wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-07-18 20:38:15 +08:00
Code Issues Actions 27 Packages Projects Releases Wiki Activity

Merge 641ce082a9 into 0f42ba5146

Browse Source
This commit is contained in:
Silver 2026-07-18 02:24:28 +10:00 committed by GitHub
commit 54a60da2cf
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 129 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

120
comfy/ldm/krea2/model.py
View File

@ -158,11 +158,54 @@ class SingleStreamBlock(nn.Module):
self.attn = Attention(features, heads, kvheads=kvheads, bias=bias, device=device, dtype=dtype, operations=operations)
self.mlp = SwiGLU(features, multiplier, bias, device=device, dtype=dtype, operations=operations)
def forward(self, x, vec, freqs, mask=None, transformer_options={}):
prescale, preshift, pregate, postscale, postshift, postgate = self.mod(vec)
x = x + pregate * self.attn((1 + prescale) * self.prenorm(x) + preshift, freqs, mask, transformer_options=transformer_options)
x = x + postgate * self.mlp((1 + postscale) * self.postnorm(x) + postshift)
return x
def forward(self, x, vec, freqs, mask=None, transformer_options={}, vec_ref=None, split=None):
if vec_ref is not None and split is not None:
m = self.mod(vec)
r = self.mod(vec_ref)
# prenorm and attention
h = self.prenorm(x)
h_mod = torch.cat(
(
(1 + m[0]) * h[:, :split] + m[1],
(1 + r[0]) * h[:, split:] + r[1]
),
dim=1
)
attn_out = self.attn(h_mod, freqs, mask, transformer_options=transformer_options)
attn_gate = torch.cat(
(
m[2] * attn_out[:, :split],
r[2] * attn_out[:, split:]
),
dim=1
)
x = x + attn_gate
# postnorm and mlp
h = self.postnorm(x)
h_mod = torch.cat(
(
(1 + m[3]) * h[:, :split] + m[4],
(1 + r[3]) * h[:, split:] + r[4]
),
dim=1
)
mlp_out = self.mlp(h_mod)
mlp_gate = torch.cat(
(
m[5] * mlp_out[:, :split],
r[5] * mlp_out[:, split:]
),
dim=1
)
x = x + mlp_gate
return x
else:
prescale, preshift, pregate, postscale, postshift, postgate = self.mod(vec)
x = x + pregate * self.attn((1 + prescale) * self.prenorm(x) + preshift, freqs, mask, transformer_options=transformer_options)
x = x + postgate * self.mlp((1 + postscale) * self.postnorm(x) + postshift)
return x
class LastLayer(nn.Module):
@ -253,21 +296,47 @@ class SingleStreamDiT(nn.Module):
context = self.txtmlp(context)
txtlen, imglen = context.shape[1], img.shape[1]
combined = torch.cat((context, img), dim=1)
ref_latents = kwargs.get("ref_latents", None)
ref_latents_method = kwargs.get("ref_latents_method", "index_timestep_zero")
if ref_latents_method is None:
ref_latents_method = "index_timestep_zero"
if ref_latents_method != "index_timestep_zero":
raise ValueError(f"Unsupported Krea2 reference latent method: {ref_latents_method}")
device = img.device
ref_tokens_list, ref_pos_ids_list, ref_num_tokens = self._process_ref_latents(
ref_latents, device, bs, h_, w_, img.dtype
)
if len(ref_num_tokens) > 0:
transformer_options = transformer_options.copy()
if "reference_image_num_tokens" not in transformer_options:
transformer_options["reference_image_num_tokens"] = []
transformer_options["reference_image_num_tokens"].extend(ref_num_tokens)
combined = torch.cat([context, img] + ref_tokens_list, dim=1)
# Position ids: text at 0, image at (0, h_idx, w_idx).
device = combined.device
txtpos = torch.zeros(bs, txtlen, 3, device=device, dtype=torch.float32)
imgids = torch.zeros(h_, w_, 3, device=device, dtype=torch.float32)
imgids[..., 1] = torch.arange(h_, device=device, dtype=torch.float32)[:, None]
