mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-06-13 01:30:32 +08:00
model loaded and able to run however vector direction still wrong tho
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lodestone-rock
parent
54099360bd
commit
033a534f7c
@ -991,14 +991,12 @@ class SimpleMLPAdaLN(nn.Module):
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out_channels: int,
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z_channels: int,
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num_res_blocks: int,
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patch_size: int,
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max_freqs: int = 8,
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):
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super().__init__()
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self.patch_size = patch_size
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# Project backbone hidden state → per-position conditioning
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self.cond_embed = nn.Linear(z_channels, patch_size ** 2 * model_channels)
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# Project backbone hidden state → per-patch conditioning
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self.cond_embed = nn.Linear(z_channels, model_channels)
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nn.init.xavier_uniform_(self.cond_embed.weight)
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nn.init.constant_(self.cond_embed.bias, 0)
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@ -1018,12 +1016,15 @@ class SimpleMLPAdaLN(nn.Module):
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self.final_layer = DCTFinalLayer(model_channels, out_channels)
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def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
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# x: [B*N, P^2, C], c: [B*N, dim]
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x = self.input_embedder(x) # [B*N, P^2, model_channels]
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y = self.cond_embed(c).reshape(c.shape[0], self.patch_size ** 2, -1) # [B*N, P^2, model_channels]
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# x: [B*N, 1, P^2*C], c: [B*N, dim]
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original_dtype = x.dtype
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weight_dtype = self.cond_embed.weight.dtype
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x = self.input_embedder(x) # [B*N, 1, model_channels]
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y = self.cond_embed(c.to(weight_dtype)).unsqueeze(1) # [B*N, 1, model_channels]
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x = x.to(weight_dtype)
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for block in self.res_blocks:
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x = block(x, y)
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return self.final_layer(x) # [B*N, P^2, C]
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return self.final_layer(x).to(original_dtype) # [B*N, 1, P^2*C]
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#############################################################################
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@ -1052,6 +1053,7 @@ class NextDiTPixelSpace(NextDiT):
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decoder_hidden_size: int = 3840,
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decoder_num_res_blocks: int = 4,
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decoder_max_freqs: int = 8,
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decoder_in_channels: int = None, # full flattened patch size (patch_size^2 * in_channels)
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use_x0: bool = False,
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# all NextDiT args forwarded unchanged
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**kwargs,
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@ -1065,13 +1067,15 @@ class NextDiTPixelSpace(NextDiT):
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in_channels = kwargs.get("in_channels", 4)
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dim = kwargs.get("dim", 4096)
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# decoder_in_channels is the full flattened patch: patch_size^2 * in_channels
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dec_in_ch = decoder_in_channels if decoder_in_channels is not None else patch_size ** 2 * in_channels
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self.dec_net = SimpleMLPAdaLN(
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in_channels=in_channels,
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in_channels=dec_in_ch,
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model_channels=decoder_hidden_size,
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out_channels=in_channels,
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out_channels=dec_in_ch,
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z_channels=dim,
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num_res_blocks=decoder_num_res_blocks,
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patch_size=patch_size,
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max_freqs=decoder_max_freqs,
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)
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@ -1079,99 +1083,99 @@ class NextDiTPixelSpace(NextDiT):
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self.register_buffer("__x0__", torch.tensor([]))
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# ------------------------------------------------------------------
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# Override patchify_and_embed to also return the raw pixel patches
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# Forward — mirrors NextDiT._forward exactly, replacing final_layer
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# with the pixel-space dec_net decoder.
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# ------------------------------------------------------------------
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def patchify_and_embed(self, x, cap_feats, cap_mask, t, num_tokens, transformer_options={}):
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# Run the parent implementation unchanged; we capture pixel values
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# separately in _forward before calling this.
