diff --git a/docs/installation/050_INSTALLING_MODELS.md b/docs/installation/050_INSTALLING_MODELS.md index d1c0cfacca..d455d2146f 100644 --- a/docs/installation/050_INSTALLING_MODELS.md +++ b/docs/installation/050_INSTALLING_MODELS.md @@ -171,3 +171,16 @@ subfolders and organize them as you wish. The location of the autoimport directories are controlled by settings in `invokeai.yaml`. See [Configuration](../features/CONFIGURATION.md). + +### Installing models that live in HuggingFace subfolders + +On rare occasions you may need to install a diffusers-style model that +lives in a subfolder of a HuggingFace repo id. In this event, simply +add ":_subfolder-name_" to the end of the repo id. For example, if the +repo id is "monster-labs/control_v1p_sd15_qrcode_monster" and the model +you wish to fetch lives in a subfolder named "v2", then the repo id to +pass to the various model installers should be + +``` +monster-labs/control_v1p_sd15_qrcode_monster:v2 +``` diff --git a/invokeai/backend/install/model_install_backend.py b/invokeai/backend/install/model_install_backend.py index 8ce5dc5322..073bdf28d2 100644 --- a/invokeai/backend/install/model_install_backend.py +++ b/invokeai/backend/install/model_install_backend.py @@ -2,6 +2,7 @@ Utility (backend) functions used by model_install.py """ import os +import re import shutil import warnings from dataclasses import dataclass, field @@ -88,6 +89,7 @@ class ModelLoadInfo: base_type: BaseModelType path: Optional[Path] = None repo_id: Optional[str] = None + subfolder: Optional[str] = None description: str = "" installed: bool = False recommended: bool = False @@ -126,7 +128,10 @@ class ModelInstall(object): value["name"] = name value["base_type"] = base value["model_type"] = model_type - model_dict[key] = ModelLoadInfo(**value) + model_info = ModelLoadInfo(**value) + if model_info.subfolder and model_info.repo_id: + model_info.repo_id += f":{model_info.subfolder}" + model_dict[key] = model_info # supplement with entries in models.yaml installed_models = [x for x in self.mgr.list_models()] @@ -317,46 +322,63 @@ class ModelInstall(object): return self._install_path(Path(models_path), info) def _install_repo(self, repo_id: str) -> AddModelResult: + # hack to recover models stored in subfolders -- + # Required to get the "v2" model of monster-labs/control_v1p_sd15_qrcode_monster + subfolder = None + if match := re.match(r"^([^/]+/[^/]+):(\w+)$", repo_id): + repo_id = match.group(1) + subfolder = match.group(2) + hinfo = HfApi().model_info(repo_id) # we try to figure out how to download this most economically # list all the files in the repo files = [x.rfilename for x in hinfo.siblings] + if subfolder: + files = [x for x in files if x.startswith("v2/")] + prefix = f"{subfolder}/" if subfolder else "" + location = None with TemporaryDirectory(dir=self.config.models_path) as staging: staging = Path(staging) - if "model_index.json" in files: - location = self._download_hf_pipeline(repo_id, staging) # pipeline - elif "unet/model.onnx" in files: + if f"{prefix}model_index.json" in files: + location = self._download_hf_pipeline(repo_id, staging, subfolder=subfolder) # pipeline + elif f"{prefix}unet/model.onnx" in files: location = self._download_hf_model(repo_id, files, staging) else: for suffix in ["safetensors", "bin"]: - if f"pytorch_lora_weights.{suffix}" in files: - location = self._download_hf_model(repo_id, ["pytorch_lora_weights.bin"], staging) # LoRA + if f"{prefix}pytorch_lora_weights.{suffix}" in files: + location = self._download_hf_model( + repo_id, ["pytorch_lora_weights.bin"], staging, subfolder=subfolder + ) # LoRA break elif ( - self.config.precision == "float16" and f"diffusion_pytorch_model.fp16.{suffix}" in files + self.config.precision == "float16" and f"{prefix}diffusion_pytorch_model.fp16.