diff --git a/README.md b/README.md
index a0f6de6..0b392b7 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,24 @@
 # XFusion
 Deep learning-based spatiotemporal fusion for high-fidelity ultra-high-speed x-ray radiography  
 A model to reconstruct high quality x-ray images by combining the high spatial resolution of high-speed camera and high temporal resolution of ultra-high-speed camera image sequences.  
+Models and methods for spatial and temporal calibration are also included.
 
 ## Prerequisites
-This implementation is based on the [BasicSR toolbox](https://github.com/XPixelGroup/BasicSR). Data for model pre-training are collected from the [REDS dataset](https://seungjunnah.github.io/Datasets/reds).  
+This implementation is based on the [BasicSR toolbox](https://github.com/XPixelGroup/BasicSR) and the [RAFT model](https://github.com/princeton-vl/RAFT). Data for model pre-training are collected from the [REDS dataset](https://seungjunnah.github.io/Datasets/reds).  
 
 ## Usage
 ### Package description
-Currently, xfusion supports 2 model familities for high-quality xray image sequence reconstruction- the EDVR and Swin vision transformer, respectively.
+Currently, xfusion supports 2 model familities for high-quality xray image sequence reconstruction- the EDVR and Swin vision transformer, respectively.  
+In addition, xfusion is functional with x-ray image sequences from both the actual (data type: actual) and virtual (data type: virtual) experiments.  
+* Actual experiments utilize the dual-detector setup for data acquisition and virtual experiments synthetize dual-detector data from single-camera experiments.  
+
+With the actual dual-detector experimental data, xfusion first conducts the spatial and temporal calibration of the two image sequences.  
+* Temporal calibration is specific to the choice of the Phantom TMX7510 (Vision Research Inc., USA) camera and Shimadzu HPV-X2 (Shimadzu Corp., Japan) camera.  
+* Spatial calibration is based on the use of an electroformed copper grid (G400, Gilder Grids Ltd., UK).  
+
+Then at inference time, xfusion traverses the dual-camera image sequences in two ways: continuous and double-interval.  
+* Under the continuous mode, high-quality xray images corresponding to each Shimadzu camera image are reconstructed.  
+* Under the double-interval mode, independent high-quality xray images corresponding to each Phantom camera image are reconstructed to evaluate performance metrics.
 
 ### Package installation
 Navigate to the project root directory and then run
@@ -19,7 +30,7 @@ to install the package to the selected virtual environment.
 ### Initialization
 Run
 <pre>
-xfusion init --model_type <i>[EDVRModel or SwinIRModel]</i>
+xfusion init --model_type <i>[EDVRModel or SwinIRModel]</i> --data_type <i>[virtual or actual]</i>
 </pre>
 After initialization, a configuration file "xfusion.conf" will be generated in the home directory. This configuration file will be updated automatically within the workflow of the xfusion package.
 
@@ -38,9 +49,27 @@ xfusion convert --dir-lo-convert <i>[directory/to/low resolution/RGB/training im
 </pre>
 
 ### Training
-Run
+To train the model with data from the virtual experiments, run
+<pre>
+xfusion train --dir-lo-train <i>[directory/to/low resolution/gray-scale/training image]</i> --dir-hi-train <i>[directory/to/high resolution/gray-scale/training image]</i> --dir-lo-val <i>[directory/to/low resolution/gray-scale/validation image]</i> --dir-hi-val <i>[directory/ti/high resolution/gray-scale/validation image]</i> --opt <i>directory/to/training setting/yaml file</i> --path-train-meta-info-file <i>[directory/to/training image/meta data]</i> --path-val-meta-info-file <i>[directory/to/validation image/meta data]</i> --pretrain_network_g <i>[path/to/model weight/file/for/model initialization]</i>
+</pre>
+To train the model with data from the actual experiments, run
+<pre>
+xfusion train --dir-lo-train <i>[directory/to/low resolution/gray-scale/paired training image]</i> --dir-hi-train <i>[directory/to/high resolution/gray-scale/paired training image]</i> --dir-lo-val <i>[directory/to/low resolution/gray-scale/paired validation image]</i> --dir-hi-val <i>[directory/ti/high resolution/gray-scale/paired validation image]</i> --dataroot_context <i>[directory/to/low resolution/gray-scale/training image full]</i> --opt <i>directory/to/training setting/yaml file</i> --path-train-meta-info-file <i>[directory/to/training image/meta data]</i> --path-val-meta-info-file <i>[directory/to/validation image/meta data]</i> --meta_info_context <i>[directory/to/paired/high/lo/resolution/training image/index map]</i> --pretrain_network_g <i>[path/to/model weight/file/for/model initialization]</i>
+</pre>
+
+### Train calibration model
+To train the calibration model with data from the virtual experiments (pretrain), run
+<pre>
+xfusion train_calibration --image_root <i>[root/directory/to/training images]</i> --meta_info_file <i>[directory/to/training image/meta data]</i>
+--evaluator_weights <i>[path/to/spatiotemporal/fusion/model weight/file/for/training loss]</i>
+--flow_weights None --full_weights None
+</pre>
+To train the calibration model with data from the actual experiments (fine tune), run
 <pre>
-xfusion train --dir-lo-train <i>[directory/to/low resolution/gray-scale/training image]</i> --dir-hi-train <i>[directory/to/high resolution/gray-scale/training image]</i> --dir-lo-val <i>[directory/to/low resolution/gray-scale/validation image]</i> --dir-hi-val <i>[directory/ti/high resolution/gray-scale/validation image]</i> --opt <i>directory/to/training setting/yaml file</i> --path-train-meta-info-file <i>[directory/to/training image/meta data]</i> --path-val-meta-info-file <i>[directory/to/validation image/meta data]</i> --pretrain_network_g <i>[directory/to/model weight/file/for/model initialization]</i>
+xfusion train_calibration --image_root <i>[root/directory/to/training images]</i> --meta_info_file <i>[directory/to/training image/meta data]</i>
+--evaluator_weights <i>[path/to/spatiotemporal/fusion/model weight/file/for/training loss]</i>
+--full_weights <i>[path/to/pretrained/calibration/model weight/file/for/model initialization]</i>
 </pre>
 
