add support for mps device

README.md

@@ -63,13 +63,14 @@
 
 ## <a name="update"></a>:new:Update
 
-- **2023.09.14**: Integrate a patch-based sampling strategy ([mixture-of-diffusers](https://github.com/albarji/mixture-of-diffusers)). [**Try it!**](#general_image_inference) Here is an [example](https://imgsli.com/MjA2MDA1) with a resolution of 2396 x 1596. GPU memory usage will continue to be optimized in the future and we are looking forward to your pull requests!
-- **2023.09.14**: Add support for background upsampler(DiffBIR/[RealESRGAN](https://github.com/xinntao/Real-ESRGAN)) in face enhancement! :rocket: [**Try it!** >](#unaligned_face_inference)
-- **2023.09.13**: Provide online demo (DiffBIR-official) in [OpenXLab](https://openxlab.org.cn/apps/detail/linxinqi/DiffBIR-official), which integrates both general model and face model. Please have a try! [camenduru](https://github.com/camenduru) also implements an online demo, thanks for his work.:hugs:
-- **2023.09.12**: Upload inference code of latent image guidance and release [real47](inputs/real47) testset.
-- **2023.09.08**: Add support for restoring unaligned faces.
-- **2023.09.06**: Update [colab demo](https://colab.research.google.com/github/camenduru/DiffBIR-colab/blob/main/DiffBIR_colab.ipynb). Thanks to [camenduru](https://github.com/camenduru)!:hugs:
-- **2023.08.30**: Repo is released.
+- **2023.09.19**: ✅ Add support to inference on **MPS/CPU** device for Apple Silicon! Check [installation_xOS.md](assets/docs/installation_xOS.md).
+- **2023.09.14**: ✅ Integrate a patch-based sampling strategy ([mixture-of-diffusers](https://github.com/albarji/mixture-of-diffusers)). [**Try it!**](#general_image_inference) Here is an [example](https://imgsli.com/MjA2MDA1) with a resolution of 2396 x 1596. GPU memory usage will continue to be optimized in the future and we are looking forward to your pull requests!
+- **2023.09.14**: ✅ Add support for background upsampler(DiffBIR/[RealESRGAN](https://github.com/xinntao/Real-ESRGAN)) in face enhancement! :rocket: [**Try it!** >](#unaligned_face_inference)
+- **2023.09.13**: :rocket: Provide online demo (DiffBIR-official) in [OpenXLab](https://openxlab.org.cn/apps/detail/linxinqi/DiffBIR-official), which integrates both general model and face model. Please have a try! [camenduru](https://github.com/camenduru) also implements an online demo, thanks for his work.:hugs:
+- **2023.09.12**: ✅ Upload inference code of latent image guidance and release [real47](inputs/real47) testset.
+- **2023.09.08**: ✅ Add support for restoring unaligned faces.
+- **2023.09.06**: :rocket: Update [colab demo](https://colab.research.google.com/github/camenduru/DiffBIR-colab/blob/main/DiffBIR_colab.ipynb). Thanks to [camenduru](https://github.com/camenduru)!:hugs:
+- **2023.08.30**: This repo is released.
 <!-- - [**History Updates** >]() -->
 
 
@@ -83,7 +84,7 @@
 - [x] Add a patch-based sampling schedule:mag:.
 - [x] Upload inference code of latent image guidance:page_facing_up:.
 - [ ] Improve the performance:superhero:.
-- [ ] Support MPS acceleration for MacOS users.
+- [x] Support MPS acceleration for MacOS users.
 
 ## <a name="installation"></a>:gear:Installation
 <!-- - **Python** >= 3.9
@@ -171,7 +172,8 @@ python inference.py \
 Remove the brackets to enable tiled sampling. If you are confused about where the `reload_swinir` option came from, please refer to the [degradation details](#degradation-details).
 
