diff --git a/configs/alt-diffusion-inference.yaml b/configs/alt-diffusion-inference.yaml
index cfbee72d71b..4944ab5c8dc 100644
--- a/configs/alt-diffusion-inference.yaml
+++ b/configs/alt-diffusion-inference.yaml
@@ -40,7 +40,7 @@ model:
         use_spatial_transformer: True
         transformer_depth: 1
         context_dim: 768
-        use_checkpoint: True
+        use_checkpoint: False
         legacy: False
 
     first_stage_config:
diff --git a/configs/alt-diffusion-m18-inference.yaml b/configs/alt-diffusion-m18-inference.yaml
index 41a031d55f0..c60dca8c7b3 100644
--- a/configs/alt-diffusion-m18-inference.yaml
+++ b/configs/alt-diffusion-m18-inference.yaml
@@ -41,7 +41,7 @@ model:
         use_linear_in_transformer: True
         transformer_depth: 1
         context_dim: 1024
-        use_checkpoint: True
+        use_checkpoint: False
         legacy: False
 
     first_stage_config:
diff --git a/configs/instruct-pix2pix.yaml b/configs/instruct-pix2pix.yaml
index 4e896879dd7..564e50ae246 100644
--- a/configs/instruct-pix2pix.yaml
+++ b/configs/instruct-pix2pix.yaml
@@ -45,7 +45,7 @@ model:
         use_spatial_transformer: True
         transformer_depth: 1
         context_dim: 768
-        use_checkpoint: True
+        use_checkpoint: False
         legacy: False
 
     first_stage_config:
diff --git a/configs/sd_xl_inpaint.yaml b/configs/sd_xl_inpaint.yaml
index 3bad372186f..f40f45e3316 100644
--- a/configs/sd_xl_inpaint.yaml
+++ b/configs/sd_xl_inpaint.yaml
@@ -21,7 +21,7 @@ model:
       params:
         adm_in_channels: 2816
         num_classes: sequential
-        use_checkpoint: True
+        use_checkpoint: False
         in_channels: 9
         out_channels: 4
         model_channels: 320
diff --git a/configs/v1-inference.yaml b/configs/v1-inference.yaml
index d4effe569e8..25c4d9ed066 100644
--- a/configs/v1-inference.yaml
+++ b/configs/v1-inference.yaml
@@ -40,7 +40,7 @@ model:
         use_spatial_transformer: True
         transformer_depth: 1
         context_dim: 768
-        use_checkpoint: True
+        use_checkpoint: False
         legacy: False
 
     first_stage_config:
diff --git a/configs/v1-inpainting-inference.yaml b/configs/v1-inpainting-inference.yaml
index f9eec37d24b..68c199f99c3 100644
--- a/configs/v1-inpainting-inference.yaml
+++ b/configs/v1-inpainting-inference.yaml
@@ -40,7 +40,7 @@ model:
         use_spatial_transformer: True
         transformer_depth: 1
         context_dim: 768
-        use_checkpoint: True
+        use_checkpoint: False
         legacy: False
 
     first_stage_config:
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 42b14dc239d..aee4e9d9ca7 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -378,13 +378,18 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
         self.network_weights_backup = weights_backup
 
     bias_backup = getattr(self, "network_bias_backup", None)
-    if bias_backup is None:
+    if bias_backup is None and wanted_names != ():
         if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
             bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
         elif getattr(self, 'bias', None) is not None:
             bias_backup = self.bias.to(devices.cpu, copy=True)
         else:
             bias_backup = None
+
+        # Unlike weight which always has value, some modules don't have bias.
+        # Only report if bias is not None and current bias are not unchanged.
+        if bias_backup is not None and current_names != ():
+            raise RuntimeError("no backup bias found and current bias are not unchanged")
         self.network_bias_backup = bias_backup
 
     if current_names != wanted_names:
diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index 016a33d1057..2b32d138665 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -41,7 +41,7 @@
 parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
 parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "half", "autocast"], default="autocast")
 parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
@@ -69,7 +69,8 @@
 parser.add_argument("--opt-sdp-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization for automatic choice of optimization; requires PyTorch 2.*")
 parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="prefer scaled dot product cross-attention layer optimization without memory efficient attention for automatic choice of optimization, makes image generation deterministic; requires PyTorch 2.*")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="prefer no cross-attention layer optimization for automatic choice of optimization")
-parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
+parser.add_argument("--disable-nan-check", action='store_true', help="[Deprecated] do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
+parser.add_argument("--enable-nan-check", action='store_true', help="Check if produced images/latent spaces have nans at extra performance cost. (~20ms/it)")
 parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
 parser.add_argument("--use-ipex", action="store_true", help="use Intel XPU as torch device")
 parser.add_argument("--disable-model-loading-ram-optimization", action='store_true', help="disable an optimization that reduces RAM use when loading a model")
diff --git a/modules/devices.py b/modules/devices.py
index e4f671ac659..0e19ed2769a 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -114,6 +114,9 @@ def enable_tf32():
 