 ### Test data
@@ -49,9 +78,28 @@ To download test data, run
 xfusion download --dir-inf <i>[tomobank/link/address/of/test/dataset]</i> --out-dir-inf <i>[directory/to/testing image]</i>
 </pre>
 
-### Inference
+### Spatial calibration
 Run
 <pre>
-xfusion inference --opt <i>directory/to/testing dataset/setting/yaml file</i> --arch-opt <i>directory/to/training setting/yaml file</i> --model_file <i>[path/to/model file]</i> --machine <i>tomo or polaris</i>
+xfusion calibrate --cal_model_file <i>[path/to/calibration/model file]</i> --cal_dir <i>[root/directory/to/calibration/experiment/data]</i>
+--cal_id <i>[calibration experiment subdirectory name]</i>
+</pre>
+
+### Inference
+For virtual experiment data, run
+<pre>
+xfusion inference --opt <i>directory/to/testing dataset/setting/yaml file</i> --arch-opt <i>directory/to/model architecture/yaml file</i> --img-class <i>dataset1 or dataset2</i> --model_file <i>[path/to/model file]</i> --machine <i>tomo or polaris</i>
 </pre>
-Currently to work for EDVRModel in the single-process mode and SwinIRModel in the multi-process mode.
+For actual experiment data, run
+<pre>
+xfusion inference --arch-opt <i>directory/to/model architecture/yaml file</i> --img-class <i>name of high-speed experiment</i> --model_file <i>[path/to/model file]</i> --traverse_mode <i>double or continuous</i> --tfm_file <i>[path/to/rigid/body/spatial/transformation/file]</i> --meta_file_scale <i>[path/to/nonuniform/scaling/parameters]</i> --input_dir <i>[root/directory/to/high/speed/experiment/data]</i> --case_list <i>experiment subdirectory names</i>
+</pre>
+
+### Suggested usage
+* For virtual experiment data, currently xfusion works for EDVRModel in the single-process mode and SwinIRModel in the multi-process mode.  
+When working on the virtual experiment data, first initialize the software, and then do the inference.  
+
+* For actual experiment data, xfusion works for both models in a unified way.  
+Do an independent initialization every time the model (EDVR-STF or SWIN-XVR) or the type of data (virtual or actual) changes.  
+When working on the actual experiment data, first initialize the software, then do the spatial calibration, and last do the inference.  
+Multiple experiments sharing the same spatial calibration can be grouped to run the model inference.
diff --git a/xfusion/config.py b/xfusion/config.py
index ea144f0..d571050 100644
--- a/xfusion/config.py
+++ b/xfusion/config.py
@@ -20,11 +20,19 @@
            'parse_known_args',
            'write']
 def list_of_ints(arg):
+    if ',' in arg:
+        return list(map(int,arg.split(',')))
+    else:
+        if arg.lower() != 'none':
+            return [int(arg)]
+        else:
+            return None
+
+def list_of_strs(arg):
     if ',' in arg:
         return list(arg.split(','))
     else:
         return [arg]
-
 CONFIG_FILE_NAME = os.path.join(str(pathlib.Path.home()), 'xfusion.conf')
 XFUSION_HOME = os.path.join(str(pathlib.Path.home()), 'xfusion')
 XFUSION_TRAIN_HOME = os.path.join(str(XFUSION_HOME), 'train')
@@ -188,10 +196,49 @@ def list_of_ints(arg):
         'help': 'Relative frame separation in the hr images after and before the augmentation'
     },
     'poisson_noise_b0_exp': {
-        'default': [],
+        'default': 'none',
         'type': list_of_ints,
         'help': 'upper and lower bounds of the blank scan factor in log scale'
     },
+     # newly added ddp configs
+    'iter_div_factor': {
+            'default': 10,
+            'type': int,
+            'help': "Divisor of the total number of iterations"
+    },
+    'batch_size': {
+            'default': 1,
+            'type': int,
+            'help': "Number of samples in a batch"
+    },
+    'num_workers': {
+            'default': 3,
+            'type': int,
+            'help': "Number of workers for data loader"},
+    'warmup_iter': {
+        'default': -1,
+        'type': int,
+        'help': "Number of iterations for training warm up"},
+    'dataset_enlarge_ratio': {
+        'default': 10.0,
+        'type': float,
+        'help': "Multiplier of the original number of training samples"},
+    'sub_dataset': {
+        'default': False,
+        'type': lambda x: bool(strtobool(x)),
+        'help': "When set the original training data are randomly sub-sampled for training"},
+    'sub_dataset_frac': {
+        'default': 1.0,
+        'type': float,
+        'help': "Fraction of the original training data set size to sub-sample"},
+    'single_epoch_ok': {
+        'default': False,
+        'type': lambda x: bool(strtobool(x)),
+        'help': "If to only train data for one epoch"},
+    'profiler_iter_num': {
+        'default': 3,
+        'type': int,
+        'help': "Number of subsequent iterations to track computation performance for HTA"},
   }
 