imgids[..., 2] = torch.arange(w_, device=device, dtype=torch.float32)[None, :]
imgpos = imgids.reshape(1, h_ * w_, 3).repeat(bs, 1, 1)
pos = torch.cat((txtpos, imgpos), dim=1)
pos = torch.cat([txtpos, imgpos] + ref_pos_ids_list, dim=1)
freqs = self.pe_embedder(pos)
for block in self.blocks:
combined = block(combined, tvec, freqs, None, transformer_options=transformer_options)
if len(ref_num_tokens) > 0:
# Compute tvec0 for timestep=0 (reference)
t0 = self.tmlp(timestep_embedding(torch.zeros_like(timesteps), self.tdim).unsqueeze(1).to(img.dtype))
tvec0 = self.tproj(t0)
split = txtlen + imglen
for block in self.blocks:
combined = block(combined, tvec, freqs, None, transformer_options=transformer_options, vec_ref=tvec0, split=split)
else:
for block in self.blocks:
combined = block(combined, tvec, freqs, None, transformer_options=transformer_options)
final = self.last(combined, t)
out = final[:, txtlen:txtlen + imglen, :]
@ -288,3 +357,34 @@ class SingleStreamDiT(nn.Module):
f"Load the text encoder with CLIPLoader type 'krea2'."
)
return context.reshape(b, seq, self.txtlayers, self.txtdim)
def _process_ref_latents(self, ref_latents, device, bs, h_main, w_main, dtype):
ref_tokens_list = []
ref_pos_ids_list = []
ref_num_tokens = []
patch = self.patch
if ref_latents is not None:
for i, ref in enumerate(ref_latents):
if ref.ndim == 5:
ref_b5, ref_c5, ref_t5, ref_h5, ref_w5 = ref.shape
ref = ref.movedim(2, 1).reshape(ref_b5 * ref_t5, ref_c5, ref_h5, ref_w5)
ref_pad = comfy.ldm.common_dit.pad_to_patch_size(ref, (patch, patch))
ref_pad = comfy.utils.repeat_to_batch_size(ref_pad, bs)
ref_pad = ref_pad.to(device=device, dtype=dtype)
ref_gh = ref_pad.shape[-2] // patch
ref_gw = ref_pad.shape[-1] // patch
ref_tokens = rearrange(ref_pad, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch, pw=patch)
ref_tokens = self.first(ref_tokens)
ref_tokens_list.append(ref_tokens)
ref_num_tokens.append(ref_tokens.shape[1])
ref_pos_ids = torch.zeros(ref_gh, ref_gw, 3, device=device, dtype=torch.float32)
ref_pos_ids[..., 0] = i + 1.0
ref_pos_ids[..., 1] = torch.arange(ref_gh, device=device, dtype=torch.float32)[:, None]
ref_pos_ids[..., 2] = torch.arange(ref_gw, device=device, dtype=torch.float32)[None, :]
ref_pos_ids = ref_pos_ids.reshape(1, ref_gh * ref_gw, 3).repeat(bs, 1, 1)
ref_pos_ids_list.append(ref_pos_ids)
return ref_tokens_list, ref_pos_ids_list, ref_num_tokens

19
comfy/model_base.py
View File

@ -2317,12 +2317,31 @@ class Ideogram4(BaseModel):
class Krea2(BaseModel):
def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.krea2.model.SingleStreamDiT)
self.memory_usage_factor_conds = ("ref_latents",)
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
cross_attn = kwargs.get("cross_attn", None)
if cross_attn is not None:
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
ref_latents = kwargs.get("reference_latents", None)
if ref_latents is not None:
latents = []
for lat in ref_latents:
latents.append(self.process_latent_in(lat))
out['ref_latents'] = comfy.conds.CONDList(latents)
ref_latents_method = kwargs.get("reference_latents_method", None)
if ref_latents_method is not None:
out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method)
return out
def extra_conds_shapes(self, **kwargs):
out = {}
ref_latents = kwargs.get("reference_latents", None)
if ref_latents is not None:
out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
return out
class HunyuanImage21(BaseModel):