Merge 5c6fcbda91babbefc6d907a7febe3c0450f05f74 into bed12674a1d2c4bfdfbdd098686390f807996c90

comfy_extras/nodes_dype.py

@@ -0,0 +1,366 @@
+# adapted from https://github.com/guyyariv/DyPE
+
+import math
+
+import numpy as np
+import torch
+from typing_extensions import override
+
+from comfy.model_patcher import ModelPatcher
+from comfy_api.latest import ComfyExtension, io
+
+
+def find_correction_factor(num_rotations, dim, base, max_position_embeddings):
+    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+        2 * math.log(base)
+    )  # Inverse dim formula to find number of rotations
+
+
+def find_correction_range(low_ratio, high_ratio, dim, base, ori_max_pe_len):
+    """Find the correction range for NTK-by-parts interpolation"""
+    low = np.floor(find_correction_factor(low_ratio, dim, base, ori_max_pe_len))
+    high = np.ceil(find_correction_factor(high_ratio, dim, base, ori_max_pe_len))
+    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
+
+
+def linear_ramp_mask(min, max, dim):
+    if min == max:
+        max += 0.001  # Prevent singularity
+
+    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+    ramp_func = torch.clamp(linear_func, 0, 1)
+    return ramp_func
+
+
+def find_newbase_ntk(dim, base, scale):
+    """Calculate the new base for NTK-aware scaling"""
+    return base * (scale ** (dim / (dim - 2)))
+
+
+def get_1d_rotary_pos_embed(
+    dim: int,
+    pos: np.ndarray | int,
+    theta: float = 10000.0,
+    use_real=False,
+    linear_factor=1.0,
+    ntk_factor=1.0,
+    repeat_interleave_real=True,
+    freqs_dtype=torch.float32,
+    yarn=False,
+    max_pe_len=None,
+    ori_max_pe_len=64,
+    dype=False,
+    current_timestep=1.0,
+):
+    """
+    Precompute the frequency tensor for complex exponentials with RoPE.
+    Supports YARN interpolation for vision transformers.
+
+    Args:
+        dim (`int`):
+            Dimension of the frequency tensor.
+        pos (`np.ndarray` or `int`):
+            Position indices for the frequency tensor. [S] or scalar.
+        theta (`float`, *optional*, defaults to 10000.0):
+            Scaling factor for frequency computation.
+        use_real (`bool`, *optional*, defaults to False):
+            If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+        linear_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for linear interpolation.
+        ntk_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor for NTK-Aware RoPE.
+        repeat_interleave_real (`bool`, *optional*, defaults to True):
+            If True and use_real, real and imaginary parts are interleaved with themselves to reach dim.
+            Otherwise, they are concatenated.
+        freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
+            Data type of the frequency tensor.
+        yarn (`bool`, *optional*, defaults to False):
+            If True, use YARN interpolation combining NTK, linear, and base methods.
+        max_pe_len (`int`, *optional*):
+            Maximum position encoding length (current patches for vision models).
+        ori_max_pe_len (`int`, *optional*, defaults to 64):
+            Original maximum position encoding length (base patches for vision models).
+        dype (`bool`, *optional*, defaults to False):
+            If True, enable DyPE (Dynamic Position Encoding) with timestep-aware scaling.
+        current_timestep (`float`, *optional*, defaults to 1.0):
+            Current timestep for DyPE, normalized to [0, 1] where 1 is pure noise.
+
+    Returns:
+        `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
+            If use_real=True, returns tuple of (cos, sin) tensors.
+    """
+    assert dim % 2 == 0
+
+    if isinstance(pos, int):
+        pos = torch.arange(pos)
+    if isinstance(pos, np.ndarray):
+        pos = torch.from_numpy(pos)
+
+    device = pos.device
+
+    if yarn and max_pe_len is not None and max_pe_len > ori_max_pe_len:
+        if not isinstance(max_pe_len, torch.Tensor):
+            max_pe_len = torch.tensor(max_pe_len, dtype=freqs_dtype, device=device)
+
+        scale = torch.clamp_min(max_pe_len / ori_max_pe_len, 1.0)
+
+        beta_0 = 1.25
+        beta_1 = 0.75
+        gamma_0 = 16
+        gamma_1 = 2
+
+        freqs_base = 1.0 / (
+            theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim)
