Align DyPE with paper

Author: JPPhoto

invokeai/app/invocations/flux_denoise.py

@@ -477,7 +477,7 @@ def _run_diffusion(
                 )
                 context.logger.info(
                     f"DyPE enabled: resolution={self.width}x{self.height}, preset={self.dype_preset}, "
-                    f"method={dype_config.method}, scale={dype_config.dype_scale:.2f}, "
+                    f"scale={dype_config.dype_scale:.2f}, "
                     f"exponent={dype_config.dype_exponent:.2f}, start_sigma={dype_config.dype_start_sigma:.2f}, "
                     f"base_resolution={dype_config.base_resolution}"
                 )

invokeai/backend/flux/denoise.py

@@ -96,15 +96,18 @@ def denoise(
                 timestep = scheduler.timesteps[step_index]
                 # Convert scheduler timestep (0-1000) to normalized (0-1) for the model
                 t_curr = timestep.item() / scheduler.config.num_train_timesteps
+                dype_sigma = DyPEExtension.resolve_step_sigma(
+                    fallback_sigma=t_curr,
+                    step_index=step_index,
+                    scheduler_sigmas=getattr(scheduler, "sigmas", None),
+                )
                 t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
 
                 # DyPE: Update step state for timestep-dependent scaling
                 if dype_extension is not None and dype_embedder is not None:
                     dype_extension.update_step_state(
                         embedder=dype_embedder,
-                        timestep=t_curr,
-                        timestep_index=user_step,
-                        total_steps=total_steps,
+                        sigma=dype_sigma,
                     )
 
                 # For Heun scheduler, track if we're in first or second order step
@@ -264,9 +267,7 @@ def denoise(
             if dype_extension is not None and dype_embedder is not None:
                 dype_extension.update_step_state(
                     embedder=dype_embedder,
-                    timestep=t_curr,
-                    timestep_index=step_index,
-                    total_steps=total_steps,
+                    sigma=t_curr,
                 )
 
             t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)

invokeai/backend/flux/dype/__init__.py

@@ -1,9 +1,9 @@
 """Dynamic Position Extrapolation (DyPE) for FLUX models.
 
-DyPE enables high-resolution image generation (4K+) with pretrained FLUX models
-by dynamically scaling RoPE position embeddings during the denoising process.
+DyPE enables high-resolution image generation with pretrained FLUX models by
+dynamically modulating RoPE extrapolation during denoising.
 
-Based on: https://github.com/wildminder/ComfyUI-DyPE
+Based on the official DyPE project: https://github.com/guyyariv/DyPE
 """
 
 from invokeai.backend.flux.dype.base import DyPEConfig

invokeai/backend/flux/dype/base.py

@@ -1,8 +1,6 @@
-"""DyPE base configuration and utilities."""
+"""DyPE base configuration and utilities for FLUX vision_yarn RoPE."""
 
-import math
 from dataclasses import dataclass
-from typing import Literal
 
 import torch
 from torch import Tensor
@@ -14,72 +12,39 @@ class DyPEConfig:
 
     enable_dype: bool = True
     base_resolution: int = 1024  # Native training resolution
-    method: Literal["vision_yarn", "yarn", "ntk", "base"] = "vision_yarn"
     dype_scale: float = 2.0  # Magnitude λs (0.0-8.0)
     dype_exponent: float = 2.0  # Decay speed λt (0.0-1000.0)
     dype_start_sigma: float = 1.0  # When DyPE decay starts
 
 
-def get_mscale(scale: float, mscale_factor: float = 1.0) -> float:
-    """Calculate magnitude scaling factor.
-
-    Args:
-        scale: The resolution scaling factor
-        mscale_factor: Adjustment factor for the scaling
-
-    Returns:
-        The magnitude scaling factor
-    """
-    if scale <= 1.0:
-        return 1.0
-    return mscale_factor * math.log(scale) + 1.0
-
-
-def get_timestep_mscale(
-    scale: float,
+def get_timestep_kappa(
     current_sigma: float,
     dype_scale: float,
     dype_exponent: float,
     dype_start_sigma: float,
 ) -> float:
-    """Calculate timestep-dependent magnitude scaling.
+    """Calculate the paper-style DyPE scheduler value κ(t).
 
     The key insight of DyPE: early steps focus on low frequencies (global structure),
-    late steps on high frequencies (details). This function modulates the scaling
-    based on the current timestep/sigma.
+    late steps on high frequencies (details). DyPE expresses this as a direct
+    timestep scheduler over the positional extrapolation strength:
+
+        κ(t) = λs * t^λt
 
     Args:
-        scale: Resolution scaling factor
         current_sigma: Current noise level (1.0 = full noise, 0.0 = clean)
         dype_scale: DyPE magnitude (λs)
         dype_exponent: DyPE decay speed (λt)
         dype_start_sigma: Sigma threshold to start decay
 
