="0" flood-opacity="0.9" stdDeviation="0.5"/>
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<feDropShadow flood-color="black" dx="0" dy="0" flood-opacity="0.9" stdDeviation="0.5"/>
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</filter>
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<path style="filter:url(#shadow);" fill="#FFFFFF" d="M13.18 19C13.35 19.72 13.64 20.39 14.03 21H5C3.9 21 3 20.11 3 19V5C3 3.9 3.9 3 5 3H19C20.11 3 21 3.9 21 5V11.18C20.5 11.07 20 11 19.5 11C19.33 11 19.17 11 19 11.03V5H5V19H13.18M11.21 15.83L9.25 13.47L6.5 17H13.03C13.14 15.54 13.73 14.22 14.64 13.19L13.96 12.29L11.21 15.83M19 13.5V12L16.75 14.25L19 16.5V15C20.38 15 21.5 16.12 21.5 17.5C21.5 17.9 21.41 18.28 21.24 18.62L22.33 19.71C22.75 19.08 23 18.32 23 17.5C23 15.29 21.21 13.5 19 13.5M19 20C17.62 20 16.5 18.88 16.5 17.5C16.5 17.1 16.59 16.72 16.76 16.38L15.67 15.29C15.25 15.92 15 16.68 15 17.5C15 19.71 16.79 21.5 19 21.5V23L21.25 20.75L19 18.5V20Z" />
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</svg>
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After Width: | Height: | Size: 989 B |
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@ -1,74 +1,96 @@
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addEventListener('keydown', (event) => {
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function keyupEditAttention(event){
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let target = event.originalTarget || event.composedPath()[0];
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if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
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if (! (event.metaKey || event.ctrlKey)) return;
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let plus = "ArrowUp"
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let minus = "ArrowDown"
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if (event.key != plus && event.key != minus) return;
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let isPlus = event.key == "ArrowUp"
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let isMinus = event.key == "ArrowDown"
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if (!isPlus && !isMinus) return;
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let selectionStart = target.selectionStart;
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let selectionEnd = target.selectionEnd;
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// If the user hasn't selected anything, let's select their current parenthesis block
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if (selectionStart === selectionEnd) {
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let text = target.value;
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function selectCurrentParenthesisBlock(OPEN, CLOSE){
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if (selectionStart !== selectionEnd) return false;
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// Find opening parenthesis around current cursor
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const before = target.value.substring(0, selectionStart);
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let beforeParen = before.lastIndexOf("(");
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if (beforeParen == -1) return;
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let beforeParenClose = before.lastIndexOf(")");
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const before = text.substring(0, selectionStart);
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let beforeParen = before.lastIndexOf(OPEN);
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if (beforeParen == -1) return false;
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let beforeParenClose = before.lastIndexOf(CLOSE);
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while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
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beforeParen = before.lastIndexOf("(", beforeParen - 1);
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beforeParenClose = before.lastIndexOf(")", beforeParenClose - 1);
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beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
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beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
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}
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// Find closing parenthesis around current cursor
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const after = target.value.substring(selectionStart);
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let afterParen = after.indexOf(")");
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if (afterParen == -1) return;
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let afterParenOpen = after.indexOf("(");
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const after = text.substring(selectionStart);
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let afterParen = after.indexOf(CLOSE);
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if (afterParen == -1) return false;
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let afterParenOpen = after.indexOf(OPEN);
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while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
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afterParen = after.indexOf(")", afterParen + 1);
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afterParenOpen = after.indexOf("(", afterParenOpen + 1);
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afterParen = after.indexOf(CLOSE, afterParen + 1);
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afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
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}
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if (beforeParen === -1 || afterParen === -1) return;
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if (beforeParen === -1 || afterParen === -1) return false;
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// Set the selection to the text between the parenthesis
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const parenContent = target.value.substring(beforeParen + 1, selectionStart + afterParen);
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const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
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const lastColon = parenContent.lastIndexOf(":");
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selectionStart = beforeParen + 1;
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selectionEnd = selectionStart + lastColon;
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target.setSelectionRange(selectionStart, selectionEnd);
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}
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return true;
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}
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// If the user hasn't selected anything, let's select their current parenthesis block
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if(! selectCurrentParenthesisBlock('<', '>')){
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selectCurrentParenthesisBlock('(', ')')
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}
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event.preventDefault();
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if (selectionStart == 0 || target.value[selectionStart - 1] != "(") {
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target.value = target.value.slice(0, selectionStart) +
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"(" + target.value.slice(selectionStart, selectionEnd) + ":1.0)" +
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target.value.slice(selectionEnd);
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closeCharacter = ')'
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delta = opts.keyedit_precision_attention
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target.focus();
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target.selectionStart = selectionStart + 1;
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target.selectionEnd = selectionEnd + 1;
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if (selectionStart > 0 && text[selectionStart - 1] == '<'){
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closeCharacter = '>'
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delta = opts.keyedit_precision_extra
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} else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
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} else {
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end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
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weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
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if (isNaN(weight)) return;
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if (event.key == minus) weight -= 0.1;
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if (event.key == plus) weight += 0.1;
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// do not include spaces at the end
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while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
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selectionEnd -= 1;
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}
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if(selectionStart == selectionEnd){
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return
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}
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weight = parseFloat(weight.toPrecision(12));
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text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
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target.value = target.value.slice(0, selectionEnd + 1) +
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weight +
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target.value.slice(selectionEnd + 1 + end - 1);
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selectionStart += 1;
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selectionEnd += 1;
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}
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target.focus();
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target.selectionStart = selectionStart;
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target.selectionEnd = selectionEnd;
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}
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end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
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weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
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if (isNaN(weight)) return;
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weight += isPlus ? delta : -delta;
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weight = parseFloat(weight.toPrecision(12));
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if(String(weight).length == 1) weight += ".0"
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text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
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target.focus();
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target.value = text;
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target.selectionStart = selectionStart;
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target.selectionEnd = selectionEnd;
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updateInput(target)
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});
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}
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addEventListener('keydown', (event) => {
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keyupEditAttention(event);
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});
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@ -13,10 +13,10 @@ function setupExtraNetworksForTab(tabname){
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tabs.appendChild(close)
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search.addEventListener("input", function(evt){
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searchTerm = search.value
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searchTerm = search.value.toLowerCase()
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gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
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text = elem.querySelector('.name').textContent
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text = elem.querySelector('.name').textContent.toLowerCase()
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elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
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})
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});
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|
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@ -107,7 +107,10 @@ titles = {
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"Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",
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"Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",
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"Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",
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"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders."
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"Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
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"Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
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"Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
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"Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
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}
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|
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@ -104,11 +104,6 @@ function create_tab_index_args(tabId, args){
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return res
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}
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function get_extras_tab_index(){
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const [,,...args] = [...arguments]
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return [get_tab_index('mode_extras'), get_tab_index('extras_resize_mode'), ...args]
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}
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function get_img2img_tab_index() {
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let res = args_to_array(arguments)
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res.splice(-2)
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|
|
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11
launch.py
11
launch.py
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@ -179,7 +179,7 @@ def run_extensions_installers(settings_file):
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def prepare_environment():
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global skip_install
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||||
|
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torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
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torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
|
||||
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
|
||||
commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
|
||||
|
||||
|
|
@ -187,8 +187,6 @@ def prepare_environment():
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clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
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openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
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|
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xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
|
||||
|
||||
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
|
||||
taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
|
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k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
|
||||
|
|
@ -210,6 +208,7 @@ def prepare_environment():
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sys.argv, _ = extract_arg(sys.argv, '-f')
|
||||
sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
|
||||
sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
|
||||
sys.argv, reinstall_torch = extract_arg(sys.argv, '--reinstall-torch')
|
||||
sys.argv, update_check = extract_arg(sys.argv, '--update-check')
|
||||
sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
|
||||
sys.argv, skip_install = extract_arg(sys.argv, '--skip-install')
|
||||
|
|
@ -221,7 +220,7 @@ def prepare_environment():
|
|||
print(f"Python {sys.version}")
|
||||
print(f"Commit hash: {commit}")
|
||||
|
||||
if not is_installed("torch") or not is_installed("torchvision"):
|
||||
if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
|
||||
run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch")
|
||||
|
||||
if not skip_torch_cuda_test:
|
||||
|
|
@ -239,14 +238,14 @@ def prepare_environment():
|
|||
if (not is_installed("xformers") or reinstall_xformers) and xformers:
|
||||
if platform.system() == "Windows":
|
||||
if platform.python_version().startswith("3.10"):
|
||||
run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
|
||||
run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
|
||||
else:
|
||||
print("Installation of xformers is not supported in this version of Python.")