+        )
+
+        freqs_linear = 1.0 / torch.einsum(
+            "..., f -> ... f",
+            scale,
+            (
+                theta
+                ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim)
+            ),
+        )
+
+        new_base = find_newbase_ntk(dim, theta, scale)
+        if new_base.dim() > 0:
+            new_base = new_base.view(-1, 1)
+        freqs_ntk = 1.0 / torch.pow(
+            new_base, (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim)
+        )
+        if freqs_ntk.dim() > 1:
+            freqs_ntk = freqs_ntk.squeeze()
+
+        if dype:
+            beta_0 = beta_0 ** (2.0 * (current_timestep**2.0))
+            beta_1 = beta_1 ** (2.0 * (current_timestep**2.0))
+
+        low, high = find_correction_range(beta_0, beta_1, dim, theta, ori_max_pe_len)
+        low = max(0, low)
+        high = min(dim // 2, high)
+
+        freqs_mask = 1 - linear_ramp_mask(low, high, dim // 2).to(device).to(
+            freqs_dtype
+        )
+        freqs = freqs_linear * (1 - freqs_mask) + freqs_ntk * freqs_mask
+
+        if dype:
+            gamma_0 = gamma_0 ** (2.0 * (current_timestep**2.0))
+            gamma_1 = gamma_1 ** (2.0 * (current_timestep**2.0))
+
+        low, high = find_correction_range(gamma_0, gamma_1, dim, theta, ori_max_pe_len)
+        low = max(0, low)
+        high = min(dim // 2, high)
+
+        freqs_mask = 1 - linear_ramp_mask(low, high, dim // 2).to(device).to(
+            freqs_dtype
+        )
+        freqs = freqs * (1 - freqs_mask) + freqs_base * freqs_mask
+
+    else:
+        theta_ntk = theta * ntk_factor
+        freqs = (
+            1.0
+            / (
+                theta_ntk
+                ** (torch.arange(0, dim, 2, dtype=freqs_dtype, device=device) / dim)
+            )
+            / linear_factor
+        )
+
+    freqs = pos.unsqueeze(-1) * freqs
+
+    is_npu = freqs.device.type == "npu"
+    if is_npu:
+        freqs = freqs.float()
+
+    if use_real and repeat_interleave_real:
+        freqs_cos = (
+            freqs.cos()
+            .repeat_interleave(2, dim=-1, output_size=freqs.shape[-1] * 2)
+            .float()
+        )
+        freqs_sin = (
+            freqs.sin()
+            .repeat_interleave(2, dim=-1, output_size=freqs.shape[-1] * 2)
+            .float()
+        )
+
+        if yarn and max_pe_len is not None and max_pe_len > ori_max_pe_len:
+            mscale = torch.where(
+                scale <= 1.0, torch.tensor(1.0), 0.1 * torch.log(scale) + 1.0
+            ).to(scale)
+            freqs_cos = freqs_cos * mscale
+            freqs_sin = freqs_sin * mscale
+
+        return freqs_cos, freqs_sin
+    elif use_real:
+        freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float()
+        freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float()
+        return freqs_cos, freqs_sin
+    else:
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+        return freqs_cis
+
+
+class FluxPosEmbed(torch.nn.Module):
+    def __init__(
+        self,
+        theta: int,
+        axes_dim: list[int],
+        method: str = "yarn",
+        dype: bool = True,
+    ):
+        super().__init__()
+        self.theta = theta
+        self.axes_dim = axes_dim
+        self.base_resolution = 1024
+        self.base_patches = (self.base_resolution // 8) // 2
+        self.method = method
+        self.dype = dype if method != "base" else False
+        self.current_timestep = 1.0
+
+    def set_timestep(self, timestep: float):
+        """Set current timestep for DyPE. Timestep normalized to [0, 1] where 1 is pure noise."""
+        self.current_timestep = timestep
+
+    def forward(self, ids: torch.Tensor) -> torch.Tensor:
+        n_axes = ids.shape[-1]
+        cos_out = []
+        sin_out = []
+        pos = ids.float()
+        freqs_dtype = torch.bfloat16 if ids.device.type == "cuda" else torch.float32
+
+        for i in range(n_axes):
+            axis_dim = self.axes_dim[i]
+            axis_pos = pos[..., i]
+
+            common_kwargs = {
+                "dim": axis_dim,
+                "pos": axis_pos,
+                "theta": self.theta,
+                "repeat_interleave_real": True,
+                "use_real": True,
+                "freqs_dtype": freqs_dtype,
+            }
+
+            if i > 0:
+                max_pos = axis_pos.max().item()
+                current_patches = max_pos + 1
+
+                if self.method == "yarn" and current_patches > self.base_patches:
+                    max_pe_len = torch.tensor(
+                        current_patches, dtype=freqs_dtype, device=pos.device
+                    )
+                    cos, sin = get_1d_rotary_pos_embed(
+                        **common_kwargs,
+                        yarn=True,
+                        max_pe_len=max_pe_len,
+                        ori_max_pe_len=self.base_patches,
+                        dype=self.dype,
+                        current_timestep=self.current_timestep,
+                    )
+
+                elif self.method == "ntk" and current_patches > self.base_patches:
+                    base_ntk = (current_patches / self.base_patches) ** (
+                        self.axes_dim[i] / (self.axes_dim[i] - 2)
+                    )
+                    ntk_factor = (
+                        base_ntk ** (2.0 * (self.current_timestep**2.0))
+                        if self.dype
+                        else base_ntk
+                    )
+                    ntk_factor = max(1.0, ntk_factor)
+
+                    cos, sin = get_1d_rotary_pos_embed(
+                        **common_kwargs, ntk_factor=ntk_factor
+                    )
+
+                else:
+                    cos, sin = get_1d_rotary_pos_embed(**common_kwargs)
+            else:
+                cos, sin = get_1d_rotary_pos_embed(**common_kwargs)
+
+            cos_out.append(cos)
+            sin_out.append(sin)
+
+        emb_parts = []
+        for cos, sin in zip(cos_out, sin_out):
+            cos_reshaped = cos.view(*cos.shape[:-1], -1, 2)[..., :1]
+            sin_reshaped = sin.view(*sin.shape[:-1], -1, 2)[..., :1]
+            row1 = torch.cat([cos_reshaped, -sin_reshaped], dim=-1)
+            row2 = torch.cat([sin_reshaped, cos_reshaped], dim=-1)
+            matrix = torch.stack([row1, row2], dim=-2)
+            emb_parts.append(matrix)
+
+        emb = torch.cat(emb_parts, dim=-3)
+        return emb.unsqueeze(1).to(ids.device)
+
+
+def apply_dype_flux(model: ModelPatcher, method: str) -> ModelPatcher:
+    if getattr(model.model, "_dype", None) == method:
+        return model
+
+    m = model.clone()
+    m.model._dype = method
+
+    _pe_embedder = m.model.diffusion_model.pe_embedder
+    _theta, _axes_dim = _pe_embedder.theta, _pe_embedder.axes_dim
+
+    pos_embedder = FluxPosEmbed(_theta, _axes_dim, method, dype=True)
+    m.add_object_patch("diffusion_model.pe_embedder", pos_embedder)
+
+    sigma_max = m.model.model_sampling.sigma_max.item()
+
+    def dype_wrapper_function(model_function, args_dict):
+        timestep_tensor = args_dict.get("timestep")
+        if timestep_tensor is not None and timestep_tensor.numel() > 0:
+            current_sigma = timestep_tensor.flatten()[0].item()
+
+            if sigma_max > 0:
+                normalized_timestep = min(max(current_sigma / sigma_max, 0.0), 1.0)
+                pos_embedder.set_timestep(normalized_timestep)
+
+        input_x, c = args_dict.get("input"), args_dict.get("c", {})
+        return model_function(input_x, args_dict.get("timestep"), **c)
+
+    m.set_model_unet_function_wrapper(dype_wrapper_function)
+
+    return m
+
+
+class DyPEPatchModelFlux(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DyPEPatchModelFlux",
+            display_name="DyPE Patch Model (Flux)",
+            category="_for_testing",
+            inputs=[
+                io.Model.Input("model"),
+                io.Combo.Input(
+                    "method",
+                    options=["yarn", "ntk", "base"],
+                    default="yarn",
+                ),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model: ModelPatcher, method: str) -> io.NodeOutput:
+        m = apply_dype_flux(model, method)
+        return io.NodeOutput(m)
+
+
+class DyPEExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            DyPEPatchModelFlux,
+        ]
+
+
+async def comfy_entrypoint():
+    return DyPEExtension()

nodes.py

@@ -2357,6 +2357,7 @@ async def init_builtin_extra_nodes():
         "nodes_rope.py",
         "nodes_logic.py",
         "nodes_nop.py",
+        "nodes_dype.py",
     ]
 
     import_failed = []