InvokeAI/ldm/invoke/readline.py

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"""
Readline helper functions for invoke.py.
You may import the global singleton `completer` to get access to the
completer object itself. This is useful when you want to autocomplete
seeds:
from ldm.invoke.readline import completer
completer.add_seed(18247566)
completer.add_seed(9281839)
"""
import os
import re
import atexit
from ldm.invoke.args import Args
Embedding merging (#1526) * add whole <style token> to vocab for concept library embeddings * add ability to load multiple concept .bin files * make --log_tokenization respect custom tokens * start working on concept downloading system * preliminary support for dynamic loading and merging of multiple embedded models - The embedding_manager is now enhanced with ldm.invoke.concepts_lib, which handles dynamic downloading and caching of embedded models from the Hugging Face concepts library (https://huggingface.co/sd-concepts-library) - Downloading of a embedded model is triggered by the presence of one or more <concept> tags in the prompt. - Once the embedded model is downloaded, its trigger phrase will be loaded into the embedding manager and the prompt's <concept> tag will be replaced with the <trigger_phrase> - The downloaded model stays on disk for fast loading later. - The CLI autocomplete will complete partial <concept> tags for you. Type a '<' and hit tab to get all ~700 concepts. BUGS AND LIMITATIONS: - MODEL NAME VS TRIGGER PHRASE You must use the name of the concept embed model from the SD library, and not the trigger phrase itself. Usually these are the same, but not always. For example, the model named "hoi4-leaders" corresponds to the trigger "<HOI4-Leader>" One reason for this design choice is that there is no apparent constraint on the uniqueness of the trigger phrases and one trigger phrase may map onto multiple models. So we use the model name instead. The second reason is that there is no way I know of to search Hugging Face for models with certain trigger phrases. So we'd have to download all 700 models to index the phrases. The problem this presents is that this may confuse users, who will want to reuse prompts from distributions that use the trigger phrase directly. Usually this will work, but not always. - WON'T WORK ON A FIREWALLED SYSTEM If the host running IAI has no internet connection, it can't download the concept libraries. I will add a script that allows users to preload a list of concept models. - BUG IN PROMPT REPLACEMENT WHEN MODEL NOT FOUND There's a small bug that occurs when the user provides an invalid model name. The <concept> gets replaced with <None> in the prompt. * fix loading .pt embeddings; allow multi-vector embeddings; warn on dupes * simplify replacement logic and remove cuda assumption * download list of concepts from hugging face * remove misleading customization of '*' placeholder the existing code as-is did not do anything; unclear what it was supposed to do. the obvious alternative -- setting using 'placeholder_strings' instead of 'placeholder_tokens' to match model.params.personalization_config.params.placeholder_strings -- caused a crash. i think this is because the passed string also needed to be handed over on init of the PersonalizedBase as the 'placeholder_token' argument. this is weird config dict magic and i don't want to touch it. put a breakpoint in personalzied.py line 116 (top of PersonalizedBase.__init__) if you want to have a crack at it yourself. * address all the issues raised by damian0815 in review of PR #1526 * actually resize the token_embeddings * multiple improvements to the concept loader based on code reviews 1. Activated the --embedding_directory option (alias --embedding_path) to load a single embedding or an entire directory of embeddings at startup time. 2. Can turn off automatic loading of embeddings using --no-embeddings. 3. Embedding checkpoints are scanned with the pickle scanner. 4. More informative error messages when a concept can't be loaded due either to a 404 not found error or a network error. * autocomplete terms end with ">" now * fix startup error and network unreachable 1. If the .invokeai file does not contain the --root and --outdir options, invoke.py will now fix it. 2. Catch and handle network problems when downloading hugging face textual inversion concepts. * fix misformatted error string Co-authored-by: Damian Stewart <d@damianstewart.com>
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from ldm.invoke.concepts_lib import Concepts
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from ldm.invoke.globals import Globals
# ---------------readline utilities---------------------
try:
import readline
readline_available = True
except (ImportError,ModuleNotFoundError) as e:
print(f'** An error occurred when loading the readline module: {str(e)}')
readline_available = False
IMG_EXTENSIONS = ('.png','.jpg','.jpeg','.PNG','.JPG','.JPEG','.gif','.GIF')
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
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WEIGHT_EXTENSIONS = ('.ckpt','.bae')
TEXT_EXTENSIONS = ('.txt','.TXT')
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
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CONFIG_EXTENSIONS = ('.yaml','.yml')
COMMANDS = (
'--steps','-s',
'--seed','-S',
'--iterations','-n',
'--width','-W','--height','-H',
'--cfg_scale','-C',
'--threshold',
'--perlin',
'--grid','-g',
'--individual','-i',
'--save_intermediates',
'--init_img','-I',
'--init_mask','-M',
'--init_color',
'--strength','-f',
'--variants','-v',
'--outdir','-o',
'--sampler','-A','-m',
'--embedding_path',
'--device',
'--grid','-g',
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'--facetool','-ft',
'--facetool_strength','-G',
'--codeformer_fidelity','-cf',
'--upscale','-U',
'-save_orig','--save_original',
'--skip_normalize','-x',
'--log_tokenization','-t',
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'--hires_fix',
'--inpaint_replace','-r',
'--png_compression','-z',
'--text_mask','-tm',
'!fix','!fetch','!replay','!history','!search','!clear',
'!models','!switch','!import_model','!edit_model','!del_model',
'!mask',
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
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)
MODEL_COMMANDS = (
'!switch',
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
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'!edit_model',
'!del_model',
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
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)
WEIGHT_COMMANDS = (
'!import_model',
)
IMG_PATH_COMMANDS = (
'--outdir[=\s]',
)
TEXT_PATH_COMMANDS=(
'!replay',
)
IMG_FILE_COMMANDS=(
'!fix',
'!fetch',
'!mask',
'--init_img[=\s]','-I',
'--init_mask[=\s]','-M',
'--init_color[=\s]',
'--embedding_path[=\s]',
)
Embedding merging (#1526) * add whole <style token> to vocab for concept library embeddings * add ability to load multiple concept .bin files * make --log_tokenization respect custom tokens * start working on concept downloading system * preliminary support for dynamic loading and merging of multiple embedded models - The embedding_manager is now enhanced with ldm.invoke.concepts_lib, which handles dynamic downloading and caching of embedded models from the Hugging Face concepts library (https://huggingface.co/sd-concepts-library) - Downloading of a embedded model is triggered by the presence of one or more <concept> tags in the prompt. - Once the embedded model is downloaded, its trigger phrase will be loaded into the embedding manager and the prompt's <concept> tag will be replaced with the <trigger_phrase> - The downloaded model stays on disk for fast loading later. - The CLI autocomplete will complete partial <concept> tags for you. Type a '<' and hit tab to get all ~700 concepts. BUGS AND LIMITATIONS: - MODEL NAME VS TRIGGER PHRASE You must use the name of the concept embed model from the SD library, and not the trigger phrase itself. Usually these are the same, but not always. For example, the model named "hoi4-leaders" corresponds to the trigger "<HOI4-Leader>" One reason for this design choice is that there is no apparent constraint on the uniqueness of the trigger phrases and one trigger phrase may map onto multiple models. So we use the model name instead. The second reason is that there is no way I know of to search Hugging Face for models with certain trigger phrases. So we'd have to download all 700 models to index the phrases. The problem this presents is that this may confuse users, who will want to reuse prompts from distributions that use the trigger phrase directly. Usually this will work, but not always. - WON'T WORK ON A FIREWALLED SYSTEM If the host running IAI has no internet connection, it can't download the concept libraries. I will add a script that allows users to preload a list of concept models. - BUG IN PROMPT REPLACEMENT WHEN MODEL NOT FOUND There's a small bug that occurs when the user provides an invalid model name. The <concept> gets replaced with <None> in the prompt. * fix loading .pt embeddings; allow multi-vector embeddings; warn on dupes * simplify replacement logic and remove cuda assumption * download list of concepts from hugging face * remove misleading customization of '*' placeholder the existing code as-is did not do anything; unclear what it was supposed to do. the obvious alternative -- setting using 'placeholder_strings' instead of 'placeholder_tokens' to match model.params.personalization_config.params.placeholder_strings -- caused a crash. i think this is because the passed string also needed to be handed over on init of the PersonalizedBase as the 'placeholder_token' argument. this is weird config dict magic and i don't want to touch it. put a breakpoint in personalzied.py line 116 (top of PersonalizedBase.