diff --git a/.github/workflows/on_pull_request.yaml b/.github/workflows/on_pull_request.yaml index 7b7219fd..78e608ee 100644 --- a/.github/workflows/on_pull_request.yaml +++ b/.github/workflows/on_pull_request.yaml @@ -1,4 +1,4 @@ -name: Run Linting/Formatting on Pull Requests +name: Linter on: - push @@ -6,7 +6,9 @@ on: jobs: lint-python: + name: ruff runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code uses: actions/checkout@v3 @@ -18,11 +20,13 @@ jobs: # not to have GHA download an (at the time of writing) 4 GB cache # of PyTorch and other dependencies. - name: Install Ruff - run: pip install ruff==0.0.265 + run: pip install ruff==0.0.272 - name: Run Ruff run: ruff . lint-js: + name: eslint runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code uses: actions/checkout@v3 diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index 226cf759..3dafaf8d 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -1,4 +1,4 @@ -name: Run basic features tests on CPU with empty SD model +name: Tests on: - push @@ -6,7 +6,9 @@ on: jobs: test: + name: tests on CPU with empty model runs-on: ubuntu-latest + if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name steps: - name: Checkout Code uses: actions/checkout@v3 @@ -39,10 +41,11 @@ jobs: --skip-prepare-environment --skip-torch-cuda-test --test-server + --do-not-download-clip --no-half --disable-opt-split-attention --use-cpu all - --add-stop-route + --api-server-stop 2>&1 | tee output.txt & - name: Run tests run: | @@ -50,7 +53,7 @@ jobs: python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test - name: Kill test server if: always() - run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10 + run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10 - name: Show coverage run: | python -m coverage combine .coverage* diff --git a/.github/workflows/warns_merge_master.yml b/.github/workflows/warns_merge_master.yml new file mode 100644 index 00000000..ae2aab6b --- /dev/null +++ b/.github/workflows/warns_merge_master.yml @@ -0,0 +1,19 @@ +name: Pull requests can't target master branch + +"on": + pull_request: + types: + - opened + - synchronize + - reopened + branches: + - master + +jobs: + check: + runs-on: ubuntu-latest + steps: + - name: Warning marge into master + run: | + echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch." + exit 1 diff --git a/CHANGELOG.md b/CHANGELOG.md index 925403a9..63a2c7d3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,68 @@ +## 1.5.0 + +### Features: + * SD XL support + * user metadata system for custom networks + * extended Lora metadata editor: set activation text, default weight, view tags, training info + * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension) + * show github stars for extenstions + * img2img batch mode can read extra stuff from png info + * img2img batch works with subdirectories + * hotkeys to move prompt elements: alt+left/right + * restyle time taken/VRAM display + * add textual inversion hashes to infotext + * optimization: cache git extension repo information + * move generate button next to the generated picture for mobile clients + * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface + * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds + +### Minor: + * checkbox to check/uncheck all extensions in the Installed tab + * add gradio user to infotext and to filename patterns + * allow gif for extra network previews + * add options to change colors in grid + * use natural sort for items in extra networks + * Mac: use empty_cache() from torch 2 to clear VRAM + * added automatic support for installing the right libraries for Navi3 (AMD) + * add option SWIN_torch_compile to accelerate SwinIR upscale + * suppress printing TI embedding info at start to console by default + * speedup extra networks listing + * added `[none]` filename token. + * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs) + * add always_discard_next_to_last_sigma option to XYZ plot + * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag. + +### Extensions and API: + * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop + * allow Script to have custom metaclass + * add model exists status check /sdapi/v1/options + * rename --add-stop-route to --api-server-stop + * add `before_hr` script callback + * add callback `after_extra_networks_activate` + * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable + * return http 404 when thumb file not found + * allow replacing extensions index with environment variable + +### Bug Fixes: + * fix for catch errors when retrieving extension index #11290 + * fix very slow loading speed of .safetensors files when reading from network drives + * API cache cleanup + * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode + * fix warning of 'has_mps' deprecated from PyTorch + * fix problem with extra network saving images as previews losing generation info + * fix throwing exception when trying to resize image with I;16 mode + * fix for #11534: canvas zoom and pan extension hijacking shortcut keys + * fixed launch script to be runnable from any directory + * don't add "Seed Resize: -1x-1" to API image metadata + * correctly remove end parenthesis with ctrl+up/down + * fixing --subpath on newer gradio version + * fix: check fill size none zero when resize (fixes #11425) + * use submit and blur for quick settings textbox + * save img2img batch with images.save_image() + * prevent running preload.py for disabled extensions + * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included + + ## 1.4.1 ### Bug Fixes: diff --git a/README.md b/README.md index 688d925a..e7407473 100644 --- a/README.md +++ b/README.md @@ -193,8 +193,11 @@ and replace the path in step 3 with `git clone https://github.com/anapnoe/stable Here's how to add code to the original repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) ## Documentation + The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki). +For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki). + ## Credits Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file. @@ -223,5 +226,6 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al - Security advice - RyotaK - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd +- LyCORIS - KohakuBlueleaf - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. - (You) diff --git a/extensions-builtin/LDSR/ldsr_model_arch.py b/extensions-builtin/LDSR/ldsr_model_arch.py index 7f450086..7cac36ce 100644 --- a/extensions-builtin/LDSR/ldsr_model_arch.py +++ b/extensions-builtin/LDSR/ldsr_model_arch.py @@ -12,7 +12,7 @@ import safetensors.torch from ldm.models.diffusion.ddim import DDIMSampler from ldm.util import instantiate_from_config, ismap -from modules import shared, sd_hijack +from modules import shared, sd_hijack, devices cached_ldsr_model: torch.nn.Module = None @@ -112,8 +112,7 @@ class LDSR: gc.collect() - if torch.cuda.is_available: - torch.cuda.empty_cache() + devices.torch_gc() im_og = image width_og, height_og = im_og.size @@ -150,8 +149,7 @@ class LDSR: del model gc.collect() - if torch.cuda.is_available: - torch.cuda.empty_cache() + devices.torch_gc() return a diff --git a/extensions-builtin/LDSR/scripts/ldsr_model.py b/extensions-builtin/LDSR/scripts/ldsr_model.py index dbd6d331..bd78dece 100644 --- a/extensions-builtin/LDSR/scripts/ldsr_model.py +++ b/extensions-builtin/LDSR/scripts/ldsr_model.py @@ -1,7 +1,6 @@ import os -from basicsr.utils.download_util import load_file_from_url - +from modules.modelloader import load_file_from_url from modules.upscaler import Upscaler, UpscalerData from ldsr_model_arch import LDSR from modules import shared, script_callbacks, errors @@ -43,20 +42,17 @@ class UpscalerLDSR(Upscaler): if local_safetensors_path is not None and os.path.exists(local_safetensors_path): model = local_safetensors_path else: - model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True) + model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt") - yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True) + yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml") - try: - return LDSR(model, yaml) - except Exception: - errors.report("Error importing LDSR", exc_info=True) - return None + return LDSR(model, yaml) def do_upscale(self, img, path): - ldsr = self.load_model(path) - if ldsr is None: - print("NO LDSR!") + try: + ldsr = self.load_model(path) + except Exception: + errors.report(f"Failed loading LDSR model {path}", exc_info=True) return img ddim_steps = shared.opts.ldsr_steps return ldsr.super_resolution(img, ddim_steps, self.scale) diff --git a/extensions-builtin/Lora/extra_networks_lora.py b/extensions-builtin/Lora/extra_networks_lora.py index 66ee9c85..ba2945c6 100644 --- a/extensions-builtin/Lora/extra_networks_lora.py +++ b/extensions-builtin/Lora/extra_networks_lora.py @@ -1,5 +1,5 @@ from modules import extra_networks, shared -import lora +import networks class ExtraNetworkLora(extra_networks.ExtraNetwork): @@ -9,24 +9,38 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): def activate(self, p, params_list): additional = shared.opts.sd_lora - if additional != "None" and additional in lora.available_loras and not any(x for x in params_list if x.items[0] == additional): + if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional): p.all_prompts = [x + f"" for x in p.all_prompts] params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier])) names = [] - multipliers = [] + te_multipliers = [] + unet_multipliers = [] + dyn_dims = [] for params in params_list: assert params.items - names.append(params.items[0]) - multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0) + names.append(params.positional[0]) - lora.load_loras(names, multipliers) + te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0 + te_multiplier = float(params.named.get("te", te_multiplier)) + + unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier + unet_multiplier = float(params.named.get("unet", unet_multiplier)) + + dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None + dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim + + te_multipliers.append(te_multiplier) + unet_multipliers.append(unet_multiplier) + dyn_dims.append(dyn_dim) + + networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims) if shared.opts.lora_add_hashes_to_infotext: - lora_hashes = [] - for item in lora.loaded_loras: - shorthash = item.lora_on_disk.shorthash + network_hashes = [] + for item in networks.loaded_networks: + shorthash = item.network_on_disk.shorthash if not shorthash: continue @@ -36,10 +50,10 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork): alias = alias.replace(":", "").replace(",", "") - lora_hashes.append(f"{alias}: {shorthash}") + network_hashes.append(f"{alias}: {shorthash}") - if lora_hashes: - p.extra_generation_params["Lora hashes"] = ", ".join(lora_hashes) + if network_hashes: + p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes) def deactivate(self, p): pass diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 34ff57dd..9365aa74 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -1,506 +1,9 @@ -import os -import re -import torch -from typing import Union +import networks -from modules import shared, devices, sd_models, errors, scripts, sd_hijack, hashes +list_available_loras = networks.list_available_networks -metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} - -re_digits = re.compile(r"\d+") -re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") -re_compiled = {} - -suffix_conversion = { - "attentions": {}, - "resnets": { - "conv1": "in_layers_2", - "conv2": "out_layers_3", - "time_emb_proj": "emb_layers_1", - "conv_shortcut": "skip_connection", - } -} - - -def convert_diffusers_name_to_compvis(key, is_sd2): - def match(match_list, regex_text): - regex = re_compiled.get(regex_text) - if regex is None: - regex = re.compile(regex_text) - re_compiled[regex_text] = regex - - r = re.match(regex, key) - if not r: - return False - - match_list.clear() - match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) - return True - - m = [] - - if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): - suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) - return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" - - if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): - suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) - return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" - - if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): - suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) - return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" - - if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"): - return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op" - - if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"): - return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv" - - if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"): - if is_sd2: - if 'mlp_fc1' in m[1]: - return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" - elif 'mlp_fc2' in m[1]: - return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" - else: - return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" - - return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}" - - return key - - -class LoraOnDisk: - def __init__(self, name, filename): - self.name = name - self.filename = filename - self.metadata = {} - self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" - - if self.is_safetensors: - try: - self.metadata = sd_models.read_metadata_from_safetensors(filename) - except Exception as e: - errors.display(e, f"reading lora {filename}") - - if self.metadata: - m = {} - for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)): - m[k] = v - - self.metadata = m - - self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text - self.alias = self.metadata.get('ss_output_name', self.name) - - self.hash = None - self.shorthash = None - self.set_hash( - self.metadata.get('sshs_model_hash') or - hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or - '' - ) - - def set_hash(self, v): - self.hash = v - self.shorthash = self.hash[0:12] - - if self.shorthash: - available_lora_hash_lookup[self.shorthash] = self - - def read_hash(self): - if not self.hash: - self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '') - - def get_alias(self): - if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in forbidden_lora_aliases: - return self.name - else: - return self.alias - - -class LoraModule: - def __init__(self, name, lora_on_disk: LoraOnDisk): - self.name = name - self.lora_on_disk = lora_on_disk - self.multiplier = 1.0 - self.modules = {} - self.mtime = None - - self.mentioned_name = None - """the text that was used to add lora to prompt - can be either name or an alias""" - - -class LoraUpDownModule: - def __init__(self): - self.up = None - self.down = None - self.alpha = None - - -def assign_lora_names_to_compvis_modules(sd_model): - lora_layer_mapping = {} - - for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules(): - lora_name = name.replace(".", "_") - lora_layer_mapping[lora_name] = module - module.lora_layer_name = lora_name - - for name, module in shared.sd_model.model.named_modules(): - lora_name = name.replace(".", "_") - lora_layer_mapping[lora_name] = module - module.lora_layer_name = lora_name - - sd_model.lora_layer_mapping = lora_layer_mapping - - -def load_lora(name, lora_on_disk): - lora = LoraModule(name, lora_on_disk) - lora.mtime = os.path.getmtime(lora_on_disk.filename) - - sd = sd_models.read_state_dict(lora_on_disk.filename) - - # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0 - if not hasattr(shared.sd_model, 'lora_layer_mapping'): - assign_lora_names_to_compvis_modules(shared.sd_model) - - keys_failed_to_match = {} - is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping - - for key_diffusers, weight in sd.items(): - key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1) - key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2) - - sd_module = shared.sd_model.lora_layer_mapping.get(key, None) - - if sd_module is None: - m = re_x_proj.match(key) - if m: - sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None) - - if sd_module is None: - keys_failed_to_match[key_diffusers] = key - continue - - lora_module = lora.modules.get(key, None) - if lora_module is None: - lora_module = LoraUpDownModule() - lora.modules[key] = lora_module - - if lora_key == "alpha": - lora_module.alpha = weight.item() - continue - - if type(sd_module) == torch.nn.Linear: - module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) - elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear: - module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) - elif type(sd_module) == torch.nn.MultiheadAttention: - module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) - elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1): - module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) - elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3): - module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False) - else: - print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}') - continue - raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}") - - with torch.no_grad(): - module.weight.copy_(weight) - - module.to(device=devices.cpu, dtype=devices.dtype) - - if lora_key == "lora_up.weight": - lora_module.up = module - elif lora_key == "lora_down.weight": - lora_module.down = module - else: - raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha") - - if keys_failed_to_match: - print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}") - - return lora - - -def load_loras(names, multipliers=None): - already_loaded = {} - - for lora in loaded_loras: - if lora.name in names: - already_loaded[lora.name] = lora - - loaded_loras.clear() - - loras_on_disk = [available_lora_aliases.get(name, None) for name in names] - if any(x is None for x in loras_on_disk): - list_available_loras() - - loras_on_disk = [available_lora_aliases.get(name, None) for name in names] - - failed_to_load_loras = [] - - for i, name in enumerate(names): - lora = already_loaded.get(name, None) - - lora_on_disk = loras_on_disk[i] - - if lora_on_disk is not None: - if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime: - try: - lora = load_lora(name, lora_on_disk) - except Exception as e: - errors.display(e, f"loading Lora {lora_on_disk.filename}") - continue - - lora.mentioned_name = name - - lora_on_disk.read_hash() - - if lora is None: - failed_to_load_loras.append(name) - print(f"Couldn't find Lora with name {name}") - continue - - lora.multiplier = multipliers[i] if multipliers else 1.0 - loaded_loras.append(lora) - - if failed_to_load_loras: - sd_hijack.model_hijack.comments.append("Failed to find Loras: " + ", ".join(failed_to_load_loras)) - - -def lora_calc_updown(lora, module, target): - with torch.no_grad(): - up = module.up.weight.to(target.device, dtype=target.dtype) - down = module.down.weight.to(target.device, dtype=target.dtype) - - if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1): - updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3) - elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3): - updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3) - else: - updown = up @ down - - updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) - - return updown - - -def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]): - weights_backup = getattr(self, "lora_weights_backup", None) - - if weights_backup is None: - return - - if isinstance(self, torch.nn.MultiheadAttention): - self.in_proj_weight.copy_(weights_backup[0]) - self.out_proj.weight.copy_(weights_backup[1]) - else: - self.weight.copy_(weights_backup) - - -def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]): - """ - Applies the currently selected set of Loras to the weights of torch layer self. - If weights already have this particular set of loras applied, does nothing. - If not, restores orginal weights from backup and alters weights according to loras. - """ - - lora_layer_name = getattr(self, 'lora_layer_name', None) - if lora_layer_name is None: - return - - current_names = getattr(self, "lora_current_names", ()) - wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras) - - weights_backup = getattr(self, "lora_weights_backup", None) - if weights_backup is None: - if isinstance(self, torch.nn.MultiheadAttention): - weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True)) - else: - weights_backup = self.weight.to(devices.cpu, copy=True) - - self.lora_weights_backup = weights_backup - - if current_names != wanted_names: - lora_restore_weights_from_backup(self) - - for lora in loaded_loras: - module = lora.modules.get(lora_layer_name, None) - if module is not None and hasattr(self, 'weight'): - self.weight += lora_calc_updown(lora, module, self.weight) - continue - - module_q = lora.modules.get(lora_layer_name + "_q_proj", None) - module_k = lora.modules.get(lora_layer_name + "_k_proj", None) - module_v = lora.modules.get(lora_layer_name + "_v_proj", None) - module_out = lora.modules.get(lora_layer_name + "_out_proj", None) - - if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: - updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight) - updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight) - updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight) - updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) - - self.in_proj_weight += updown_qkv - self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight) - continue - - if module is None: - continue - - print(f'failed to calculate lora weights for layer {lora_layer_name}') - - self.lora_current_names = wanted_names - - -def lora_forward(module, input, original_forward): - """ - Old way of applying Lora by executing operations during layer's forward. - Stacking many loras this way results in big performance degradation. - """ - - if len(loaded_loras) == 0: - return original_forward(module, input) - - input = devices.cond_cast_unet(input) - - lora_restore_weights_from_backup(module) - lora_reset_cached_weight(module) - - res = original_forward(module, input) - - lora_layer_name = getattr(module, 'lora_layer_name', None) - for lora in loaded_loras: - module = lora.modules.get(lora_layer_name, None) - if module is None: - continue - - module.up.to(device=devices.device) - module.down.to(device=devices.device) - - res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0) - - return res - - -def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): - self.lora_current_names = () - self.lora_weights_backup = None - - -def lora_Linear_forward(self, input): - if shared.opts.lora_functional: - return lora_forward(self, input, torch.nn.Linear_forward_before_lora) - - lora_apply_weights(self) - - return torch.nn.Linear_forward_before_lora(self, input) - - -def lora_Linear_load_state_dict(self, *args, **kwargs): - lora_reset_cached_weight(self) - - return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs) - - -def lora_Conv2d_forward(self, input): - if shared.opts.lora_functional: - return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora) - - lora_apply_weights(self) - - return torch.nn.Conv2d_forward_before_lora(self, input) - - -def lora_Conv2d_load_state_dict(self, *args, **kwargs): - lora_reset_cached_weight(self) - - return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs) - - -def lora_MultiheadAttention_forward(self, *args, **kwargs): - lora_apply_weights(self) - - return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs) - - -def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs): - lora_reset_cached_weight(self) - - return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs) - - -def list_available_loras(): - available_loras.clear() - available_lora_aliases.clear() - forbidden_lora_aliases.clear() - available_lora_hash_lookup.clear() - forbidden_lora_aliases.update({"none": 1, "Addams": 1}) - - os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) - - candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) - for filename in sorted(candidates, key=str.lower): - if os.path.isdir(filename): - continue - - name = os.path.splitext(os.path.basename(filename))[0] - try: - entry = LoraOnDisk(name, filename) - except OSError: # should catch FileNotFoundError and PermissionError etc. - errors.report(f"Failed to load LoRA {name} from {filename}", exc_info=True) - continue - - available_loras[name] = entry - - if entry.alias in available_lora_aliases: - forbidden_lora_aliases[entry.alias.lower()] = 1 - - available_lora_aliases[name] = entry - available_lora_aliases[entry.alias] = entry - - -re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)") - - -def infotext_pasted(infotext, params): - if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]: - return # if the other extension