 cpu: torch.device = torch.device("cpu")
 fp8: bool = False
+# Force fp16 for all models in inference. No casting during inference.
+# This flag is controlled by "--precision half" command line arg.
+force_fp16: bool = False
 device: torch.device = None
 device_interrogate: torch.device = None
 device_gfpgan: torch.device = None
@@ -127,6 +130,8 @@ def enable_tf32():
 
 
 def cond_cast_unet(input):
+    if force_fp16:
+        return input.to(torch.float16)
     return input.to(dtype_unet) if unet_needs_upcast else input
 
 
@@ -206,6 +211,11 @@ def autocast(disable=False):
     if disable:
         return contextlib.nullcontext()
 
+    if force_fp16:
+        # No casting during inference if force_fp16 is enabled.
+        # All tensor dtype conversion happens before inference.
+        return contextlib.nullcontext()
+
     if fp8 and device==cpu:
         return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
 
@@ -230,7 +240,7 @@ class NansException(Exception):
 
 
 def test_for_nans(x, where):
-    if shared.cmd_opts.disable_nan_check:
+    if not shared.cmd_opts.enable_nan_check:
         return
 
     if not torch.all(torch.isnan(x)).item():
@@ -250,8 +260,6 @@ def test_for_nans(x, where):
     else:
         message = "A tensor with all NaNs was produced."
 
-    message += " Use --disable-nan-check commandline argument to disable this check."
-
     raise NansException(message)
 
 
@@ -269,3 +277,17 @@ def first_time_calculation():
     x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
     conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
     conv2d(x)
+
+
+def force_model_fp16():
+    """
+    ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which
+    force conversion of input to float32. If force_fp16 is enabled, we need to
+    prevent this casting.
+    """
+    assert force_fp16
+    import sgm.modules.diffusionmodules.util as sgm_util
+    import ldm.modules.diffusionmodules.util as ldm_util
+    sgm_util.GroupNorm32 = torch.nn.GroupNorm
+    ldm_util.GroupNorm32 = torch.nn.GroupNorm
+    print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.")
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 5812b0e5855..ddc411076ba 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -440,6 +440,10 @@ def prepare_environment():
         git_pull_recursive(extensions_dir)
         startup_timer.record("update extensions")
 
+    if args.disable_nan_check:
+        print("Nan check disabled by default. --disable-nan-check argument is now ignored. "
+              "Use --enable-nan-check to re-enable nan check.")
+
     if "--exit" in sys.argv:
         print("Exiting because of --exit argument")
         exit(0)
@@ -454,8 +458,8 @@ def configure_for_tests():
         sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
     if "--skip-torch-cuda-test" not in sys.argv:
         sys.argv.append("--skip-torch-cuda-test")
-    if "--disable-nan-check" not in sys.argv:
-        sys.argv.append("--disable-nan-check")
+    if "--enable-nan-check" in sys.argv:
+        sys.argv.remove("--enable-nan-check")
 
     os.environ['COMMANDLINE_ARGS'] = ""
 
diff --git a/modules/processing.py b/modules/processing.py
index 76557dd7f5e..fff2595e70a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -115,20 +115,17 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
 
     else:
-        sd = sd_model.model.state_dict()
-        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
-        if diffusion_model_input is not None:
-            if diffusion_model_input.shape[1] == 9:
-                # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
-                image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
-                image_conditioning = images_tensor_to_samples(image_conditioning,
-                                                              approximation_indexes.get(opts.sd_vae_encode_method))
+        if getattr(sd_model.model, "is_sdxl_inpaint", False):
+            # The "masked-image" in this case will just be all 0.5 since the entire image is masked.
+            image_conditioning = torch.ones(x.shape[0], 3, height, width, device=x.device) * 0.5
+            image_conditioning = images_tensor_to_samples(image_conditioning,
+                                                            approximation_indexes.get(opts.sd_vae_encode_method))
 