__init__) if you want to have a crack at it yourself. * address all the issues raised by damian0815 in review of PR #1526 * actually resize the token_embeddings * multiple improvements to the concept loader based on code reviews 1. Activated the --embedding_directory option (alias --embedding_path) to load a single embedding or an entire directory of embeddings at startup time. 2. Can turn off automatic loading of embeddings using --no-embeddings. 3. Embedding checkpoints are scanned with the pickle scanner. 4. More informative error messages when a concept can't be loaded due either to a 404 not found error or a network error. * autocomplete terms end with ">" now * fix startup error and network unreachable 1. If the .invokeai file does not contain the --root and --outdir options, invoke.py will now fix it. 2. Catch and handle network problems when downloading hugging face textual inversion concepts. * fix misformatted error string Co-authored-by: Damian Stewart <d@damianstewart.com>
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path_regexp = '(' + '|'.join(IMG_PATH_COMMANDS+IMG_FILE_COMMANDS) + ')\s*\S*$'
weight_regexp = '(' + '|'.join(WEIGHT_COMMANDS) + ')\s*\S*$'
text_regexp = '(' + '|'.join(TEXT_PATH_COMMANDS) + ')\s*\S*$'
class Completer(object):
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
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def __init__(self, options, models=[]):
self.options = sorted(options)
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
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self.models = sorted(models)
self.seeds = set()
self.matches = list()
self.default_dir = None
self.linebuffer = None
self.auto_history_active = True
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
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self.extensions = None
Embedding merging (#1526) * add whole <style token> to vocab for concept library embeddings * add ability to load multiple concept .bin files * make --log_tokenization respect custom tokens * start working on concept downloading system * preliminary support for dynamic loading and merging of multiple embedded models - The embedding_manager is now enhanced with ldm.invoke.concepts_lib, which handles dynamic downloading and caching of embedded models from the Hugging Face concepts library (https://huggingface.co/sd-concepts-library) - Downloading of a embedded model is triggered by the presence of one or more <concept> tags in the prompt. - Once the embedded model is downloaded, its trigger phrase will be loaded into the embedding manager and the prompt's <concept> tag will be replaced with the <trigger_phrase> - The downloaded model stays on disk for fast loading later. - The CLI autocomplete will complete partial <concept> tags for you. Type a '<' and hit tab to get all ~700 concepts. BUGS AND LIMITATIONS: - MODEL NAME VS TRIGGER PHRASE You must use the name of the concept embed model from the SD library, and not the trigger phrase itself. Usually these are the same, but not always. For example, the model named "hoi4-leaders" corresponds to the trigger "<HOI4-Leader>" One reason for this design choice is that there is no apparent constraint on the uniqueness of the trigger phrases and one trigger phrase may map onto multiple models. So we use the model name instead. The second reason is that there is no way I know of to search Hugging Face for models with certain trigger phrases. So we'd have to download all 700 models to index the phrases. The problem this presents is that this may confuse users, who will want to reuse prompts from distributions that use the trigger phrase directly. Usually this will work, but not always. - WON'T WORK ON A FIREWALLED SYSTEM If the host running IAI has no internet connection, it can't download the concept libraries. I will add a script that allows users to preload a list of concept models. - BUG IN PROMPT REPLACEMENT WHEN MODEL NOT FOUND There's a small bug that occurs when the user provides an invalid model name. The <concept> gets replaced with <None> in the prompt. * fix loading .pt embeddings; allow multi-vector embeddings; warn on dupes * simplify replacement logic and remove cuda assumption * download list of concepts from hugging face * remove misleading customization of '*' placeholder the existing code as-is did not do anything; unclear what it was supposed to do. the obvious alternative -- setting using 'placeholder_strings' instead of 'placeholder_tokens' to match model.params.personalization_config.params.placeholder_strings -- caused a crash. i think this is because the passed string also needed to be handed over on init of the PersonalizedBase as the 'placeholder_token' argument. this is weird config dict magic and i don't want to touch it. put a breakpoint in personalzied.py line 116 (top of PersonalizedBase.