is active, it will handle those fields, no need to do anything - - added = [] - - for k in params: - if not k.startswith("AddNet Model "): - continue - - num = k[13:] - - if params.get("AddNet Module " + num) != "LoRA": - continue - - name = params.get("AddNet Model " + num) - if name is None: - continue - - m = re_lora_name.match(name) - if m: - name = m.group(1) - - multiplier = params.get("AddNet Weight A " + num, "1.0") - - added.append(f"") - - if added: - params["Prompt"] += "\n" + "".join(added) - - -available_loras = {} -available_lora_aliases = {} -available_lora_hash_lookup = {} -forbidden_lora_aliases = {} -loaded_loras = [] - -list_available_loras() +available_loras = networks.available_networks +available_lora_aliases = networks.available_network_aliases +available_lora_hash_lookup = networks.available_network_hash_lookup +forbidden_lora_aliases = networks.forbidden_network_aliases +loaded_loras = networks.loaded_networks diff --git a/extensions-builtin/Lora/lyco_helpers.py b/extensions-builtin/Lora/lyco_helpers.py new file mode 100644 index 00000000..279b34bc --- /dev/null +++ b/extensions-builtin/Lora/lyco_helpers.py @@ -0,0 +1,21 @@ +import torch + + +def make_weight_cp(t, wa, wb): + temp = torch.einsum('i j k l, j r -> i r k l', t, wb) + return torch.einsum('i j k l, i r -> r j k l', temp, wa) + + +def rebuild_conventional(up, down, shape, dyn_dim=None): + up = up.reshape(up.size(0), -1) + down = down.reshape(down.size(0), -1) + if dyn_dim is not None: + up = up[:, :dyn_dim] + down = down[:dyn_dim, :] + return (up @ down).reshape(shape) + + +def rebuild_cp_decomposition(up, down, mid): + up = up.reshape(up.size(0), -1) + down = down.reshape(down.size(0), -1) + return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down) diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py new file mode 100644 index 00000000..8ecfa29a --- /dev/null +++ b/extensions-builtin/Lora/network.py @@ -0,0 +1,154 @@ +import os +from collections import namedtuple +import enum + +from modules import sd_models, cache, errors, hashes, shared + +NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module']) + +metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20} + + +class SdVersion(enum.Enum): + Unknown = 1 + SD1 = 2 + SD2 = 3 + SDXL = 4 + + +class NetworkOnDisk: + def __init__(self, name, filename): + self.name = name + self.filename = filename + self.metadata = {} + self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors" + + def read_metadata(): + metadata = sd_models.read_metadata_from_safetensors(filename) + metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text + + return metadata + + if self.is_safetensors: + try: + self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata) + except Exception as e: + errors.display(e, f"reading lora {filename}") + + if self.metadata: + m = {} + for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)): + m[k] = v + + self.metadata = m + + self.alias = self.metadata.get('ss_output_name', self.name) + + self.hash = None + self.shorthash = None + self.set_hash( + self.metadata.get('sshs_model_hash') or + hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or + '' + ) + + self.sd_version = self.detect_version() + + def detect_version(self): + if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"): + return SdVersion.SDXL + elif str(self.metadata.get('ss_v2', "")) == "True": + return SdVersion.SD2 + elif len(self.metadata): + return SdVersion.SD1 + + return SdVersion.Unknown + + def set_hash(self, v): + self.hash = v + self.shorthash = self.hash[0:12] + + if self.shorthash: + import networks + networks.available_network_hash_lookup[self.shorthash] = self + + def read_hash(self): + if not self.hash: + self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '') + + def get_alias(self): + import networks + if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases: + return self.name + else: + return self.alias + + +class Network: # LoraModule + def __init__(self, name, network_on_disk: NetworkOnDisk): + self.name = name + self.network_on_disk = network_on_disk + self.te_multiplier = 1.0 + self.unet_multiplier = 1.0 + self.dyn_dim = None + self.modules = {} + self.mtime = None + + self.mentioned_name = None + """the text that was used to add the network to prompt - can be either name or an alias""" + + +class ModuleType: + def create_module(self, net: Network, weights: NetworkWeights) -> Network | None: + return None + + +class NetworkModule: + def __init__(self, net: Network, weights: NetworkWeights): + self.network = net + self.network_key = weights.network_key + self.sd_key = weights.sd_key + self.sd_module = weights.sd_module + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.dim = None + self.bias = weights.w.get("bias") + self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None + self.scale = weights.w["scale"].item() if "scale" in weights.w else None + + def multiplier(self): + if 'transformer' in self.sd_key[:20]: + return self.network.te_multiplier + else: + return self.network.unet_multiplier + + def calc_scale(self): + if self.scale is not None: + return self.scale + if self.dim is not None and self.alpha is not None: + return self.alpha / self.dim + + return 1.0 + + def finalize_updown(self, updown, orig_weight, output_shape): + if self.bias is not None: + updown = updown.reshape(self.bias.shape) + updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype) + updown = updown.reshape(output_shape) + + if len(output_shape) == 4: + updown = updown.reshape(output_shape) + + if orig_weight.size().numel() == updown.size().numel(): + updown = updown.reshape(orig_weight.shape) + + return updown * self.calc_scale() * self.multiplier() + + def calc_updown(self, target): + raise NotImplementedError() + + def forward(self, x, y): + raise NotImplementedError() + diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py new file mode 100644 index 00000000..109b4c2c --- /dev/null +++ b/extensions-builtin/Lora/network_full.py @@ -0,0 +1,22 @@ +import network + + +class ModuleTypeFull(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["diff"]): + return NetworkModuleFull(net, weights) + + return None + + +class NetworkModuleFull(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.weight = weights.w.get("diff") + + def calc_updown(self, orig_weight): + output_shape = self.weight.shape + updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype) + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_hada.py b/extensions-builtin/Lora/network_hada.py new file mode 100644 index 00000000..5fcb0695 --- /dev/null +++ b/extensions-builtin/Lora/network_hada.py @@ -0,0 +1,55 @@ +import lyco_helpers +import network + + +class ModuleTypeHada(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]): + return NetworkModuleHada(net, weights) + + return None + + +class NetworkModuleHada(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + if hasattr(self.sd_module, 'weight'): + self.shape = self.sd_module.weight.shape + + self.w1a = weights.w["hada_w1_a"] + self.w1b = weights.w["hada_w1_b"] + self.dim = self.w1b.shape[0] + self.w2a = weights.w["hada_w2_a"] + self.w2b = weights.w["hada_w2_b"] + + self.t1 = weights.w.get("hada_t1") + self.t2 = weights.w.get("hada_t2") + + def calc_updown(self, orig_weight): + w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) + w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [w1a.size(0), w1b.size(1)] + + if self.t1 is not None: + output_shape = [w1a.size(1), w1b.size(1)] + t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype) + updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b) + output_shape += t1.shape[2:] + else: + if len(w1b.shape) == 4: + output_shape += w1b.shape[2:] + updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape) + + if self.t2 is not None: + t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype) + updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) + else: + updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape) + + updown = updown1 * updown2 + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_ia3.py b/extensions-builtin/Lora/network_ia3.py new file mode 100644 index 00000000..7edc4249 --- /dev/null +++ b/extensions-builtin/Lora/network_ia3.py @@ -0,0 +1,30 @@ +import network + + +class ModuleTypeIa3(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["weight"]): + return NetworkModuleIa3(net, weights) + + return None + + +class NetworkModuleIa3(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w = weights.w["weight"] + self.on_input = weights.w["on_input"].item() + + def calc_updown(self, orig_weight): + w = self.w.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [w.size(0), orig_weight.size(1)] + if self.on_input: + output_shape.reverse() + else: + w = w.reshape(-1, 1) + + updown = orig_weight * w + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_lokr.py b/extensions-builtin/Lora/network_lokr.py new file mode 100644 index 00000000..340acdab --- /dev/null +++ b/extensions-builtin/Lora/network_lokr.py @@ -0,0 +1,64 @@ +import torch + +import lyco_helpers +import network + + +class ModuleTypeLokr(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w) + has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w) + if has_1 and has_2: + return NetworkModuleLokr(net, weights) + + return None + + +def make_kron(orig_shape, w1, w2): + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + w2 = w2.contiguous() + return torch.kron(w1, w2).reshape(orig_shape) + + +class NetworkModuleLokr(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.w1 = weights.w.get("lokr_w1") + self.w1a = weights.w.get("lokr_w1_a") + self.w1b = weights.w.get("lokr_w1_b") + self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim + self.w2 = weights.w.get("lokr_w2") + self.w2a = weights.w.get("lokr_w2_a") + self.w2b = weights.w.get("lokr_w2_b") + self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim + self.t2 = weights.w.get("lokr_t2") + + def calc_updown(self, orig_weight): + if self.w1 is not None: + w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype) + else: + w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype) + w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype) + w1 = w1a @ w1b + + if self.w2 is not None: + w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype) + elif self.t2 is None: + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + w2 = w2a @ w2b + else: + t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype) + w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype) + w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype) + w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b) + + output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)] + if len(orig_weight.shape) == 4: + output_shape = orig_weight.shape + + updown = make_kron(output_shape, w1, w2) + + return self.finalize_updown(updown, orig_weight, output_shape) diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py new file mode 100644 index 00000000..26c0a72c --- /dev/null +++ b/extensions-builtin/Lora/network_lora.py @@ -0,0 +1,86 @@ +import torch + +import lyco_helpers +import network +from modules import devices + + +class ModuleTypeLora(network.ModuleType): + def create_module(self, net: network.Network, weights: network.NetworkWeights): + if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]): + return NetworkModuleLora(net, weights) + + return None + + +class NetworkModuleLora(network.NetworkModule): + def __init__(self, net: network.Network, weights: network.NetworkWeights): + super().