mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
added controlnet models to frontend; backend needs to be done
This commit is contained in:
Lincoln Stein
parent
dc54cbb1fc
commit
c9ee42450e
@ -359,6 +359,7 @@ setting environment variables INVOKEAI_<setting>.
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conf_path : Path = Field(default='configs/models.yaml', description='Path to models definition file', category='Paths')
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embedding_dir : Path = Field(default='embeddings', description='Path to InvokeAI textual inversion aembeddings directory', category='Paths')
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gfpgan_model_dir : Path = Field(default="./models/gfpgan/GFPGANv1.4.pth", description='Path to GFPGAN models directory.', category='Paths')
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controlnet_dir : Path = Field(default="controlnet", description='Path to directory of ControlNet models.', category='Paths')
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legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
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lora_dir : Path = Field(default='loras', description='Path to InvokeAI LoRA model directory', category='Paths')
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outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
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@ -465,6 +466,13 @@ setting environment variables INVOKEAI_<setting>.
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'''
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return self._resolve(self.lora_dir) if self.lora_dir else None
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@property
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def controlnet_path(self)->Path:
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'''
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Path to the controlnet models directory.
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'''
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return self._resolve(self.controlnet_dir) if self.controlnet_dir else None
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@property
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def autoconvert_path(self)->Path:
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'''
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@ -12,7 +12,6 @@ print("Loading Python libraries...\n",file=sys.stderr)
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import argparse
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import io
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import os
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import re
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import shutil
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import traceback
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import warnings
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@ -67,14 +66,9 @@ config = get_invokeai_config()
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Model_dir = "models"
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Weights_dir = "ldm/stable-diffusion-v1/"
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# the initial "configs" dir is now bundled in the `invokeai.configs` package
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Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
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Default_config_file = config.model_conf_path
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SD_Configs = config.legacy_conf_path
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Datasets = OmegaConf.load(Dataset_path)
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# minimum size for the UI
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MIN_COLS = 135
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MIN_LINES = 45
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@ -49,9 +49,9 @@ Config_preamble = """
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def default_config_file():
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print(config.root_dir)
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return config.model_conf_path
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def sd_configs():
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return config.legacy_conf_path
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@ -59,8 +59,7 @@ def initial_models():
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global Datasets
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if Datasets:
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return Datasets
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return (Datasets := OmegaConf.load(Dataset_path))
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return (Datasets := OmegaConf.load(Dataset_path)['diffusers'])
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def install_requested_models(
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install_initial_models: List[str] = None,
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@ -79,7 +78,7 @@ def install_requested_models(
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if not config_file_path.exists():
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open(config_file_path, "w")
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model_manager = ModelManager(OmegaConf.load(config_file_path), precision=precision)
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model_manager = ModelManager(OmegaConf.load(config_file_path)['diffusers'], precision=precision)
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if remove_models and len(remove_models) > 0:
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print("== DELETING UNCHECKED STARTER MODELS ==")
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@ -1,83 +1,98 @@
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stable-diffusion-1.5:
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description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
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repo_id: runwayml/stable-diffusion-v1-5
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: True
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default: True
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sd-inpainting-1.5:
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description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
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repo_id: runwayml/stable-diffusion-inpainting
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: True
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stable-diffusion-2.1:
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description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
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repo_id: stabilityai/stable-diffusion-2-1
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format: diffusers
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recommended: True
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sd-inpainting-2.0:
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description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
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repo_id: stabilityai/stable-diffusion-2-inpainting