__init__) if you want to have a crack at it yourself. * address all the issues raised by damian0815 in review of PR #1526 * actually resize the token_embeddings * multiple improvements to the concept loader based on code reviews 1. Activated the --embedding_directory option (alias --embedding_path) to load a single embedding or an entire directory of embeddings at startup time. 2. Can turn off automatic loading of embeddings using --no-embeddings. 3. Embedding checkpoints are scanned with the pickle scanner. 4. More informative error messages when a concept can't be loaded due either to a 404 not found error or a network error. * autocomplete terms end with ">" now * fix startup error and network unreachable 1. If the .invokeai file does not contain the --root and --outdir options, invoke.py will now fix it. 2. Catch and handle network problems when downloading hugging face textual inversion concepts. * fix misformatted error string Co-authored-by: Damian Stewart <d@damianstewart.com>
2022-11-28 07:40:24 +00:00
self.concepts = Concepts().list_concepts()
return
def complete(self, text, state):
'''
Completes invoke command line.
BUG: it doesn't correctly complete files that have spaces in the name.
'''
buffer = readline.get_line_buffer()
if state == 0:
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
2022-10-14 03:48:07 +00:00
# extensions defined, so go directly into path completion mode
if self.extensions is not None:
self.matches = self._path_completions(text, state, self.extensions)
# looking for an image file
elif re.search(path_regexp,buffer):
do_shortcut = re.search('^'+'|'.join(IMG_FILE_COMMANDS),buffer)
self.matches = self._path_completions(text, state, IMG_EXTENSIONS,shortcut_ok=do_shortcut)
# looking for a seed
elif re.search('(-S\s*|--seed[=\s])\d*$',buffer):
self.matches= self._seed_completions(text,state)
Embedding merging (#1526) * add whole <style token> to vocab for concept library embeddings * add ability to load multiple concept .bin files * make --log_tokenization respect custom tokens * start working on concept downloading system * preliminary support for dynamic loading and merging of multiple embedded models - The embedding_manager is now enhanced with ldm.invoke.concepts_lib, which handles dynamic downloading and caching of embedded models from the Hugging Face concepts library (https://huggingface.co/sd-concepts-library) - Downloading of a embedded model is triggered by the presence of one or more <concept> tags in the prompt. - Once the embedded model is downloaded, its trigger phrase will be loaded into the embedding manager and the prompt's <concept> tag will be replaced with the <trigger_phrase> - The downloaded model stays on disk for fast loading later. - The CLI autocomplete will complete partial <concept> tags for you. Type a '<' and hit tab to get all ~700 concepts. BUGS AND LIMITATIONS: - MODEL NAME VS TRIGGER PHRASE You must use the name of the concept embed model from the SD library, and not the trigger phrase itself. Usually these are the same, but not always. For example, the model named "hoi4-leaders" corresponds to the trigger "<HOI4-Leader>" One reason for this design choice is that there is no apparent constraint on the uniqueness of the trigger phrases and one trigger phrase may map onto multiple models. So we use the model name instead. The second reason is that there is no way I know of to search Hugging Face for models with certain trigger phrases. So we'd have to download all 700 models to index the phrases. The problem this presents is that this may confuse users, who will want to reuse prompts from distributions that use the trigger phrase directly. Usually this will work, but not always. - WON'T WORK ON A FIREWALLED SYSTEM If the host running IAI has no internet connection, it can't download the concept libraries. I will add a script that allows users to preload a list of concept models. - BUG IN PROMPT REPLACEMENT WHEN MODEL NOT FOUND There's a small bug that occurs when the user provides an invalid model name. The <concept> gets replaced with <None> in the prompt. * fix loading .pt embeddings; allow multi-vector embeddings; warn on dupes * simplify replacement logic and remove cuda assumption * download list of concepts from hugging face * remove misleading customization of '*' placeholder the existing code as-is did not do anything; unclear what it was supposed to do. the obvious alternative -- setting using 'placeholder_strings' instead of 'placeholder_tokens' to match model.params.personalization_config.params.placeholder_strings -- caused a crash. i think this is because the passed string also needed to be handed over on init of the PersonalizedBase as the 'placeholder_token' argument. this is weird config dict magic and i don't want to touch it. put a breakpoint in personalzied.py line 116 (top of PersonalizedBase.