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from typing import Any
import types
import torch
def _rope_encode_with_source_id(
wan_model,
t,
h,
w,
*,
t_start=0,
steps_t=None,
steps_h=None,
steps_w=None,
device=None,
dtype=None,
transformer_options=None,
source_id=0,
):
freqs = wan_model.rope_encode(
t,
h,
w,
t_start=t_start,
steps_t=steps_t,
steps_h=steps_h,
steps_w=steps_w,
device=device,
dtype=dtype,
transformer_options=transformer_options or {},
)
if source_id:
from comfy.ldm.flux.math import rope
d = wan_model.dim // wan_model.num_heads
pos = torch.tensor([[float(source_id)]], device=freqs.device, dtype=torch.float32)
id_rot = rope(pos, d, wan_model.rope_embedder.theta).reshape(1, 1, 1, d // 2, 2, 2).to(freqs.dtype)
freqs = torch.einsum("...ij,...jk->...ik", freqs, id_rot)
return freqs
def _bernini_forward_orig(
wan_model,
x,
t,
context,
*,
clip_fea=None,
freqs=None,
transformer_options=None,
**kwargs,
):
import comfy.ldm.wan.model as wan_model_module
if transformer_options is None:
transformer_options = {}
x = wan_model.patch_embedding(x.float()).to(x.dtype)
grid_sizes = x.shape[2:]
transformer_options["grid_sizes"] = grid_sizes
x = x.flatten(2).transpose(1, 2)
e = wan_model.time_embedding(
wan_model_module.sinusoidal_embedding_1d(wan_model.freq_dim, t.flatten()).to(dtype=x[0].dtype)
)
e = e.reshape(t.shape[0], -1, e.shape[-1])
e0 = wan_model.time_projection(e).unflatten(2, (6, wan_model.dim))
full_ref = None
if wan_model.ref_conv is not None:
full_ref = kwargs.get("reference_latent", None)
if full_ref is not None:
full_ref = wan_model.ref_conv(full_ref).flatten(2).transpose(1, 2)
x = torch.concat((full_ref, x), dim=1)
context_latents = kwargs.get("context_latents", None)
main_len = x.shape[1]
if context_latents is not None:
for lat in context_latents:
cl = wan_model.patch_embedding(lat.float().to(x.device)).to(x.dtype).flatten(2).transpose(1, 2)
x = torch.cat([x, cl], dim=1)
context = wan_model.text_embedding(context)
context_img_len = None
if clip_fea is not None:
if wan_model.img_emb is not None:
context_clip = wan_model.img_emb(clip_fea)
context = torch.concat([context_clip, context], dim=1)
context_img_len = clip_fea.shape[-2]
patches_replace = transformer_options.get("patches_replace", {})
blocks_replace = patches_replace.get("dit", {})
transformer_options["total_blocks"] = len(wan_model.blocks)
transformer_options["block_type"] = "double"
for i, block in enumerate(wan_model.blocks):
transformer_options["block_index"] = i
if ("double_block", i) in blocks_replace:
def block_wrap(args):
return {
"img": block(
args["img"],
context=args["txt"],
e=args["vec"],
freqs=args["pe"],
context_img_len=context_img_len,
transformer_options=args["transformer_options"],
)
}
out = blocks_replace[("double_block", i)](
{
"img": x,
"txt": context,
"vec": e0,
"pe": freqs,
"transformer_options": transformer_options,
},
{"original_block": block_wrap},
)
x = out["img"]
else:
x = block(
x,
e=e0,
freqs=freqs,
context=context,
context_img_len=context_img_len,
transformer_options=transformer_options,
)
x = wan_model.head(x, e)
if context_latents is not None:
x = x[:, :main_len]
if full_ref is not None:
x = x[:, full_ref.shape[1] :]
return wan_model.unpatchify(x, grid_sizes)
def bind_bernini_conds(model):
import comfy.conds
base_model = model.model
if getattr(base_model, "_bernini_conds", False):
return
orig_extra_conds = base_model.extra_conds
orig_resize_cond = base_model.resize_cond_for_context_window
def extra_conds(self, **kwargs):
out = orig_extra_conds(**kwargs)
context_latents = kwargs.get("context_latents", None)
if context_latents is not None:
out["context_latents"] = comfy.conds.CONDList([self.process_latent_in(lat) for lat in context_latents])
return out
def resize_cond_for_context_window(self, cond_key, cond_value, window, x_in, device, retain_index_list=[]):
if cond_key == "context_latents" and isinstance(getattr(cond_value, "cond", None), list):
dim = window.dim
out = []
for lat in cond_value.cond:
if lat.ndim > dim and lat.shape[dim] > 1 and lat.shape[dim] == x_in.shape[dim]:
idx = tuple([slice(None)] * dim + [window.index_list])
out.append(lat[idx].to(device))
else:
out.append(lat.to(device))
return cond_value._copy_with(out)
return orig_resize_cond(cond_key, cond_value, window, x_in, device, retain_index_list=retain_index_list)
base_model.extra_conds = types.MethodType(extra_conds, base_model)
base_model.resize_cond_for_context_window = types.MethodType(resize_cond_for_context_window, base_model)
base_model._bernini_conds = True
def bernini_forward_wrapper(
executor,
x,
timestep,
context,
clip_fea=None,
time_dim_concat=None,
transformer_options=None,
**kwargs,
):
import comfy.ldm.common_dit
context_latents = kwargs.get("context_latents", None)
if context_latents is None:
return executor(x, timestep, context, clip_fea, time_dim_concat, transformer_options, **kwargs)
if transformer_options is None:
transformer_options = {}
wan_model = executor.class_obj
_, _, t, h, w = x.shape
x = comfy.ldm.common_dit.pad_to_patch_size(x, wan_model.patch_size)
t_len = t
if time_dim_concat is not None:
time_dim_concat = comfy.ldm.common_dit.pad_to_patch_size(time_dim_concat, wan_model.patch_size)
x = torch.cat([x, time_dim_concat], dim=2)
t_len = x.shape[2]
if wan_model.ref_conv is not None and "reference_latent" in kwargs:
t_len += 1
freqs = wan_model.rope_encode(t_len, h, w, device=x.device, dtype=x.dtype, transformer_options=transformer_options)
padded_context_latents = [
comfy.ldm.common_dit.pad_to_patch_size(lat, wan_model.patch_size) for lat in context_latents
]
for i, lat in enumerate(padded_context_latents):
freqs = torch.cat(
[
freqs,
_rope_encode_with_source_id(
wan_model,
lat.shape[-3],
lat.shape[-2],
lat.shape[-1],
device=x.device,
dtype=x.dtype,
transformer_options=transformer_options,
source_id=i + 1,
),
],
dim=1,
)
kwargs = {**kwargs, "context_latents": padded_context_latents}
out = _bernini_forward_orig(
wan_model,
x,
timestep,
context,
clip_fea=clip_fea,
freqs=freqs,
transformer_options=transformer_options,
**kwargs,
)
return out[:, :, :t, :h, :w] |