     Returns:
-        Timestep-modulated scaling factor
+        Timestep scheduler value κ(t)
     """
-    if scale <= 1.0:
-        return 1.0
-
-    # Normalize sigma to [0, 1] range relative to start_sigma
-    if current_sigma >= dype_start_sigma:
-        t_normalized = 1.0
-    else:
-        t_normalized = current_sigma / dype_start_sigma
-
-    # Apply exponential decay: stronger extrapolation early, weaker late
-    # decay = exp(-λt * (1 - t))  where t=1 is early (high sigma), t=0 is late
-    decay = math.exp(-dype_exponent * (1.0 - t_normalized))
-
-    # Base mscale from resolution
-    base_mscale = get_mscale(scale)
+    if dype_scale <= 0.0 or dype_start_sigma <= 0.0:
+        return 0.0
 
-    # Interpolate between base_mscale and 1.0 based on decay and dype_scale
-    # When decay=1 (early): use scaled value
-    # When decay=0 (late): use base value
-    scaled_mscale = 1.0 + (base_mscale - 1.0) * dype_scale * decay
-
-    return scaled_mscale
+    t_normalized = max(0.0, min(current_sigma / dype_start_sigma, 1.0))
+    return dype_scale * (t_normalized**dype_exponent)
 
 
 def compute_vision_yarn_freqs(
@@ -117,35 +82,23 @@ def compute_vision_yarn_freqs(
     """
     assert dim % 2 == 0
 
-    # Use the larger scale for NTK calculation
     scale = max(scale_h, scale_w)
 
     device = pos.device
     dtype = torch.float64 if device.type != "mps" else torch.float32
 
-    # NTK-aware theta scaling: extends position coverage for high-res
-    # Formula: theta_scaled = theta * scale^(dim/(dim-2))
-    # This increases the wavelength of position encodings proportionally
+    # DyPE applies a direct timestep scheduler to the NTK extrapolation exponent.
+    # Early steps keep strong extrapolation; late steps relax smoothly back
+    # toward the training-time RoPE.
     if scale > 1.0:
-        ntk_alpha = scale ** (dim / (dim - 2))
-
-        # Apply timestep-dependent DyPE modulation
-        # mscale controls how strongly we apply the NTK extrapolation
-        # Early steps (high sigma): stronger extrapolation for global structure
-        # Late steps (low sigma): weaker extrapolation for fine details
-        mscale = get_timestep_mscale(
-            scale=scale,
+        ntk_exponent = dim / (dim - 2)
+        kappa = get_timestep_kappa(
             current_sigma=current_sigma,
             dype_scale=dype_config.dype_scale,
             dype_exponent=dype_config.dype_exponent,
             dype_start_sigma=dype_config.dype_start_sigma,
         )
-
-        # Modulate NTK alpha by mscale
-        # When mscale > 1: interpolate towards stronger extrapolation
-        # When mscale = 1: use base NTK alpha
-        modulated_alpha = 1.0 + (ntk_alpha - 1.0) * mscale
-        scaled_theta = theta * modulated_alpha
+        scaled_theta = theta * (scale ** (ntk_exponent * kappa))
     else:
         scaled_theta = theta
 
@@ -160,101 +113,3 @@ def compute_vision_yarn_freqs(
     sin = torch.sin(angles)
 
     return cos.to(pos.dtype), sin.to(pos.dtype)
-
-
-def compute_yarn_freqs(
-    pos: Tensor,
-    dim: int,
-    theta: int,
-    scale: float,
-    current_sigma: float,
-    dype_config: DyPEConfig,
-) -> tuple[Tensor, Tensor]:
-    """Compute RoPE frequencies using YARN/NTK method.
-
-    Uses NTK-aware theta scaling for high-resolution support with
-    timestep-dependent DyPE modulation.
-
-    Args:
-        pos: Position tensor
-        dim: Embedding dimension
-        theta: RoPE base frequency
-        scale: Uniform scaling factor
-        current_sigma: Current noise level (1.0 = full noise, 0.0 = clean)
-        dype_config: DyPE configuration
-
-    Returns:
-        Tuple of (cos, sin) frequency tensors
-    """
-    assert dim % 2 == 0
-
-    device = pos.device
-    dtype = torch.float64 if device.type != "mps" else torch.float32
-
-    # NTK-aware theta scaling with DyPE modulation
-    if scale > 1.0:
-        ntk_alpha = scale ** (dim / (dim - 2))
-
-        # Apply timestep-dependent DyPE modulation
-        mscale = get_timestep_mscale(
-            scale=scale,
-            current_sigma=current_sigma,
-            dype_scale=dype_config.dype_scale,
-            dype_exponent=dype_config.dype_exponent,
-            dype_start_sigma=dype_config.dype_start_sigma,
-        )
-
-        # Modulate NTK alpha by mscale
-        modulated_alpha = 1.0 + (ntk_alpha - 1.0) * mscale
-        scaled_theta = theta * modulated_alpha
-    else:
-        scaled_theta = theta
-
-    freq_seq = torch.arange(0, dim, 2, dtype=dtype, device=device) / dim
-    freqs = 1.0 / (scaled_theta**freq_seq)
-
-    angles = torch.einsum("...n,d->...nd", pos.to(dtype), freqs)
-
-    cos = torch.cos(angles)
-    sin = torch.sin(angles)
-
-    return cos.to(pos.dtype), sin.to(pos.dtype)
-
-
-def compute_ntk_freqs(
-    pos: Tensor,
-    dim: int,
-    theta: int,
-    scale: float,
-) -> tuple[Tensor, Tensor]:
-    """Compute RoPE frequencies using NTK method.
-
-    Neural Tangent Kernel approach - continuous frequency scaling without
-    timestep dependency.
-
-    Args:
-        pos: Position tensor
-        dim: Embedding dimension
-        theta: RoPE base frequency
-        scale: Scaling factor
-
-    Returns:
-        Tuple of (cos, sin) frequency tensors
-    """
-    assert dim % 2 == 0
-
-    device = pos.device
-    dtype = torch.float64 if device.type != "mps" else torch.float32
-
-    # NTK scaling
-    scaled_theta = theta * (scale ** (dim / (dim - 2)))
-
-    freq_seq = torch.arange(0, dim, 2, dtype=dtype, device=device) / dim
-    freqs = 1.0 / (scaled_theta**freq_seq)
-
-    angles = torch.einsum("...n,d->...nd", pos.to(dtype), freqs)
-
-    cos = torch.cos(angles)
-    sin = torch.sin(angles)
-
-    return cos.to(pos.dtype), sin.to(pos.dtype)