 MODEL_TYPES = {'train':OrderedDict(),'inference':OrderedDict()}
@@ -225,50 +272,17 @@ def list_of_ints(arg):
             'type': int,
             'help': "Size of convolution window if used in the vit residual connection"},
         # newly added ddp configs
-        'iter_div_factor': {
-            'default': 10,
-            'type': int,
-            'help': "Divisor of the total number of iterations"},
+        
         'num_frame': {
             'default': 3,
             'type': int,
             'help': "Number of input low resolution frames to the model"},
-        'batch_size': {
-            'default': 1,
-            'type': int,
-            'help': "Number of samples in a batch"},
+        
         'initial_lr': {
-            'default': 2e-4,
+            'default': 5e-6,
             'type': float,
             'help': "Initial learning rate"},
-        'num_workers': {
-            'default': 3,
-            'type': int,
-            'help': "Number of workers for data loader"},
-        'warmup_iter': {
-            'default': -1,
-            'type': int,
-            'help': "Number of iterations for training warm up"},
-        'dataset_enlarge_ratio': {
-            'default': 10.0,
-            'type': float,
-            'help': "Multiplier of the original number of training samples"},
-        'sub_dataset': {
-            'default': False,
-            'type': lambda x: bool(strtobool(x)),
-            'help': "When set the original training data are randomly sub-sampled for training"},
-        'sub_dataset_frac': {
-            'default': 1.0,
-            'type': float,
-            'help': "Fraction of the original training data set size to sub-sample"},
-        'single_epoch_ok': {
-            'default': False,
-            'type': lambda x: bool(strtobool(x)),
-            'help': "If to only train data for one epoch"},
-        'profiler_iter_num': {
-            'default': 3,
-            'type': int,
-            'help': "Number of subsequent iterations to track computation performance for HTA"},}
+        }
 
 MODEL_TYPES['train']['EDVRModel'] = {
     'initial_lr': {
@@ -326,7 +340,7 @@ def list_of_ints(arg):
         'default': 'none',
         'type': Path,
         'help': "When set the path to the model weights to initialize the model"},
-    'num_frame': {
+    'num_frame_infer': {
         'default': 3,
         'type': int,
         'help': "Number of input low resolution frames to the model"},
@@ -510,7 +524,7 @@ def list_of_ints(arg):
         'help': 'when set the model has separate encoders for the hr/lr input image tensors'
     },
     'cal_train_poisson_b0_exponent': {
-        'default': [],
+        'default': 'none',
         'type': list_of_ints,
         'help': 'upper and lower bounds of the blank scan factor in log scale'
     },
@@ -688,7 +702,7 @@ def list_of_ints(arg):
         'help': "Full path to the user experiment data"
     },
     'case_list': {'default': [],
-        'type': list_of_ints,
+        'type': list_of_strs,
         'help': "Names of the individual experiment subdirectories"
     },
     'device': {'default': 0,
diff --git a/xfusion/inference/infer_swin_ddp.py b/xfusion/inference/infer_swin_ddp.py
index f9c6858..4f14786 100644
--- a/xfusion/inference/infer_swin_ddp.py
+++ b/xfusion/inference/infer_swin_ddp.py
@@ -59,10 +59,10 @@ def run_inference(ts, args):
 
     opt['network_g']['embed_dim'] = args.embed_dim
     opt['network_g']['num_feat_ext'] = args.embed_dim
-    opt['network_g']['num_frame'] = args.num_frame
+    opt['network_g']['num_frame'] = args.num_frame_infer
     
     opt['network_g']['depths'] = [d * args.scale_depth for d in opt['network_g']['depths']]
-    opt['network_g']['window_size'][0] += (args.num_frame - 3)
+    opt['network_g']['window_size'][0] += (args.num_frame_infer - 3)
     opt['network_g']['window_size'][1:] = [args.window_size_spatial] * len(opt['network_g']['window_size'][1:])
     
     opt['network_g']['num_heads'] = [int(np.ceil(h * args.scale_mha)) for h in opt['network_g']['num_heads']]
@@ -75,7 +75,7 @@ def run_inference(ts, args):
     dataroot = config.get_inf_data_dirs(img_class)
     inf_home_dir = Path(dataroot).parent
 
-    out_dir = Path(inf_home_dir / f'{test_set_name}_{mode}_align_{args.align_features_ok}_prepostx_fixed_lr_r_{lo_frame_sep}_hr_d_{hi_frame_sep*2}_b0_{b0}_em_{args.embed_dim}_nf_{args.num_frame}_dep_{args.scale_depth}_nh_{args.scale_mha}_win_{args.window_size_spatial}_model_{args.model_file.parent.stem}_ddp_swinVRHP_rem1_{ts}')
+    out_dir = Path(inf_home_dir / f'{test_set_name}_{mode}_align_{args.align_features_ok}_prepostx_fixed_lr_r_{lo_frame_sep}_hr_d_{hi_frame_sep*2}_b0_{b0}_em_{args.embed_dim}_nf_{args.num_frame_infer}_dep_{args.scale_depth}_nh_{args.scale_mha}_win_{args.window_size_spatial}_model_{args.model_file.parent.stem}_ddp_swinVRHP_rem1_{ts}')
     out_dir.mkdir(exist_ok=True,parents=True)
 