__init__(net, weights) + + self.up_model = self.create_module(weights.w, "lora_up.weight") + self.down_model = self.create_module(weights.w, "lora_down.weight") + self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True) + + self.dim = weights.w["lora_down.weight"].shape[0] + + def create_module(self, weights, key, none_ok=False): + weight = weights.get(key) + + if weight is None and none_ok: + return None + + is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention] + is_conv = type(self.sd_module) in [torch.nn.Conv2d] + + if is_linear: + weight = weight.reshape(weight.shape[0], -1) + module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False) + elif is_conv and key == "lora_down.weight" or key == "dyn_up": + if len(weight.shape) == 2: + weight = weight.reshape(weight.shape[0], -1, 1, 1) + + if weight.shape[2] != 1 or weight.shape[3] != 1: + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) + else: + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) + elif is_conv and key == "lora_mid.weight": + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False) + elif is_conv and key == "lora_up.weight" or key == "dyn_down": + module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False) + else: + raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}') + + with torch.no_grad(): + if weight.shape != module.weight.shape: + weight = weight.reshape(module.weight.shape) + module.weight.copy_(weight) + + module.to(device=devices.cpu, dtype=devices.dtype) + module.weight.requires_grad_(False) + + return module + + def calc_updown(self, orig_weight): + up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) + down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) + + output_shape = [up.size(0), down.size(1)] + if self.mid_model is not None: + # cp-decomposition + mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype) + updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid) + output_shape += mid.shape[2:] + else: + if len(down.shape) == 4: + output_shape += down.shape[2:] + updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim) + + return self.finalize_updown(updown, orig_weight, output_shape) + + def forward(self, x, y): + self.up_model.to(device=devices.device) + self.down_model.to(device=devices.device) + + return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale() + + diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py new file mode 100644 index 00000000..17cbe1bb --- /dev/null +++ b/extensions-builtin/Lora/networks.py @@ -0,0 +1,468 @@ +import os +import re + +import network +import network_lora +import network_hada +import network_ia3 +import network_lokr +import network_full + +import torch +from typing import Union + +from modules import shared, devices, sd_models, errors, scripts, sd_hijack + +module_types = [ + network_lora.ModuleTypeLora(), + network_hada.ModuleTypeHada(), + network_ia3.ModuleTypeIa3(), + network_lokr.ModuleTypeLokr(), + network_full.ModuleTypeFull(), +] + + +re_digits = re.compile(r"\d+") +re_x_proj = re.compile(r"(.*)_([qkv]_proj)$") +re_compiled = {} + +suffix_conversion = { + "attentions": {}, + "resnets": { + "conv1": "in_layers_2", + "conv2": "out_layers_3", + "time_emb_proj": "emb_layers_1", + "conv_shortcut": "skip_connection", + } +} + + +def convert_diffusers_name_to_compvis(key, is_sd2): + def match(match_list, regex_text): + regex = re_compiled.get(regex_text) + if regex is None: + regex = re.compile(regex_text) + re_compiled[regex_text] = regex + + r = re.match(regex, key) + if not r: + return False + + match_list.clear() + match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()]) + return True + + m = [] + + if match(m, r"lora_unet_conv_in(.*)"): + return f'diffusion_model_input_blocks_0_0{m[0]}' + + if match(m, r"lora_unet_conv_out(.*)"): + return f'diffusion_model_out_2{m[0]}' + + if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"): + return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}" + + if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" + + if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2]) + return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}" + + if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"): + suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3]) + return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}" + + if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"): + return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op" + + if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"): + return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv" + + if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"): + if is_sd2: + if 'mlp_fc1' in m[1]: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" + elif 'mlp_fc2' in m[1]: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" + else: + return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" + + return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}" + + if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"): + if 'mlp_fc1' in m[1]: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}" + elif 'mlp_fc2' in m[1]: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}" + else: + return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}" + + return key + + +def assign_network_names_to_compvis_modules(sd_model): + network_layer_mapping = {} + + if shared.sd_model.is_sdxl: + for i, embedder in enumerate(shared.sd_model.conditioner.embedders): + if not hasattr(embedder, 'wrapped'): + continue + + for name, module in embedder.wrapped.named_modules(): + network_name = f'{i}_{name.replace(".", "_")}' + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + else: + for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules(): + network_name = name.replace(".", "_") + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + + for name, module in shared.sd_model.model.named_modules(): + network_name = name.replace(".", "_") + network_layer_mapping[network_name] = module + module.network_layer_name = network_name + + sd_model.network_layer_mapping = network_layer_mapping + + +def load_network(name, network_on_disk): + net = network.Network(name, network_on_disk) + net.mtime = os.path.getmtime(network_on_disk.filename) + + sd = sd_models.read_state_dict(network_on_disk.filename) + + # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0 + if not hasattr(shared.sd_model, 'network_layer_mapping'): + assign_network_names_to_compvis_modules(shared.sd_model) + + keys_failed_to_match = {} + is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping + + matched_networks = {} + + for key_network, weight in sd.items(): + key_network_without_network_parts, network_part = key_network.split(".", 1) + + key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2) + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + if sd_module is None: + m = re_x_proj.match(key) + if m: + sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None) + + # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model" + if sd_module is None and "lora_unet" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("lora_unet", "diffusion_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts: + key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + # some SD1 Loras also have correct compvis keys + if sd_module is None: + key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model") + sd_module = shared.sd_model.network_layer_mapping.get(key, None) + + if sd_module is None: + keys_failed_to_match[key_network] = key + continue + + if key not in matched_networks: + matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module) + + matched_networks[key].w[network_part] = weight + + for key, weights in matched_networks.items(): + net_module = None + for nettype in module_types: + net_module = nettype.create_module(net, weights) + if net_module is not None: + break + + if net_module is None: + raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}") + + net.modules[key] = net_module + + if keys_failed_to_match: + print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}") + + return net + + +def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None): + already_loaded = {} + + for net in loaded_networks: + if net.name in names: + already_loaded[net.name] = net + + loaded_networks.clear() + + networks_on_disk = [available_network_aliases.get(name, None) for name in names] + if any(x is None for x in networks_on_disk): + list_available_networks() + + networks_on_disk = [available_network_aliases.get(name, None) for name in names] + + failed_to_load_networks = [] + + for i, name in enumerate(names): + net = already_loaded.get(name, None) + + network_on_disk = networks_on_disk[i] + + if network_on_disk is not None: + if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime: + try: + net = load_network(name, network_on_disk) + except Exception as e: + errors.display(e, f"loading network {network_on_disk.filename}") + continue + + net.mentioned_name = name + + network_on_disk.read_hash() + + if net is None: + failed_to_load_networks.append(name) + print(f"Couldn't find network with name {name}") + continue + + net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0 + net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0 + net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0 + loaded_networks.append(net) + + if failed_to_load_networks: + sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks)) + + +def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]): + weights_backup = getattr(self, "network_weights_backup", None) + + if weights_backup is None: + return + + if isinstance(self, torch.nn.MultiheadAttention): + self.in_proj_weight.copy_(weights_backup[0]) + self.out_proj.weight.copy_(weights_backup[1]) + else: + self.weight.copy_(weights_backup) + + +def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]): + """ + Applies the currently selected set of networks to the weights of torch layer self. + If weights already have this particular set of networks applied, does nothing. + If not, restores orginal weights from backup and alters weights according to networks. + """ + + network_layer_name = getattr(self, 'network_layer_name', None) + if network_layer_name is None: + return + + current_names = getattr(self, "network_current_names", ()) + wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks) + + weights_backup = getattr(self, "network_weights_backup", None) + if weights_backup is None: + if isinstance(self, torch.nn.MultiheadAttention): + weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True)) + else: + weights_backup = self.weight.to(devices.cpu, copy=True) + + self.network_weights_backup = weights_backup + + if current_names != wanted_names: + network_restore_weights_from_backup(self) + + for net in loaded_networks: + module = net.modules.get(network_layer_name, None) + if module is not None and hasattr(self, 'weight'): + with torch.no_grad(): + updown = module.calc_updown(self.weight) + + if len(self.weight.shape) == 4 and self.weight.shape[1] == 9: + # inpainting model. zero pad updown to make channel[1] 4 to 9 + updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5)) + + self.weight += updown + continue + + module_q = net.modules.get(network_layer_name + "_q_proj", None) + module_k = net.modules.get(network_layer_name + "_k_proj", None) + module_v = net.modules.get(network_layer_name + "_v_proj", None) + module_out = net.modules.get(network_layer_name + "_out_proj", None) + + if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out: + with torch.no_grad(): + updown_q = module_q.calc_updown(self.in_proj_weight) + updown_k = module_k.calc_updown(self.in_proj_weight) + updown_v = module_v.calc_updown(self.in_proj_weight) + updown_qkv = torch.vstack([updown_q, updown_k, updown_v]) + updown_out = module_out.calc_updown(self.out_proj.weight) + + self.in_proj_weight += updown_qkv + self.out_proj.weight += updown_out + continue + + if module is None: + continue + + print(f'failed to calculate network weights for layer {network_layer_name}') + + self.network_current_names = wanted_names + + +def network_forward(module, input, original_forward): + """ + Old way of applying Lora by executing operations during layer's forward. + Stacking many loras this way results in big performance degradation. + """ + + if len(loaded_networks) == 0: + return original_forward(module, input) + + input = devices.cond_cast_unet(input) + + network_restore_weights_from_backup(module) + network_reset_cached_weight(module) + + y = original_forward(module, input) + + network_layer_name = getattr(module, 'network_layer_name', None) + for lora in loaded_networks: + module = lora.modules.get(network_layer_name, None) + if module is None: + continue + + y = module.forward(y, input) + + return y + + +def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]): + self.network_current_names = () + self.network_weights_backup = None + + +def network_Linear_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, torch.nn.Linear_forward_before_network) + + network_apply_weights(self) + + return torch.nn.Linear_forward_before_network(self, input) + + +def network_Linear_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs) + + +def network_Conv2d_forward(self, input): + if shared.opts.lora_functional: + return