-                # Add the fake full 1s mask to the first dimension.
-                image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-                image_conditioning = image_conditioning.to(x.dtype)
+            # Add the fake full 1s mask to the first dimension.
+            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+            image_conditioning = image_conditioning.to(x.dtype)
 
-                return image_conditioning
+            return image_conditioning
 
         # Dummy zero conditioning if we're not using inpainting or unclip models.
         # Still takes up a bit of memory, but no encoder call.
@@ -390,11 +387,8 @@ def img2img_image_conditioning(self, source_image, latent_image, image_mask=None
         if self.sampler.conditioning_key == "crossattn-adm":
             return self.unclip_image_conditioning(source_image)
 
-        sd = self.sampler.model_wrap.inner_model.model.state_dict()
-        diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
-        if diffusion_model_input is not None:
-            if diffusion_model_input.shape[1] == 9:
-                return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+        if getattr(self.sampler.model_wrap.inner_model.model, "is_sdxl_inpaint", False):
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
 
         # Dummy zero conditioning if we're not using inpainting or depth model.
         return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
diff --git a/modules/sd_hijack_checkpoint.py b/modules/sd_hijack_checkpoint.py
index 2604d969f91..b2f05bbdcf0 100644
--- a/modules/sd_hijack_checkpoint.py
+++ b/modules/sd_hijack_checkpoint.py
@@ -4,16 +4,19 @@
 import ldm.modules.diffusionmodules.openaimodel
 
 
+# Setting flag=False so that torch skips checking parameters.
+# parameters checking is expensive in frequent operations.
+
 def BasicTransformerBlock_forward(self, x, context=None):
-    return checkpoint(self._forward, x, context)
+    return checkpoint(self._forward, x, context, flag=False)
 
 
 def AttentionBlock_forward(self, x):
-    return checkpoint(self._forward, x)
+    return checkpoint(self._forward, x, flag=False)
 
 
 def ResBlock_forward(self, x, emb):
-    return checkpoint(self._forward, x, emb)
+    return checkpoint(self._forward, x, emb, flag=False)
 
 
 stored = []
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 7f9e328d05a..4c2dc56d45d 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -486,7 +486,19 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
     k_in = self.to_k(context_k)
     v_in = self.to_v(context_v)
 
-    q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
+    def _reshape(t):
+        """rearrange(t, 'b n (h d) -> b n h d', h=h).
+        Using torch native operations to avoid overhead as this function is
+        called frequently. (70 times/it for SDXL)
+        """
+        b, n, _ = t.shape  # Get the batch size (b) and sequence length (n)
+        d = t.shape[2] // h  # Determine the depth per head
+        return t.reshape(b, n, h, d)
+
+    q = _reshape(q_in)
+    k = _reshape(k_in)
+    v = _reshape(v_in)
+
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -497,7 +509,9 @@ def xformers_attention_forward(self, x, context=None, mask=None, **kwargs):
 
     out = out.to(dtype)
 
-    out = rearrange(out, 'b n h d -> b n (h d)', h=h)
+    # out = rearrange(out, 'b n h d -> b n (h d)', h=h)
+    b, n, h, d = out.shape
+    out = out.reshape(b, n, h * d)
     return self.to_out(out)
 
 
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 2101f1a0415..41955313a31 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -36,7 +36,7 @@ def cat(self, tensors, *args, **kwargs):
 
 # Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
 def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
-
+    """Always make sure inputs to unet are in correct dtype."""
     if isinstance(cond, dict):
         for y in cond.keys():
             if isinstance(cond[y], list):
@@ -45,7 +45,11 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
                 cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
 
     with devices.autocast():
-        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
+        result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
+        if devices.unet_needs_upcast:
+            return result.float()
+        else:
+            return result
 
 
 class GELUHijack(torch.nn.GELU, torch.nn.Module):
@@ -64,12 +68,11 @@ def hijack_ddpm_edit():
     if not ddpm_edit_hijack:
         CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
         CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
-        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model)
 
 
 unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
-CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
-CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+
 if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
     CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
     CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
@@ -81,5 +84,17 @@ def hijack_ddpm_edit():
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
 CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
 
-CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
-CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
+CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
+
+
+def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
+    if devices.unet_needs_upcast and timesteps.dtype == torch.int64:
+        dtype = torch.float32
+    else:
+        dtype = devices.dtype_unet
+    return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
+
+
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
+CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
diff --git a/modules/sd_hijack_utils.py b/modules/sd_hijack_utils.py
index 79bf6e46862..546f2eda4ec 100644
--- a/modules/sd_hijack_utils.py
+++ b/modules/sd_hijack_utils.py
@@ -1,7 +1,11 @@
 import importlib
 
+
+always_true_func = lambda *args, **kwargs: True
+
+
 class CondFunc:
-    def __new__(cls, orig_func, sub_func, cond_func):
+    def __new__(cls, orig_func, sub_func, cond_func=always_true_func):
         self = super(CondFunc, cls).__new__(cls)
         if isinstance(orig_func, str):
             func_path = orig_func.split('.')
@@ -20,13 +24,13 @@ def __new__(cls, orig_func, sub_func, cond_func):
                 print(f"Warning: Failed to resolve {orig_func} for CondFunc hijack")
                 pass
         self.__init__(orig_func, sub_func, cond_func)
-        return lambda *args, **kwargs: self(*args, **kwargs)
-    def __init__(self, orig_func, sub_func, cond_func):
-        self.__orig_func = orig_func
-        self.__sub_func = sub_func
-        self.__cond_func = cond_func
-    def __call__(self, *args, **kwargs):
-        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
-            return self.__sub_func(self.__orig_func, *args, **kwargs)
-        else:
-            return self.__orig_func(*args, **kwargs)
+        return lambda *args, **kwargs: self(*args, **kwargs)
+    def __init__(self, orig_func, sub_func, cond_func):
+        self.__orig_func = orig_func
+        self.__sub_func = sub_func
+        self.__cond_func = cond_func
+    def __call__(self, *args, **kwargs):
+        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+            return self.__sub_func(self.__orig_func, *args, **kwargs)
+        else:
+            return self.__orig_func(*args, **kwargs)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index ff245b7a668..aed58e19d36 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -380,6 +380,13 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
     model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
     model.is_sd1 = not model.is_sdxl and not model.is_sd2
     model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
+    # Set is_sdxl_inpaint flag.
+    diffusion_model_input = state_dict.get('diffusion_model.input_blocks.0.0.weight', None)
+    model.is_sdxl_inpaint = (
+        model.is_sdxl and
+        diffusion_model_input is not None and
+        diffusion_model_input.shape[1] == 9
+    )
     if model.is_sdxl:
         sd_models_xl.extend_sdxl(model)
 
@@ -403,6 +410,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
         model.float()
         model.alphas_cumprod_original = model.alphas_cumprod
         devices.dtype_unet = torch.float32
+        assert shared.cmd_opts.precision != "half", "Cannot use --precision half with --no-half"
         timer.record("apply float()")
     else:
         vae = model.first_stage_model
@@ -540,7 +548,7 @@ def repair_config(sd_config):
     if hasattr(sd_config.model.params, 'unet_config'):
         if shared.cmd_opts.no_half:
             sd_config.model.params.unet_config.params.use_fp16 = False
-        elif shared.cmd_opts.upcast_sampling:
+        elif shared.cmd_opts.upcast_sampling or shared.cmd_opts.precision == "half":
             sd_config.model.params.unet_config.params.use_fp16 = True
 
     if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
@@ -551,6 +559,14 @@ def repair_config(sd_config):
         karlo_path = os.path.join(paths.models_path, 'karlo')
         sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)
 
+    # Do not use checkpoint for inference.
+    # This helps prevent extra performance overhead on checking parameters.
+    # The perf overhead is about 100ms/it on 4090 for SDXL.
+    if hasattr(sd_config.model.params, "network_config"):
+        sd_config.model.params.network_config.params.use_checkpoint = False
+    if hasattr(sd_config.model.params, "unet_config"):
+        sd_config.model.params.unet_config.params.use_checkpoint = False
+
 
 def rescale_zero_terminal_snr_abar(alphas_cumprod):
     alphas_bar_sqrt = alphas_cumprod.sqrt()
diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py
index b38137eb5a9..9cec4f13dc2 100644
--- a/modules/sd_models_config.py
+++ b/modules/sd_models_config.py
@@ -35,7 +35,7 @@ def is_using_v_parameterization_for_sd2(state_dict):
 
     with sd_disable_initialization.DisableInitialization():
         unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
-            use_checkpoint=True,
+            use_checkpoint=False,
             use_fp16=False,
             image_size=32,
             in_channels=4,
diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index 94ff973fb84..1242a59369f 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -35,11 +35,10 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
 
