Version 1.1.0: Support Wan
This commit is contained in:
Yael Walker
@@ -1,9 +1,13 @@
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# Regional Adaptive Sampling
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*** Version 1.1.0***: Support for Wan/VACE.
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It's currently experimental status. I tested with Wan2.1 Vace 1.3B T2I and Wan2.2 T2I 5B.
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Please feel free to open an issue if your wan workflow doesn't work (there are a lot of Wan variants, and I might not have handled all cases correctly).
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[Regional Adaptive Sampling](https://github.com/microsoft/RAS) is a new technique for accelerating the inference of diffusion transformers.
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It essentially works as a KV Cache inside the model, picking regions that are likely to be updated by each diffusion step and passing in only those tokens.
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This implementation is simple to use, and compatible with Flux (dev & schnell) at HunYuanVideo. I may add support for other models in the future.
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This implementation is simple to use, and compatible with Flux (dev & schnell), HunYuanVideo, and some Wan variants.
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## Usage
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Apply the `Regional Adaptive Sampling` node to the desired model. It has the following parameters:
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@@ -11,3 +15,6 @@ Apply the `Regional Adaptive Sampling` node to the desired model. It has the fol
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- **warmup_steps**: The number of steps to do without RAS at the beginning. Setting higher will decrease the speedup, and setting it lower will degrade the composition.
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- **hydrate_every**: Every `hydrate_every` steps, we do a full run through the model with all tokens, to refresh the stale cache. Set to 0 to disable and do full RAS.
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- **starvation_scale**: Controls how the model decides which part of the image to focus on. Increasing it will probably shift quality from the main subject to the background. The default of 0.1 is what's used in the paper, and I haven't tried anything else.
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## Todos:
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support batch size > 1 or cfg (makes the token caching logic more complicated)
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "comfyui-regional-adaptive-sampling"
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description = "ComfyUI implementation of Regional Adaptive Sampling, (original implementation at https://github.com/microsoft/RAS)."
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version = "1.0.0"
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version = "1.1.0"
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license = {file = "LICENSE"}
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dependencies = ["torch", "einops", "diffusers"]
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@@ -1,6 +1,5 @@
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from dataclasses import dataclass
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from types import MethodType
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from time import sleep
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import torch
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from torch import nn
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@@ -92,10 +91,12 @@ class RASManager:
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wrapped = SingleStreamBlockWrapper(layer, self, last)
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elif isinstance(layer, LastLayer):
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wrapped = LastLayerWrapper(layer, self)
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elif isinstance(layer, WanAttentionBlock):
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wrapped = WanAttentionBlockWrapper(layer, self, first, last)
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# note: vacewaneattentionblock is a subclass of wanattentionblock
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# so we have to check for the vace block first
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elif isinstance(layer, VaceWanAttentionBlock):
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wrapped = VaceWanAttentionBlockWrapper(layer, self, first, last)
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elif isinstance(layer, WanAttentionBlock):
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wrapped = WanAttentionBlockWrapper(layer, self, first, last)
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elif isinstance(layer, Head):
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wrapped = HeadWrapper(layer, self)
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else:
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@@ -145,6 +146,19 @@ class RASManager:
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)
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patcher.add_object_patch(f"diffusion_model.blocks.{i}", layer)
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# todo, add the vace blocks as well here
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if hasattr(model, "vace_blocks"):
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for i, v in enumerate(model.vace_blocks):
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layer = self.wrap_layer(
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v,
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first=(i == 0),
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# we DONT put back the conditioning tokens
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# because the last vace block is well before the last real block
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last=False,
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# last=(i == (len(model.vace_blocks) - 1))
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)
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patcher.add_object_patch(f"diffusion_model.vace_blocks.{i}", layer)
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# wrap the forward_orig method, to be able to get the timestep
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forward_orig = model.forward_orig
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@@ -641,13 +655,17 @@ class WanAttentionBlockWrapper(WanAttentionBlock):
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return x
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class VaceWanAttentionBlockWrapper(VaceWanAttentionBlock):
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class VaceWanAttentionBlockWrapper(WanAttentionBlockWrapper):
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"""
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Same as the VaceWanAttentionBlock, but uses a RASManager and RASCache to do KV caching.