network_forward(self, input, torch.nn.Conv2d_forward_before_network) + + network_apply_weights(self) + + return torch.nn.Conv2d_forward_before_network(self, input) + + +def network_Conv2d_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs) + + +def network_MultiheadAttention_forward(self, *args, **kwargs): + network_apply_weights(self) + + return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs) + + +def network_MultiheadAttention_load_state_dict(self, *args, **kwargs): + network_reset_cached_weight(self) + + return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs) + + +def list_available_networks(): + available_networks.clear() + available_network_aliases.clear() + forbidden_network_aliases.clear() + available_network_hash_lookup.clear() + forbidden_network_aliases.update({"none": 1, "Addams": 1}) + + os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True) + + candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) + candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"])) + for filename in candidates: + if os.path.isdir(filename): + continue + + name = os.path.splitext(os.path.basename(filename))[0] + try: + entry = network.NetworkOnDisk(name, filename) + except OSError: # should catch FileNotFoundError and PermissionError etc. + errors.report(f"Failed to load network {name} from {filename}", exc_info=True) + continue + + available_networks[name] = entry + + if entry.alias in available_network_aliases: + forbidden_network_aliases[entry.alias.lower()] = 1 + + available_network_aliases[name] = entry + available_network_aliases[entry.alias] = entry + + +re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)") + + +def infotext_pasted(infotext, params): + if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]: + return # if the other extension is active, it will handle those fields, no need to do anything + + added = [] + + for k in params: + if not k.startswith("AddNet Model "): + continue + + num = k[13:] + + if params.get("AddNet Module " + num) != "LoRA": + continue + + name = params.get("AddNet Model " + num) + if name is None: + continue + + m = re_network_name.match(name) + if m: + name = m.group(1) + + multiplier = params.get("AddNet Weight A " + num, "1.0") + + added.append(f"") + + if added: + params["Prompt"] += "\n" + "".join(added) + + +available_networks = {} +available_network_aliases = {} +loaded_networks = [] +available_network_hash_lookup = {} +forbidden_network_aliases = {} + +list_available_networks() diff --git a/extensions-builtin/Lora/preload.py b/extensions-builtin/Lora/preload.py index 863dc5c0..50961be3 100644 --- a/extensions-builtin/Lora/preload.py +++ b/extensions-builtin/Lora/preload.py @@ -4,3 +4,4 @@ from modules import paths def preload(parser): parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora')) + parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS')) diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index e650f469..cd28afc9 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -4,69 +4,76 @@ import torch import gradio as gr from fastapi import FastAPI -import lora +import network +import networks +import lora # noqa:F401 import extra_networks_lora import ui_extra_networks_lora from modules import script_callbacks, ui_extra_networks, extra_networks, shared def unload(): - torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora - torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora - torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora - torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora - torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora - torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora + torch.nn.Linear.forward = torch.nn.Linear_forward_before_network + torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network + torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network + torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network + torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network + torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network def before_ui(): ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora()) - extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora()) + + extra_network = extra_networks_lora.ExtraNetworkLora() + extra_networks.register_extra_network(extra_network) + extra_networks.register_extra_network_alias(extra_network, "lyco") -if not hasattr(torch.nn, 'Linear_forward_before_lora'): - torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward +if not hasattr(torch.nn, 'Linear_forward_before_network'): + torch.nn.Linear_forward_before_network = torch.nn.Linear.forward -if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'): - torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict +if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'): + torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict -if not hasattr(torch.nn, 'Conv2d_forward_before_lora'): - torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward +if not hasattr(torch.nn, 'Conv2d_forward_before_network'): + torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward -if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'): - torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict +if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'): + torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict -if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'): - torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward +if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'): + torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward -if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'): - torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict +if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'): + torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict -torch.nn.Linear.forward = lora.lora_Linear_forward -torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict -torch.nn.Conv2d.forward = lora.lora_Conv2d_forward -torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict -torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward -torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict +torch.nn.Linear.forward = networks.network_Linear_forward +torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict +torch.nn.Conv2d.forward = networks.network_Conv2d_forward +torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict +torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward +torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict -script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules) +script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules) script_callbacks.on_script_unloaded(unload) script_callbacks.on_before_ui(before_ui) -script_callbacks.on_infotext_pasted(lora.infotext_pasted) +script_callbacks.on_infotext_pasted(networks.infotext_pasted) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), { - "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras), + "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks), "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}), "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"), + "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"), + "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}), })) shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), { - "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"), + "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"), })) -def create_lora_json(obj: lora.LoraOnDisk): +def create_lora_json(obj: network.NetworkOnDisk): return { "name": obj.name, "alias": obj.alias, @@ -75,17 +82,17 @@ def create_lora_json(obj: lora.LoraOnDisk): } -def api_loras(_: gr.Blocks, app: FastAPI): +def api_networks(_: gr.Blocks, app: FastAPI): @app.get("/sdapi/v1/loras") async def get_loras(): - return [create_lora_json(obj) for obj in lora.available_loras.values()] + return [create_lora_json(obj) for obj in networks.available_networks.values()] @app.post("/sdapi/v1/refresh-loras") async def refresh_loras(): - return lora.list_available_loras() + return networks.list_available_networks() -script_callbacks.on_app_started(api_loras) +script_callbacks.on_app_started(api_networks) re_lora = re.compile("= 16 + + +re_word = re.compile(r"[-_\w']+") +re_comma = re.compile(r" *, *") + + +def build_tags(metadata): + tags = {} + + for _, tags_dict in metadata.get("ss_tag_frequency", {}).items(): + for tag, tag_count in tags_dict.items(): + tag = tag.strip() + tags[tag] = tags.get(tag, 0) + int(tag_count) + + if tags and is_non_comma_tagset(tags): + new_tags = {} + + for text, text_count in tags.items(): + for word in re.findall(re_word, text): + if len(word) < 3: + continue + + new_tags[word] = new_tags.get(word, 0) + text_count + + tags = new_tags + + ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True) + + return [(tag, tags[tag]) for tag in ordered_tags] + + +class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor): + def __init__(self, ui, tabname, page): + super().__init__(ui, tabname, page) + + self.select_sd_version = None + + self.taginfo = None + self.edit_activation_text = None + self.slider_preferred_weight = None + self.edit_notes = None + + def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes): + user_metadata = self.get_user_metadata(name) + user_metadata["description"] = desc + user_metadata["sd version"] = sd_version + user_metadata["activation text"] = activation_text + user_metadata["preferred weight"] = preferred_weight + user_metadata["notes"] = notes + + self.write_user_metadata(name, user_metadata) + + def get_metadata_table(self, name): + table = super().get_metadata_table(name) + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + + keys = { + 'ss_sd_model_name': "Model:", + 'ss_clip_skip': "Clip skip:", + 'ss_network_module': "Kohya module:", + } + + for key, label in keys.items(): + value = metadata.get(key, None) + if value is not None and str(value) != "None": + table.append((label, html.escape(value))) + + ss_training_started_at = metadata.get('ss_training_started_at') + if ss_training_started_at: + table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M'))) + + ss_bucket_info = metadata.get("ss_bucket_info") + if ss_bucket_info and "buckets" in ss_bucket_info: + resolutions = {} + for _, bucket in ss_bucket_info["buckets"].items(): + resolution = bucket["resolution"] + resolution = f'{resolution[1]}x{resolution[0]}' + + resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"]) + + resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True) + resolutions_text = html.escape(", ".join(resolutions_list[0:4])) + if len(resolutions) > 4: + resolutions_text += ", ..." + resolutions_text = f"{resolutions_text}" + + table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text)) + + image_count = 0 + for _, params in metadata.get("ss_dataset_dirs", {}).items(): + image_count += int(params.get("img_count", 0)) + + if image_count: + table.append(("Dataset size:", image_count)) + + return table + + def put_values_into_components(self, name): + user_metadata = self.get_user_metadata(name) + values = super().put_values_into_components(name) + + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + + tags = build_tags(metadata) + gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]] + + return [ + *values[0:5], + item.get("sd_version", "Unknown"), + gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False), + user_metadata.get('activation text', ''), + float(user_metadata.get('preferred weight', 0.0)), + gr.update(visible=True if tags else False), + gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False), + ] + + def generate_random_prompt(self, name): + item = self.page.items.get(name, {}) + metadata = item.get("metadata") or {} + tags = build_tags(metadata) + + return self.generate_random_prompt_from_tags(tags) + + def generate_random_prompt_from_tags(self, tags): + max_count = None + res = [] + for tag, count in tags: + if not max_count: + max_count = count + + v = random.random() * max_count + if count > v: + res.append(tag) + + return ", ".join(sorted(res)) + + def create_extra_default_items_in_left_column(self): + + # this would be a lot better as gr.Radio but I can't make it work + self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True) + + def create_editor(self): + self.create_default_editor_elems() + + self.taginfo = gr.HighlightedText(label="Training dataset tags") + self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora") + self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01) + + with gr.Row() as row_random_prompt: + with gr.Column(scale=8): + random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False) + + with gr.Column(scale=1, min_width=120): + generate_random_prompt = gr.Button('Generate').style(full_width=True, size="lg") + + self.edit_notes = gr.TextArea(label='Notes', lines=4) + + generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False) + + def select_tag(activation_text, evt: gr.SelectData): + tag = evt.value[0] + + words = re.split(re_comma, activation_text) + if tag in words: + words = [x for x in words if x != tag and x.strip()] + return ", ".join(words) + + return activation_text + ", " + tag if activation_text else tag + + self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False) + + self.create_default_buttons() + + viewed_components = [ + self.edit_name, + self.edit_description, + self.html_filedata, + self.html_preview, + self.edit_notes, + self.select_sd_version, + self.taginfo, + self.edit_activation_text, + self.slider_preferred_weight, + row_random_prompt, + random_prompt, + ] + + self.button_edit\ + .