 #### Face Image
-Download [face_full_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_full_v1.ckpt) to `weights/` and run the following command.
+<!-- Download [face_full_v1.ckpt](https://huggingface.co/lxq007/DiffBIR/resolve/main/face_full_v1.ckpt) to `weights/` and run the following command. -->
+<!-- The model can be downloaded from the internet automatically. -->
 
 ```shell
 # for aligned face inputs

assets/docs/installation_xOS.md

@@ -8,53 +8,44 @@ You can choose to run on **CPU** without `xformers` and `triton` installed.
 To use **CUDA**, please refer to [issue#24](https://github.com/XPixelGroup/DiffBIR/issues/24) to try solve the problem of `triton` installation.
 
 # MacOS
-Currenly only CPU device is supported to run DiffBIR on Apple Silicon since most GPU acceleration packages are compatiable with CUDA only. 
-
-We are still trying to support MPS device. Stay tuned for our progress!
-
-You can try to set up according to the following steps.
-
-1. Install **torch** according to the [official document](https://pytorch.org/get-started/locally/).
-
-```bash
-pip install torch torchvision
-```
-
-2. Package `triton` and `xformers` is not needed since they work with CUDA.
-
-Remove torch & cuda related packages. Your requirements.txt looks like:
-```bash
-# requirements.txt
-pytorch_lightning==1.4.2
-einops
-open-clip-torch
-omegaconf
-torchmetrics==0.6.0
-opencv-python-headless
-scipy
-matplotlib
-lpips
-gradio
-chardet
-transformers
-facexlib
-```
-
-```bash
-pip install -r requirements.txt
-```
-
-3. Run the inference script using CPU. Ensure you've downloaded the model weights.
-```bash
-python inference.py \
---input inputs/demo/general \
---config configs/model/cldm.yaml \
---ckpt weights/general_full_v1.ckpt \
---reload_swinir --swinir_ckpt weights/general_swinir_v1.ckpt \
---steps 50 \
---sr_scale 4 \
---image_size 512 \
---color_fix_type wavelet --resize_back \
---output results/demo/general \
---device cpu
-```
\ No newline at end of file
+<!-- Currenly only CPU device is supported to run DiffBIR on Apple Silicon since most GPU acceleration packages are compatiable with CUDA only. 
+
+We are still trying to support MPS device. Stay tuned for our progress! -->
+
+You can try to set up according to the following steps to use CPU or MPS device.
+
+1. Install **torch (Preview/Nighly version)**.
+
+    ```bash
+    # MPS acceleration is available on MacOS 12.3+
+    pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
+    ```
+    Check more details in [official document](https://pytorch.org/get-started/locally/).
+
+2. Package `triton` and `xformers` is not needed since they work with CUDA. Remove the related packages. 
+
+    Your requirements.txt should look like:
+    ```bash
+    # requirements.txt
+    pytorch_lightning==1.4.2
+    einops
+    open-clip-torch
+    omegaconf
+    torchmetrics==0.6.0
+    opencv-python-headless
+    scipy
+    matplotlib
+    lpips
+    gradio
+    chardet
+    transformers
+    facexlib
+    ```
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+3. [Run the inference script](https://github.com/XPixelGroup/DiffBIR#general_image_inference) and specify `--device cpu` or `--device mps`. Using MPS can accelarate your inference.
+
+    You can specify `--tiled` and related arguments to avoid OOM. 
\ No newline at end of file

inference.py

@@ -127,17 +127,39 @@ def parse_args() -> Namespace:
     parser.add_argument("--skip_if_exist", action="store_true")
     
     parser.add_argument("--seed", type=int, default=231)
-    parser.add_argument("--device", type=str, default="cuda", choices=["cpu", "cuda"])
+    parser.add_argument("--device", type=str, default="cuda", choices=["cpu", "cuda", "mps"])
     
     return parser.parse_args()
 
+def check_device(device):
+    if device == "cuda":
+        # check if CUDA is available
+        if not torch.cuda.is_available():
+            print("CUDA not available because the current PyTorch install was not "
+                "built with CUDA enabled.")
+            device = "cpu"
+    else:
+        # xformers only support CUDA. Disable xformers when using cpu or mps.
+        disable_xformers()
+        if device == "mps":
+            # check if MPS is available
+            if not torch.backends.mps.is_available():
+                if not torch.backends.mps.is_built():
+                    print("MPS not available because the current PyTorch install was not "
+                        "built with MPS enabled.")
+                    device = "cpu"
+                else:
+                    print("MPS not available because the current MacOS version is not 12.3+ "
+                        "and/or you do not have an MPS-enabled device on this machine.")
