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enable downloading from subfolders for repo_ids (#4725)

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[## What type of PR is this? (check all applicable)

- [X] Feature

## Have you discussed this change with the InvokeAI team?
- [X] Yes
      
## Have you updated all relevant documentation?
- [X] Yes

## Description

Very rarely a model lives in the subfolder of a non-pipeline HuggingFace
repo_id. The example I've been working with is
https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster/tree/main,
where the improved monster QR code controlnet model lives in the `v2`
subdirectory.

In order to accommodate installing such files, I have made two changes
to the model installer.

1. At installation/configuration time, if a stanza in
`INITIAL_MODELS.yaml` contains the field `subfolder`, then the model
will be installed from the indicated subfolder. The syntax in this case
is:
```
sd-1/controlnet/qrcode_monster:
   repo_id: monster-labs/control_v1p_sd15_qrcode_monster
   subfolder: v2
```
2. From within the Web GUI or the installer TUI, if you wish to indicate
that the model resides in a subfolder, you can tack ":_subfoldername_"
to the end of the repo_id. The resulting repo_id will look like:
```
monster-labs/control_v1p_sd15_qrcode_monster:v2
```

The code for introducing these changes is obscure and somewhat hacky.
However, the whole installer code base has been rewritten for the model
manager refactor (#4252 ) and I will reimplement this feature in a more
elegant way in that PR.
This commit is contained in:
Millun Atluri 2023-09-28 15:26:18 +10:00 committed by GitHub
commit 309e2414ce
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3 changed files with 59 additions and 20 deletions
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13
docs/installation/050_INSTALLING_MODELS.md
View File

@ -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
```

63
invokeai/backend/install/model_install_backend.py
View File

@ -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)

3
invokeai/configs/INITIAL_MODELS.yaml
View File

@ -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