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return super().patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options)
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def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, ref_latents=[], ref_contexts=[], siglip_feats=[], transformer_options={}, **kwargs):
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omni = len(ref_latents) > 0
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if omni:
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timesteps = torch.cat([timesteps * 0, timesteps], dim=0)
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# ------------------------------------------------------------------
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# Forward
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# ------------------------------------------------------------------
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def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
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t = 1.0 - timesteps
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cap_feats = context
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cap_mask = attention_mask
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bs, c, h, w = x.shape
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x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
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t = self.t_embedder(t * self.time_scale, dtype=x.dtype) # (N, D)
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t = self.t_embedder(t * self.time_scale, dtype=x.dtype)
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adaln_input = t
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cap_feats = self.cap_embedder(cap_feats)
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if self.clip_text_pooled_proj is not None:
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pooled = kwargs.get("clip_text_pooled", None)
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if pooled is not None:
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pooled = self.clip_text_pooled_proj(pooled)
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else:
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pooled = torch.zeros((1, self.clip_text_dim), device=x.device, dtype=x.dtype)
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pooled = torch.zeros((x.shape[0], self.clip_text_dim), device=x.device, dtype=x.dtype)
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adaln_input = self.time_text_embed(torch.cat((t, pooled), dim=-1))
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# ---- capture raw pixel patches before backbone embedding ----
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# ---- capture raw pixel patches before patchify_and_embed embeds them ----
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pH = pW = self.patch_size
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B, C, H, W = x.shape
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# [B, N, P*P*C] (same layout as what x_embedder receives)
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pixel_patches = (
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x.view(B, C, H // pH, pH, W // pW, pW)
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.permute(0, 2, 4, 3, 5, 1) # [B, Ht, Wt, pH, pW, C]
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.flatten(3) # [B, Ht, Wt, pH*pW*C]
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.flatten(1, 2) # [B, N, pH*pW*C]
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)
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# reshape to [B*N, P^2, C] for the decoder
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N = pixel_patches.shape[1]
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pixel_values = pixel_patches.reshape(B * N, pH * pW, C)
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# decoder sees one token per patch: [B*N, 1, P^2*C]
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pixel_values = pixel_patches.reshape(B * N, 1, pH * pW * C)
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patches = transformer_options.get("patches", {})
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x_is_tensor = isinstance(x, torch.Tensor)
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img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(
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x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options
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img, mask, img_size, cap_size, freqs_cis, timestep_zero_index = self.patchify_and_embed(
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x, cap_feats, cap_mask, adaln_input, num_tokens,
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ref_latents=ref_latents, ref_contexts=ref_contexts,
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siglip_feats=siglip_feats, transformer_options=transformer_options
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)
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freqs_cis = freqs_cis.to(img.device)
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transformer_options["total_blocks"] = len(self.layers)
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transformer_options["block_type"] = "double"
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img_input = img
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for i, layer in enumerate(self.layers):
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img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
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transformer_options["block_index"] = i
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img = layer(img, mask, freqs_cis, adaln_input, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options)
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if "double_block" in patches:
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for p in patches["double_block"]:
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out = p({"img": img[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
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out = p({"img": img[:, cap_size[0]:], "img_input": img_input[:, cap_size[0]:], "txt": img[:, :cap_size[0]], "pe": freqs_cis[:, cap_size[0]:], "vec": adaln_input, "x": x, "block_index": i, "transformer_options": transformer_options})
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if "img" in out:
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img[:, cap_size[0]:] = out["img"]
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if "txt" in out:
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img[:, :cap_size[0]] = out["txt"]
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# ---- pixel-space decoder ----
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# img: [B, txt_len+N, dim] → extract image tokens → [B, N, dim]
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img_hidden = img[:, cap_size[0]:, :] # [B, N, dim]
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# per-patch conditioning: [B*N, dim]
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decoder_cond = img_hidden.reshape(B * N, self.dim)
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# ---- pixel-space decoder (replaces final_layer + unpatchify) ----
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# img may have padding tokens beyond N; only the first N are real image patches
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img_hidden = img[:, cap_size[0]:cap_size[0] + N, :] # [B, N, dim]
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decoder_cond = img_hidden.reshape(B * N, self.dim) # [B*N, dim]
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# decode: [B*N, P^2, C]
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output = self.dec_net(pixel_values, decoder_cond)
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output = self.dec_net(pixel_values, decoder_cond) # [B*N, 1, P^2*C]
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output = output.reshape(B, N, -1) # [B, N, P^2*C]
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# reshape back: [B*N, P^2, C] → [B, N, P^2*C]
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output = output.reshape(B, N, -1)
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# unpatchify expects [B, txt_len+N, P^2*C] with cap tokens prepended
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# re-prepend a zero placeholder for the cap positions so unpatchify works
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# prepend zero cap placeholder so unpatchify indexing works unchanged
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cap_placeholder = torch.zeros(
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B, cap_size[0], output.shape[-1], device=output.device, dtype=output.dtype
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)
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img_out = torch.cat([cap_placeholder, output], dim=1)
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img_out = self.unpatchify(img_out, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
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img_out = self.unpatchify(
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torch.cat([cap_placeholder, output], dim=1),
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img_size, cap_size, return_tensor=x_is_tensor
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)[:, :, :h, :w]
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return -img_out
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def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
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# _forward returns -x0 (negated decoder output, matching the latent-space convention).