|
||||
print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
|
||||
if not is_installed("xformers"):
|
||||
exit(0)
|
||||
elif platform.system() == "Linux":
|
||||
run_pip("install xformers", "xformers")
|
||||
run_pip("install xformers==0.0.16rc425", "xformers")
|
||||
|
||||
if not is_installed("pyngrok") and ngrok:
|
||||
run_pip("install pyngrok", "ngrok")
|
||||
|
|
|
|||
|
|
@ -11,10 +11,9 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
|
|||
from secrets import compare_digest
|
||||
|
||||
import modules.shared as shared
|
||||
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
|
||||
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
|
||||
from modules.api.models import *
|
||||
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
|
||||
from modules.extras import run_extras
|
||||
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
|
||||
from modules.textual_inversion.preprocess import preprocess
|
||||
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
|
||||
|
|
@ -23,6 +22,8 @@ from modules.sd_models import checkpoints_list, find_checkpoint_config
|
|||
from modules.realesrgan_model import get_realesrgan_models
|
||||
from modules import devices
|
||||
from typing import List
|
||||
import piexif
|
||||
import piexif.helper
|
||||
|
||||
def upscaler_to_index(name: str):
|
||||
try:
|
||||
|
|
@ -45,32 +46,46 @@ def validate_sampler_name(name):
|
|||
|
||||
def setUpscalers(req: dict):
|
||||
reqDict = vars(req)
|
||||
reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
|
||||
reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
|
||||
reqDict.pop('upscaler_1')
|
||||
reqDict.pop('upscaler_2')
|
||||
reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
|
||||
reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
|
||||
return reqDict
|
||||
|
||||
def decode_base64_to_image(encoding):
|
||||
if encoding.startswith("data:image/"):
|
||||
encoding = encoding.split(";")[1].split(",")[1]
|
||||
return Image.open(BytesIO(base64.b64decode(encoding)))
|
||||
try:
|
||||
image = Image.open(BytesIO(base64.b64decode(encoding)))
|
||||
return image
|
||||
except Exception as err:
|
||||
raise HTTPException(status_code=500, detail="Invalid encoded image")
|
||||
|
||||
def encode_pil_to_base64(image):
|
||||
with io.BytesIO() as output_bytes:
|
||||
|
||||
# Copy any text-only metadata
|
||||
use_metadata = False
|
||||
metadata = PngImagePlugin.PngInfo()
|
||||
for key, value in image.info.items():
|
||||
if isinstance(key, str) and isinstance(value, str):
|
||||
metadata.add_text(key, value)
|
||||
use_metadata = True
|
||||
if opts.samples_format.lower() == 'png':
|
||||
use_metadata = False
|
||||
metadata = PngImagePlugin.PngInfo()
|
||||
for key, value in image.info.items():
|
||||
if isinstance(key, str) and isinstance(value, str):
|
||||
metadata.add_text(key, value)
|
||||
use_metadata = True
|
||||
image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
|
||||
|
||||
elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
|
||||
parameters = image.info.get('parameters', None)
|
||||
exif_bytes = piexif.dump({
|
||||
"Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
|
||||
})
|
||||
if opts.samples_format.lower() in ("jpg", "jpeg"):
|
||||
image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
|
||||
else:
|
||||
image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
|
||||
|
||||
else:
|
||||
raise HTTPException(status_code=500, detail="Invalid image format")
|
||||
|
||||
image.save(
|
||||
output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
|
||||
)
|
||||
bytes_data = output_bytes.getvalue()
|
||||
|
||||
return base64.b64encode(bytes_data)
|
||||
|
||||
def api_middleware(app: FastAPI):
|
||||
|
|
@ -244,7 +259,7 @@ class Api:
|
|||
reqDict['image'] = decode_base64_to_image(reqDict['image'])
|
||||
|
||||
with self.queue_lock:
|
||||
result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||
result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||
|
||||
return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
|
||||
|
||||
|
|
@ -260,7 +275,7 @@ class Api:
|
|||
reqDict.pop('imageList')
|
||||
|
||||
with self.queue_lock:
|
||||
result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||
result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
|
||||
|
||||
return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
|
||||
|
||||
|
|
@ -360,13 +375,16 @@ class Api:
|
|||
return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
|
||||
|
||||
def get_upscalers(self):
|
||||
upscalers = []
|
||||
|
||||
for upscaler in shared.sd_upscalers:
|
||||
u = upscaler.scaler
|
||||
upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
|
||||
|
||||
return upscalers
|
||||
return [
|
||||
{
|
||||
"name": upscaler.name,
|
||||
"model_name": upscaler.scaler.model_name,
|
||||
"model_path": upscaler.data_path,
|
||||
"model_url": None,
|
||||
"scale": upscaler.scale,
|
||||
}
|
||||
for upscaler in shared.sd_upscalers
|
||||
]
|
||||
|
||||
def get_sd_models(self):
|
||||
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
|
||||
|
|
|
|||
|
|
@ -220,6 +220,7 @@ class UpscalerItem(BaseModel):
|
|||
model_name: Optional[str] = Field(title="Model Name")
|
||||
model_path: Optional[str] = Field(title="Path")
|
||||
model_url: Optional[str] = Field(title="URL")
|
||||
scale: Optional[float] = Field(title="Scale")
|
||||
|
||||
class SDModelItem(BaseModel):
|
||||
title: str = Field(title="Title")
|
||||
|
|
|
|||
|
|
@ -24,6 +24,18 @@ See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable
|
|||
""")
|
||||
|
||||
|
||||
already_displayed = {}
|
||||
|
||||
|
||||
def display_once(e: Exception, task):
|
||||
if task in already_displayed:
|
||||
return
|
||||
|
||||
display(e, task)
|
||||
|
||||
already_displayed[task] = 1
|
||||
|
||||
|
||||
def run(code, task):
|
||||
try:
|
||||
code()
|
||||
|
|
|
|||
|
|
@ -1,230 +1,16 @@
|
|||
from __future__ import annotations
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
import re
|
||||
import shutil
|
||||
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
import torch
|
||||
import tqdm
|
||||
|
||||
from typing import Callable, List, OrderedDict, Tuple
|
||||
from functools import partial
|
||||
from dataclasses import dataclass
|
||||
|
||||
from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae
|
||||
from modules.shared import opts
|
||||
import modules.gfpgan_model
|
||||
from modules.ui import plaintext_to_html
|
||||
import modules.codeformer_model
|
||||
from modules import shared, images, sd_models, sd_vae
|
||||
from modules.ui_common import plaintext_to_html
|
||||
import gradio as gr
|
||||
import safetensors.torch
|
||||
|
||||
class LruCache(OrderedDict):
|
||||
@dataclass(frozen=True)
|
||||
class Key:
|
||||
image_hash: int
|
||||
info_hash: int
|
||||
args_hash: int
|
||||
|
||||
@dataclass
|
||||
class Value:
|
||||
image: Image.Image
|
||||
info: str
|
||||
|
||||
def __init__(self, max_size: int = 5, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self._max_size = max_size
|
||||
|
||||
def get(self, key: LruCache.Key) -> LruCache.Value:
|
||||
ret = super().get(key)
|
||||
if ret is not None:
|
||||
self.move_to_end(key) # Move to end of eviction list
|
||||
return ret
|
||||
|
||||
def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
|
||||
self[key] = value
|
||||
while len(self) > self._max_size:
|
||||
self.popitem(last=False)
|
||||
|
||||
|
||||
cached_images: LruCache = LruCache(max_size=5)
|
||||
|
||||
|
||||
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
|
||||
devices.torch_gc()
|
||||
|
||||
shared.state.begin()
|
||||
shared.state.job = 'extras'
|
||||
|
||||
imageArr = []
|
||||
# Also keep track of original file names
|
||||
imageNameArr = []
|
||||
outputs = []
|
||||
|
||||
if extras_mode == 1:
|
||||
#convert file to pillow image
|
||||
for img in image_folder:
|
||||
image = Image.open(img)
|
||||
imageArr.append(image)
|
||||
imageNameArr.append(os.path.splitext(img.orig_name)[0])
|
||||
elif extras_mode == 2:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
|
||||
|
||||
if input_dir == '':
|
||||
return outputs, "Please select an input directory.", ''
|
||||
image_list = shared.listfiles(input_dir)
|
||||
for img in image_list:
|
||||
try:
|
||||
image = Image.open(img)
|
||||
except Exception:
|
||||
continue
|
||||
imageArr.append(image)
|
||||
imageNameArr.append(img)
|
||||
else:
|
||||
imageArr.append(image)
|
||||
imageNameArr.append(None)
|
||||
|
||||
if extras_mode == 2 and output_dir != '':
|
||||
outpath = output_dir
|
||||
else:
|
||||
outpath = opts.outdir_samples or opts.outdir_extras_samples
|
||||
|
||||
# Extra operation definitions
|
||||
|
||||
def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
||||
shared.state.job = 'extras-gfpgan'
|
||||
restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if gfpgan_visibility < 1.0:
|
||||
res = Image.blend(image, res, gfpgan_visibility)
|
||||
|
||||
info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
|
||||
return (res, info)
|
||||
|
||||
def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
||||
shared.state.job = 'extras-codeformer'
|
||||
restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if codeformer_visibility < 1.0:
|
||||
res = Image.blend(image, res, codeformer_visibility)
|
||||
|
||||
info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
|
||||
return (res, info)
|
||||
|
||||
def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
|
||||
shared.state.job = 'extras-upscale'
|
||||
upscaler = shared.sd_upscalers[scaler_index]
|
||||
res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
|
||||
if mode == 1 and crop:
|
||||
cropped = Image.new("RGB", (resize_w, resize_h))
|
||||
cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
|
||||
res = cropped
|
||||
return res
|
||||
|
||||
def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
||||
# Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
|
||||
nonlocal upscaling_resize
|
||||
if resize_mode == 1:
|
||||
upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
|
||||
crop_info = " (crop)" if upscaling_crop else ""
|
||||
info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
|
||||
return (image, info)
|
||||
|
||||
@dataclass
|
||||
class UpscaleParams:
|
||||
upscaler_idx: int
|
||||
blend_alpha: float
|
||||
|
||||
def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
|
||||
blended_result: Image.Image = None
|
||||
image_hash: str = hash(np.array(image.getdata()).tobytes())
|
||||
for upscaler in params:
|
||||
upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
|
||||
upscaling_resize_w, upscaling_resize_h, upscaling_crop)
|
||||
cache_key = LruCache.Key(image_hash=image_hash,
|
||||
info_hash=hash(info),
|
||||
args_hash=hash(upscale_args))
|
||||
cached_entry = cached_images.get(cache_key)
|
||||
if cached_entry is None:
|
||||
res = upscale(image, *upscale_args)
|
||||
info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
|
||||
cached_images.put(cache_key, LruCache.Value(image=res, info=info))
|
||||
else:
|
||||
res, info = cached_entry.image, cached_entry.info
|
||||
|
||||
if blended_result is None:
|
||||
blended_result = res
|
||||
else:
|
||||
blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
|
||||
return (blended_result, info)
|
||||
|
||||
# Build a list of operations to run
|
||||
facefix_ops: List[Callable] = []
|
||||
facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
|
||||
facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
|
||||
|
||||
upscale_ops: List[Callable] = []
|
||||
upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
|
||||
|
||||
if upscaling_resize != 0:
|
||||
step_params: List[UpscaleParams] = []
|
||||
step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
|
||||
if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
|
||||
step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
|
||||
|
||||
upscale_ops.append(partial(run_upscalers_blend, step_params))
|
||||
|
||||
extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
|
||||
|
||||
for image, image_name in zip(imageArr, imageNameArr):
|
||||
if image is None:
|
||||
return outputs, "Please select an input image.", ''
|
||||
|
||||
shared.state.textinfo = f'Processing image {image_name}'
|
||||
|
||||
existing_pnginfo = image.info or {}
|
||||
|
||||
image = image.convert("RGB")
|
||||
info = ""
|
||||
# Run each operation on each image
|
||||
for op in extras_ops:
|
||||
image, info = op(image, info)
|
||||
|
||||
if opts.use_original_name_batch and image_name is not None:
|
||||
basename = os.path.splitext(os.path.basename(image_name))[0]
|
||||
else:
|
||||
basename = ''
|
||||
|
||||
if opts.enable_pnginfo: # append info before save
|
||||
image.info = existing_pnginfo
|
||||
image.info["extras"] = info
|
||||
|
||||
if save_output:
|
||||
# Add upscaler name as a suffix.
|
||||
suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
|
||||
# Add second upscaler if applicable.
|
||||
if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
|
||||
suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
|
||||
|
||||
images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
|
||||
no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
|
||||
|
||||
if extras_mode != 2 or show_extras_results :
|
||||
outputs.append(image)
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
return outputs, plaintext_to_html(info), ''
|
||||
|
||||
def clear_cache():
|
||||
cached_images.clear()
|
||||
|
||||
|
||||
def run_pnginfo(image):
|
||||
if image is None:
|
||||
|
|
@ -285,7 +71,7 @@ def to_half(tensor, enable):
|
|||
return tensor
|
||||
|
||||
|
||||
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae):
|
||||
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
|
||||
shared.state.begin()
|
||||
shared.state.job = 'model-merge'
|
||||
|
||||
|
|
@ -430,6 +216,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
|
|||
for key in theta_0.keys():
|
||||
theta_0[key] = to_half(theta_0[key], save_as_half)
|
||||
|
||||
if discard_weights:
|
||||
regex = re.compile(discard_weights)
|
||||
for key in list(theta_0):
|
||||
if re.search(regex, key):
|
||||
theta_0.pop(key, None)
|
||||
|
||||
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
|
||||
|
||||
filename = filename_generator() if custom_name == '' else custom_name
|
||||
|
|
|
|||
|
|
@ -715,6 +715,8 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||
do_not_save_samples=True,
|
||||
)
|
||||
|
||||
p.disable_extra_networks = True
|
||||
|
||||
if preview_from_txt2img:
|
||||
p.prompt = preview_prompt
|
||||
p.negative_prompt = preview_negative_prompt
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import os
|
|||
import sys
|
||||
import traceback
|
||||
from collections import namedtuple
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
import torch
|
||||
|
|
@ -20,19 +21,20 @@ Category = namedtuple("Category", ["name", "topn", "items"])
|
|||
|
||||
re_topn = re.compile(r"\.top(\d+)\.")
|
||||
|
||||
def category_types():
|
||||
return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
|
||||
|
||||
|
||||
def download_default_clip_interrogate_categories(content_dir):
|
||||
print("Downloading CLIP categories...")
|
||||
|
||||
tmpdir = content_dir + "_tmp"
|
||||
category_types = ["artists", "flavors", "mediums", "movements"]
|
||||
|
||||
try:
|
||||
os.makedirs(tmpdir)
|
||||
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/artists.txt", os.path.join(tmpdir, "artists.txt"))
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/flavors.txt", os.path.join(tmpdir, "flavors.top3.txt"))
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/mediums.txt", os.path.join(tmpdir, "mediums.txt"))
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/movements.txt", os.path.join(tmpdir, "movements.txt"))
|
||||
|
||||
for category_type in category_types:
|
||||
torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
|
||||
os.rename(tmpdir, content_dir)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -51,32 +53,38 @@ class InterrogateModels:
|
|||
|
||||
def __init__(self, content_dir):
|
||||
self.loaded_categories = None
|
||||
self.skip_categories = []
|
||||
self.content_dir = content_dir
|
||||
self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
|
||||
|
||||
def categories(self):
|
||||
if self.loaded_categories is not None:
|
||||
return self.loaded_categories
|
||||
|
||||
self.loaded_categories = []
|
||||
|
||||
if not os.path.exists(self.content_dir):
|
||||
download_default_clip_interrogate_categories(self.content_dir)
|
||||
|
||||
if os.path.exists(self.content_dir):
|
||||
for filename in os.listdir(self.content_dir):
|
||||
m = re_topn.search(filename)
|
||||
topn = 1 if m is None else int(m.group(1))
|
||||
if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
|
||||
return self.loaded_categories
|
||||
|
||||
with open(os.path.join(self.content_dir, filename), "r", encoding="utf8") as file:
|
||||
self.loaded_categories = []
|
||||
|
||||
if os.path.exists(self.content_dir):
|
||||
self.skip_categories = shared.opts.interrogate_clip_skip_categories
|
||||
category_types = []
|
||||
for filename in Path(self.content_dir).glob('*.txt'):
|
||||
category_types.append(filename.stem)
|
||||
if filename.stem in self.skip_categories:
|
||||
continue
|
||||
m = re_topn.search(filename.stem)
|
||||
topn = 1 if m is None else int(m.group(1))
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
lines = [x.strip() for x in file.readlines()]
|
||||
|
||||
self.loaded_categories.append(Category(name=filename, topn=topn, items=lines))
|
||||
self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
|
||||
|
||||
return self.loaded_categories
|
||||
|
||||
def load_blip_model(self):
|
||||
import models.blip
|
||||
with paths.Prioritize("BLIP"):
|
||||
import models.blip
|
||||
|
||||
files = modelloader.load_models(
|
||||
model_path=os.path.join(paths.models_path, "BLIP"),
|
||||
|
|
@ -139,6 +147,8 @@ class InterrogateModels:
|
|||
def rank(self, image_features, text_array, top_count=1):
|
||||
import clip
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
if shared.opts.interrogate_clip_dict_limit != 0:
|
||||
text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
|
||||
|
||||
|
|
|
|||
|
|
@ -38,3 +38,17 @@ for d, must_exist, what, options in path_dirs:
|
|||
else:
|
||||
sys.path.append(d)
|
||||
paths[what] = d
|
||||
|
||||
|
||||
class Prioritize:
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
self.path = None
|
||||
|
||||
def __enter__(self):
|
||||
self.path = sys.path.copy()
|
||||
sys.path = [paths[self.name]] + sys.path
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
sys.path = self.path
|
||||
self.path = None
|
||||
|
|
|
|||
|
|
@ -0,0 +1,103 @@
|
|||
import os
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
|
||||
from modules.shared import opts
|
||||
|
||||
|
||||
def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
|
||||
devices.torch_gc()
|
||||
|
||||
shared.state.begin()
|
||||
shared.state.job = 'extras'
|
||||
|
||||
image_data = []
|
||||
image_names = []
|
||||
outputs = []
|
||||
|
||||
if extras_mode == 1:
|
||||
for img in image_folder:
|
||||
image = Image.open(img)
|
||||
image_data.append(image)
|
||||
image_names.append(os.path.splitext(img.orig_name)[0])
|
||||
elif extras_mode == 2:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
|
||||
assert input_dir, 'input directory not selected'
|
||||
|
||||
image_list = shared.listfiles(input_dir)
|
||||
for filename in image_list:
|
||||
try:
|
||||
image = Image.open(filename)
|
||||
except Exception:
|
||||
continue
|
||||
image_data.append(image)
|
||||
image_names.append(filename)
|
||||
else:
|
||||
assert image, 'image not selected'
|
||||
|
||||
image_data.append(image)
|
||||
image_names.append(None)
|
||||
|
||||
if extras_mode == 2 and output_dir != '':
|
||||
outpath = output_dir
|
||||
else:
|
||||
outpath = opts.outdir_samples or opts.outdir_extras_samples
|
||||
|
||||
infotext = ''
|
||||
|
||||
for image, name in zip(image_data, image_names):
|
||||
shared.state.textinfo = name
|
||||
|
||||
existing_pnginfo = image.info or {}
|
||||
|
||||
pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
|
||||
|
||||
scripts.scripts_postproc.run(pp, args)
|
||||
|
||||
if opts.use_original_name_batch and name is not None:
|
||||
basename = os.path.splitext(os.path.basename(name))[0]
|
||||
else:
|
||||
basename = ''
|
||||
|
||||
infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
|
||||
|
||||
if opts.enable_pnginfo:
|
||||
pp.image.info = existing_pnginfo
|
||||
pp.image.info["postprocessing"] = infotext
|
||||
|
||||
if save_output:
|
||||
images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
|
||||
|
||||
if extras_mode != 2 or show_extras_results:
|
||||
outputs.append(pp.image)
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
return outputs, ui_common.plaintext_to_html(infotext), ''
|
||||
|
||||
|
||||
def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
|
||||
"""old handler for API"""
|
||||
|
||||
args = scripts.scripts_postproc.create_args_for_run({
|
||||
"Upscale": {
|
||||
"upscale_mode": resize_mode,
|
||||
"upscale_by": upscaling_resize,
|
||||
"upscale_to_width": upscaling_resize_w,
|
||||
"upscale_to_height": upscaling_resize_h,
|
||||
"upscale_crop": upscaling_crop,
|
||||
"upscaler_1_name": extras_upscaler_1,
|
||||
"upscaler_2_name": extras_upscaler_2,
|
||||
"upscaler_2_visibility": extras_upscaler_2_visibility,
|
||||
},
|
||||
"GFPGAN": {
|
||||
"gfpgan_visibility": gfpgan_visibility,
|
||||
},
|
||||
"CodeFormer": {
|
||||
"codeformer_visibility": codeformer_visibility,
|
||||
"codeformer_weight": codeformer_weight,
|
||||
},
|
||||
})
|
||||
|
||||
return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)
|
||||
|
|
@ -140,6 +140,7 @@ class StableDiffusionProcessing:
|
|||
self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
|
||||
self.override_settings_restore_afterwards = override_settings_restore_afterwards
|
||||
self.is_using_inpainting_conditioning = False
|
||||
self.disable_extra_networks = False
|
||||
|
||||
if not seed_enable_extras:
|
||||
self.subseed = -1
|
||||
|
|
@ -579,7 +580,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
with devices.autocast():
|
||||
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
|
||||
|
||||
extra_networks.activate(p, extra_network_data)
|
||||
if not p.disable_extra_networks:
|
||||
extra_networks.activate(p, extra_network_data)
|
||||
|
||||
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
|
|
@ -723,7 +725,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
if opts.grid_save:
|
||||
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
||||
|
||||
extra_networks.deactivate(p, extra_network_data)
|
||||
if not p.disable_extra_networks:
|
||||
extra_networks.deactivate(p, extra_network_data)
|
||||
|
||||
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)
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from collections import namedtuple
|
|||
import gradio as gr
|
||||
|
||||
from modules.processing import StableDiffusionProcessing
|
||||
from modules import shared, paths, script_callbacks, extensions, script_loading
|
||||
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
|
||||
|
||||
AlwaysVisible = object()
|
||||
|
||||
|
|
@ -150,8 +150,10 @@ def basedir():
|
|||
return current_basedir
|
||||
|
||||
|
||||
scripts_data = []
|
||||
ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
|
||||
|
||||
scripts_data = []
|
||||
postprocessing_scripts_data = []
|
||||
ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
|
||||
|
||||
|
||||
|
|
@ -190,23 +192,31 @@ def list_files_with_name(filename):
|
|||
def load_scripts():
|
||||
global current_basedir
|
||||
scripts_data.clear()
|
||||
postprocessing_scripts_data.clear()
|
||||
script_callbacks.clear_callbacks()
|
||||
|
||||
scripts_list = list_scripts("scripts", ".py")
|
||||
|
||||
syspath = sys.path
|
||||
|
||||
def register_scripts_from_module(module):
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) != type:
|
||||
continue
|
||||
|
||||
if issubclass(script_class, Script):
|
||||
scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
|
||||
elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
|
||||
postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
|
||||
|
||||
for scriptfile in sorted(scripts_list):
|
||||
try:
|
||||
if scriptfile.basedir != paths.script_path:
|
||||
sys.path = [scriptfile.basedir] + sys.path
|
||||
current_basedir = scriptfile.basedir
|
||||
|
||||
module = script_loading.load_module(scriptfile.path)
|
||||
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) == type and issubclass(script_class, Script):
|
||||
scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
|
||||
script_module = script_loading.load_module(scriptfile.path)
|
||||
register_scripts_from_module(script_module)
|
||||
|
||||
except Exception:
|
||||
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
|
||||
|
|
@ -413,6 +423,7 @@ class ScriptRunner:
|
|||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
|
||||
scripts_current: ScriptRunner = None
|
||||
|
||||
|
||||
|
|
@ -423,12 +434,13 @@ def reload_script_body_only():
|
|||
|
||||
|
||||
def reload_scripts():
|
||||
global scripts_txt2img, scripts_img2img
|
||||
global scripts_txt2img, scripts_img2img, scripts_postproc
|
||||
|
||||
load_scripts()
|
||||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
|
||||
|
||||
|
||||
def IOComponent_init(self, *args, **kwargs):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,147 @@
|
|||
import os
|
||||
import gradio as gr
|
||||
|
||||
from modules import errors, shared
|
||||
|
||||
|
||||
class PostprocessedImage:
|
||||
def __init__(self, image):
|
||||
self.image = image
|
||||
self.info = {}
|
||||
|
||||
|
||||
class ScriptPostprocessing:
|
||||
filename = None
|
||||
controls = None
|
||||
args_from = None
|
||||
args_to = None
|
||||
|
||||
order = 1000
|
||||
"""scripts will be ordred by this value in postprocessing UI"""
|
||||
|
||||
name = None
|
||||
"""this function should return the title of the script."""
|
||||
|
||||
group = None
|
||||
"""A gr.Group component that has all script's UI inside it"""
|
||||
|
||||
def ui(self):
|
||||
"""
|
||||
This function should create gradio UI elements. See https://gradio.app/docs/#components
|
||||
The return value should be a dictionary that maps parameter names to components used in processing.
|
||||
Values of those components will be passed to process() function.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def process(self, pp: PostprocessedImage, **args):
|
||||
"""
|
||||
This function is called to postprocess the image.
|
||||
args contains a dictionary with all values returned by components from ui()
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def image_changed(self):
|
||||
pass
|
||||
|
||||
|
||||
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
|
||||
try:
|
||||
res = func(*args, **kwargs)
|
||||
return res
|
||||
except Exception as e:
|
||||
errors.display(e, f"calling {filename}/{funcname}")
|
||||
|
||||
return default
|
||||
|
||||
|
||||
class ScriptPostprocessingRunner:
|
||||
def __init__(self):
|
||||
self.scripts = None
|
||||
self.ui_created = False
|
||||
|
||||
def initialize_scripts(self, scripts_data):
|
||||
self.scripts = []
|
||||
|
||||
for script_class, path, basedir, script_module in scripts_data:
|
||||
script: ScriptPostprocessing = script_class()
|
||||
script.filename = path
|
||||
|
||||
self.scripts.append(script)
|
||||
|
||||
def create_script_ui(self, script, inputs):
|
||||
script.args_from = len(inputs)
|
||||
script.args_to = len(inputs)
|
||||
|
||||
script.controls = wrap_call(script.ui, script.filename, "ui")
|
||||
|
||||
for control in script.controls.values():
|
||||
control.custom_script_source = os.path.basename(script.filename)
|
||||
|
||||
inputs += list(script.controls.values())
|
||||
script.args_to = len(inputs)
|
||||
|
||||
def scripts_in_preferred_order(self):
|
||||
if self.scripts is None:
|
||||
import modules.scripts
|
||||
self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
|
||||
|
||||
scripts_order = [x.lower().strip() for x in shared.opts.postprocessing_scipts_order.split(",")]
|
||||
|
||||
def script_score(name):
|
||||
name = name.lower()
|
||||
for i, possible_match in enumerate(scripts_order):
|
||||
if possible_match in name:
|
||||
return i
|
||||
|
||||
return len(self.scripts)
|
||||
|
||||
script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
|
||||
|
||||
return sorted(self.scripts, key=lambda x: script_scores[x.name])
|
||||
|
||||
def setup_ui(self):
|
||||
inputs = []
|
||||
|
||||
for script in self.scripts_in_preferred_order():
|
||||
with gr.Box() as group:
|
||||
self.create_script_ui(script, inputs)
|
||||
|
||||
script.group = group
|
||||
|
||||
self.ui_created = True
|
||||
return inputs
|
||||
|
||||
def run(self, pp: PostprocessedImage, args):
|
||||
for script in self.scripts_in_preferred_order():
|
||||
shared.state.job = script.name
|
||||
|
||||
script_args = args[script.args_from:script.args_to]
|
||||
|
||||
process_args = {}
|
||||
for (name, component), value in zip(script.controls.items(), script_args):
|
||||
process_args[name] = value
|
||||
|
||||
script.process(pp, **process_args)
|
||||
|
||||
def create_args_for_run(self, scripts_args):
|
||||
if not self.ui_created:
|
||||
with gr.Blocks(analytics_enabled=False):
|
||||
self.setup_ui()
|
||||
|
||||
scripts = self.scripts_in_preferred_order()
|
||||
args = [None] * max([x.args_to for x in scripts])
|
||||
|
||||
for script in scripts:
|
||||
script_args_dict = scripts_args.get(script.name, None)
|
||||
if script_args_dict is not None:
|
||||
|
||||
for i, name in enumerate(script.controls):
|
||||
args[script.args_from + i] = script_args_dict.get(name, None)
|
||||
|
||||
return args
|
||||
|
||||
def image_changed(self):
|
||||
for script in self.scripts_in_preferred_order():
|
||||
script.image_changed()
|
||||
|
|
@ -41,7 +41,9 @@ class DisableInitialization:
|
|||
return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
|
||||
|
||||
def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
|
||||
res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
|
||||
res.name_or_path = pretrained_model_name_or_path
|
||||
return res
|
||||
|
||||
def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
|
||||
args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from torch import einsum
|
|||
from ldm.util import default
|
||||
from einops import rearrange
|
||||
|
||||
from modules import shared
|
||||
from modules import shared, errors
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
from .sub_quadratic_attention import efficient_dot_product_attention
|
||||
|
|
@ -279,6 +279,21 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
|
|||
)
|
||||
|
||||
|
||||
def get_xformers_flash_attention_op(q, k, v):
|
||||
if not shared.cmd_opts.xformers_flash_attention:
|
||||
return None
|
||||
|
||||
try:
|
||||
flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
|
||||
fw, bw = flash_attention_op
|
||||
if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
|
||||
return flash_attention_op
|
||||
except Exception as e:
|
||||
errors.display_once(e, "enabling flash attention")
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
q_in = self.to_q(x)
|
||||
|
|
@ -290,7 +305,8 @@ def xformers_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
|
||||
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
|
||||
|
||||
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
|
@ -365,7 +381,7 @@ def xformers_attnblock_forward(self, x):
|
|||
q = q.contiguous()
|
||||
k = k.contiguous()
|
||||
v = v.contiguous()
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v)
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
|
||||
out = self.proj_out(out)
|
||||
return x + out
|
||||
|
|
|
|||
|
|
@ -57,6 +57,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
|
|||
parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
|
||||
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
|
||||
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
||||
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
|
||||
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
|
||||
parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
|
||||
|
|
@ -398,7 +399,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
|
||||
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
|
||||
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
|
||||
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
|
||||
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
|
||||
|
|
@ -406,7 +407,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
|
|
@ -422,6 +424,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
|||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
|
||||
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
|
||||
|
|
@ -429,6 +432,10 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
|||
"deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
||||
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
|
|
@ -443,9 +450,12 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"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"),
|
||||
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
"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}),
|
||||
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
||||
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
|
|
@ -470,6 +480,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
|||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
||||
'postprocessing_scipts_order': OptionInfo("upscale, gfpgan, codeformer", "Postprocessing operation order"),
|
||||
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
"disabled_extensions": OptionInfo([], "Disable those extensions"),
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
|
|
|
|||
313
modules/ui.py
313
modules/ui.py
|
|
@ -5,7 +5,6 @@ import mimetypes
|
|||
import os