     log_file = os.path.join(out_dir, f"inference.log")
@@ -129,7 +129,7 @@ def run_inference(ts, args):
         #    continue
         dataset_opt['scale'] = 4
         dataset_opt['gt_size'] = gt_size[:2]
-        dataset_opt['num_frame'] = args.num_frame
+        dataset_opt['num_frame'] = args.num_frame_infer
         #test_set = VideoTestDatasetSTF(dataset_opt)
         #test_set = build_dataset(dataset_opt)
         test_set = XrayVideoTestDatasetSTF(dataset_opt)
diff --git a/xfusion/inference/infer_with_auto_calibration_main4_continuous2.py b/xfusion/inference/infer_with_auto_calibration_main4_continuous2.py
index b5f2ec1..fa9ec71 100644
--- a/xfusion/inference/infer_with_auto_calibration_main4_continuous2.py
+++ b/xfusion/inference/infer_with_auto_calibration_main4_continuous2.py
@@ -79,7 +79,7 @@ def run_inference(args):
     exp_root = Path(config.get_inf_data_dirs(input_dir.name))
     device = args.device
     
-    opt_path = args.opt
+    opt_path = args.arch_opt
     opt = yaml_load(opt_path)
     opt['manual_seed'] = 10
     torch.manual_seed(opt['manual_seed'])
diff --git a/xfusion/train/basicsr/metrics/metric_util.py b/xfusion/train/basicsr/metrics/metric_util.py
index 06d81d4..79f4f6d 100644
--- a/xfusion/train/basicsr/metrics/metric_util.py
+++ b/xfusion/train/basicsr/metrics/metric_util.py
@@ -15,8 +15,6 @@
 
 import numpy as np
 
-from xfusion.train.basicsr.utils import bgr2ycbcr
-
 
 def reorder_image(img, input_order='HWC'):
     """Reorder images to 'HWC' order.
@@ -42,19 +40,3 @@ def reorder_image(img, input_order='HWC'):
     if input_order == 'CHW':
         img = img.transpose(1, 2, 0)
     return img
-
-
-def to_y_channel(img):
-    """Change to Y channel of YCbCr.
-
-    Args:
-        img (ndarray): Images with range [0, 255].
-
-    Returns:
-        (ndarray): Images with range [0, 255] (float type) without round.
-    """
-    img = img.astype(np.float32) / 255.
-    if img.ndim == 3 and img.shape[2] == 3:
-        img = bgr2ycbcr(img, y_only=True)
-        img = img[..., None]
-    return img * 255.
diff --git a/xfusion/train/basicsr/metrics/niqe.py b/xfusion/train/basicsr/metrics/niqe.py
index 74a6237..6e8d5c3 100644
--- a/xfusion/train/basicsr/metrics/niqe.py
+++ b/xfusion/train/basicsr/metrics/niqe.py
@@ -20,7 +20,7 @@
 from scipy.ndimage import convolve
 from scipy.special import gamma
 
-from xfusion.train.basicsr.metrics.metric_util import reorder_image, to_y_channel
+from xfusion.train.basicsr.metrics.metric_util import reorder_image
 from xfusion.train.basicsr.utils.matlab_functions import imresize
 from xfusion.train.basicsr.utils.registry import METRIC_REGISTRY
 
@@ -198,7 +198,7 @@ def calculate_niqe(img, crop_border, input_order='HWC', convert_to='y', **kwargs
     if input_order != 'HW':
         img = reorder_image(img, input_order=input_order)
         if convert_to == 'y':
-            img = to_y_channel(img)
+            raise Exception('not implemented yet...')
         elif convert_to == 'gray':
             img = cv2.cvtColor(img / 255., cv2.COLOR_BGR2GRAY) * 255.
         img = np.squeeze(img)
diff --git a/xfusion/train/basicsr/metrics/niqe_pris_params.npz b/xfusion/train/basicsr/metrics/niqe_pris_params.npz
deleted file mode 100644
index 204ddce..0000000
Binary files a/xfusion/train/basicsr/metrics/niqe_pris_params.npz and /dev/null differ
diff --git a/xfusion/train/basicsr/metrics/psnr_ssim.py b/xfusion/train/basicsr/metrics/psnr_ssim.py
index 25f4f42..423f276 100644
--- a/xfusion/train/basicsr/metrics/psnr_ssim.py
+++ b/xfusion/train/basicsr/metrics/psnr_ssim.py
@@ -18,8 +18,7 @@
 import torch
 import torch.nn.functional as F
 
-from xfusion.train.basicsr.metrics.metric_util import reorder_image, to_y_channel
-from xfusion.train.basicsr.utils.color_util import rgb2ycbcr_pt
+from xfusion.train.basicsr.metrics.metric_util import reorder_image
 from xfusion.train.basicsr.utils.registry import METRIC_REGISTRY
 
 
@@ -51,8 +50,7 @@ def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=Fal
         img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
 
     if test_y_channel:
-        img = to_y_channel(img)
-        img2 = to_y_channel(img2)
+        raise Exception('not implemented yet...')
 
