Remove some useless code.

comfy/ldm/pixart/pixart.py

@@ -1,196 +0,0 @@
-# Based on:
-# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
-# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
-import torch
-import torch.nn as nn
-
-from .blocks import (
-    t2i_modulate,
-    CaptionEmbedder,
-    AttentionKVCompress,
-    MultiHeadCrossAttention,
-    T2IFinalLayer,
-)
-from comfy.ldm.modules.diffusionmodules.mmdit import PatchEmbed, TimestepEmbedder, Mlp, get_1d_sincos_pos_embed_from_grid_torch
-
-
-class PixArtBlock(nn.Module):
-    """
-    A PixArt block with adaptive layer norm (adaLN-single) conditioning.
-    """
-    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0, input_size=None, sampling=None, sr_ratio=1, qk_norm=False, **block_kwargs):
-        super().__init__()
-        self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.attn = AttentionKVCompress(
-            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
-            qk_norm=qk_norm, **block_kwargs
-        )
-        self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs)
-        self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        # to be compatible with lower version pytorch
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.mlp = Mlp(in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0)
-        self.drop_path = nn.Identity() #DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
-        self.sampling = sampling
-        self.sr_ratio = sr_ratio
-
-    def forward(self, x, y, t, mask=None, **kwargs):
-        B, N, C = x.shape
-
-        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None] + t.reshape(B, 6, -1)).chunk(6, dim=1)
-        x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C))
-        x = x + self.cross_attn(x, y, mask)
-        x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
-
-        return x
-
-
-### Core PixArt Model ###
-class PixArt(nn.Module):
-    """
-    Diffusion model with a Transformer backbone.
-    """
-    def __init__(
-            self,
-            input_size=32,
-            patch_size=2,
-            in_channels=4,
-            hidden_size=1152,
-            depth=28,
-            num_heads=16,
-            mlp_ratio=4.0,
-            class_dropout_prob=0.1,
-            pred_sigma=True,
-            drop_path: float = 0.,
-            caption_channels=4096,
-            pe_interpolation=1.0,
-            pe_precision=None,
-            config=None,
-            model_max_length=120,
-            qk_norm=False,
-            kv_compress_config=None,
-            **kwargs,
-    ):
-        super().__init__()
-        self.pred_sigma = pred_sigma
-        self.in_channels = in_channels
-        self.out_channels = in_channels * 2 if pred_sigma else in_channels
-        self.patch_size = patch_size
-        self.num_heads = num_heads
-        self.pe_interpolation = pe_interpolation
-        self.pe_precision = pe_precision
-        self.depth = depth
-
-        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
-        self.t_embedder = TimestepEmbedder(hidden_size)
-        num_patches = self.x_embedder.num_patches
-        self.base_size = input_size // self.patch_size
-        # Will use fixed sin-cos embedding:
-        self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
-
-        approx_gelu = lambda: nn.GELU(approximate="tanh")
-        self.t_block = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
-        )
-        self.y_embedder = CaptionEmbedder(
-            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
-            act_layer=approx_gelu, token_num=model_max_length
-		)
-        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
-        self.kv_compress_config = kv_compress_config
-        if kv_compress_config is None:
-            self.kv_compress_config = {
-                'sampling': None,
-                'scale_factor': 1,
-                'kv_compress_layer': [],
-            }
-        self.blocks = nn.ModuleList([
-            PixArtBlock(
-                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
-                input_size=(input_size // patch_size, input_size // patch_size),
-                sampling=self.kv_compress_config['sampling'],
-                sr_ratio=int(
-                    self.kv_compress_config['scale_factor']
-                ) if i in self.kv_compress_config['kv_compress_layer'] else 1,
-                qk_norm=qk_norm,
-            )
-            for i in range(depth)
-        ])
-        self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
-
-    def forward_raw(self, x, t, y, mask=None, data_info=None):
-        """
-        Original forward pass of PixArt.
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        t: (N,) tensor of diffusion timesteps
-        y: (N, 1, 120, C) tensor of class labels
-        """
-        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
-        t = self.t_embedder(timestep)  # (N, D)
-        t0 = self.t_block(t)
-        y = self.y_embedder(y, self.training)  # (N, 1, L, D)
-        if mask is not None:
-            if mask.shape[0] != y.shape[0]:
-                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
-            mask = mask.squeeze(1).squeeze(1)
-            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
-            y_lens = mask.sum(dim=1).tolist()
-        else:
-            y_lens = None
-            y = y.squeeze(1).view(1, -1, x.shape[-1])
-        for block in self.blocks:
-            x = block(x, y, t0, y_lens)  # (N, T, D)
-        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
-        x = self.unpatchify(x)  # (N, out_channels, H, W)
-        return x
-
-    def forward(self, x, timesteps, context, y=None, **kwargs):
-        """
-        Forward pass that adapts comfy input to original forward function
-        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
-        timesteps: (N,) tensor of diffusion timesteps
-        context: (N, 1, 120, C) conditioning
-        y: extra conditioning.
-        """
-        ## Still accepts the input w/o that dim but returns garbage
-        if len(context.shape) == 3:
-            context = context.unsqueeze(1)
-
-        ## run original forward pass
-        out = self.forward_raw(
-            x = x,
-            t = timesteps,
-            y = context,
-        )
-
-        ## only return EPS
-        eps, _ = out[:, :self.in_channels], out[:, self.in_channels:]
-        return eps
-
-    def unpatchify(self, x):
-        """
-        x: (N, T, patch_size**2 * C)
-        imgs: (N, H, W, C)
-        """
-        c = self.out_channels
-        p = self.x_embedder.patch_size[0]
-        h = w = int(x.shape[1] ** 0.5)
-        assert h * w == x.shape[1]
-
-        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
-        x = torch.einsum('nhwpqc->nchpwq', x)
-        imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
-        return imgs
-
-def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
-    grid_h, grid_w = torch.meshgrid(
-        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
-        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
-        indexing='ij'
-    )
-    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
-    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
-    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
-    return emb

comfy/ldm/pixart/pixartms.py

@@ -12,10 +12,20 @@ from .blocks import (
     T2IFinalLayer,
     SizeEmbedder,
 )
-from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
-from .pixart import PixArt, get_2d_sincos_pos_embed_torch
 
 
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
+    grid_h, grid_w = torch.meshgrid(
+        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
+        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
+        indexing='ij'
+    )
+    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
 class PixArtMSBlock(nn.Module):
     """
     A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
@@ -53,7 +63,7 @@ class PixArtMSBlock(nn.Module):
 
 
 ### Core PixArt Model ###
-class PixArtMS(PixArt):
+class PixArtMS(nn.Module):
     """
     Diffusion model with a Transformer backbone.
     """