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format: diffusers
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recommended: False
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analog-diffusion-1.0:
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description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
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repo_id: wavymulder/Analog-Diffusion
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format: diffusers
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recommended: false
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deliberate-1.0:
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description: Versatile model that produces detailed images up to 768px (4.27 GB)
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format: diffusers
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repo_id: XpucT/Deliberate
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recommended: False
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d&d-diffusion-1.0:
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description: Dungeons & Dragons characters (2.13 GB)
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format: diffusers
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repo_id: 0xJustin/Dungeons-and-Diffusion
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recommended: False
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dreamlike-photoreal-2.0:
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description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
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format: diffusers
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repo_id: dreamlike-art/dreamlike-photoreal-2.0
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recommended: False
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inkpunk-1.0:
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description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
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format: diffusers
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repo_id: Envvi/Inkpunk-Diffusion
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recommended: False
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openjourney-4.0:
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description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
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format: diffusers
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repo_id: prompthero/openjourney
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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portrait-plus-1.0:
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description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
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format: diffusers
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repo_id: wavymulder/portraitplus
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recommended: False
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seek-art-mega-1.0:
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description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
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repo_id: coreco/seek.art_MEGA
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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trinart-2.0:
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description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
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repo_id: naclbit/trinart_stable_diffusion_v2
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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waifu-diffusion-1.4:
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description: An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)
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repo_id: hakurei/waifu-diffusion
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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diffusers:
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stable-diffusion-1.5:
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description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
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repo_id: runwayml/stable-diffusion-v1-5
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: True
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default: True
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sd-inpainting-1.5:
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description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
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repo_id: runwayml/stable-diffusion-inpainting
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: True
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stable-diffusion-2.1:
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description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
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repo_id: stabilityai/stable-diffusion-2-1
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format: diffusers
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recommended: True
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sd-inpainting-2.0:
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description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
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repo_id: stabilityai/stable-diffusion-2-inpainting
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format: diffusers
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recommended: False
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analog-diffusion-1.0:
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description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
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repo_id: wavymulder/Analog-Diffusion
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format: diffusers
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recommended: false
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deliberate-1.0:
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description: Versatile model that produces detailed images up to 768px (4.27 GB)
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format: diffusers
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repo_id: XpucT/Deliberate
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recommended: False
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d&d-diffusion-1.0:
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description: Dungeons & Dragons characters (2.13 GB)
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format: diffusers
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repo_id: 0xJustin/Dungeons-and-Diffusion
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recommended: False
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dreamlike-photoreal-2.0:
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description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
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format: diffusers
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repo_id: dreamlike-art/dreamlike-photoreal-2.0
