mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Feat: z-image pixel space (model still training atm) (#12709)
* draft zeta (z-image pixel space) * revert gitignore * model loaded and able to run however vector direction still wrong tho * flip the vector direction to original again this time * Move wrongly positioned Z image pixel space class * inherit Radiance LatentFormat class * Fix parameters in classes for Zeta x0 dino * remove arbitrary nn.init instances * Remove unused import of lru_cache --------- Co-authored-by: silveroxides <ishimarukaito@gmail.com>
This commit is contained in:
Lodestone
@@ -776,3 +776,10 @@ class ChromaRadiance(LatentFormat):
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def process_out(self, latent):
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return latent
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class ZImagePixelSpace(ChromaRadiance):
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"""Pixel-space latent format for ZImage DCT variant.
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No VAE encoding/decoding — the model operates directly on RGB pixels.
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"""
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pass
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@@ -14,6 +14,7 @@ from comfy.ldm.flux.layers import EmbedND
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from comfy.ldm.flux.math import apply_rope
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import comfy.patcher_extension
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import comfy.utils
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from comfy.ldm.chroma_radiance.layers import NerfEmbedder
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def invert_slices(slices, length):
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@@ -858,3 +859,267 @@ class NextDiT(nn.Module):
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img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
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return -img
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#############################################################################
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# Pixel Space Decoder Components #
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#############################################################################
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def _modulate_shift_scale(x, shift, scale):
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return x * (1 + scale) + shift
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class PixelResBlock(nn.Module):
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"""
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Residual block with AdaLN modulation, zero-initialised so it starts as
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an identity at the beginning of training.
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"""
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def __init__(self, channels: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.in_ln = operations.LayerNorm(channels, eps=1e-6, dtype=dtype, device=device)
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self.mlp = nn.Sequential(
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operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),
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nn.SiLU(),
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operations.Linear(channels, channels, bias=True, dtype=dtype, device=device),
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)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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operations.Linear(channels, 3 * channels, bias=True, dtype=dtype, device=device),
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)
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def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
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shift, scale, gate = self.adaLN_modulation(y).chunk(3, dim=-1)
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h = _modulate_shift_scale(self.in_ln(x), shift, scale)
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h = self.mlp(h)
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return x + gate * h
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class DCTFinalLayer(nn.Module):
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"""Zero-initialised output projection (adopted from DiT)."""
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def __init__(self, model_channels: int, out_channels: int, dtype=None, device=None, operations=None):
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super().__init__()
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self.norm_final = operations.LayerNorm(model_channels, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
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self.linear = operations.Linear(model_channels, out_channels, bias=True, dtype=dtype, device=device)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.linear(self.norm_final(x))
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class SimpleMLPAdaLN(nn.Module):
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"""
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Small MLP decoder head for the pixel-space variant.
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Takes per-patch pixel values and a per-patch conditioning vector from the
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transformer backbone and predicts the denoised pixel values.
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x : [B*N, P^2, C] – noisy pixel values per patch position
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c : [B*N, dim] – backbone hidden state per patch (conditioning)
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→ [B*N, P^2, C]
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"""
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def __init__(
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self,
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in_channels: int,
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model_channels: int,
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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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max_freqs: int = 8,
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dtype=None,
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device=None,
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operations=None,
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):
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super().__init__()
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self.dtype = dtype
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# Project backbone hidden state → per-patch conditioning
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self.cond_embed = operations.Linear(z_channels, model_channels, dtype=dtype, device=device)
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# Input projection with DCT positional encoding
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self.input_embedder = NerfEmbedder(
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in_channels=in_channels,
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hidden_size_input=model_channels,
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max_freqs=max_freqs,
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dtype=dtype,
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device=device,
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operations=operations,
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)
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# Residual blocks
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self.res_blocks = nn.ModuleList([
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PixelResBlock(model_channels, dtype=dtype, device=device, operations=operations) for _ in range(num_res_blocks)
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])
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# Output projection
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self.final_layer = DCTFinalLayer(model_channels, out_channels, dtype=dtype, device=device, operations=operations)
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def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
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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 if hasattr(self.cond_embed, "weight") and self.cond_embed.weight is not None else (self.dtype or x.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).to(original_dtype) # [B*N, 1, P^2*C]
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#############################################################################
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# NextDiT – Pixel Space #
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#############################################################################
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class NextDiTPixelSpace(NextDiT):
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"""
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Pixel-space variant of NextDiT.