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\ + .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box]) + + edited_components = [ + self.edit_description, + self.select_sd_version, + self.edit_activation_text, + self.slider_preferred_weight, + self.edit_notes, + ] + + self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components) diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py index 7b928008..1311b2f4 100644 --- a/extensions-builtin/Lora/ui_extra_networks_lora.py +++ b/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -1,8 +1,11 @@ -import json import os -import lora + +import network +import networks from modules import shared, ui_extra_networks +from modules.ui_extra_networks import quote_js +from ui_edit_user_metadata import LoraUserMetadataEditor class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): @@ -10,27 +13,66 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): super().__init__('Lora') def refresh(self): - lora.list_available_loras() + networks.list_available_networks() + + def create_item(self, name, index=None, enable_filter=True): + lora_on_disk = networks.available_networks.get(name) + + path, ext = os.path.splitext(lora_on_disk.filename) + + alias = lora_on_disk.get_alias() + + item = { + "name": name, + "filename": lora_on_disk.filename, + "preview": self.find_preview(path) if self.find_preview(path) else './file=html/card-no-preview.png', + "description": self.find_description(path), + "search_term": self.search_terms_from_path(lora_on_disk.filename), + "local_preview": f"{path}.{shared.opts.samples_format}", + "metadata": lora_on_disk.metadata, + "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)}, + "sd_version": lora_on_disk.sd_version.name, + } + + self.read_user_metadata(item) + activation_text = item["user_metadata"].get("activation text") + preferred_weight = item["user_metadata"].get("preferred weight", 0.0) + item["prompt"] = quote_js(f"") + + if activation_text: + item["prompt"] += " + " + quote_js(" " + activation_text) + + sd_version = item["user_metadata"].get("sd version") + if sd_version in network.SdVersion.__members__: + item["sd_version"] = sd_version + sd_version = network.SdVersion[sd_version] + else: + sd_version = lora_on_disk.sd_version + + if shared.opts.lora_show_all or not enable_filter: + pass + elif sd_version == network.SdVersion.Unknown: + model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1 + if model_version.name in shared.opts.lora_hide_unknown_for_versions: + return None + elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL: + return None + elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2: + return None + elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1: + return None + + return item def list_items(self): - for index, (name, lora_on_disk) in enumerate(lora.available_loras.items()): - path, ext = os.path.splitext(lora_on_disk.filename) - - alias = lora_on_disk.get_alias() - - yield { - "name": name, - "filename": path, - "preview": self.find_preview(path) if self.find_preview(path) else './file=html/card-no-preview.png', - "description": self.find_description(path), - "search_term": self.search_terms_from_path(lora_on_disk.filename), - "prompt": json.dumps(f""), - "local_preview": f"{path}.{shared.opts.samples_format}", - "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None, - "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)}, - - } + for index, name in enumerate(networks.available_networks): + item = self.create_item(name, index) + + if item is not None: + yield item def allowed_directories_for_previews(self): - return [shared.cmd_opts.lora_dir] + return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat] + def create_user_metadata_editor(self, ui, tabname): + return LoraUserMetadataEditor(ui, tabname, self) diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py index 85b4505f..167d2f64 100644 --- a/extensions-builtin/ScuNET/scripts/scunet_model.py +++ b/extensions-builtin/ScuNET/scripts/scunet_model.py @@ -1,4 +1,3 @@ -import os.path import sys import PIL.Image @@ -6,12 +5,11 @@ import numpy as np import torch from tqdm import tqdm -from basicsr.utils.download_util import load_file_from_url - import modules.upscaler from modules import devices, modelloader, script_callbacks, errors -from scunet_model_arch import SCUNet as net +from scunet_model_arch import SCUNet +from modules.modelloader import load_file_from_url from modules.shared import opts @@ -28,7 +26,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): scalers = [] add_model2 = True for file in model_paths: - if "http" in file: + if file.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(file) @@ -87,11 +85,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler): def do_upscale(self, img: PIL.Image.Image, selected_file): - torch.cuda.empty_cache() + devices.torch_gc() - model = self.load_model(selected_file) - if model is None: - print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr) + try: + model = self.load_model(selected_file) + except Exception as e: + print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr) return img device = devices.get_device_for('scunet') @@ -111,7 +110,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler): torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy() del torch_img, torch_output - torch.cuda.empty_cache() + devices.torch_gc() output = np_output.transpose((1, 2, 0)) # CHW to HWC output = output[:, :, ::-1] # BGR to RGB @@ -119,15 +118,12 @@ class UpscalerScuNET(modules.upscaler.Upscaler): def load_model(self, path: str): device = devices.get_device_for('scunet') - if "http" in path: - filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True) + if path.startswith("http"): + # TODO: this doesn't use `path` at all? + filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth") else: filename = path - if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None: - print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr) - return None - - model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) + model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64) model.load_state_dict(torch.load(filename), strict=True) model.eval() for _, v in model.named_parameters(): diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py index 1c7bf325..ae0d0e6a 100644 --- a/extensions-builtin/SwinIR/scripts/swinir_model.py +++ b/extensions-builtin/SwinIR/scripts/swinir_model.py @@ -1,34 +1,35 @@ -import os +import sys +import platform import numpy as np import torch from PIL import Image -from basicsr.utils.download_util import load_file_from_url from tqdm import tqdm from modules import modelloader, devices, script_callbacks, shared from modules.shared import opts, state -from swinir_model_arch import SwinIR as net -from swinir_model_arch_v2 import Swin2SR as net2 +from swinir_model_arch import SwinIR +from swinir_model_arch_v2 import Swin2SR from modules.upscaler import Upscaler, UpscalerData +SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth" device_swinir = devices.get_device_for('swinir') class UpscalerSwinIR(Upscaler): def __init__(self, dirname): + self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs + self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings self.name = "SwinIR" - self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \ - "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \ - "-L_x4_GAN.pth " + self.model_url = SWINIR_MODEL_URL self.model_name = "SwinIR 4x" self.user_path = dirname super().__init__() scalers = [] model_files = self.find_models(ext_filter=[".pt", ".pth"]) for model in model_files: - if "http" in model: + if model.startswith("http"): name = self.model_name else: name = modelloader.friendly_name(model) @@ -37,42 +38,54 @@ class UpscalerSwinIR(Upscaler): self.scalers = scalers def do_upscale(self, img, model_file): - model = self.load_model(model_file) - if model is None: - return img - model = model.to(device_swinir, dtype=devices.dtype) + use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \ + and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows" + current_config = (model_file, opts.SWIN_tile) + + if use_compile and self._cached_model_config == current_config: + model = self._cached_model + else: + self._cached_model = None + try: + model = self.load_model(model_file) + except Exception as e: + print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr) + return img + model = model.to(device_swinir, dtype=devices.dtype) + if use_compile: + model = torch.compile(model) + self._cached_model = model + self._cached_model_config = current_config img = upscale(img, model) - try: - torch.cuda.empty_cache() - except Exception: - pass + devices.torch_gc() return img def load_model(self, path, scale=4): - if "http" in path: - dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth") - filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True) + if path.startswith("http"): + filename = modelloader.load_file_from_url( + url=path, + model_dir=self.model_download_path, + file_name=f"{self.model_name.replace(' ', '_')}.pth", + ) else: filename = path - if filename is None or not os.path.exists(filename): - return None if filename.endswith(".v2.pth"): - model = net2( - upscale=scale, - in_chans=3, - img_size=64, - window_size=8, - img_range=1.0, - depths=[6, 6, 6, 6, 6, 6], - embed_dim=180, - num_heads=[6, 6, 6, 6, 6, 6], - mlp_ratio=2, - upsampler="nearest+conv", - resi_connection="1conv", + model = Swin2SR( + upscale=scale, + in_chans=3, + img_size=64, + window_size=8, + img_range=1.0, + depths=[6, 6, 6, 6, 6, 6], + embed_dim=180, + num_heads=[6, 6, 6, 6, 6, 6], + mlp_ratio=2, + upsampler="nearest+conv", + resi_connection="1conv", ) params = None else: - model = net( + model = SwinIR( upscale=scale, in_chans=3, img_size=64, @@ -172,6 +185,8 @@ def on_ui_settings(): shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling"))) shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling"))) + if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows + shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run")) script_callbacks.on_ui_settings(on_ui_settings) diff --git a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js index 5ebd2073..30199dcd 100644 --- a/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js +++ b/extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js @@ -200,7 +200,8 @@ onUiLoaded(async() => { canvas_hotkey_move: "KeyF", canvas_hotkey_overlap: "KeyO", canvas_disabled_functions: [], - canvas_show_tooltip: true + canvas_show_tooltip: true, + canvas_blur_prompt: false }; const functionMap = { @@ -608,6 +609,19 @@ onUiLoaded(async() => { // Handle keydown events function handleKeyDown(event) { + // Disable key locks to make pasting from the buffer work correctly + if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { + return; + } + } + + const hotkeyActions = { [hotkeysConfig.canvas_hotkey_reset]: resetZoom, [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, @@ -686,6 +700,20 @@ onUiLoaded(async() => { // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. function handleMoveKeyDown(e) { + + // Disable key locks to make pasting from the buffer work correctly + if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && event.code === 'KeyC') || e.