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recommended: False
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inkpunk-1.0:
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description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
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format: diffusers
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repo_id: Envvi/Inkpunk-Diffusion
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recommended: False
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openjourney-4.0:
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description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
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format: diffusers
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repo_id: prompthero/openjourney
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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portrait-plus-1.0:
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description: An SD-1.5 model trained on close range portraits of people; prompt with "portrait+" (2.13 GB)
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format: diffusers
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repo_id: wavymulder/portraitplus
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recommended: False
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seek-art-mega-1.0:
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description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
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repo_id: coreco/seek.art_MEGA
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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trinart-2.0:
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description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
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repo_id: naclbit/trinart_stable_diffusion_v2
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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waifu-diffusion-1.4:
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description: An SD-1.5 model trained on 680k anime/manga-style images (2.13 GB)
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repo_id: hakurei/waifu-diffusion
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format: diffusers
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vae:
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repo_id: stabilityai/sd-vae-ft-mse
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recommended: False
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controlnet:
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canny: lllyasviel/control_v11p_sd15_canny
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inpaint: lllyasviel/control_v11p_sd15_inpaint
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mlsd: lllyasviel/control_v11p_sd15_mlsd
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depth: lllyasviel/control_v11f1p_sd15_depth
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normal_bae: lllyasviel/control_v11p_sd15_normalbae
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seg: lllyasviel/control_v11p_sd15_seg
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lineart: lllyasviel/control_v11p_sd15_lineart
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lineart_anime: lllyasviel/control_v11p_sd15s2_lineart_anime
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scribble: lllyasviel/control_v11p_sd15_scribble
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softedge: lllyasviel/control_v11p_sd15_softedge
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shuffle: lllyasviel/control_v11e_sd15_shuffle
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tile: lllyasviel/control_v11f1e_sd15_tile
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ip2p: lllyasviel/control_v11e_sd15_ip2p
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@ -53,14 +53,17 @@ class addModelsForm(npyscreen.FormMultiPage):
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def __init__(self, parentApp, name, multipage=False, *args, **keywords):
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self.multipage = multipage
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self.initial_models = OmegaConf.load(Dataset_path)
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self.initial_models = OmegaConf.load(Dataset_path)['diffusers']
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self.control_net_models = OmegaConf.load(Dataset_path)['controlnet']
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self.installed_cn_models = self._get_installed_cn_models()
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try:
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self.existing_models = OmegaConf.load(default_config_file())
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except:
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self.existing_models = dict()
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self.starter_model_list = [
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x for x in list(self.initial_models.keys()) if x not in self.existing_models
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]
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# self.starter_model_list = [
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# x for x in list(self.initial_models.keys()) if x not in self.existing_models
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# ]
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self.starter_model_list = list(self.initial_models.keys())
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self.installed_models = dict()
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super().__init__(parentApp=parentApp, name=name, *args, **keywords)
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@ -75,6 +78,9 @@ class addModelsForm(npyscreen.FormMultiPage):
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self.installed_models = sorted(
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[x for x in list(self.initial_models.keys()) if x in self.existing_models]
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)
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cn_model_list = sorted(self.control_net_models.keys())
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self.nextrely -= 1
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self.add_widget_intelligent(
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npyscreen.FixedText,
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@ -89,44 +95,44 @@ class addModelsForm(npyscreen.FormMultiPage):
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color="CAUTION",
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)
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self.nextrely += 1
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if len(self.installed_models) > 0:
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self.add_widget_intelligent(
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CenteredTitleText,
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name="== INSTALLED STARTER MODELS ==",
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editable=False,
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color="CONTROL",