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# Reference x0→v conversion: v = (noisy - out) / t, where out = -x0
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# → v = (noisy - (-x0)) / t = (noisy + x0) / t
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# Since neg_x0 = -x0: v = (x - neg_x0) / t
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# _forward returns -x0 (negated decoder output, same convention as NextDiT).
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#
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# ComfyUI uses CONST sampling: calculate_denoised = x - model_output * sigma
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# We need calculate_denoised to return x0, so model_output must equal (x - x0) / sigma.
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#
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# _forward returns neg_x0 = -x0, so:
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# model_output = (x + neg_x0) / sigma → (x + (-x0)) / sigma = (x - x0) / sigma ✓
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# Then: x - model_output * sigma = x - (x - x0) = x0 ✓
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neg_x0 = comfy.patcher_extension.WrapperExecutor.new_class_executor(
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self._forward,
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self,
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comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, kwargs.get("transformer_options", {}))
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).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
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return (x - neg_x0) / timesteps.view(-1, 1, 1, 1)
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return (x + neg_x0) / timesteps.view(-1, 1, 1, 1)
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@ -1217,7 +1217,8 @@ class Lumina2(BaseModel):
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class ZImagePixelSpace(Lumina2):
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def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace)
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BaseModel.__init__(self, model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiTPixelSpace)
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self.memory_usage_factor_conds = ("ref_latents",)
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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@ -467,28 +467,23 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
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dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix)
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if dec_cond_key in state_dict_keys: # pixel-space variant
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dit_config["image_model"] = "zimage_pixel"
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w = state_dict[dec_cond_key] # [patch_size^2 * decoder_hidden_size, dim]
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dit_config["decoder_hidden_size"] = w.shape[0] // (dit_config["patch_size"] ** 2)
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# patch_size and in_channels are derived from x_embedder:
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# x_embedder: Linear(patch_size * patch_size * in_channels, dim)
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# The decoder also receives the full flat patch, so decoder_in_channels = x_embedder input dim.
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x_emb_in = state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1]
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dec_out = state_dict['{}dec_net.final_layer.linear.weight'.format(key_prefix)].shape[0]
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# patch_size: infer from decoder final layer output matching x_embedder input
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# in_channels: infer from dec_net input_embedder (in_features = dec_in_ch + max_freqs^2)
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embedder_w = state_dict['{}dec_net.input_embedder.embedder.0.weight'.format(key_prefix)]
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dec_in_ch = dec_out # decoder in == decoder out (same pixel space)
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dit_config["patch_size"] = round((x_emb_in / 3) ** 0.5) # assume RGB (in_channels=3)
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dit_config["in_channels"] = 3
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dit_config["decoder_in_channels"] = dec_in_ch
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dit_config["decoder_hidden_size"] = state_dict[dec_cond_key].shape[0]
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dit_config["decoder_num_res_blocks"] = count_blocks(
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state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.'
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)
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dit_config["decoder_max_freqs"] = 8 # fixed in NerfEmbedder
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dit_config["in_channels"] = w.shape[1] // dit_config["dim"] if False else \
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state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1] // (dit_config["patch_size"] ** 2)
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if '{}__x0__'.format(key_prefix) in state_dict_keys:
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dit_config["use_x0"] = True
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dec_cond_key = '{}dec_net.cond_embed.weight'.format(key_prefix)
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if dec_cond_key in state_dict_keys: # pixel-space variant
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dit_config["image_model"] = "zimage_pixel"
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w = state_dict[dec_cond_key] # [patch_size^2 * decoder_hidden_size, dim]
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dit_config["decoder_hidden_size"] = w.shape[0] // (dit_config["patch_size"] ** 2)
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dit_config["decoder_num_res_blocks"] = count_blocks(
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state_dict_keys, '{}dec_net.res_blocks.'.format(key_prefix) + '{}.'
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)
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dit_config["decoder_max_freqs"] = 8 # fixed in NerfEmbedder
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dit_config["in_channels"] = w.shape[1] // dit_config["dim"] if False else \
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state_dict['{}x_embedder.weight'.format(key_prefix)].shape[1] // (dit_config["patch_size"] ** 2)
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dit_config["decoder_max_freqs"] = int((embedder_w.shape[1] - dec_in_ch) ** 0.5)
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if '{}__x0__'.format(key_prefix) in state_dict_keys:
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dit_config["use_x0"] = True
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@ -1734,6 +1734,6 @@ class LongCatImage(supported_models_base.BASE):
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hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
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return supported_models_base.ClipTarget(comfy.text_encoders.longcat_image.LongCatImageTokenizer, comfy.text_encoders.longcat_image.te(**hunyuan_detect))
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models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
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models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima]
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models += [SVD_img2vid]
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