|
||||
import platform
|
||||
import random
|
||||
import subprocess as sp
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
|
|
@ -20,7 +19,7 @@ import numpy as np
|
|||
from PIL import Image, PngImagePlugin
|
||||
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
|
||||
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
|
||||
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
|
||||
from modules.paths import script_path
|
||||
|
||||
|
|
@ -41,6 +40,7 @@ from modules.sd_samplers import samplers, samplers_for_img2img
|
|||
from modules.textual_inversion import textual_inversion
|
||||
import modules.hypernetworks.ui
|
||||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
import modules.extras
|
||||
|
||||
warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
|
||||
|
||||
|
|
@ -75,6 +75,7 @@ css_hide_progressbar = """
|
|||
.wrap .m-12::before { content:"Loading..." }
|
||||
.wrap .z-20 svg { display:none!important; }
|
||||
.wrap .z-20::before { content:"Loading..." }
|
||||
.wrap.cover-bg .z-20::before { content:"" }
|
||||
.progress-bar { display:none!important; }
|
||||
.meta-text { display:none!important; }
|
||||
.meta-text-center { display:none!important; }
|
||||
|
|
@ -85,7 +86,6 @@ css_hide_progressbar = """
|
|||
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
||||
reuse_symbol = '\u267b\ufe0f' # ♻️
|
||||
paste_symbol = '\u2199\ufe0f' # ↙
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
save_style_symbol = '\U0001f4be' # 💾
|
||||
apply_style_symbol = '\U0001f4cb' # 📋
|
||||
|
|
@ -94,78 +94,14 @@ extra_networks_symbol = '\U0001F3B4' # 🎴
|
|||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
return text
|
||||
return ui_common.plaintext_to_html(text)
|
||||
|
||||
|
||||
def send_gradio_gallery_to_image(x):
|
||||
if len(x) == 0:
|
||||
return None
|
||||
return image_from_url_text(x[0])
|
||||
|
||||
def save_files(js_data, images, do_make_zip, index):
|
||||
import csv
|
||||
filenames = []
|
||||
fullfns = []
|
||||
|
||||
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
def __init__(self, d=None):
|
||||
if d is not None:
|
||||
for key, value in d.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
data = json.loads(js_data)
|
||||
|
||||
p = MyObject(data)
|
||||
path = opts.outdir_save
|
||||
save_to_dirs = opts.use_save_to_dirs_for_ui
|
||||
extension: str = opts.samples_format
|
||||
start_index = 0
|
||||
|
||||
if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
|
||||
images = [images[index]]
|
||||
start_index = index
|
||||
|
||||
os.makedirs(opts.outdir_save, exist_ok=True)
|
||||
|
||||
with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
||||
at_start = file.tell() == 0
|
||||
writer = csv.writer(file)
|
||||
if at_start:
|
||||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
for image_index, filedata in enumerate(images, start_index):
|
||||
image = image_from_url_text(filedata)
|
||||
|
||||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
fullfns.append(fullfn)
|
||||
if txt_fullfn:
|
||||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
zip_filepath = os.path.join(path, "images.zip")
|
||||
|
||||
from zipfile import ZipFile
|
||||
with ZipFile(zip_filepath, "w") as zip_file:
|
||||
for i in range(len(fullfns)):
|
||||
with open(fullfns[i], mode="rb") as f:
|
||||
zip_file.writestr(filenames[i], f.read())
|
||||
fullfns.insert(0, zip_filepath)
|
||||
|
||||
return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def visit(x, func, path=""):
|
||||
if hasattr(x, 'children'):
|
||||
for c in x.children:
|
||||
|
|
@ -443,19 +379,6 @@ def apply_setting(key, value):
|
|||
opts.save(shared.config_filename)
|
||||
return getattr(opts, key)
|
||||
|
||||
|
||||
def update_generation_info(generation_info, html_info, img_index):
|
||||
try:
|
||||
generation_info = json.loads(generation_info)
|
||||
if img_index < 0 or img_index >= len(generation_info["infotexts"]):
|
||||
return html_info, gr.update()
|
||||
return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
|
||||
except Exception:
|
||||
pass
|
||||
# if the json parse or anything else fails, just return the old html_info
|
||||
return html_info, gr.update()
|
||||
|
||||
|
||||
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
|
||||
def refresh():
|
||||
refresh_method()
|
||||
|
|
@ -476,107 +399,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
|
|||
|
||||
|
||||
def create_output_panel(tabname, outdir):
|
||||
def open_folder(f):
|
||||
if not os.path.exists(f):
|
||||
print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
|
||||
return
|
||||
elif not os.path.isdir(f):
|
||||
print(f"""
|
||||
WARNING
|
||||
An open_folder request was made with an argument that is not a folder.
|
||||
This could be an error or a malicious attempt to run code on your computer.
|
||||
Requested path was: {f}
|
||||
""", file=sys.stderr)
|
||||
return
|
||||
|
||||
if not shared.cmd_opts.hide_ui_dir_config:
|
||||
path = os.path.normpath(f)
|
||||
if platform.system() == "Windows":
|
||||
os.startfile(path)
|
||||
elif platform.system() == "Darwin":
|
||||
sp.Popen(["open", path])
|
||||
elif "microsoft-standard-WSL2" in platform.uname().release:
|
||||
sp.Popen(["wsl-open", path])
|
||||
else:
|
||||
sp.Popen(["xdg-open", path])
|
||||
|
||||
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
||||
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
||||
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
|
||||
|
||||
generation_info = None
|
||||
with gr.Column():
|
||||
with gr.Row(elem_id=f"image_buttons_{tabname}"):
|
||||
open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
|
||||
|
||||
if tabname != "extras":
|
||||
save = gr.Button('Save', elem_id=f'save_{tabname}')
|
||||
save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
|
||||
|
||||
buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
|
||||
|
||||
open_folder_button.click(
|
||||
fn=lambda: open_folder(opts.outdir_samples or outdir),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
if tabname != "extras":
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
|
||||
if tabname == 'txt2img' or tabname == 'img2img':
|
||||
generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
|
||||
generation_info_button.click(
|
||||
fn=update_generation_info,
|
||||
_js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
|
||||
inputs=[generation_info, html_info, html_info],
|
||||
outputs=[html_info, html_info],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
save_zip.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
]
|
||||
)
|
||||
|
||||
else:
|
||||
html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
|
||||
return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
|
||||
return ui_common.create_output_panel(tabname, outdir)
|
||||
|
||||
|
||||
def create_sampler_and_steps_selection(choices, tabname):
|
||||
|
|
@ -935,7 +758,7 @@ def create_ui():
|
|||
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
|
||||
|
||||
elif category == "checkboxes":
|
||||
with FormRow(elem_id="img2img_checkboxes"):
|
||||
with FormRow(elem_id="img2img_checkboxes", variant="compact"):
|
||||
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
|
||||
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
|
||||
|
||||
|
|
@ -1122,86 +945,7 @@ def create_ui():
|
|||
modules.scripts.scripts_current = None
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='compact'):
|
||||
with gr.Tabs(elem_id="mode_extras"):
|
||||
with gr.TabItem('Single Image', elem_id="extras_single_tab"):
|
||||
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
||||
|
||||
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
||||
|
||||
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
|
||||
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
||||
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
|
||||
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
|
||||
|
||||
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
||||
|
||||
with gr.Tabs(elem_id="extras_resize_mode"):
|
||||
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
|
||||
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
|
||||
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
|
||||
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
|
||||
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
|
||||
|
||||
with gr.Group():
|
||||
extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
|
||||
|
||||
with gr.Group():
|
||||
extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
|
||||
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1, elem_id="extras_upscaler_2_visibility")
|
||||
|
||||
with gr.Group():
|
||||
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan, elem_id="extras_gfpgan_visibility")
|
||||
|
||||
with gr.Group():
|
||||
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_visibility")
|
||||
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_weight")
|
||||
|
||||
with gr.Group():
|
||||
upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
|
||||
|
||||
result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
|
||||
|
||||
submit.click(
|
||||
fn=wrap_gradio_gpu_call(modules.extras.run_extras, extra_outputs=[None, '']),
|
||||
_js="get_extras_tab_index",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
dummy_component,
|
||||
extras_image,
|
||||
image_batch,
|
||||
extras_batch_input_dir,
|
||||
extras_batch_output_dir,
|
||||
show_extras_results,
|
||||
gfpgan_visibility,
|
||||
codeformer_visibility,
|
||||
codeformer_weight,
|
||||
upscaling_resize,
|
||||
upscaling_resize_w,
|
||||
upscaling_resize_h,
|
||||
upscaling_crop,
|
||||
extras_upscaler_1,
|
||||
extras_upscaler_2,
|
||||
extras_upscaler_2_visibility,
|
||||
upscale_before_face_fix,
|
||||
],
|
||||
outputs=[
|
||||
result_images,
|
||||
html_info_x,
|
||||
html_info,
|
||||
]
|
||||
)
|
||||
parameters_copypaste.add_paste_fields("extras", extras_image, None)
|
||||
|
||||
extras_image.change(
|
||||
fn=modules.extras.clear_cache,
|
||||
inputs=[], outputs=[]
|
||||
)
|
||||
ui_postprocessing.create_ui()
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
|
|
@ -1222,10 +966,19 @@ def create_ui():
|
|||
outputs=[html, generation_info, html2],
|
||||
)
|
||||
|
||||
def update_interp_description(value):
|
||||
interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
|
||||
interp_descriptions = {
|
||||
"No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
|
||||
"Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
|
||||
"Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
|
||||
}
|
||||
return interp_descriptions[value]