invokeai/backend/flux/dype/presets.py

@@ -31,7 +31,6 @@ class DyPEPresetConfig:
     """Preset configuration values."""
 
     base_resolution: int
-    method: str
     dype_scale: float
     dype_exponent: float
     dype_start_sigma: float
@@ -41,7 +40,6 @@ class DyPEPresetConfig:
 DYPE_PRESETS: dict[DyPEPreset, DyPEPresetConfig] = {
     DYPE_PRESET_4K: DyPEPresetConfig(
         base_resolution=1024,
-        method="vision_yarn",
         dype_scale=2.0,
         dype_exponent=2.0,
         dype_start_sigma=1.0,
@@ -84,7 +82,6 @@ def get_dype_config_for_resolution(
     return DyPEConfig(
         enable_dype=True,
         base_resolution=base_resolution,
-        method="vision_yarn",
         dype_scale=dynamic_dype_scale,
         dype_exponent=2.0,
         dype_start_sigma=1.0,
@@ -111,24 +108,24 @@ def get_dype_config_for_area(
         return None
 
     area_ratio = area / base_area
-    effective_side_ratio = math.sqrt(area_ratio)  # 1.0 at base, 2.0 at 2K (if base is 1K)
-
-    # Strength: 0 at base area, 8 at sat_area, clamped thereafter.
-    sat_area = 2027520  # Determined by experimentation where a vertical line appears
-    sat_side_ratio = math.sqrt(sat_area / base_area)
-    dynamic_dype_scale = 8.0 * (effective_side_ratio - 1.0) / (sat_side_ratio - 1.0)
+    effective_side_ratio = math.sqrt(area_ratio)
+    aspect_ratio = max(width, height) / min(width, height)
+    aspect_attenuation = 1.0 if aspect_ratio <= 2.0 else 2.0 / aspect_ratio
+
+    # Retune area mode to be "auto, but area-aware" instead of dramatically
+    # stronger than auto. This keeps it closer to the paper-style core DyPE.
+    dynamic_dype_scale = 2.4 * effective_side_ratio
+    dynamic_dype_scale *= aspect_attenuation
     dynamic_dype_scale = max(0.0, min(dynamic_dype_scale, 8.0))
 
-    # Continuous exponent schedule:
-    # r=1 -> 0.5, r=2 -> 1.0, r=4 -> 2.0 (exact), smoothly varying in between.
-    x = math.log2(effective_side_ratio)
-    dype_exponent = 0.25 * (x**2) + 0.25 * x + 0.5
-    dype_exponent = max(0.5, min(dype_exponent, 2.0))
+    # Use a narrower, higher exponent range than the old area heuristic so the
+    # paper-style scheduler decays more conservatively and artifacts are reduced.
+    exponent_progress = max(0.0, min(effective_side_ratio - 1.0, 1.0))
+    dype_exponent = 1.25 + 0.75 * exponent_progress
 
     return DyPEConfig(
         enable_dype=True,
         base_resolution=base_resolution,
-        method="vision_yarn",
         dype_scale=dynamic_dype_scale,
         dype_exponent=dype_exponent,
         dype_start_sigma=1.0,
@@ -165,7 +162,6 @@ def get_dype_config_from_preset(
         return DyPEConfig(
             enable_dype=True,
             base_resolution=1024,
-            method="vision_yarn",
             dype_scale=custom_scale if custom_scale is not None else dynamic_dype_scale,
             dype_exponent=custom_exponent if custom_exponent is not None else 2.0,
             dype_start_sigma=1.0,
@@ -196,7 +192,6 @@ def get_dype_config_from_preset(
     return DyPEConfig(
         enable_dype=True,
         base_resolution=preset_config.base_resolution,
-        method=preset_config.method,
         dype_scale=preset_config.dype_scale,
         dype_exponent=preset_config.dype_exponent,
         dype_start_sigma=preset_config.dype_start_sigma,