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)
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self.nextrely -= 1
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self.add_widget_intelligent(
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CenteredTitleText,
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name="Currently installed starter models. Uncheck to delete:",
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editable=False,
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labelColor="CAUTION",
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)
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self.nextrely -= 1
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columns = self._get_columns()
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self.previously_installed_models = self.add_widget_intelligent(
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MultiSelectColumns,
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columns=columns,
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values=self.installed_models,
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value=[x for x in range(0, len(self.installed_models))],
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max_height=1 + len(self.installed_models) // columns,
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relx=4,
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slow_scroll=True,
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scroll_exit=True,
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)
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self.purge_deleted = self.add_widget_intelligent(
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npyscreen.Checkbox,
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name="Purge deleted models from disk",
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value=False,
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scroll_exit=True,
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relx=4,
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)
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self.nextrely += 1
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# if len(self.installed_models) > 0:
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# self.add_widget_intelligent(
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# CenteredTitleText,
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# name="== INSTALLED STARTER MODELS ==",
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# editable=False,
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# color="CONTROL",
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# )
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# self.nextrely -= 1
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# self.add_widget_intelligent(
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# CenteredTitleText,
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# name="Currently installed starter models. Uncheck to delete:",
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# editable=False,
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# labelColor="CAUTION",
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# )
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# self.nextrely -= 1
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# columns = self._get_columns()
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# self.previously_installed_models = self.add_widget_intelligent(
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# MultiSelectColumns,
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# columns=columns,
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# values=self.installed_models,
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# value=[x for x in range(0, len(self.installed_models))],
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# max_height=1 + len(self.installed_models) // columns,
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# relx=4,
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# slow_scroll=True,
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# scroll_exit=True,
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# )
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# self.purge_deleted = self.add_widget_intelligent(
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# npyscreen.Checkbox,
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# name="Purge deleted models from disk",
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# value=False,
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# scroll_exit=True,
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# relx=4,
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# )
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# self.nextrely += 1
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if len(self.starter_model_list) > 0:
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self.add_widget_intelligent(
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CenteredTitleText,
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name="== STARTER MODELS (recommended ones selected) ==",
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name="== DIFFUSERS MODELS (recommended ones selected) ==",
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editable=False,
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color="CONTROL",
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)
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@ -148,12 +154,42 @@ class addModelsForm(npyscreen.FormMultiPage):
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value=[
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self.starter_model_list.index(x)
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for x in self.starter_model_list
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if show_recommended and x in recommended_models
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if (show_recommended and x in recommended_models)\
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or (x in self.existing_models)
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],
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max_height=len(starter_model_labels) + 1,
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relx=4,
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scroll_exit=True,
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)
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self.add_widget_intelligent(
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CenteredTitleText,
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name="== CONTROLNET MODELS ==",
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editable=False,
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color="CONTROL",
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)
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columns=6
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self.cn_models_selected = self.add_widget_intelligent(
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MultiSelectColumns,
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columns=columns,
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name="Install ControlNet Models",
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values=cn_model_list,