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='compact'):
|
||||
gr.HTML(value="<p style='margin-bottom: 2.5em'>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
|
||||
interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
|
||||
|
||||
with FormRow(elem_id="modelmerger_models"):
|
||||
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
|
||||
|
|
@ -1240,6 +993,7 @@ def create_ui():
|
|||
custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
|
||||
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
|
||||
interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
|
||||
interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
|
||||
|
||||
with FormRow():
|
||||
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
|
||||
|
|
@ -1254,6 +1008,9 @@ def create_ui():
|
|||
bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
|
||||
create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
|
||||
|
||||
with FormRow():
|
||||
discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
|
||||
|
||||
with gr.Row():
|
||||
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
|
||||
|
||||
|
|
@ -1265,7 +1022,7 @@ def create_ui():
|
|||
with gr.Row().style(equal_height=False):
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
|
||||
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Row(variant="compact").style(equal_height=False):
|
||||
with gr.Tabs(elem_id="train_tabs"):
|
||||
|
||||
with gr.Tab(label="Create embedding"):
|
||||
|
|
@ -1844,6 +1601,7 @@ def create_ui():
|
|||
checkpoint_format,
|
||||
config_source,
|
||||
bake_in_vae,
|
||||
discard_weights,
|
||||
],
|
||||
outputs=[
|
||||
primary_model_name,
|
||||
|
|
@ -1931,28 +1689,27 @@ def create_ui():
|
|||
with open(ui_config_file, "w", encoding="utf8") as file:
|
||||
json.dump(ui_settings, file, indent=4)
|
||||
|
||||
# Required as a workaround for change() event not triggering when loading values from ui-config.json
|
||||
interp_description.value = update_interp_description(interp_method.value)
|
||||
|
||||
return demo
|
||||
|
||||
|
||||
def reload_javascript():
|
||||
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
|
||||
javascript = f'<script>{jsfile.read()}</script>'
|
||||
|
||||
scripts_list = modules.scripts.list_scripts("javascript", ".js")
|
||||
|
||||
for basedir, filename, path in scripts_list:
|
||||
with open(path, "r", encoding="utf8") as jsfile:
|
||||
javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"
|
||||
head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}"></script>\n'
|
||||
|
||||
inline = f"{localization.localization_js(shared.opts.localization)};"
|
||||
if cmd_opts.theme is not None:
|
||||
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
||||
inline += f"set_theme('{cmd_opts.theme}');"
|
||||
|
||||
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
||||
for script in modules.scripts.list_scripts("javascript", ".js"):
|
||||
head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
|
||||
|
||||
head += f'<script type="text/javascript">{inline}</script>\n'
|
||||
|
||||
def template_response(*args, **kwargs):
|
||||
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
|
||||
res.body = res.body.replace(
|
||||
b'</head>', f'{javascript}</head>'.encode("utf8"))
|
||||
res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
|
||||
res.init_headers()
|
||||
return res
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,202 @@
|
|||
import json
|
||||
import html
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
|
||||
import gradio as gr
|
||||
import subprocess as sp
|
||||
|
||||
from modules import call_queue, shared
|
||||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
import modules.images
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
||||
|
||||
def update_generation_info(generation_info, html_info, img_index):
|
||||
try:
|
||||
generation_info = json.loads(generation_info)
|
||||
if img_index < 0 or img_index >= len(generation_info["infotexts"]):
|
||||
return html_info, gr.update()
|
||||
return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
|
||||
except Exception:
|
||||
pass
|
||||
# if the json parse or anything else fails, just return the old html_info
|
||||
return html_info, gr.update()
|
||||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
return text
|
||||
|
||||
|
||||
def save_files(js_data, images, do_make_zip, index):
|
||||
import csv
|
||||
filenames = []
|
||||
fullfns = []
|
||||
|
||||
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
def __init__(self, d=None):
|
||||
if d is not None:
|
||||
for key, value in d.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
data = json.loads(js_data)
|
||||
|
||||
p = MyObject(data)
|
||||
path = shared.opts.outdir_save
|
||||
save_to_dirs = shared.opts.use_save_to_dirs_for_ui
|
||||
extension: str = shared.opts.samples_format
|
||||
start_index = 0
|
||||
|
||||
if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
|
||||
images = [images[index]]
|
||||
start_index = index
|
||||
|
||||
os.makedirs(shared.opts.outdir_save, exist_ok=True)
|
||||
|
||||
with open(os.path.join(shared.opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
||||
at_start = file.tell() == 0
|
||||
writer = csv.writer(file)
|
||||
if at_start:
|
||||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
for image_index, filedata in enumerate(images, start_index):
|
||||
image = image_from_url_text(filedata)
|
||||
|
||||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
fullfns.append(fullfn)
|
||||
if txt_fullfn:
|
||||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
zip_filepath = os.path.join(path, "images.zip")
|
||||
|
||||
from zipfile import ZipFile
|
||||
with ZipFile(zip_filepath, "w") as zip_file:
|
||||
for i in range(len(fullfns)):
|
||||
with open(fullfns[i], mode="rb") as f:
|
||||
zip_file.writestr(filenames[i], f.read())
|
||||
fullfns.insert(0, zip_filepath)
|
||||
|
||||
return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def create_output_panel(tabname, outdir):
|
||||
from modules import shared
|
||||
import modules.generation_parameters_copypaste as parameters_copypaste
|
||||
|
||||
def open_folder(f):
|
||||
if not os.path.exists(f):
|
||||
print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
|
||||
return
|
||||
elif not os.path.isdir(f):
|
||||
print(f"""
|
||||
WARNING
|
||||
An open_folder request was made with an argument that is not a folder.
|
||||
This could be an error or a malicious attempt to run code on your computer.
|
||||
Requested path was: {f}
|
||||
""", file=sys.stderr)
|
||||
return
|
||||
|
||||
if not shared.cmd_opts.hide_ui_dir_config:
|
||||
path = os.path.normpath(f)
|
||||
if platform.system() == "Windows":
|
||||
os.startfile(path)
|
||||
elif platform.system() == "Darwin":
|
||||
sp.Popen(["open", path])
|
||||
elif "microsoft-standard-WSL2" in platform.uname().release:
|
||||
sp.Popen(["wsl-open", path])
|
||||
else:
|
||||
sp.Popen(["xdg-open", path])
|
||||
|
||||
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
||||
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
||||
result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
|
||||
|
||||
generation_info = None
|
||||
with gr.Column():
|
||||
with gr.Row(elem_id=f"image_buttons_{tabname}"):
|
||||
open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
|
||||
|
||||
if tabname != "extras":
|
||||
save = gr.Button('Save', elem_id=f'save_{tabname}')
|
||||
save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
|
||||
|
||||
buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
|
||||
|
||||
open_folder_button.click(
|
||||
fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
if tabname != "extras":
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
|
||||
if tabname == 'txt2img' or tabname == 'img2img':
|
||||
generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
|
||||
generation_info_button.click(
|
||||
fn=update_generation_info,
|
||||
_js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
|
||||
inputs=[generation_info, html_info, html_info],
|
||||
outputs=[html_info, html_info],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=call_queue.wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
save_zip.click(
|
||||
fn=call_queue.wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
]
|
||||
)
|
||||
|
||||
else:
|
||||
html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
|
||||
return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
|
||||
|
|
@ -47,3 +47,4 @@ class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
|
|||
|
||||
def get_block_name(self):
|
||||
return "colorpicker"
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import os.path
|
|||
from modules import shared
|
||||
import gradio as gr
|
||||
import json
|
||||
import html
|
||||
|
||||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
|
||||
|
|
@ -26,6 +27,7 @@ class ExtraNetworksPage:
|
|||
pass
|
||||
|
||||
def create_html(self, tabname):
|
||||
view = shared.opts.extra_networks_default_view
|
||||
items_html = ''
|
||||
|
||||
for item in self.list_items():
|
||||
|
|
@ -36,7 +38,7 @@ class ExtraNetworksPage:
|
|||
items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
|
||||
|
||||
res = f"""
|
||||
<div id='{tabname}_{self.name}_cards' class='extra-network-cards'>
|
||||
<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
|
||||
{items_html}
|
||||
</div>
|
||||
"""
|
||||
|
|
@ -53,12 +55,13 @@ class ExtraNetworksPage:
|
|||
preview = item.get("preview", None)
|
||||
|
||||
args = {
|
||||
"preview_html": "style='background-image: url(" + json.dumps(preview) + ")'" if preview else '',
|
||||
"prompt": json.dumps(item["prompt"]),
|
||||
"preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
|
||||
"prompt": item["prompt"],
|
||||
"tabname": json.dumps(tabname),
|
||||
"local_preview": json.dumps(item["local_preview"]),
|
||||
"name": item["name"],
|
||||
"allow_negative_prompt": "true" if self.allow_negative_prompt else "false",
|
||||
"card_clicked": '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"',
|
||||
"save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
|
||||
}
|
||||
|
||||
return self.card_page.format(**args)
|
||||
|
|
@ -79,10 +82,26 @@ class ExtraNetworksUi:
|
|||
self.tabname = None
|
||||
|
||||
|
||||
def pages_in_preferred_order(pages):
|
||||
tab_order = [x.lower().strip() for x in shared.opts.ui_extra_networks_tab_reorder.split(",")]
|
||||
|
||||
def tab_name_score(name):
|
||||
name = name.lower()
|
||||
for i, possible_match in enumerate(tab_order):
|
||||
if possible_match in name:
|
||||
return i
|
||||
|
||||