     img = img.astype(np.float64)
     img2 = img2.astype(np.float64)
@@ -86,8 +84,7 @@ def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs):
         img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border]
 
     if test_y_channel:
-        img = rgb2ycbcr_pt(img, y_only=True)
-        img2 = rgb2ycbcr_pt(img2, y_only=True)
+        raise Exception('not implemented yet...')
 
     img = img.to(torch.float64)
     img2 = img2.to(torch.float64)
@@ -131,8 +128,7 @@ def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=Fal
         img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...]
 
     if test_y_channel:
-        img = to_y_channel(img)
-        img2 = to_y_channel(img2)
+        raise Exception('not implemented yet...')
 
     img = img.astype(np.float64)
     img2 = img2.astype(np.float64)
@@ -172,8 +168,7 @@ def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs):
         img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border]
 
     if test_y_channel:
-        img = rgb2ycbcr_pt(img, y_only=True)
-        img2 = rgb2ycbcr_pt(img2, y_only=True)
+        raise Exception('not implemented yet...')
 
     img = img.to(torch.float64)
     img2 = img2.to(torch.float64)
diff --git a/xfusion/train/train_EDVR_L_x4_STF_REDS_mono_prepost_xfull_plus_mult_poiss_u1v3.yml b/xfusion/train/train_EDVR_L_x4_STF_REDS_mono_prepost_xfull_plus_mult_poiss_u1v3.yml
new file mode 100644
index 0000000..830ae5e
--- /dev/null
+++ b/xfusion/train/train_EDVR_L_x4_STF_REDS_mono_prepost_xfull_plus_mult_poiss_u1v3.yml
@@ -0,0 +1,141 @@
+# Copyright 2018-2022 BasicSR Authors
+
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+
+#        http://www.apache.org/licenses/LICENSE-2.0
+
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+# Modifications Copyright 2025 xfusion authors
+
+# general settings
+name: 104_EDVR_L_x4_STF_REDS_600k_B4G8_valREDS4_103pretrain_wandb_mono_prepost_xfull_plus_mult_poiss
+model_type: EDVRModel
+scale: 4
+num_gpu: 4  # set num_gpu: 0 for cpu mode
+manual_seed: 10
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: XRAY
+    type: XrayDatasetSTF
+    flag: grayscale
+    dataroot_gt: [/train/HR]
+    dataroot_lq: [/train/LR]
+    dataroot_flow: ~
+    meta_info_file: [meta_info_xray_GT_u1_.txt]
+    val_partition: official #REDS4  # set to 'official' when use the official validation partition
+    io_backend:
+      type: disk
+
+    num_frame: 3
+    num_frame_hi: 2
+    gt_size: 256
+    interval_list: [1]
+    hi_interval_mult: 1
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+
+    # data loader
+    num_worker_per_gpu: 3
+    batch_size_per_gpu: 1
+    dataset_enlarge_ratio: 400
+    prefetch_mode: ~
+
+  val:
+    name: XRAY
+    type: XrayVideoTestDatasetSTF
+    flag: grayscale
+    dataroot_gt: [/val/HR]
+    dataroot_lq: [/val/LR]
+    meta_info_file: [meta_info_ADDMA_test_GT.txt] #basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
+    # change to 'meta_info_REDSofficial4_test_GT' when use the official validation partition
+    io_backend:
+      type: disk
+
+    cache_data: false
+    num_frame: 3
+    num_frame_hi: 2
+    padding: reflection_circle
+
+# network structures
+network_g:
+  #ema_decay: 0.999
+  type: EDVRSTF
+  num_in_ch: 1
+  num_out_ch: 1
+  num_feat: 128
+  num_frame: 3
+  num_frame_hi: 2
+  deformable_groups: 8
+  num_extract_block: 5
+  num_reconstruct_block: 40
+  center_frame_idx: ~
+  hr_in: false
+  with_predeblur: false
+  with_tsa: true
+
+# path
+path:
+  pretrain_network_g: net_g_latest.pth
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  optim_g:
+    type: Adam
+    lr: !!float 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: CosineAnnealingRestartLR
+    periods: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+  tsa_iter: 0
+  dcn_lr_mul: 1
+
+  # losses
+  pixel_opt:
+    type: CharbonnierLoss
+    loss_weight: 1.0
+    reduction: sum
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 0
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500
+
+find_unused_parameters: true
diff --git a/xfusion/train/train_SwinVR_VSTFx4_xray_scratch_remove1_u1v3_contextmap.yml b/xfusion/train/train_SwinVR_VSTFx4_xray_scratch_remove1_u1v3_contextmap.yml