+                    device = "cpu"
+    print(f'using device {device}')
+    return device
 
 def main() -> None:
     args = parse_args()
     pl.seed_everything(args.seed)
     
-    if args.device == "cpu":
-        disable_xformers()
+    args.device = check_device(args.device)
     
     model: ControlLDM = instantiate_from_config(OmegaConf.load(args.config))
     load_state_dict(model, torch.load(args.ckpt, map_location="cpu"), strict=True)

inference_face.py

@@ -15,7 +15,7 @@ from utils.image import auto_resize, pad
 from utils.file import load_file_from_url
 from utils.face_restoration_helper import FaceRestoreHelper
 
-from inference import process
+from inference import process, check_device
 
 pretrained_models = {
     'general_v1': {
@@ -54,7 +54,9 @@ def parse_args() -> Namespace:
 
     # Loading two DiffBIR models requires huge GPU memory capacity. Choose RealESRGAN as an alternative.
     parser.add_argument('--bg_upsampler', type=str, default='RealESRGAN', choices=['DiffBIR', 'RealESRGAN'], help='Background upsampler.')
+    # TODO: support tiled for DiffBIR background upsampler
     parser.add_argument('--bg_tile', type=int, default=400, help='Tile size for background sampler.')
+    parser.add_argument('--bg_tile_stride', type=int, default=200, help='Tile stride for background sampler.')
     
     # postprocessing and saving
     parser.add_argument("--color_fix_type", type=str, default="wavelet", choices=["wavelet", "adain", "none"])
@@ -64,8 +66,7 @@ def parse_args() -> Namespace:
     
     # change seed to finte-tune your restored images! just specify another random number.
     parser.add_argument("--seed", type=int, default=231)
-    # TODO: support mps device for MacOS devices
-    parser.add_argument("--device", type=str, default="cuda", choices=["cpu", "cuda"])
+    parser.add_argument("--device", type=str, default="cuda", choices=["cpu", "cuda", "mps"])
     
     return parser.parse_args()
 
@@ -115,7 +116,7 @@ def main() -> None:
     
     assert os.path.isdir(args.input)
 
-    auto_xformers_status(args.device)
+    args.device = check_device(args.device)
     model = build_diffbir_model(args.config, args.ckpt, args.swinir_ckpt).to(args.device)
 
     # ------------------ set up FaceRestoreHelper -------------------
@@ -131,15 +132,12 @@ def main() -> None:
     if args.bg_upsampler == 'DiffBIR':
         # Loading two DiffBIR models consumes huge GPU memory capacity.
         bg_upsampler = build_diffbir_model(args.config, 'weights/general_full_v1.pth')
-        # try:
         bg_upsampler = bg_upsampler.to(args.device)
-        # except:
-        #     # put the bg_upsampler on cpu to avoid OOM
-        #     gpu_alternate = True
     elif args.bg_upsampler == 'RealESRGAN':
         from utils.realesrgan.realesrganer import set_realesrgan
-        # support official RealESRGAN x2 & x4 upsample model
-        bg_upscale = int(args.sr_scale) if int(args.sr_scale) in [2, 4] else 4
+        # support official RealESRGAN x2 & x4 upsample model.
+        # Using x2 upsampler as default if scale is not specified as 4.
+        bg_upscale = int(args.sr_scale) if int(args.sr_scale) in [2, 4] else 2
         print(f'Loading RealESRGAN_x{bg_upscale}plus.pth for background upsampling...')
         bg_upsampler = set_realesrgan(args.bg_tile, args.device, bg_upscale)
     else:

model/spaced_sampler.py

@@ -74,7 +74,13 @@ def _extract_into_tensor(arr, timesteps, broadcast_shape):
                             dimension equal to the length of timesteps.
     :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
     """
-    res = torch.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    try:
+        # float64 as default. float64 is not supported by mps device.
+        res = torch.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
+    except:
+        # to be compatiable with mps
+        res = torch.from_numpy(arr.astype(np.float32)).to(device=timesteps.device)[timesteps].float()
+        
     while len(res.shape) < len(broadcast_shape):
         res = res[..., None]
     return res.expand(broadcast_shape)

utils/face_restoration_helper.py

@@ -6,9 +6,9 @@ from torchvision.transforms.functional import normalize
 
 from facexlib.detection import init_detection_model
 from facexlib.parsing import init_parsing_model
-from facexlib.utils.misc import img2tensor, imwrite # , adain_npy, isgray, bgr2gray, 
-from basicsr.utils.download_util import load_file_from_url
-# from basicsr.utils.misc import get_device
+from facexlib.utils.misc import img2tensor, imwrite
+
+from .file import load_file_from_url
 
 def get_largest_face(det_faces, h, w):