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Identical transformer backbone to NextDiT, but the output head is replaced
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with a small MLP decoder (SimpleMLPAdaLN) that operates on raw pixel values
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per patch rather than a single affine projection.
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Key differences vs NextDiT:
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• ``final_layer`` is removed; ``dec_net`` (SimpleMLPAdaLN) is used instead.
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• ``_forward`` stores the raw patchified pixel values before the backbone
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embedding and feeds them to ``dec_net`` together with the per-patch
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backbone hidden states.
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• Supports optional x0 prediction via ``use_x0``.
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"""
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def __init__(
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self,
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# decoder-specific
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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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):
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super().__init__(**kwargs)
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# Remove the latent-space final layer – not used in pixel space
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del self.final_layer
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patch_size = kwargs.get("patch_size", 2)
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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=dec_in_ch,
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model_channels=decoder_hidden_size,
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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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max_freqs=decoder_max_freqs,
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dtype=kwargs.get("dtype"),
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device=kwargs.get("device"),
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operations=kwargs.get("operations"),
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)
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if use_x0:
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self.register_buffer("__x0__", torch.tensor([]))
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# ------------------------------------------------------------------
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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 _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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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)
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adaln_input = t
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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((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 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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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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N = pixel_patches.shape[1]
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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, 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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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]:], "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 (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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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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# 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 = 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 neg_x0 = -x0 (negated decoder output).
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#
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# Reference inference (working_inference_reference.py):
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# out = _forward(img, t) # = -x0
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# pred = (img - out) / t # = (img + x0) / t [_apply_x0_residual]
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# img += (t_prev - t_curr) * pred # Euler step
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#
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# ComfyUI's Euler sampler does the same:
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# x_next = x + (sigma_next - sigma) * model_output
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# So model_output must equal pred = (x - neg_x0) / t = (x - (-x0)) / t = (x + x0) / t
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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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@@ -1263,6 +1263,11 @@ class Lumina2(BaseModel):
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out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
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return out
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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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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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class WAN21(BaseModel):
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def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
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super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)
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@@ -464,6 +464,29 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
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if sig_weight is not None:
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dit_config["siglip_feat_dim"] = sig_weight.shape[0]
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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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# 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"] = 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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return dit_config
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if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1
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@@ -1118,6 +1118,20 @@ class ZImage(Lumina2):
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hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen3_4b.transformer.".format(pref))
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return supported_models_base.ClipTarget(comfy.text_encoders.z_image.ZImageTokenizer, comfy.text_encoders.z_image.te(**hunyuan_detect))
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class ZImagePixelSpace(ZImage):
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unet_config = {
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"image_model": "zimage_pixel",
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}
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# Pixel-space model: no spatial compression, operates on raw RGB patches.
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latent_format = latent_formats.ZImagePixelSpace
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# Much lower memory than latent-space models (no VAE, small patches).
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memory_usage_factor = 0.05 # TODO: figure out the optimal value for this.
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def get_model(self, state_dict, prefix="", device=None):
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return model_base.ZImagePixelSpace(self, device=device)
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class WAN21_T2V(supported_models_base.BASE):
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unet_config = {
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"image_model": "wan2.1",
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@@ -1720,6 +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]
|
||||
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]
|
||||
|
||||
models += [SVD_img2vid]
|
||||
|
||||
Reference in New Issue
Block a user