new file mode 100644
index 0000000..e06fd34
--- /dev/null
+++ b/xfusion/train/train_SwinVR_VSTFx4_xray_scratch_remove1_u1v3_contextmap.yml
@@ -0,0 +1,143 @@
+# Copyright 2018-2022 BasicSR Authors
+
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+
+#        http://www.apache.org/licenses/LICENSE-2.0
+
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+# Modifications Copyright 2025 xfusion authors
+
+# general settings
+name: train_SwinVR_STFx4_scratch_P48W8_DIV2K_500k_B4G8_s18_x2_0.4
+model_type: SwinIRModel
+scale: 4
+num_gpu: 4
+manual_seed: 0
+
+# dataset and data loader settings
+datasets:
+  train:
+    name: XRAY
+    type: XrayDatasetSTF
+    flag: grayscale
+    dataroot_gt: [/train/HR]
+    dataroot_lq: [/train/LR]
+    dataroot_context: [/train/LRC]
+    dataroot_flow: ~
+    meta_info_file: [meta_info_xray_GT_u1.txt]
+    meta_info_context: [context_map.csv]
+    use_nearest_context: true
+    val_partition: official #REDS4  # set to 'official' when use the official validation partition
+    io_backend:
+      type: disk
+
+    num_frame: 3
+    num_frame_hi: 2
+    gt_size: 96
+    interval_list: [1]
+    hi_interval_mult: 1
+    random_reverse: false
+    use_hflip: true
+    use_rot: true
+    use_local_renorm: false
+    poisson_noise_b0_exp: [1,7]
+
+    # data loader
+    num_worker_per_gpu: 3
+    batch_size_per_gpu: 1
+    dataset_enlarge_ratio: 400
+    prefetch_mode: ~
+
+  val:
+    name: XRAY
+    type: XrayVideoTestDatasetSTF
+    flag: grayscale
+    dataroot_gt: [/val/HR]
+    dataroot_lq: [/val/LR]
+    meta_info_file: [meta_info_ADDMA_test_GT.txt] #basicsr/data/meta_info/meta_info_REDS4_test_GT.txt
+    # change to 'meta_info_REDSofficial4_test_GT' when use the official validation partition
+    io_backend:
+      type: disk
+
+    cache_data: false
+    num_frame: 3
+    num_frame_hi: 2
+    padding: reflection_circle
+    use_local_renorm: false
+
+# network structures
+network_g:
+  type: PatchTransformerSTF
+  upscale: 4
+  num_in_ch: 1
+  num_frame: 3
+  #img_size: 48
+  window_size: [5,8,8]
+  #img_range: 1.
+  depths: [2,2,18,2]
+  embed_dim: 192
+  num_heads: [3,3,3,3]
+  mlp_ratio: 2
+  upsampler: 'pixelshuffle'
+  resi_connection: '1conv'
+
+# path
+path:
+  pretrain_network_g: net_g_latest.pth
+  strict_load_g: true
+  resume_state: ~
+
+# training settings
+train:
+  ema_decay: 0.999
+  optim_g:
+    type: Adam
+    lr: !!float 2e-4
+    weight_decay: 0
+    betas: [0.9, 0.99]
+
+  scheduler:
+    type: CosineAnnealingRestartLR
+    periods: [300000]
+    restart_weights: [1]
+    eta_min: !!float 1e-7
+
+  total_iter: 300000
+  warmup_iter: -1  # no warm up
+
+  # losses
+  pixel_opt:
+    type: L1Loss
+    loss_weight: 1.0
+    reduction: mean
+
+# validation settings
+val:
+  val_freq: !!float 5e3
+  save_img: false
+
+  metrics:
+    psnr: # metric name, can be arbitrary
+      type: calculate_psnr
+      crop_border: 4
+      test_y_channel: false
+
+# logging settings
+logger:
+  print_freq: 100
+  save_checkpoint_freq: !!float 5e3
+  use_tb_logger: true
+  wandb:
+    project: ~
+    resume_id: ~
+
+# dist training settings
+dist_params:
+  backend: nccl
+  port: 29500
diff --git a/xfusion/train/train_simple_gray4_VSWINIR_xray_multinode_ddp_new4.py b/xfusion/train/train_simple_gray4_VSWINIR_xray_multinode_ddp_new4.py
index e7ee2ca..631013d 100644
--- a/xfusion/train/train_simple_gray4_VSWINIR_xray_multinode_ddp_new4.py
+++ b/xfusion/train/train_simple_gray4_VSWINIR_xray_multinode_ddp_new4.py
@@ -68,6 +68,8 @@ def parse_options_(root_path,args,opt):
     opt['datasets']['train']['interval_list'] = args.interval_list
     opt['datasets']['train']['hi_interval_mult'] = args.hi_interval_mult
     opt['datasets']['val']['num_frame'] = args.num_frame
+    if args.poisson_noise_b0_exp is not None:
+        opt['datasets']['train']['poisson_noise_b0_exp'] = args.poisson_noise_b0_exp
     opt['network_g']['num_frame'] = args.num_frame
     opt['network_g']['window_size'][0] += (args.num_frame - 3)
     opt['network_g']['window_size'][1:] = [args.window_size_spatial] * len(opt['network_g']['window_size'][1:])
@@ -257,91 +259,91 @@ def train_pipeline(args,opt):
     opt['root_path'] = root_path
 