return len(pages)
|
||||
|
||||
tab_scores = {page.name: (tab_name_score(page.name), original_index) for original_index, page in enumerate(pages)}
|
||||
|
||||
return sorted(pages, key=lambda x: tab_scores[x.name])
|
||||
|
||||
|
||||
def create_ui(container, button, tabname):
|
||||
ui = ExtraNetworksUi()
|
||||
ui.pages = []
|
||||
ui.stored_extra_pages = extra_pages.copy()
|
||||
ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
|
||||
ui.tabname = tabname
|
||||
|
||||
with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import json
|
||||
import os
|
||||
|
||||
from modules import shared, ui_extra_networks
|
||||
|
|
@ -25,7 +26,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
|
|||
"name": name,
|
||||
"filename": path,
|
||||
"preview": preview,
|
||||
"prompt": f"<hypernet:{name}:1.0>",
|
||||
"prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
|
||||
"local_preview": path + ".png",
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import json
|
||||
import os
|
||||
|
||||
from modules import ui_extra_networks, sd_hijack
|
||||
|
|
@ -24,7 +25,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
|
|||
"name": embedding.name,
|
||||
"filename": embedding.filename,
|
||||
"preview": preview,
|
||||
"prompt": embedding.name,
|
||||
"prompt": json.dumps(embedding.name),
|
||||
"local_preview": path + ".preview.png",
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,57 @@
|
|||
import gradio as gr
|
||||
from modules import scripts_postprocessing, scripts, shared, gfpgan_model, codeformer_model, ui_common, postprocessing, call_queue
|
||||
import modules.generation_parameters_copypaste as parameters_copypaste
|
||||
|
||||
|
||||
def create_ui():
|
||||
tab_index = gr.State(value=0)
|
||||
|
||||
with gr.Row().style(equal_height=False, variant='compact'):
|
||||
with gr.Column(variant='compact'):
|
||||
with gr.Tabs(elem_id="mode_extras"):
|
||||
with gr.TabItem('Single Image', elem_id="extras_single_tab") as tab_single:
|
||||
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
||||
|
||||
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
||||
|
||||
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
|
||||
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
||||
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
|
||||
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
|
||||
|
||||
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
||||
|
||||
script_inputs = scripts.scripts_postproc.setup_ui()
|
||||
|
||||
with gr.Column():
|
||||
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
|
||||
|
||||
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
|
||||
tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
|
||||
tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
|
||||
|
||||
submit.click(
|
||||
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
|
||||
inputs=[
|
||||
tab_index,
|
||||
extras_image,
|
||||
image_batch,
|
||||
extras_batch_input_dir,
|
||||
extras_batch_output_dir,
|
||||
show_extras_results,
|
||||
*script_inputs
|
||||
],
|
||||
outputs=[
|
||||
result_images,
|
||||
html_info_x,
|
||||
html_info,
|
||||
]
|
||||
)
|
||||
|
||||
parameters_copypaste.add_paste_fields("extras", extras_image, None)
|
||||
|
||||
extras_image.change(
|
||||
fn=scripts.scripts_postproc.image_changed,
|
||||
inputs=[], outputs=[]
|
||||
)
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, codeformer_model
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "CodeFormer"
|
||||
order = 3000
|
||||
|
||||
def ui(self):
|
||||
with FormRow():
|
||||
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, elem_id="extras_codeformer_visibility")
|
||||
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, elem_id="extras_codeformer_weight")
|
||||
|
||||
return {
|
||||
"codeformer_visibility": codeformer_visibility,
|
||||
"codeformer_weight": codeformer_weight,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
|
||||
if codeformer_visibility == 0:
|
||||
return
|
||||
|
||||
restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if codeformer_visibility < 1.0:
|
||||
res = Image.blend(pp.image, res, codeformer_visibility)
|
||||
|
||||
pp.image = res
|
||||
pp.info["CodeFormer visibility"] = round(codeformer_visibility, 3)
|
||||
pp.info["CodeFormer weight"] = round(codeformer_weight, 3)
|
||||
|
|
@ -0,0 +1,33 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, gfpgan_model
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "GFPGAN"
|
||||
order = 2000
|
||||
|
||||
def ui(self):
|
||||
with FormRow():
|
||||
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
|
||||
|
||||
return {
|
||||
"gfpgan_visibility": gfpgan_visibility,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
|
||||
if gfpgan_visibility == 0:
|
||||
return
|
||||
|
||||
restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if gfpgan_visibility < 1.0:
|
||||
res = Image.blend(pp.image, res, gfpgan_visibility)
|
||||
|
||||
pp.image = res
|
||||
pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, shared
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
upscale_cache = {}
|
||||
|
||||
|
||||
class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Upscale"
|
||||
order = 1000
|
||||
|
||||
def ui(self):
|
||||
selected_tab = gr.State(value=0)
|
||||
|
||||
with gr.Tabs(elem_id="extras_resize_mode"):
|
||||
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
|
||||
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
|
||||
|
||||
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab") as tab_scale_to:
|
||||
with FormRow():
|
||||
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
|
||||
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
|
||||
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
|
||||
|
||||
with FormRow():
|
||||
extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
|
||||
|
||||
with FormRow():
|
||||
extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
|
||||
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
|
||||
|
||||
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
|
||||
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
|
||||
|
||||
return {
|
||||
"upscale_mode": selected_tab,
|
||||
"upscale_by": upscaling_resize,
|
||||
"upscale_to_width": upscaling_resize_w,
|
||||
"upscale_to_height": upscaling_resize_h,
|
||||
"upscale_crop": upscaling_crop,
|
||||
"upscaler_1_name": extras_upscaler_1,
|
||||
"upscaler_2_name": extras_upscaler_2,
|
||||
"upscaler_2_visibility": extras_upscaler_2_visibility,
|
||||
}
|
||||
|
||||
def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):
|
||||
if upscale_mode == 1:
|
||||
upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
|
||||
info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
|
||||
else:
|
||||
info["Postprocess upscale by"] = upscale_by
|
||||
|
||||
cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
cached_image = upscale_cache.pop(cache_key, None)
|
||||
|
||||
if cached_image is not None:
|
||||
image = cached_image
|
||||
else:
|
||||
image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)
|
||||
|
||||
upscale_cache[cache_key] = image
|
||||
if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache:
|
||||
upscale_cache.pop(next(iter(upscale_cache), None), None)
|
||||
|
||||
if upscale_mode == 1 and upscale_crop:
|
||||
cropped = Image.new("RGB", (upscale_to_width, upscale_to_height))
|
||||
cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2))
|
||||
image = cropped
|
||||
info["Postprocess crop to"] = f"{image.width}x{image.height}"
|
||||
|
||||
return image
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
if upscaler_1_name == "None":
|
||||
upscaler_1_name = None
|
||||
|
||||
upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None)
|
||||
assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}'
|
||||
|
||||
if not upscaler1:
|
||||
return
|
||||
|
||||
if upscaler_2_name == "None":
|
||||
upscaler_2_name = None
|
||||
|
||||
upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
|
||||
assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
|
||||
|
||||
upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
pp.info[f"Postprocess upscaler"] = upscaler1.name
|
||||
|
||||
if upscaler2 and upscaler_2_visibility > 0:
|
||||
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
|
||||
|
||||
pp.info[f"Postprocess upscaler 2"] = upscaler2.name
|
||||
|
||||
pp.image = upscaled_image
|
||||
|
||||
def image_changed(self):
|
||||
upscale_cache.clear()
|
||||
|
|
@ -165,10 +165,14 @@ class AxisOption:
|
|||
self.confirm = confirm
|
||||
self.cost = cost
|
||||
self.choices = choices
|
||||
self.is_img2img = False
|
||||
|
||||
|
||||
class AxisOptionImg2Img(AxisOption):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_img2img = True
|
||||
|
||||
class AxisOptionTxt2Img(AxisOption):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.is_img2img = False
|
||||
|
|
@ -180,10 +184,12 @@ axis_options = [
|
|||
AxisOption("Var. seed", int, apply_field("subseed")),
|
||||
AxisOption("Var. strength", float, apply_field("subseed_strength")),
|
||||
AxisOption("Steps", int, apply_field("steps")),
|
||||
AxisOptionTxt2Img("Hires steps", int, apply_field("hr_second_pass_steps")),
|
||||
AxisOption("CFG Scale", float, apply_field("cfg_scale")),
|
||||
AxisOption("Prompt S/R", str, apply_prompt, format_value=format_value),
|
||||
AxisOption("Prompt order", str_permutations, apply_order, format_value=format_value_join_list),
|
||||
AxisOption("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionTxt2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),
|
||||
AxisOptionImg2Img("Sampler", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),
|
||||
AxisOption("Checkpoint name", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),
|
||||
AxisOption("Sigma Churn", float, apply_field("s_churn")),
|
||||
AxisOption("Sigma min", float, apply_field("s_tmin")),
|
||||
|
|
@ -192,8 +198,8 @@ axis_options = [
|
|||
AxisOption("Eta", float, apply_field("eta")),
|
||||
AxisOption("Clip skip", int, apply_clip_skip),
|
||||
AxisOption("Denoising", float, apply_field("denoising_strength")),
|
||||
AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [x.name for x in shared.sd_upscalers]),
|
||||
AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")),
|
||||
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("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),
|
||||
]
|
||||
|
|
@ -288,42 +294,41 @@ class Script(scripts.Script):
|
|||
return "X/Y plot"
|
||||
|
||||
def ui(self, is_img2img):
|
||||
current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img and is_img2img]