__init__) if you want to have a crack at it yourself. * address all the issues raised by damian0815 in review of PR #1526 * actually resize the token_embeddings * multiple improvements to the concept loader based on code reviews 1. Activated the --embedding_directory option (alias --embedding_path) to load a single embedding or an entire directory of embeddings at startup time. 2. Can turn off automatic loading of embeddings using --no-embeddings. 3. Embedding checkpoints are scanned with the pickle scanner. 4. More informative error messages when a concept can't be loaded due either to a 404 not found error or a network error. * autocomplete terms end with ">" now * fix startup error and network unreachable 1. If the .invokeai file does not contain the --root and --outdir options, invoke.py will now fix it. 2. Catch and handle network problems when downloading hugging face textual inversion concepts. * fix misformatted error string Co-authored-by: Damian Stewart <d@damianstewart.com>
2022-11-28 07:40:24 +00:00
elif re.search('<[\w-]*$',buffer):
self.matches= self._concept_completions(text,state)
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
2022-10-14 03:48:07 +00:00
# looking for a model
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
2022-10-12 06:14:59 +00:00
elif re.match('^'+'|'.join(MODEL_COMMANDS),buffer):
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
2022-10-14 03:48:07 +00:00
self.matches= self._model_completions(text, state)
elif re.search(weight_regexp,buffer):
2022-11-28 17:44:32 +00:00
self.matches = self._path_completions(
text,
state,
WEIGHT_EXTENSIONS,
default_dir=Globals.root,
)
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
2022-10-12 06:14:59 +00:00
elif re.search(text_regexp,buffer):
self.matches = self._path_completions(text, state, TEXT_EXTENSIONS)
# This is the first time for this text, so build a match list.
elif text:
self.matches = [
s for s in self.options if s and s.startswith(text)
]
else:
self.matches = self.options[:]
# Return the state'th item from the match list,
# if we have that many.
try:
response = self.matches[state]
except IndexError:
response = None
return response
add ability to import and edit alternative models online - !import_model <path/to/model/weights> will import a new model, prompt the user for its name and description, write it to the models.yaml file, and load it. - !edit_model <model_name> will bring up a previously-defined model and prompt the user to edit its descriptive fields. Example of !import_model <pre> invoke> <b>!import_model models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt</b> >> Model import in process. Please enter the values needed to configure this model: Name for this model: <b>waifu-diffusion</b> Description of this model: <b>Waifu Diffusion v1.3</b> Configuration file for this model: <b>configs/stable-diffusion/v1-inference.yaml</b> Default image width: <b>512</b> Default image height: <b>512</b> >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu Diffusion v1.3 height: 512 weights: models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt width: 512 OK to import [n]? <b>y</b> >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch08-float16.ckpt | LatentDiffusion: Running in eps-prediction mode | DiffusionWrapper has 859.52 M params. | Making attention of type 'vanilla' with 512 in_channels | Working with z of shape (1, 4, 32, 32) = 4096 dimensions. | Making attention of type 'vanilla' with 512 in_channels | Using faster float16 precision </pre> Example of !edit_model <pre> invoke> <b>!edit_model waifu-diffusion</b> >> Editing model waifu-diffusion from configuration file ./configs/models.yaml description: <b>Waifu diffusion v1.4beta</b> weights: models/ldm/stable-diffusion-v1/<b>model-epoch10-float16.ckpt</b> config: configs/stable-diffusion/v1-inference.yaml width: 512 height: 512 >> New configuration: waifu-diffusion: config: configs/stable-diffusion/v1-inference.yaml description: Waifu diffusion v1.4beta weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt height: 512 width: 512 OK to import [n]? y >> Caching model stable-diffusion-1.4 in system RAM >> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt ... </pre>
2022-10-14 03:48:07 +00:00
def complete_extensions(self, extensions:list):
'''
If called with a list of extensions, will force completer
to do file path completions.
