update schedulers init signature

Signed-off-by: Vladimir Mandic <[email protected]>

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Change Log for SD.Next
 
+## Update for 2026-10-04
+
+- sdnq: support double-quant, including `nvfp4`
+- schedulers: update init signature for new configuration options
+- rocm: remove incompatible env flags
+
 ## Update for 2026-10-01
 
 ### Highlights for 2026-10-01

modules/res4lyf/deis_scheduler_alt.py

@@ -57,6 +57,8 @@ class RESDEISMultistepScheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/gauss_legendre_scheduler.py

@@ -45,6 +45,8 @@ class GaussLegendreScheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/linear_rk_scheduler.py

@@ -45,6 +45,8 @@ class LinearRKScheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/lobatto_scheduler.py

@@ -46,6 +46,8 @@ class LobattoScheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/radau_iia_scheduler.py

@@ -46,6 +46,8 @@ class RadauIIAScheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/rungekutta_44s_scheduler.py

@@ -45,6 +45,8 @@ class RungeKutta44Scheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/rungekutta_57s_scheduler.py

@@ -43,6 +43,8 @@ class RungeKutta57Scheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/rungekutta_67s_scheduler.py

@@ -44,6 +44,8 @@ class RungeKutta67Scheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/res4lyf/specialized_rk_scheduler.py

@@ -46,6 +46,8 @@ class SpecializedRKScheduler(SchedulerMixin, ConfigMixin):
         timestep_type: str = "discrete",
         rescale_betas_zero_snr: bool = False,
         final_sigmas_type: str = "zero",
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if trained_betas is not None:
             self.betas = torch.tensor(trained_betas, dtype=torch.float32)

modules/schedulers/scheduler_ersde.py

@@ -128,6 +128,8 @@ class ERSDEScheduler(SchedulerMixin, ConfigMixin):
         use_dynamic_shifting: bool = False,
         base_shift: float = 0.5,
         max_shift: float = 1.15,
+        base_image_seq_len: int = 256,
+        max_image_seq_len: int = 4096,
     ):
         if solver_order not in (1, 2, 3):
             raise ValueError(f"solver_order must be 1, 2, or 3, got {solver_order}")

modules/schedulers/scheduler_unipc_flowmatch.py

@@ -96,7 +96,9 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             rescale_betas_zero_snr: bool = False,
             timestep_spacing: str = "linspace",
             steps_offset: int = 0,
-            final_sigmas_type: Optional[str] = "zero",  # "zero", "sigma_min"
+            final_sigmas_type: Optional[str] = "zero",  # "zero", "sigma_min",
+            base_image_seq_len: int = 256,
+            max_image_seq_len: int = 4096,
     ):
 
         if solver_type not in ["bh1", "bh2"]:

requirements.txt

@@ -25,7 +25,7 @@ fastapi==0.124.4
 uvicorn==0.52.4
 rich==15.0.0
 safetensors==0.8.0
-peft==0.21.1
+peft==0.21.2
 httpx==0.28.1
 requests==2.34.2
 tqdm==4.70.1