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { + return; + } + } + + if (e.code === hotkeysConfig.canvas_hotkey_move) { if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { e.preventDefault(); diff --git a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py index 1b6683aa..380176ce 100644 --- a/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py +++ b/extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py @@ -9,5 +9,6 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"), "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"), "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"), + "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"), "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}), })) diff --git a/extensions-builtin/mobile/javascript/mobile.js b/extensions-builtin/mobile/javascript/mobile.js new file mode 100644 index 00000000..12cae4b7 --- /dev/null +++ b/extensions-builtin/mobile/javascript/mobile.js @@ -0,0 +1,26 @@ +var isSetupForMobile = false; + +function isMobile() { + for (var tab of ["txt2img", "img2img"]) { + var imageTab = gradioApp().getElementById(tab + '_results'); + if (imageTab && imageTab.offsetParent && imageTab.offsetLeft == 0) { + return true; + } + } + + return false; +} + +function reportWindowSize() { + var currentlyMobile = isMobile(); + if (currentlyMobile == isSetupForMobile) return; + isSetupForMobile = currentlyMobile; + + for (var tab of ["txt2img", "img2img"]) { + var button = gradioApp().getElementById(tab + '_generate_box'); + var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column'); + target.insertBefore(button, target.firstElementChild); + } +} + +window.addEventListener("resize", reportWindowSize); diff --git a/html/extra-networks-card.html b/html/extra-networks-card.html index 2719de6b..0cf61664 100644 --- a/html/extra-networks-card.html +++ b/html/extra-networks-card.html @@ -1,7 +1,8 @@
-
+
{metadata_button} + {edit_button}
    diff --git a/javascript/badScaleChecker.js b/javascript/badScaleChecker.js new file mode 100644 index 00000000..625ad309 --- /dev/null +++ b/javascript/badScaleChecker.js @@ -0,0 +1,108 @@ +(function() { + var ignore = localStorage.getItem("bad-scale-ignore-it") == "ignore-it"; + + function getScale() { + var ratio = 0, + screen = window.screen, + ua = navigator.userAgent.toLowerCase(); + + if (window.devicePixelRatio !== undefined) { + ratio = window.devicePixelRatio; + } else if (~ua.indexOf('msie')) { + if (screen.deviceXDPI && screen.logicalXDPI) { + ratio = screen.deviceXDPI / screen.logicalXDPI; + } + } else if (window.outerWidth !== undefined && window.innerWidth !== undefined) { + ratio = window.outerWidth / window.innerWidth; + } + + return ratio == 0 ? 0 : Math.round(ratio * 100); + } + + var showing = false; + + var div = document.createElement("div"); + div.style.position = "fixed"; + div.style.top = "0px"; + div.style.left = "0px"; + div.style.width = "100vw"; + div.style.backgroundColor = "firebrick"; + div.style.textAlign = "center"; + div.style.zIndex = 99; + + var b = document.createElement("b"); + b.innerHTML = 'Bad Scale: ??% '; + + div.appendChild(b); + + var note1 = document.createElement("p"); + note1.innerHTML = "Change your browser or your computer settings!"; + note1.title = 'Just make sure "computer-scale" * "browser-scale" = 100% ,\n' + + "you can keep your computer-scale and only change this page's scale,\n" + + "for example: your computer-scale is 125%, just use [\"CTRL\"+\"-\"] to make your browser-scale of this page to 80%."; + div.appendChild(note1); + + var note2 = document.createElement("p"); + note2.innerHTML = " Otherwise, it will cause this page to not function properly!"; + note2.title = "When you click \"Copy image to: [inpaint sketch]\" in some img2img's tab,\n" + + "if scale<100% the canvas will be invisible,\n" + + "else if scale>100% this page will take large amount of memory and CPU performance."; + div.appendChild(note2); + + var btn = document.createElement("button"); + btn.innerHTML = "Click here to ignore"; + + div.appendChild(btn); + + function tryShowTopBar(scale) { + if (showing) return; + + b.innerHTML = 'Bad Scale: ' + scale + '% '; + + var updateScaleTimer = setInterval(function() { + var newScale = getScale(); + b.innerHTML = 'Bad Scale: ' + newScale + '% '; + if (newScale == 100) { + var p = div.parentNode; + if (p != null) p.removeChild(div); + showing = false; + clearInterval(updateScaleTimer); + check(); + } + }, 999); + + btn.onclick = function() { + clearInterval(updateScaleTimer); + var p = div.parentNode; + if (p != null) p.removeChild(div); + ignore = true; + showing = false; + localStorage.setItem("bad-scale-ignore-it", "ignore-it"); + }; + + document.body.appendChild(div); + } + + function check() { + if (!ignore) { + var timer = setInterval(function() { + var scale = getScale(); + if (scale != 100 && !ignore) { + tryShowTopBar(scale); + clearInterval(timer); + } + if (ignore) { + clearInterval(timer); + } + }, 999); + } + } + + if (document.readyState != "complete") { + document.onreadystatechange = function() { + if (document.readyState != "complete") check(); + }; + } else { + check(); + } +})(); diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js index 3f673c7d..8906c892 100644 --- a/javascript/edit-attention.js +++ b/javascript/edit-attention.js @@ -1,120 +1,121 @@ -function keyupEditAttention(event) { - let target = event.originalTarget || event.composedPath()[0]; - if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; - if (!(event.metaKey || event.ctrlKey)) return; - - let isPlus = event.key == "ArrowUp"; - let isMinus = event.key == "ArrowDown"; - if (!isPlus && !isMinus) return; - - let selectionStart = target.selectionStart; - let selectionEnd = target.selectionEnd; - let text = target.value; - - function selectCurrentParenthesisBlock(OPEN, CLOSE) { - if (selectionStart !== selectionEnd) return false; - - // Find opening parenthesis around current cursor - const before = text.substring(0, selectionStart); - let beforeParen = before.lastIndexOf(OPEN); - if (beforeParen == -1) return false; - let beforeParenClose = before.lastIndexOf(CLOSE); - while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { - beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); - beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1); - } - - // Find closing parenthesis around current cursor - const after = text.substring(selectionStart); - let afterParen = after.indexOf(CLOSE); - if (afterParen == -1) return false; - let afterParenOpen = after.indexOf(OPEN); - while (afterParenOpen !== -1 && afterParen > afterParenOpen) { - afterParen = after.indexOf(CLOSE, afterParen + 1); - afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1); - } - if (beforeParen === -1 || afterParen === -1) return false; - - // Set the selection to the text between the parenthesis - const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); - const lastColon = parenContent.lastIndexOf(":"); - selectionStart = beforeParen + 1; - selectionEnd = selectionStart + lastColon; - target.setSelectionRange(selectionStart, selectionEnd); - return true; - } - - function selectCurrentWord() { - if (selectionStart !== selectionEnd) return false; - const delimiters = opts.keyedit_delimiters + " \r\n\t"; - - // seek backward until to find beggining - while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { - selectionStart--; - } - - // seek forward to find end - while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { - selectionEnd++; - } - - target.setSelectionRange(selectionStart, selectionEnd); - return true; - } - - // If the user hasn't selected anything, let's select their current parenthesis block or word - if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { - selectCurrentWord(); - } - - event.preventDefault(); - - var closeCharacter = ')'; - var delta = opts.keyedit_precision_attention; - - if (selectionStart > 0 && text[selectionStart - 1] == '<') { - closeCharacter = '>'; - delta = opts.keyedit_precision_extra; - } else if (selectionStart == 0 || text[selectionStart - 1] != "(") { - - // do not include spaces at the end - while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { - selectionEnd -= 1; - } - if (selectionStart == selectionEnd) { - return; - } - - text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); - - selectionStart += 1; - selectionEnd += 1; - } - - var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; - var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end)); - if (isNaN(weight)) return; - - weight += isPlus ? delta : -delta; - weight = parseFloat(weight.toPrecision(12)); - if (String(weight).length == 1) weight += ".0"; - - if (closeCharacter == ')' && weight == 1) { - text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5); - selectionStart--; - selectionEnd--; - } else { - text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1); - } - - target.focus(); - target.value = text; - target.selectionStart = selectionStart; - target.selectionEnd = selectionEnd; - - updateInput(target); -} - -addEventListener('keydown', (event) => { - keyupEditAttention(event); -}); +function keyupEditAttention(event) { + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!(event.metaKey || event.ctrlKey)) return; + + let isPlus = event.key == "ArrowUp"; + let isMinus = event.key == "ArrowDown"; + if (!isPlus && !isMinus) return; + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + + function selectCurrentParenthesisBlock(OPEN, CLOSE) { + if (selectionStart !== selectionEnd) return false; + + // Find opening parenthesis around current cursor + const before = text.substring(0, selectionStart); + let beforeParen = before.lastIndexOf(OPEN); + if (beforeParen == -1) return false; + let beforeParenClose = before.lastIndexOf(CLOSE); + while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { + beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); + beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1); + } + + // Find closing parenthesis around current cursor + const after = text.substring(selectionStart); + let afterParen = after.indexOf(CLOSE); + if (afterParen == -1) return false; + let afterParenOpen = after.indexOf(OPEN); + while (afterParenOpen !== -1 && afterParen > afterParenOpen) { + afterParen = after.indexOf(CLOSE, afterParen + 1); + afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1); + } + if (beforeParen === -1 || afterParen === -1) return false; + + // Set the selection to the text between the parenthesis + const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); + const lastColon = parenContent.lastIndexOf(":"); + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + lastColon; + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + function selectCurrentWord() { + if (selectionStart !== selectionEnd) return false; + const delimiters = opts.keyedit_delimiters + " \r\n\t"; + + // seek backward until to find beggining + while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { + selectionStart--; + } + + // seek forward to find end + while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { + selectionEnd++; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + // If the user hasn't selected anything, let's select their current parenthesis block or word + if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { + selectCurrentWord(); + } + + event.preventDefault(); + + var closeCharacter = ')'; + var delta = opts.keyedit_precision_attention; + + if (selectionStart > 0 && text[selectionStart - 1] == '<') { + closeCharacter = '>'; + delta = opts.keyedit_precision_extra; + } else if (selectionStart == 0 || text[selectionStart - 1] != "(") { + + // do not include spaces at the end + while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { + selectionEnd -= 1; + } + if (selectionStart == selectionEnd) { + return; + } + + text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); + + selectionStart += 1; + selectionEnd += 1; + } + + var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; + var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end)); + if (isNaN(weight)) return; + + weight += isPlus ? delta : -delta; + weight = parseFloat(weight.toPrecision(12)); + if (String(weight).length == 1) weight += ".0"; + + if (closeCharacter == ')' && weight == 1) { + var endParenPos = text.substring(selectionEnd).indexOf(')'); + text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); + selectionStart--; + selectionEnd--; + } else { + text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end); + } + + target.focus(); + target.value = text; + target.selectionStart = selectionStart; + target.selectionEnd = selectionEnd; + + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditAttention(event); +}); diff --git a/javascript/edit-order.js b/javascript/edit-order.js new file mode 100644 index 00000000..ed4ef9ac --- /dev/null +++ b/javascript/edit-order.js @@ -0,0 +1,41 @@ +/* alt+left/right moves text in prompt */ + +function keyupEditOrder(event) { + if (!opts.keyedit_move) return; + + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_toprow'] [id*='_prompt'], .prompt) textarea")) return; + if (!event.altKey) return; + + let isLeft = event.key == "ArrowLeft"; + let isRight = event.key == "ArrowRight"; + if (!isLeft && !isRight) return; + event.preventDefault(); + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + let items = text.split(","); + let indexStart = (text.slice(0, selectionStart).match(/,/g) || []).length; + let indexEnd = (text.slice(0, selectionEnd).match(/,/g) || []).length; + let range = indexEnd - indexStart + 1; + + if (isLeft && indexStart > 0) { + items.splice(indexStart - 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart - 1).join().length + (indexStart == 1 ? 