'''
self.extensions=extensions
def add_history(self,line):
'''
Pass thru to readline
'''
if not self.auto_history_active:
readline.add_history(line)
def clear_history(self):
'''
Pass clear_history() thru to readline
'''
readline.clear_history()
def search_history(self,match:str):
'''
Like show_history() but only shows items that
contain the match string.
'''
self.show_history(match)
def remove_history_item(self,pos):
readline.remove_history_item(pos)
def add_seed(self, seed):
'''
Add a seed to the autocomplete list for display when -S is autocompleted.
'''
if seed is not None:
self.seeds.add(str(seed))
def set_default_dir(self, path):
self.default_dir=path
def set_options(self,options):
self.options = options
def get_line(self,index):
try:
line = self.get_history_item(index)
except IndexError:
return None
return line
def get_current_history_length(self):
return readline.get_current_history_length()
def get_history_item(self,index):
return readline.get_history_item(index)
def show_history(self,match=None):
'''
Print the session history using the pydoc pager
'''
import pydoc
lines = list()
h_len = self.get_current_history_length()
if h_len < 1:
print('<empty history>')
return
for i in range(0,h_len):
line = self.get_history_item(i+1)
if match and match not in line:
continue
lines.append(f'[{i+1}] {line}')
pydoc.pager('\n'.join(lines))
def set_line(self,line)->None:
'''
Set the default string displayed in the next line of input.
'''
self.linebuffer = line
readline.redisplay()
def add_model(self,model_name:str)->None:
'''
add a model name to the completion list
'''
self.models.append(model_name)
def del_model(self,model_name:str)->None:
'''
removes a model name from the completion list
'''
self.models.remove(model_name)
def _seed_completions(self, text, state):
m = re.search('(-S\s?|--seed[=\s]?)(\d*)',text)
if m:
switch = m.groups()[0]
partial = m.groups()[1]
else:
switch = ''
partial = text
matches = list()
for s in self.seeds:
if s.startswith(partial):
matches.append(switch+s)
matches.sort()
return matches
Embedding merging (#1526) * add whole <style token> to vocab for concept library embeddings * add ability to load multiple concept .bin files * make --log_tokenization respect custom tokens * start working on concept downloading system * preliminary support for dynamic loading and merging of multiple embedded models - The embedding_manager is now enhanced with ldm.invoke.concepts_lib, which handles dynamic downloading and caching of embedded models from the Hugging Face concepts library (https://huggingface.co/sd-concepts-library) - Downloading of a embedded model is triggered by the presence of one or more <concept> tags in the prompt. - Once the embedded model is downloaded, its trigger phrase will be loaded into the embedding manager and the prompt's <concept> tag will be replaced with the <trigger_phrase> - The downloaded model stays on disk for fast loading later. - The CLI autocomplete will complete partial <concept> tags for you. Type a '<' and hit tab to get all ~700 concepts. BUGS AND LIMITATIONS: - MODEL NAME VS TRIGGER PHRASE You must use the name of the concept embed model from the SD library, and not the trigger phrase itself. Usually these are the same, but not always. For example, the model named "hoi4-leaders" corresponds to the trigger "<HOI4-Leader>" One reason for this design choice is that there is no apparent constraint on the uniqueness of the trigger phrases and one trigger phrase may map onto multiple models. So we use the model name instead. The second reason is that there is no way I know of to search Hugging Face for models with certain trigger phrases. So we'd have to download all 700 models to index the phrases. The problem this presents is that this may confuse users, who will want to reuse prompts from distributions that use the