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value=[
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cn_model_list.index(x)
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for x in cn_model_list
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if x in self.installed_cn_models
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],
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max_height=len(cn_model_list)//columns + 1,
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relx=4,
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scroll_exit=True,
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)
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self.nextrely += 1
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self.purge_deleted = self.add_widget_intelligent(
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npyscreen.Checkbox,
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name="Purge unchecked models from disk",
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value=False,
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scroll_exit=True,
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relx=4,
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)
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self.add_widget_intelligent(
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CenteredTitleText,
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name="== IMPORT LOCAL AND REMOTE MODELS ==",
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@ -263,6 +299,21 @@ class addModelsForm(npyscreen.FormMultiPage):
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for x in range(0, len(names))
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]
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def _get_installed_cn_models(self)->list[str]:
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with open('log.txt','w') as file:
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cn_dir = config.controlnet_path
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file.write(f'cn_dir={cn_dir}\n')
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installed_cn_models = set()
|
||||
for root, dirs, files in os.walk(cn_dir):
|
||||
for name in dirs:
|
||||
file.write(f'{root}/{name}/config.json\n')
|
||||
if Path(root, name, 'config.json').exists():
|
||||
installed_cn_models.add(name)
|
||||
inverse_dict = {name.split('/')[1]: key for key, name in self.control_net_models.items()}
|
||||
file.write(f'inverse={inverse_dict}')
|
||||
return [inverse_dict[x] for x in installed_cn_models]
|
||||
|
||||
|
||||
def _get_columns(self) -> int:
|
||||
window_width, window_height = get_terminal_size()
|
||||
cols = (
|
||||
@ -318,16 +369,20 @@ class addModelsForm(npyscreen.FormMultiPage):
|
||||
)
|
||||
else:
|
||||
starter_models = dict()
|
||||
selections.purge_deleted_models = False
|
||||
if hasattr(self, "previously_installed_models"):
|
||||
unchecked = [
|
||||
self.previously_installed_models.values[x]
|
||||
for x in range(0, len(self.previously_installed_models.values))
|
||||
if x not in self.previously_installed_models.value
|
||||
]
|
||||
starter_models.update(map(lambda x: (x, False), unchecked))
|
||||
selections.purge_deleted_models = self.purge_deleted.value
|
||||
selections.starter_models = starter_models
|
||||
selections.purge_deleted_models = self.purge_deleted.value
|
||||
|
||||
selections.install_models = [x for x in starter_models if x not in self.existing_models]
|
||||
selections.remove_models = [x for x in self.starter_model_list if x in self.existing_models and x not in starter_models]
|
||||
|
||||
selections.install_cn_models = [self.control_net_models[self.cn_models_selected.values[x]]
|
||||
for x in self.cn_models_selected.value
|
||||
if self.cn_models_selected.values[x] not in self.installed_cn_models
|
||||
]
|
||||
selections.remove_cn_models = [self.control_net_models[x]
|
||||
for x in self.cn_models_selected.values
|
||||
if x in self.installed_cn_models
|
||||
and self.cn_models_selected.values.index(x) not in self.cn_models_selected.value
|
||||
]
|
||||
|
||||
# load directory and whether to scan on startup
|
||||
if self.show_directory_fields.value:
|
||||
@ -346,8 +401,11 @@ class AddModelApplication(npyscreen.NPSAppManaged):
|
||||
super().__init__()
|
||||
self.user_cancelled = False
|
||||
self.user_selections = Namespace(
|
||||
starter_models=None,
|
||||
install_models=None,
|
||||
remove_models=None,
|
||||
purge_deleted_models=False,
|
||||
install_cn_models = None,
|
||||
remove_cn_models = None,
|
||||
scan_directory=None,
|
||||
autoscan_on_startup=None,
|
||||
import_model_paths=None,
|
||||
@ -362,28 +420,29 @@ class AddModelApplication(npyscreen.NPSAppManaged):
|
||||
|
||||
# --------------------------------------------------------
|
||||
def process_and_execute(opt: Namespace, selections: Namespace):
|
||||
models_to_remove = [
|
||||
x for x in selections.starter_models if not selections.starter_models[x]
|
||||
]
|
||||
models_to_install = [
|
||||
x for x in selections.starter_models if selections.starter_models[x]
|
||||
]
|
||||
models_to_install = selections.install_models
|
||||
models_to_remove = selections.remove_models
|
||||
directory_to_scan = selections.scan_directory
|
||||
scan_at_startup = selections.autoscan_on_startup
|
||||
potential_models_to_install = selections.import_model_paths
|
||||
|
||||
install_requested_models(
|
||||
install_initial_models=models_to_install,
|
||||
remove_models=models_to_remove,
|
||||
scan_directory=Path(directory_to_scan) if directory_to_scan else None,
|
||||
external_models=potential_models_to_install,
|
||||
scan_at_startup=scan_at_startup,
|
||||
precision="float32"
|
||||
if opt.full_precision
|
||||
else choose_precision(torch.device(choose_torch_device())),
|
||||
purge_deleted=selections.purge_deleted_models,
|
||||
config_file_path=Path(opt.config_file) if opt.config_file else None,
|
||||
)
|
||||
print('NOT INSTALLING MODELS DURING DEBUGGING')
|
||||
print('models to install:',models_to_install)
|
||||
print('models to remove:',models_to_remove)
|
||||
print('CN models to install:',selections.install_cn_models)
|
||||
print('CN models to remove:',selections.remove_cn_models)
|
||||
# install_requested_models(
|
||||
# install_initial_models=models_to_install,
|
||||
# remove_models=models_to_remove,
|
||||
# scan_directory=Path(directory_to_scan) if directory_to_scan else None,
|
||||
# external_models=potential_models_to_install,
|
||||
# scan_at_startup=scan_at_startup,
|
||||
# precision="float32"
|
||||
# if opt.full_precision
|
||||
# else choose_precision(torch.device(choose_torch_device())),
|
||||
# purge_deleted=selections.purge_deleted_models,
|
||||
# config_file_path=Path(opt.config_file) if opt.config_file else None,
|
||||
# )
|
||||
|
||||
|
||||
# --------------------------------------------------------
|
||||
@ -453,8 +512,9 @@ def main():
|
||||
opt = parser.parse_args()
|
||||
|
||||
# setting a global here
|
||||
config.root = Path(opt.root or '')
|
||||
|
||||
if opt.root and Path(opt.root).exists():
|
||||
config.root = Path(opt.root)
|
||||
|
||||
if not (config.root_dir / config.conf_path.parent).exists():
|
||||
logger.info(
|
||||
"Your InvokeAI root directory is not set up. Calling invokeai-configure."
|
||||
|
||||
Loading…
Reference in New Issue
Block a user