0 : 1); + target.selectionEnd = items.slice(0, indexEnd).join().length; + } else if (isRight && indexEnd < items.length - 1) { + items.splice(indexStart + 1, 0, ...items.splice(indexStart, range)); + target.value = items.join(); + target.selectionStart = items.slice(0, indexStart + 1).join().length + 1; + target.selectionEnd = items.slice(0, indexEnd + 2).join().length; + } + + event.preventDefault(); + updateInput(target); +} + +addEventListener('keydown', (event) => { + keyupEditOrder(event); +}); diff --git a/javascript/extensions.js b/javascript/extensions.js index 0fa39b52..29185b4e 100644 --- a/javascript/extensions.js +++ b/javascript/extensions.js @@ -1,73 +1,91 @@ -function extensions_apply(_disabled_list, _update_list, disable_all) { - var disable = []; - var update = []; - - gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { - if (x.name.startsWith("enable_") && !x.checked) { - disable.push(x.name.substring(7)); - } - - if (x.name.startsWith("update_") && x.checked) { - update.push(x.name.substring(7)); - } - }); - - restart_reload(); - - return [JSON.stringify(disable), JSON.stringify(update), disable_all]; -} - -function extensions_check() { - var disable = []; - - gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { - if (x.name.startsWith("enable_") && !x.checked) { - disable.push(x.name.substring(7)); - } - }); - - gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { - x.innerHTML = "Loading..."; - }); - - - var id = randomId(); - requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() { - - }); - - return [id, JSON.stringify(disable)]; -} - -function install_extension_from_index(button, url) { - button.disabled = "disabled"; - button.value = "Installing..."; - - var textarea = gradioApp().querySelector('#extension_to_install textarea'); - textarea.value = url; - updateInput(textarea); - - gradioApp().querySelector('#install_extension_button').click(); -} - -function config_state_confirm_restore(_, config_state_name, config_restore_type) { - if (config_state_name == "Current") { - return [false, config_state_name, config_restore_type]; - } - let restored = ""; - if (config_restore_type == "extensions") { - restored = "all saved extension versions"; - } else if (config_restore_type == "webui") { - restored = "the webui version"; - } else { - restored = "the webui version and all saved extension versions"; - } - let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + "."); - if (confirmed) { - restart_reload(); - gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { - x.innerHTML = "Loading..."; - }); - } - return [confirmed, config_state_name, config_restore_type]; -} +function extensions_apply(_disabled_list, _update_list, disable_all) { + var disable = []; + var update = []; + + gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(7)); + } + + if (x.name.startsWith("update_") && x.checked) { + update.push(x.name.substring(7)); + } + }); + + restart_reload(); + + return [JSON.stringify(disable), JSON.stringify(update), disable_all]; +} + +function extensions_check() { + var disable = []; + + gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x) { + if (x.name.startsWith("enable_") && !x.checked) { + disable.push(x.name.substring(7)); + } + }); + + gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { + x.innerHTML = "Loading..."; + }); + + + var id = randomId(); + requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() { + + }); + + return [id, JSON.stringify(disable)]; +} + +function install_extension_from_index(button, url) { + button.disabled = "disabled"; + button.value = "Installing..."; + + var textarea = gradioApp().querySelector('#extension_to_install textarea'); + textarea.value = url; + updateInput(textarea); + + gradioApp().querySelector('#install_extension_button').click(); +} + +function config_state_confirm_restore(_, config_state_name, config_restore_type) { + if (config_state_name == "Current") { + return [false, config_state_name, config_restore_type]; + } + let restored = ""; + if (config_restore_type == "extensions") { + restored = "all saved extension versions"; + } else if (config_restore_type == "webui") { + restored = "the webui version"; + } else { + restored = "the webui version and all saved extension versions"; + } + let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + "."); + if (confirmed) { + restart_reload(); + gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x) { + x.innerHTML = "Loading..."; + }); + } + return [confirmed, config_state_name, config_restore_type]; +} + +function toggle_all_extensions(event) { + gradioApp().querySelectorAll('#extensions .extension_toggle').forEach(function(checkbox_el) { + checkbox_el.checked = event.target.checked; + }); +} + +function toggle_extension() { + let all_extensions_toggled = true; + for (const checkbox_el of gradioApp().querySelectorAll('#extensions .extension_toggle')) { + if (!checkbox_el.checked) { + all_extensions_toggled = false; + break; + } + } + + gradioApp().querySelector('#extensions .all_extensions_toggle').checked = all_extensions_toggled; +} diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js index 6d712d16..9a260fba 100644 --- a/javascript/extraNetworks.js +++ b/javascript/extraNetworks.js @@ -4,11 +4,12 @@ function setupExtraNetworksForTab(tabname) { .classList.add("extra-networks"); let tabs = gradioApp().querySelector("#" + tabname + "_extra_tabs > div"); - let search = gradioApp().querySelector( - "#" + tabname + "_extra_search textarea" - ); + let search = gradioApp().querySelector("#" + tabname + "_extra_search textarea" ); let refresh = gradioApp().getElementById(tabname + "_extra_refresh"); + //let sort = gradioApp().getElementById(tabname + '_extra_sort'); + //let sortOrder = gradioApp().getElementById(tabname + '_extra_sortorder'); + let clear = document.createElement("div"); clear.id = tabname + "_extra_clear"; clear.classList.add("token-remove", "remove-all", "svelte-a6vu2r", "hide"); @@ -22,6 +23,13 @@ function setupExtraNetworksForTab(tabname) { tabs.appendChild(clear); tabs.appendChild(refresh); +/* + sort.classList.add('sort'); + sortOrder.classList.add('sortorder'); + sort.dataset.sortkey = 'sortDefault'; + tabs.appendChild(sort); + tabs.appendChild(sortOrder); */ + clear.addEventListener("click", function (evt) { search.value = ""; updateInput(search); @@ -252,6 +260,8 @@ function extraNetworksRequestMetadata(event, extraPage, cardName) { event.stopPropagation(); } + + /* function setupExtraNetworksForTab(tabname) { gradioApp().querySelector('#' + tabname + '_extra_tabs').classList.add('extra-networks'); @@ -368,7 +378,7 @@ function setupExtraNetworks() { onUiLoaded(setupExtraNetworks); -var re_extranet = /<([^:]+:[^:]+):[\d.]+>/; +var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/; var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g; function tryToRemoveExtraNetworkFromPrompt(textarea, text) { @@ -376,15 +386,22 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) { var replaced = false; var newTextareaText; if (m) { + var extraTextAfterNet = m[2]; var partToSearch = m[1]; - newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found) { + var foundAtPosition = -1; + newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) { m = found.match(re_extranet); if (m[1] == partToSearch) { replaced = true; + foundAtPosition = pos; return ""; } return found; }); + + if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) { + newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length); + } } else { newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) { if (found == text) { @@ -437,19 +454,20 @@ function extraNetworksSearchButton(tabs_id, event) { var globalPopup = null; var globalPopupInner = null; +function closePopup() { + if (!globalPopup) return; + + globalPopup.style.display = "none"; +} function popup(contents) { if (!globalPopup) { globalPopup = document.createElement('div'); - globalPopup.onclick = function() { - globalPopup.style.display = "none"; - }; + globalPopup.onclick = closePopup; globalPopup.classList.add('global-popup'); var close = document.createElement('div'); close.classList.add('global-popup-close'); - close.onclick = function() { - globalPopup.style.display = "none"; - }; + close.onclick = closePopup; close.title = "Close"; globalPopup.appendChild(close); @@ -460,7 +478,7 @@ function popup(contents) { globalPopupInner.classList.add('global-popup-inner'); globalPopup.appendChild(globalPopupInner); - gradioApp().appendChild(globalPopup); + gradioApp().querySelector('.main').appendChild(globalPopup); } globalPopupInner.innerHTML = ''; @@ -518,4 +536,44 @@ function extraNetworksRequestMetadata(event, extraPage, cardName) { event.stopPropagation(); } -*/ + +var extraPageUserMetadataEditors = {}; + +function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) { + var id = tabname + '_' + extraPage + '_edit_user_metadata'; + + var editor = extraPageUserMetadataEditors[id]; + if (!editor) { + editor = {}; + editor.page = gradioApp().getElementById(id); + editor.nameTextarea = gradioApp().querySelector("#" + id + "_name" + ' textarea'); + editor.button = gradioApp().querySelector("#" + id + "_button"); + extraPageUserMetadataEditors[id] = editor; + } + + editor.nameTextarea.value = cardName; + updateInput(editor.nameTextarea); + + editor.button.click(); + + popup(editor.page); + + event.stopPropagation(); +} + +function extraNetworksRefreshSingleCard(page, tabname, name) { + requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) { + if (data && data.html) { + var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function + + var newDiv = document.createElement('DIV'); + newDiv.innerHTML = data.html; + var newCard = newDiv.firstElementChild; + + newCard.style = ''; + card.parentElement.insertBefore(newCard, card); + card.parentElement.removeChild(card); + } + }); +} +*/ \ No newline at end of file diff --git a/javascript/hints.js b/javascript/hints.js index 5e0a829e..f3dd9019 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -94,8 +94,6 @@ var titles = { "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.", "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.", - "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).", - "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.", "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", @@ -120,7 +118,6 @@ var titles = { "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.", "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.", "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.", - "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.", "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.", "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order listed.", "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction." diff --git a/javascript/ui.js b/javascript/ui.js index 65d40d63..da748446 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -1075,7 +1075,7 @@ onUiUpdate(function() { checked_header = "checked"; } radio_hidden_html += - '"; radio_header_html += - '