trigger phrase directly. Usually this will work, but not always. - WON'T WORK ON A FIREWALLED SYSTEM If the host running IAI has no internet connection, it can't download the concept libraries. I will add a script that allows users to preload a list of concept models. - BUG IN PROMPT REPLACEMENT WHEN MODEL NOT FOUND There's a small bug that occurs when the user provides an invalid model name. The <concept> gets replaced with <None> in the prompt. * fix loading .pt embeddings; allow multi-vector embeddings; warn on dupes * simplify replacement logic and remove cuda assumption * download list of concepts from hugging face * remove misleading customization of '*' placeholder the existing code as-is did not do anything; unclear what it was supposed to do. the obvious alternative -- setting using 'placeholder_strings' instead of 'placeholder_tokens' to match model.params.personalization_config.params.placeholder_strings -- caused a crash. i think this is because the passed string also needed to be handed over on init of the PersonalizedBase as the 'placeholder_token' argument. this is weird config dict magic and i don't want to touch it. put a breakpoint in personalzied.py line 116 (top of PersonalizedBase.__init__) if you want to have a crack at it yourself. * address all the issues raised by damian0815 in review of PR #1526 * actually resize the token_embeddings * multiple improvements to the concept loader based on code reviews 1. Activated the --embedding_directory option (alias --embedding_path) to load a single embedding or an entire directory of embeddings at startup time. 2. Can turn off automatic loading of embeddings using --no-embeddings. 3. Embedding checkpoints are scanned with the pickle scanner. 4. More informative error messages when a concept can't be loaded due either to a 404 not found error or a network error. * autocomplete terms end with ">" now * fix startup error and network unreachable 1. If the .invokeai file does not contain the --root and --outdir options, invoke.py will now fix it. 2. Catch and handle network problems when downloading hugging face textual inversion concepts. * fix misformatted error string Co-authored-by: Damian Stewart <d@damianstewart.com>
2022-11-28 07:40:24 +00:00
def add_embedding_terms(self, terms:list[str]):
self.concepts = Concepts().list_concepts()
self.concepts.extend(terms)
def _concept_completions(self, text, state):
partial = text[1:] # this removes the leading '<'
if len(partial) == 0:
return self.concepts # whole dump - think if user wants this!
matches = list()
for concept in self.concepts:
if concept.startswith(partial):
matches.append(f'<{concept}>')
matches.sort()
return matches
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
2022-10-12 06:14:59 +00:00
def _model_completions(self, text, state):
m = re.search('(!switch\s+)(\w*)',text)
if m:
switch = m.groups()[0]
partial = m.groups()[1]
else:
switch = ''
partial = text
matches = list()
for s in self.models:
if s.startswith(partial):
matches.append(switch+s)
matches.sort()
return matches
def _pre_input_hook(self):
if self.linebuffer:
readline.insert_text(self.linebuffer)
readline.redisplay()
self.linebuffer = None
2022-11-28 17:44:32 +00:00
def _path_completions(self, text, state, extensions, shortcut_ok=True, default_dir:str=''):
# separate the switch from the partial path
match = re.search('^(-\w|--\w+=?)(.*)',text)
if match is None:
switch = None
partial_path = text
else:
switch,partial_path = match.groups()
partial_path = partial_path.lstrip()
matches = list()
path = os.path.expanduser(partial_path)
if os.path.isdir(path):
dir = path
elif os.path.dirname(path) != '':
dir = os.path.dirname(path)
else:
2022-11-28 17:44:32 +00:00
dir = default_dir if os.path.exists(default_dir) else ''
path= os.path.join(dir,path)
dir_list = os.listdir(dir or '.')
