Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix merge conflicts with main

Browse Source
This commit is contained in:
Lincoln Stein
2024-04-15 09:24:57 -04:00
parent f1e79d5a8f e93f4d632d
commit 470a39935c
47 changed files with 476 additions and 277 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
invokeai/backend/image_util/depth_anything/__init__.py
View File

@ -12,7 +12,7 @@ from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.devices import TorchDevice
from invokeai.backend.util.logging import InvokeAILogger
config = get_config()
@ -47,7 +47,7 @@ class DepthAnythingDetector:
self.context = context
self.model: Optional[DPT_DINOv2] = None
self.model_size: Union[Literal["large", "base", "small"], None] = None
self.device = choose_torch_device()
self.device = TorchDevice.choose_torch_device()
def load_model(self, model_size: Literal["large", "base", "small"] = "small") -> DPT_DINOv2:
depth_anything_model_path = self.context.models.download_and_cache_ckpt(DEPTH_ANYTHING_MODELS[model_size])
@ -68,7 +68,7 @@ class DepthAnythingDetector:
self.model.load_state_dict(torch.load(depth_anything_model_path.as_posix(), map_location="cpu"))
self.model.eval()
self.model.to(choose_torch_device())
self.model.to(self.device)
return self.model
def __call__(self, image: Image.Image, resolution: int = 512) -> Image.Image:
@ -81,7 +81,7 @@ class DepthAnythingDetector:
image_height, image_width = np_image.shape[:2]
np_image = transform({"image": np_image})["image"]
tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(choose_torch_device())
tensor_image = torch.from_numpy(np_image).unsqueeze(0).to(self.device)
with torch.no_grad():
depth = self.model(tensor_image)

7
invokeai/backend/image_util/dw_openpose/wholebody.py
View File

@ -4,11 +4,10 @@
import numpy as np
import onnxruntime as ort
import torch
from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.devices import TorchDevice
from .onnxdet import inference_detector
from .onnxpose import inference_pose
@ -23,9 +22,9 @@ config = get_config()
class Wholebody:
def __init__(self, context: InvocationContext):
device = choose_torch_device()
device = TorchDevice.choose_torch_device()
providers = ["CUDAExecutionProvider"] if device == torch.device("cuda") else ["CPUExecutionProvider"]
providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
onnx_det = context.models.download_and_cache_ckpt(DWPOSE_MODELS["yolox_l.onnx"])
onnx_pose = context.models.download_and_cache_ckpt(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"])

4
invokeai/backend/image_util/realesrgan/realesrgan.py
View File

@ -11,7 +11,7 @@ from tqdm import tqdm
from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
from invokeai.backend.model_manager.config import AnyModel
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.devices import TorchDevice
"""
Adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
@ -65,7 +65,7 @@ class RealESRGAN:
self.pre_pad = pre_pad
self.mod_scale: Optional[int] = None
self.half = half
self.device = choose_torch_device()
self.device = TorchDevice.choose_torch_device()
# prefer to use params_ema
if "params_ema" in loadnet:

4
invokeai/backend/image_util/safety_checker.py
View File

@ -13,7 +13,7 @@ from transformers import AutoFeatureExtractor
import invokeai.backend.util.logging as logger
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.devices import TorchDevice
from invokeai.backend.util.silence_warnings import SilenceWarnings
CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
@ -51,7 +51,7 @@ class SafetyChecker:
cls._load_safety_checker()
if cls.safety_checker is None or cls.feature_extractor is None:
return False
device = choose_torch_device()
device = TorchDevice.choose_torch_device()
features = cls.feature_extractor([image], return_tensors="pt")
features.to(device)
cls.safety_checker.to(device)