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"""
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def __init__(
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self, original: VaceWanAttentionBlock, manager: RASManager, first=False
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self,
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original: VaceWanAttentionBlock,
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manager: RASManager,
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first=False,
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last=False,
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):
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nn.Module.__init__(self)
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# Copy attributes, handling the case where some might not exist
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@@ -668,145 +686,26 @@ class VaceWanAttentionBlockWrapper(VaceWanAttentionBlock):
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"ffn",
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"modulation",
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]
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# Handle optional attributes that might not exist
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optional_attributes = ["before_proj", "after_proj"]
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take_attributes_from(original, self, attributes_to_copy)
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# Copy optional attributes if they exist
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for attr in optional_attributes:
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for attr in ["before_proj", "after_proj"]:
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if hasattr(original, attr):
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setattr(self, attr, getattr(original, attr))
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self.manager = manager
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self.k_cache: torch.Tensor
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self.v_cache: torch.Tensor
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self.first = first
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self.last = last
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def forward(
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self, c, x, e, freqs, context, context_img_len=257, transformer_options={}
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):
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# RAS: if this is the first block, then we should drop some tokens
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idx = self.manager.live_indices()
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if self.first:
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# For VaceWan models, we track the sequence length differently
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self.manager.n_txt = (
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0 # Wan doesn't have separate text tokens in the same way
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)
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# TODO: get vace working
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self.manager.n_img = c.shape[1]
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if idx is not None:
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c = c[..., idx, :]
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# VaceWan specific logic (copied from original)
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if self.block_id == 0 and hasattr(self, "before_proj"):
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def forward(self, c, x, **kwargs):
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if hasattr(self, "before_proj"):
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c = self.before_proj(c) + x
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# Call the parent WanAttentionBlock logic but with our modified attention
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# Modulation handling (copied from original)
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if e.ndim < 4:
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e = (
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comfy.model_management.cast_to(
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self.modulation, dtype=c.dtype, device=c.device
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)
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+ e
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).chunk(6, dim=1)
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else:
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e = (
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comfy.model_management.cast_to(
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self.modulation, dtype=c.dtype, device=c.device
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).unsqueeze(0)
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+ e
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).unbind(2)
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# Self-attention with RAS caching
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y = self.self_attn_with_cache(
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torch.addcmul(
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self.repeat_e(e[0], c), self.norm1(c), 1 + self.repeat_e(e[1], c)
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),
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freqs,
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idx,
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transformer_options=transformer_options,
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)
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c = torch.addcmul(c, y, self.repeat_e(e[2], c))
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# Cross-attention & ffn (unchanged from original)
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c = c + self.cross_attn(
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self.norm3(c),
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context,
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context_img_len=context_img_len,
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transformer_options=transformer_options,
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)
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y = self.ffn(
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torch.addcmul(
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self.repeat_e(e[3], c), self.norm2(c), 1 + self.repeat_e(e[4], c)
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)
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)
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c = torch.addcmul(c, y, self.repeat_e(e[5], c))
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# VaceWan specific projection
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c = super().forward(c, **kwargs)
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c_skip = self.after_proj(c)
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return c_skip, c
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def repeat_e(self, e, x):
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"""Helper function for modulation broadcasting"""
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repeats = 1
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if e.size(1) > 1:
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repeats = x.size(1) // e.size(1)
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if repeats == 1:
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return e
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if repeats * e.size(1) == x.size(1):
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return torch.repeat_interleave(e, repeats, dim=1)
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else:
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return torch.repeat_interleave(e, repeats + 1, dim=1)[:, : x.size(1)]
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def self_attn_with_cache(self, x, freqs, idx, transformer_options={}):
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"""Modified self-attention that uses KV caching"""
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# NOTE the following attention function doesn't work
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# it's fucked
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# needs to be replaced
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# just use the one from wan (above)
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b, s, n, d = *x.shape[:2], self.num_heads, x.shape[-1] // self.num_heads
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# Compute QKV like original Wan self-attention
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q = self.self_attn.norm_q(self.self_attn.q(x)).view(b, s, n, d)
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k = self.self_attn.norm_k(self.self_attn.k(x)).view(b, s, n, d)
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v = self.self_attn.v(x).view(b, s, n, d)
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# Apply rope to Q and K
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from comfy.ldm.flux.math import apply_rope1
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if idx is not None:
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freqs = freqs[:, idx]
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q = apply_rope1(q, freqs)
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k = apply_rope1(k, freqs)
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# RAS: KV Cache management
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if idx is None:
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# Full hydrate - store the complete k,v tensors as cache
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self.k_cache = k
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self.v_cache = v
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else:
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self.k_cache[:, idx, :, :] = k
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self.v_cache[:, idx, :, :] = v
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q_flat = q.view(b, s, n * d)
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k_flat = self.k_cache.view(b, self.k_cache.shape[1], n * d)
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v_flat = self.v_cache.view(b, self.v_cache.shape[1], n * d)
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attn = optimized_attention(
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q_flat,
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k_flat,
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v_flat,
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heads=n,
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transformer_options=transformer_options,
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)
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result = self.self_attn.o(attn)
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return result
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class HeadWrapper(Head):
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"""
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