if shortcut_ok and os.path.exists(self.default_dir) and dir=='':
dir_list += os.listdir(self.default_dir)
for node in dir_list:
if node.startswith('.') and len(node) > 1:
continue
full_path = os.path.join(dir, node)
if not (node.endswith(extensions) or os.path.isdir(full_path)):
continue
if path and not full_path.startswith(path):
continue
if switch is None:
match_path = os.path.join(dir,node)
matches.append(match_path+'/' if os.path.isdir(full_path) else match_path)
elif os.path.isdir(full_path):
matches.append(
switch+os.path.join(os.path.dirname(full_path), node) + '/'
)
elif node.endswith(extensions):
matches.append(
switch+os.path.join(os.path.dirname(full_path), node)
)
return matches
class DummyCompleter(Completer):
def __init__(self,options):
super().__init__(options)
self.history = list()
def add_history(self,line):
self.history.append(line)
def clear_history(self):
self.history = list()
def get_current_history_length(self):
return len(self.history)
def get_history_item(self,index):
return self.history[index-1]
def remove_history_item(self,index):
return self.history.pop(index-1)
def set_line(self,line):
print(f'# {line}')
def generic_completer(commands:list)->Completer:
if readline_available:
completer = Completer(commands,[])
readline.set_completer(completer.complete)
readline.set_pre_input_hook(completer._pre_input_hook)
readline.set_completer_delims(' ')
readline.parse_and_bind('tab: complete')
readline.parse_and_bind('set print-completions-horizontally off')
readline.parse_and_bind('set page-completions on')
readline.parse_and_bind('set skip-completed-text on')
readline.parse_and_bind('set show-all-if-ambiguous on')
else:
completer = DummyCompleter(commands)
return completer
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
2022-10-12 06:14:59 +00:00
def get_completer(opt:Args, models=[])->Completer:
if readline_available:
enable fast switching between models in invoke.py - This PR enables two new commands in the invoke.py script !models -- list the available models and their cache status !switch <model> -- switch to the indicated model Example: invoke> !models laion400m not loaded Latent Diffusion LAION400M model stable-diffusion-1.4 active Stable Diffusion inference model version 1.4 waifu-1.3 cached Waifu anime model version 1.3 invoke> !switch waifu-1.3 >> Caching model stable-diffusion-1.4 in system RAM >> Retrieving model waifu-1.3 from system RAM cache The name and descriptions of the models are taken from `config/models.yaml`. A future enhancement to `model_cache.py` will be to enable new model stanzas to be added to the file programmatically. This will be useful for the WebGUI. More details: - Use fast switching algorithm described in PR #948 - Models are selected using their configuration stanza name given in models.yaml. - To avoid filling up CPU RAM with cached models, this PR implements an LRU cache that monitors available CPU RAM. - The caching code allows the minimum value of available RAM to be adjusted, but invoke.py does not currently have a command-line argument that allows you to set it. The minimum free RAM is arbitrarily set to 2 GB. - Add optional description field to configs/models.yaml Unrelated fixes: - Added ">>" to CompViz model loading messages in order to make user experience more consistent. - When generating an image greater than defaults, will only warn about possible VRAM filling the first time. - Fixed bug that was causing help message to be printed twice. This involved moving the import line for the web backend into the section where it is called. Coauthored by: @ArDiouscuros
2022-10-12 06:14:59 +00:00
completer = Completer(COMMANDS,models)
readline.set_completer(
completer.complete
)
# pyreadline3 does not have a set_auto_history() method
try:
readline.set_auto_history(False)
completer.auto_history_active = False
except:
completer.auto_history_active = True
readline.set_pre_input_hook(completer._pre_input_hook)
readline.set_completer_delims(' ')
readline.parse_and_bind('tab: complete')
readline.parse_and_bind('set print-completions-horizontally off')
readline.parse_and_bind('set page-completions on')
readline.parse_and_bind('set skip-completed-text on')
readline.parse_and_bind('set show-all-if-ambiguous on')
histfile = os.path.join(os.path.expanduser(opt.outdir), '.invoke_history')
try:
readline.read_history_file(histfile)
readline.set_history_length(1000)
except FileNotFoundError:
pass
atexit.register(readline.write_history_file, histfile)
else:
completer = DummyCompleter(COMMANDS)
return completer