 
 def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
-    sd = self.model.state_dict()
-    diffusion_model_input = sd.get('diffusion_model.input_blocks.0.0.weight', None)
-    if diffusion_model_input is not None:
-        if diffusion_model_input.shape[1] == 9:
-            x = torch.cat([x] + cond['c_concat'], dim=1)
+    """WARNING: This function is called once per denoising iteration. DO NOT add
+    expensive functionc calls such as `model.state_dict`. """
+    if self.is_sdxl_inpaint:
+        x = torch.cat([x] + cond['c_concat'], dim=1)
 
     return self.model(x, t, cond)
 
diff --git a/modules/sd_schedulers.py b/modules/sd_schedulers.py
index 75eb3ac032f..4ddb778501a 100644
--- a/modules/sd_schedulers.py
+++ b/modules/sd_schedulers.py
@@ -4,6 +4,9 @@
 
 import k_diffusion
 
+import numpy as np
+
+from modules import shared
 
 @dataclasses.dataclass
 class Scheduler:
@@ -30,6 +33,33 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
     sigs += [0.0]
     return torch.FloatTensor(sigs).to(device)
 
+def get_align_your_steps_sigmas(n, sigma_min, sigma_max, device='cpu'):
+    # https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+    def loglinear_interp(t_steps, num_steps):
+        """
+        Performs log-linear interpolation of a given array of decreasing numbers.
+        """
+        xs = np.linspace(0, 1, len(t_steps))
+        ys = np.log(t_steps[::-1])
+
+        new_xs = np.linspace(0, 1, num_steps)
+        new_ys = np.interp(new_xs, xs, ys)
+
+        interped_ys = np.exp(new_ys)[::-1].copy()
+        return interped_ys
+
+    if shared.sd_model.is_sdxl:
+        sigmas = [14.615, 6.315, 3.771, 2.181, 1.342, 0.862, 0.555, 0.380, 0.234, 0.113, 0.029]
+    else:
+        # Default to SD 1.5 sigmas.
+        sigmas = [14.615, 6.475, 3.861, 2.697, 1.886, 1.396, 0.963, 0.652, 0.399, 0.152, 0.029]
+
+    if n != len(sigmas):
+        sigmas = np.append(loglinear_interp(sigmas, n), [0.0])
+    else:
+        sigmas.append(0.0)
+
+    return torch.FloatTensor(sigmas).to(device)
 
 schedulers = [
     Scheduler('automatic', 'Automatic', None),
@@ -38,6 +68,7 @@ def sgm_uniform(n, sigma_min, sigma_max, inner_model, device):
     Scheduler('exponential', 'Exponential', k_diffusion.sampling.get_sigmas_exponential),
     Scheduler('polyexponential', 'Polyexponential', k_diffusion.sampling.get_sigmas_polyexponential, default_rho=1.0),
     Scheduler('sgm_uniform', 'SGM Uniform', sgm_uniform, need_inner_model=True, aliases=["SGMUniform"]),
+    Scheduler('align_your_steps', 'Align Your Steps', get_align_your_steps_sigmas),
 ]
 
 schedulers_map = {**{x.name: x for x in schedulers}, **{x.label: x for x in schedulers}}
diff --git a/modules/shared_init.py b/modules/shared_init.py
index 935e3a21cf2..a6ad0433d6f 100644
--- a/modules/shared_init.py
+++ b/modules/shared_init.py
@@ -31,6 +31,14 @@ def initialize():
     devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
     devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
 
+    if cmd_opts.precision == "half":
+        msg = "--no-half and --no-half-vae conflict with --precision half"
+        assert devices.dtype == torch.float16, msg
+        assert devices.dtype_vae == torch.float16, msg
+        assert devices.dtype_inference == torch.float16, msg
+        devices.force_fp16 = True
+        devices.force_model_fp16()
+
     shared.device = devices.device
     shared.weight_load_location = None if cmd_opts.lowram else "cpu"
 
diff --git a/requirements.txt b/requirements.txt
index 9e2ecfe4d67..d723775e99a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,4 +30,6 @@ torch
 torchdiffeq
 torchsde
 transformers==4.30.2
-pillow-avif-plugin==1.4.3
\ No newline at end of file
+pillow-avif-plugin==1.4.3
+
+xformers
\ No newline at end of file