{suffix}" in files ): # vae, controlnet or some other standalone files = ["config.json", f"diffusion_pytorch_model.fp16.{suffix}"] - location = self._download_hf_model(repo_id, files, staging) + location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder) break - elif f"diffusion_pytorch_model.{suffix}" in files: + elif f"{prefix}diffusion_pytorch_model.{suffix}" in files: files = ["config.json", f"diffusion_pytorch_model.{suffix}"] - location = self._download_hf_model(repo_id, files, staging) + location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder) break - elif f"learned_embeds.{suffix}" in files: - location = self._download_hf_model(repo_id, [f"learned_embeds.{suffix}"], staging) + elif f"{prefix}learned_embeds.{suffix}" in files: + location = self._download_hf_model( + repo_id, [f"learned_embeds.{suffix}"], staging, subfolder=subfolder + ) break - elif "image_encoder.txt" in files and f"ip_adapter.{suffix}" in files: # IP-Adapter + elif ( + f"{prefix}image_encoder.txt" in files and f"{prefix}ip_adapter.{suffix}" in files + ): # IP-Adapter files = ["image_encoder.txt", f"ip_adapter.{suffix}"] - location = self._download_hf_model(repo_id, files, staging) + location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder) break - elif f"model.{suffix}" in files and "config.json" in files: + elif f"{prefix}model.{suffix}" in files and f"{prefix}config.json" in files: # This elif-condition is pretty fragile, but it is intended to handle CLIP Vision models hosted # by InvokeAI for use with IP-Adapters. files = ["config.json", f"model.{suffix}"] - location = self._download_hf_model(repo_id, files, staging) + location = self._download_hf_model(repo_id, files, staging, subfolder=subfolder) break if not location: logger.warning(f"Could not determine type of repo {repo_id}. Skipping install.") @@ -443,9 +465,9 @@ class ModelInstall(object): else: return path - def _download_hf_pipeline(self, repo_id: str, staging: Path) -> Path: + def _download_hf_pipeline(self, repo_id: str, staging: Path, subfolder: str = None) -> Path: """ - This retrieves a StableDiffusion model from cache or remote and then + Retrieve a StableDiffusion model from cache or remote and then does a save_pretrained() to the indicated staging area. """ _, name = repo_id.split("/") @@ -460,6 +482,7 @@ class ModelInstall(object): variant=variant, torch_dtype=precision, safety_checker=None, + subfolder=subfolder, ) except Exception as e: # most errors are due to fp16 not being present. Fix this to catch other errors if "fp16" not in str(e): @@ -474,7 +497,7 @@ class ModelInstall(object): model.save_pretrained(staging / name, safe_serialization=True) return staging / name - def _download_hf_model(self, repo_id: str, files: List[str], staging: Path) -> Path: + def _download_hf_model(self, repo_id: str, files: List[str], staging: Path, subfolder: None) -> Path: _, name = repo_id.split("/") location = staging / name paths = list() @@ -485,7 +508,7 @@ class ModelInstall(object): model_dir=location / filePath.parent, model_name=filePath.name, access_token=self.access_token, - subfolder=filePath.parent, + subfolder=filePath.parent / subfolder if subfolder else filePath.parent, ) if p: paths.append(p) diff --git a/invokeai/configs/INITIAL_MODELS.yaml b/invokeai/configs/INITIAL_MODELS.yaml index f3dbc11c2a..9d27f37c4d 100644 --- a/invokeai/configs/INITIAL_MODELS.yaml +++ b/invokeai/configs/INITIAL_MODELS.yaml @@ -60,6 +60,9 @@ sd-1/main/trinart_stable_diffusion_v2: description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB) repo_id: naclbit/trinart_stable_diffusion_v2 recommended: False +sd-1/controlnet/qrcode_monster: + repo_id: monster-labs/control_v1p_sd15_qrcode_monster + subfolder: v2 sd-1/controlnet/canny: repo_id: lllyasviel/control_v11p_sd15_canny recommended: True