     if type(args.dir_hi_train) is not list:
-        if str(args.dir_hi_train) != 'none':
+        if str(args.dir_hi_train) not in ['none','.']:
             if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
                 opt['datasets']['train']['dataroot_gt'] = [str(args.dir_hi_train)]
             else:
                 opt['datasets']['train']['dataroot_gt'] = str(args.dir_hi_train)
     else:
         assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
-        if not any([True if str(dh) == 'none' else False for dh in args.dir_hi_train]):
+        if not any([True if str(dh) in ['none','.'] else False for dh in args.dir_hi_train]):
             opt['datasets']['train']['dataroot_gt'] = [str(dh) for dh in args.dir_hi_train]
     
     if type(args.dir_lo_train) is not list:
-        if str(args.dir_lo_train) != 'none':
+        if str(args.dir_lo_train) not in ['none','.']:
             if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
                 opt['datasets']['train']['dataroot_lq'] = [str(args.dir_lo_train)]
             else:
                 opt['datasets']['train']['dataroot_lq'] = str(args.dir_lo_train)
     else:
         assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
-        if not any([True if str(dl) == 'none' else False for dl in args.dir_lo_train]):
+        if not any([True if str(dl) in ['none','.'] else False for dl in args.dir_lo_train]):
             opt['datasets']['train']['dataroot_lq'] = [str(dl) for dl in args.dir_lo_train]
     
     if type(args.dir_hi_val) is not list:
-        if str(args.dir_hi_val) != 'none':
+        if str(args.dir_hi_val) not in ['none','.']:
             if opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF':
                 opt['datasets']['val']['dataroot_gt'] = [str(args.dir_hi_val)]
             else:
                 opt['datasets']['val']['dataroot_gt'] = str(args.dir_hi_val)
     else:
         assert opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF'
-        if not any([True if str(dh) == 'none' else False for dh in args.dir_hi_val]):
+        if not any([True if str(dh) in ['none','.'] else False for dh in args.dir_hi_val]):
             opt['datasets']['val']['dataroot_gt'] = [str(dh) for dh in args.dir_hi_val]
     
     if type(args.dir_lo_val) is not list:
-        if str(args.dir_lo_val) != 'none':
+        if str(args.dir_lo_val) not in ['none','.']:
             if opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF':
                 opt['datasets']['val']['dataroot_lq'] = [str(args.dir_lo_val)]
             else:
                 opt['datasets']['val']['dataroot_lq'] = str(args.dir_lo_val)
     else:
         assert opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF'
-        if not any([True if str(dl) == 'none' else False for dl in args.dir_lo_val]):
+        if not any([True if str(dl) in ['none','.'] else False for dl in args.dir_lo_val]):
             opt['datasets']['val']['dataroot_lq'] = [str(dl) for dl in args.dir_lo_val]
     
     if type(args.path_train_meta_info_file) is not list:
-        if str(args.path_train_meta_info_file) != 'none':
+        if str(args.path_train_meta_info_file) not in ['none','.']:
             if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
                 opt['datasets']['train']['meta_info_file'] = [str(args.path_train_meta_info_file)]
             else:
                 opt['datasets']['train']['meta_info_file'] = str(args.path_train_meta_info_file)
     else:
         assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
-        if not any([True if str(pt) == 'none' else False for pt in args.path_train_meta_info_file]):
+        if not any([True if str(pt) in ['none','.'] else False for pt in args.path_train_meta_info_file]):
             opt['datasets']['train']['meta_info_file'] = [str(pt) for pt in args.path_train_meta_info_file]
     
     if type(args.path_val_meta_info_file) is not list:
-        if str(args.path_val_meta_info_file) != 'none':
+        if str(args.path_val_meta_info_file) not in ['none','.']:
             if opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF':
                 opt['datasets']['val']['meta_info_file'] = [str(args.path_val_meta_info_file)]
             else:
                 opt['datasets']['val']['meta_info_file'] = str(args.path_val_meta_info_file)
     else:
         assert opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF'
-        if not any([True if str(pv) == 'none' else False for pv in args.path_val_meta_info_file]):
+        if not any([True if str(pv) in ['none','.'] else False for pv in args.path_val_meta_info_file]):
             opt['datasets']['val']['meta_info_file'] = [str(pv) for pv in args.path_val_meta_info_file]
 
     if type(args.dataroot_context) is not list:
-        if str(args.dataroot_context) != 'none':
+        if str(args.dataroot_context) not in ['none','.']:
             if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
                 opt['datasets']['train']['dataroot_context'] = [str(args.dataroot_context)]
             else:
                 opt['datasets']['train']['dataroot_context'] = str(args.dataroot_context)
     else:
         assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
-        if not any([True if str(dc) == 'none' else False for dc in args.dataroot_context]):
+        if not any([True if str(dc) in ['none','.'] else False for dc in args.dataroot_context]):
             opt['datasets']['train']['dataroot_context'] = [str(dc) for dc in args.dataroot_context]
 
     if type(args.meta_info_context) is not list:
-        if str(args.meta_info_context) != 'none':
+        if str(args.meta_info_context) not in ['none','.']:
             if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
                 opt['datasets']['train']['meta_info_context'] = [str(args.meta_info_context)]
             else:
                 opt['datasets']['train']['meta_info_context'] = str(args.meta_info_context)
     else:
         assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
-        if not any([True if str(mic) == 'none' else False for mic in args.meta_info_context]):
+        if not any([True if str(mic) in ['none','.'] else False for mic in args.meta_info_context]):
             opt['datasets']['train']['meta_info_context'] = [str(mic) for mic in args.meta_info_context]
     opt['datasets']['train']['use_nearest_context'] = args.use_nearest_context
     opt['datasets']['train']['num_worker_per_gpu'] = args.num_workers
diff --git a/xfusion/train/train_simple_gray6_xfull_mult_poiss.py b/xfusion/train/train_simple_gray6_xfull_mult_poiss.py
index d229ee8..899dccc 100644
--- a/xfusion/train/train_simple_gray6_xfull_mult_poiss.py
+++ b/xfusion/train/train_simple_gray6_xfull_mult_poiss.py
@@ -36,7 +36,8 @@ def parse_options_(root_path,args,opt):
     