|
||||
self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=19):
|
||||
with gr.Row():
|
||||
x_type = gr.Dropdown(label="X type", choices=[x.label for x in current_axis_options], value=current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
|
||||
x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
|
||||
x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
|
||||
fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_x_tool_button", visible=False)
|
||||
|
||||
with gr.Row():
|
||||
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
|
||||
y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
|
||||
y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
|
||||
fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xy_grid_fill_y_tool_button", visible=False)
|
||||
|
||||
with gr.Row(variant="compact"):
|
||||
with gr.Row(variant="compact", elem_id="axis_options"):
|
||||
draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id("draw_legend"))
|
||||
include_lone_images = gr.Checkbox(label='Include Separate Images', value=False, elem_id=self.elem_id("include_lone_images"))
|
||||
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id("no_fixed_seeds"))
|
||||
swap_axes_button = gr.Button(value="Swap axes", elem_id="xy_grid_swap_axes_button")
|
||||
|
||||
def swap_axes(x_type, x_values, y_type, y_values):
|
||||
nonlocal current_axis_options
|
||||
return current_axis_options[y_type].label, y_values, current_axis_options[x_type].label, x_values
|
||||
return self.current_axis_options[y_type].label, y_values, self.current_axis_options[x_type].label, x_values
|
||||
|
||||
swap_args = [x_type, x_values, y_type, y_values]
|
||||
swap_axes_button.click(swap_axes, inputs=swap_args, outputs=swap_args)
|
||||
|
||||
def fill(x_type):
|
||||
axis = axis_options[x_type]
|
||||
axis = self.current_axis_options[x_type]
|
||||
return ", ".join(axis.choices()) if axis.choices else gr.update()
|
||||
|
||||
fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])
|
||||
fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])
|
||||
|
||||
def select_axis(x_type):
|
||||
return gr.Button.update(visible=axis_options[x_type].choices is not None)
|
||||
return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)
|
||||
|
||||
x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])
|
||||
y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])
|
||||
|
|
@ -398,10 +403,10 @@ class Script(scripts.Script):
|
|||
|
||||
return valslist
|
||||
|
||||
x_opt = axis_options[x_type]
|
||||
x_opt = self.current_axis_options[x_type]
|
||||
xs = process_axis(x_opt, x_values)
|
||||
|
||||
y_opt = axis_options[y_type]
|
||||
y_opt = self.current_axis_options[y_type]
|
||||
ys = process_axis(y_opt, y_values)
|
||||
|
||||
def fix_axis_seeds(axis_opt, axis_list):
|
||||
|
|
@ -422,10 +427,21 @@ class Script(scripts.Script):
|
|||
total_steps = p.steps * len(xs) * len(ys)
|
||||
|
||||
if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
|
||||
total_steps *= 2
|
||||
if x_opt.label == "Hires steps":
|
||||
total_steps += sum(xs) * len(ys)
|
||||
elif y_opt.label == "Hires steps":
|
||||
total_steps += sum(ys) * len(xs)
|
||||
elif p.hr_second_pass_steps:
|
||||
total_steps += p.hr_second_pass_steps * len(xs) * len(ys)
|
||||
else:
|
||||
total_steps *= 2
|
||||
|
||||
print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
|
||||
shared.total_tqdm.updateTotal(total_steps * p.n_iter)
|
||||
total_steps *= p.n_iter
|
||||
|
||||
image_cell_count = p.n_iter * p.batch_size
|
||||
cell_console_text = f"; {image_cell_count} images per cell" if image_cell_count > 1 else ""
|
||||
print(f"X/Y plot will create {len(xs) * len(ys) * image_cell_count} images on a {len(xs)}x{len(ys)} grid{cell_console_text}. (Total steps to process: {total_steps})")
|
||||
shared.total_tqdm.updateTotal(total_steps)
|
||||
|
||||
grid_infotext = [None]
|
||||
|
||||
|
|
|
|||
77
style.css
77
style.css
|
|
@ -589,7 +589,7 @@ canvas[key="mask"] {
|
|||
|
||||
/* Extensions */
|
||||
|
||||
#tab_extensions table``{
|
||||
#tab_extensions table{
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
|
|
@ -707,12 +707,24 @@ footer {
|
|||
|
||||
#txt2img_checkboxes, #img2img_checkboxes{
|
||||
margin-bottom: 0.5em;
|
||||
margin-left: 0em;
|
||||
}
|
||||
#txt2img_checkboxes > div, #img2img_checkboxes > div{
|
||||
flex: 0;
|
||||
white-space: nowrap;
|
||||
min-width: auto;
|
||||
}
|
||||
#txt2img_hires_fix{
|
||||
margin-left: -0.8em;
|
||||
}
|
||||
|
||||
#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{
|
||||
margin-left: 0em;
|
||||
}
|
||||
|
||||
#axis_options {
|
||||
margin-left: 0em;
|
||||
}
|
||||
|
||||
.inactive{
|
||||
opacity: 0.5;
|
||||
|
|
@ -780,21 +792,78 @@ footer {
|
|||
display: inline-block;
|
||||
max-width: 16em;
|
||||
margin: 0.3em;
|
||||
align-self: center;
|
||||
}
|
||||
|
||||
.extra-network-cards .nocards{
|
||||
#txt2img_extra_view, #img2img_extra_view {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.extra-network-cards .nocards, .extra-network-thumbs .nocards{
|
||||
margin: 1.25em 0.5em 0.5em 0.5em;
|
||||
}
|
||||
|
||||
.extra-network-cards .nocards h1{
|
||||
.extra-network-cards .nocards h1, .extra-network-thumbs .nocards h1{
|
||||
font-size: 1.5em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.extra-network-cards .nocards li{
|
||||
.extra-network-cards .nocards li, .extra-network-thumbs .nocards li{
|
||||
margin-left: 0.5em;
|
||||
}
|
||||
|
||||
.extra-network-thumbs {
|
||||
display: flex;
|
||||
flex-flow: row wrap;
|
||||
gap: 10px;
|
||||
}
|
||||
|
||||
.extra-network-thumbs .card {
|
||||
height: 6em;
|
||||
width: 6em;
|
||||
cursor: pointer;
|
||||
background-image: url('./file=html/card-no-preview.png');
|
||||
background-size: cover;
|
||||
background-position: center center;
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.extra-network-thumbs .card:hover .additional a {
|
||||
display: block;
|
||||
}
|
||||
|
||||
.extra-network-thumbs .actions .additional a {
|
||||
background-image: url('./file=html/image-update.svg');
|
||||
background-repeat: no-repeat;
|
||||
background-size: cover;
|
||||
background-position: center center;
|
||||
position: absolute;
|
||||
top: 0;
|
||||
left: 0;
|
||||
width: 24px;
|
||||
height: 24px;
|
||||
display: none;
|
||||
font-size: 0;
|
||||
text-align: -9999;
|
||||
}
|
||||
|
||||
.extra-network-thumbs .actions .name {
|
||||
position: absolute;
|
||||
bottom: 0;
|
||||
font-size: 10px;
|
||||
padding: 3px;
|
||||
width: 100%;
|
||||
overflow: hidden;
|
||||
white-space: nowrap;
|
||||
text-overflow: ellipsis;
|
||||
background: rgba(0,0,0,.5);
|
||||
}
|
||||
|
||||
.extra-network-thumbs .card:hover .actions .name {
|
||||
white-space: normal;
|
||||
word-break: break-all;
|
||||
}
|
||||
|
||||
.extra-network-cards .card{
|
||||
display: inline-block;
|
||||
margin: 0.5em;
|
||||
|
|
|
|||
31
webui.py
31
webui.py
|
|
@ -1,6 +1,5 @@
|
|||
import os
|
||||
import sys
|
||||
import threading
|
||||
import time
|
||||
import importlib
|
||||
import signal
|
||||
|
|
@ -8,6 +7,10 @@ import re
|
|||
from fastapi import FastAPI
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from fastapi.middleware.gzip import GZipMiddleware
|
||||
from packaging import version
|
||||
|
||||
import logging
|
||||
logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
|
||||
|
||||
from modules import import_hook, errors, extra_networks
|
||||
from modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion
|
||||
|
|
@ -22,7 +25,6 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
|
|||
|
||||
from modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks
|
||||
import modules.codeformer_model as codeformer
|
||||
import modules.extras
|
||||
import modules.face_restoration
|
||||
import modules.gfpgan_model as gfpgan
|
||||
import modules.img2img
|
||||
|
|
@ -50,7 +52,32 @@ else:
|
|||
server_name = "0.0.0.0" if cmd_opts.listen else None
|
||||
|
||||
|
||||
def check_versions():
|
||||
expected_torch_version = "1.13.1"
|
||||
|
||||
if version.parse(torch.__version__) < version.parse(expected_torch_version):
|
||||
errors.print_error_explanation(f"""
|
||||
You are running torch {torch.__version__}.
|
||||
The program is tested to work with torch {expected_torch_version}.
|
||||
To reinstall the desired version, run with commandline flag --reinstall-torch.
|
||||
Beware that this will cause a lot of large files to be downloaded.
|
||||
""".strip())
|
||||
|
||||
expected_xformers_version = "0.0.16rc425"
|
||||
if shared.xformers_available:
|
||||
import xformers
|
||||
|
||||
if version.parse(xformers.__version__) < version.parse(expected_xformers_version):
|
||||
errors.print_error_explanation(f"""
|
||||
You are running xformers {xformers.__version__}.
|
||||
The program is tested to work with xformers {expected_xformers_version}.
|
||||
To reinstall the desired version, run with commandline flag --reinstall-xformers.
|
||||
""".strip())
|
||||
|
||||
|
||||
def initialize():
|
||||
check_versions()
|
||||
|
||||
extensions.list_extensions()
|
||||
localization.list_localizations(cmd_opts.localizations_dir)
|
||||
|
||||
|
|
|
|||
31
webui.sh
31
webui.sh
|
|
@ -104,6 +104,23 @@ then
|
|||
fi
|
||||
|
||||
# Check prerequisites
|
||||
gpu_info=$(lspci 2>/dev/null | grep VGA)
|
||||
case "$gpu_info" in
|
||||
*"Navi 1"*|*"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
|
||||
;;
|
||||
*"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half"
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
;;
|
||||
*)
|
||||
;;
|
||||
esac
|
||||
if echo "$gpu_info" | grep -q "AMD" && [[ -z "${TORCH_COMMAND}" ]]
|
||||
then
|
||||
export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2"
|
||||
fi
|
||||
|
||||
for preq in "${GIT}" "${python_cmd}"
|
||||
do
|
||||
if ! hash "${preq}" &>/dev/null
|
||||
|
|
@ -164,16 +181,6 @@ then
|
|||
else
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
printf "Launching launch.py..."
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
gpu_info=$(lspci 2>/dev/null | grep VGA)
|
||||
if echo "$gpu_info" | grep -q "AMD"
|
||||
then
|
||||
if [[ -z "${TORCH_COMMAND}" ]]
|
||||
then
|
||||
export TORCH_COMMAND="pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2"
|
||||
fi
|
||||
HSA_OVERRIDE_GFX_VERSION=10.3.0 exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
|
||||
else
|
||||
exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
|
||||
fi
|
||||
printf "\n%s\n" "${delimiter}"
|
||||
exec "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
|
||||
fi
|
||||
|
|
|
|||
Loading…
Reference in New Issue