     import random
     from xfusion.utils import _postprocess_yml_value
-
+    if args.force_yml == 'none':
+        args.force_yml = None
     # distributed settings
     if args.launcher == 'none':
         print(f"debugging...{opt}")
@@ -213,6 +214,94 @@ def train_pipeline(args,opt):
     opt['train']['total_iter'] /= args.iter_div_factor
     opt['train']['optim_g']['lr'] = args.initial_lr
     opt['val']['val_freq'] = args.val_freq
+        
+    if type(args.dir_hi_train) is not list:
+        if str(args.dir_hi_train) not in ['none','.']:
+            if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
+                opt['datasets']['train']['dataroot_gt'] = [str(args.dir_hi_train)]
+            else:
+                opt['datasets']['train']['dataroot_gt'] = str(args.dir_hi_train)
+    else:
+        assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
+        if not any([True if str(dh) in ['none','.'] else False for dh in args.dir_hi_train]):
+            opt['datasets']['train']['dataroot_gt'] = [str(dh) for dh in args.dir_hi_train]
+    
+    if type(args.dir_lo_train) is not list:
+        if str(args.dir_lo_train) not in ['none','.']:
+            if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
+                opt['datasets']['train']['dataroot_lq'] = [str(args.dir_lo_train)]
+            else:
+                opt['datasets']['train']['dataroot_lq'] = str(args.dir_lo_train)
+    else:
+        assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
+        if not any([True if str(dl) in ['none','.'] else False for dl in args.dir_lo_train]):
+            opt['datasets']['train']['dataroot_lq'] = [str(dl) for dl in args.dir_lo_train]
+    
+    if type(args.dir_hi_val) is not list:
+        if str(args.dir_hi_val) not in ['none','.']:
+            if opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF':
+                opt['datasets']['val']['dataroot_gt'] = [str(args.dir_hi_val)]
+            else:
+                opt['datasets']['val']['dataroot_gt'] = str(args.dir_hi_val)
+    else:
+        assert opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF'
+        if not any([True if str(dh) in ['none','.'] else False for dh in args.dir_hi_val]):
+            opt['datasets']['val']['dataroot_gt'] = [str(dh) for dh in args.dir_hi_val]
+    
+    if type(args.dir_lo_val) is not list:
+        if str(args.dir_lo_val) not in ['none','.']:
+            if opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF':
+                opt['datasets']['val']['dataroot_lq'] = [str(args.dir_lo_val)]
+            else:
+                opt['datasets']['val']['dataroot_lq'] = str(args.dir_lo_val)
+    else:
+        assert opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF'
+        if not any([True if str(dl) in ['none','.'] else False for dl in args.dir_lo_val]):
+            opt['datasets']['val']['dataroot_lq'] = [str(dl) for dl in args.dir_lo_val]
+    
+    if type(args.path_train_meta_info_file) is not list:
+        if str(args.path_train_meta_info_file) not in ['none','.']:
+            if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
+                opt['datasets']['train']['meta_info_file'] = [str(args.path_train_meta_info_file)]
+            else:
+                opt['datasets']['train']['meta_info_file'] = str(args.path_train_meta_info_file)
+    else:
+        assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
+        if not any([True if str(pt) in ['none','.'] else False for pt in args.path_train_meta_info_file]):
+            opt['datasets']['train']['meta_info_file'] = [str(pt) for pt in args.path_train_meta_info_file]
+    
+    if type(args.path_val_meta_info_file) is not list:
+        if str(args.path_val_meta_info_file) not in ['none','.']:
+            if opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF':
+                opt['datasets']['val']['meta_info_file'] = [str(args.path_val_meta_info_file)]
+            else:
+                opt['datasets']['val']['meta_info_file'] = str(args.path_val_meta_info_file)
+    else:
+        assert opt['datasets']['val']['type'] == 'XrayVideoTestDatasetSTF'
+        if not any([True if str(pv) in ['none','.'] else False for pv in args.path_val_meta_info_file]):
+            opt['datasets']['val']['meta_info_file'] = [str(pv) for pv in args.path_val_meta_info_file]
+
+    if type(args.dataroot_context) is not list:
+        if str(args.dataroot_context) not in ['none','.']:
+            if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
+                opt['datasets']['train']['dataroot_context'] = [str(args.dataroot_context)]
+            else:
+                opt['datasets']['train']['dataroot_context'] = str(args.dataroot_context)
+    else:
+        assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
+        if not any([True if str(dc) in ['none','.'] else False for dc in args.dataroot_context]):
+            opt['datasets']['train']['dataroot_context'] = [str(dc) for dc in args.dataroot_context]
+
+    if type(args.meta_info_context) is not list:
+        if str(args.meta_info_context) not in ['none','.']:
+            if opt['datasets']['train']['type'] == 'XrayDatasetSTF':
+                opt['datasets']['train']['meta_info_context'] = [str(args.meta_info_context)]
+            else:
+                opt['datasets']['train']['meta_info_context'] = str(args.meta_info_context)
+    else:
+        assert opt['datasets']['train']['type'] == 'XrayDatasetSTF'
+        if not any([True if str(mic) in ['none','.'] else False for mic in args.meta_info_context]):
+            opt['datasets']['train']['meta_info_context'] = [str(mic) for mic in args.meta_info_context]
     if args.poisson_noise_b0_exp is not None:
         opt['datasets']['train']['poisson_noise_b0_exp'] = args.poisson_noise_b0_exp
     torch.backends.cudnn.benchmark = True