Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/ComfyUI-Easy-Use/py/modules/dit/pixArt/models/PixArtMS.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+from timm.models.layers import DropPath
+from timm.models.vision_transformer import Mlp
+
+from .utils import auto_grad_checkpoint, to_2tuple
+from .PixArt_blocks import t2i_modulate, CaptionEmbedder, AttentionKVCompress, MultiHeadCrossAttention, T2IFinalLayer, TimestepEmbedder, SizeEmbedder
+from .PixArt import PixArt, get_2d_sincos_pos_embed
+
+
+class PatchEmbed(nn.Module):
+    """
+    2D Image to Patch Embedding
+    """
+    def __init__(
+            self,
+            patch_size=16,
+            in_chans=3,
+            embed_dim=768,
+            norm_layer=None,
+            flatten=True,
+            bias=True,
+    ):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+        self.flatten = flatten
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        x = self.norm(x)
+        return x
+
+
+class PixArtMSBlock(nn.Module):
+    """
+    A PixArt block with adaptive layer norm zero (adaLN-Zero) 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.hidden_size = hidden_size
+        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 = 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)
+
+    def forward(self, x, y, t, mask=None, HW=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), HW=HW))
+        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 PixArtMS(PixArt):
+    """
+    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,
+            learn_sigma=True,
+            pred_sigma=True,
+            drop_path: float = 0.,
+            caption_channels=4096,
+            pe_interpolation=None,
+            pe_precision=None,
+            config=None,
+            model_max_length=120,
+            micro_condition=True,
+            qk_norm=False,
+            kv_compress_config=None,
+            **kwargs,
+    ):
+        super().__init__(
+            input_size=input_size,
+            patch_size=patch_size,
+            in_channels=in_channels,
+            hidden_size=hidden_size,
+            depth=depth,
+            num_heads=num_heads,
+            mlp_ratio=mlp_ratio,
+            class_dropout_prob=class_dropout_prob,
+            learn_sigma=learn_sigma,
+            pred_sigma=pred_sigma,
+            drop_path=drop_path,
+            pe_interpolation=pe_interpolation,
+            config=config,
+            model_max_length=model_max_length,
+            qk_norm=qk_norm,
+            kv_compress_config=kv_compress_config,
+            **kwargs,
+        )
+        self.dtype = torch.get_default_dtype()
+        self.h = self.w = 0
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.t_block = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
+        )
+        self.x_embedder = PatchEmbed(patch_size, in_channels, 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)
+        self.micro_conditioning = micro_condition
+        if self.micro_conditioning:
+            self.csize_embedder = SizeEmbedder(hidden_size//3)  # c_size embed
+            self.ar_embedder = SizeEmbedder(hidden_size//3)     # aspect ratio embed
+        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
+        if kv_compress_config is None:
+            kv_compress_config = {
+                'sampling': None,
+                'scale_factor': 1,
+                'kv_compress_layer': [],
+            }
+        self.blocks = nn.ModuleList([
+            PixArtMSBlock(
+                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
+                input_size=(input_size // patch_size, input_size // patch_size),
+                sampling=kv_compress_config['sampling'],
+                sr_ratio=int(kv_compress_config['scale_factor']) if i in 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, **kwargs):
+        """
+        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
+        """
+        bs = x.shape[0]
+        x = x.to(self.dtype)
+        timestep = t.to(self.dtype)
+        y = y.to(self.dtype)
+        
+        pe_interpolation = self.pe_interpolation
+        if pe_interpolation is None or self.pe_precision is not None:
+            # calculate pe_interpolation on-the-fly
+            pe_interpolation = round((x.shape[-1]+x.shape[-2])/2.0 / (512/8.0), self.pe_precision or 0)
+
+        self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
+        pos_embed = torch.from_numpy(
+            get_2d_sincos_pos_embed(
+                self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=pe_interpolation,
+                base_size=self.base_size
+            )
+        ).unsqueeze(0).to(device=x.device, dtype=self.dtype)
+
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep)  # (N, D)
+
+        if self.micro_conditioning:
+            c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
+            csize = self.csize_embedder(c_size, bs)  # (N, D)
+            ar = self.ar_embedder(ar, bs)  # (N, D)
+            t = t + torch.cat([csize, ar], dim=1)
+
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, 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 = [y.shape[2]] * y.shape[0]
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+        for block in self.blocks:
+            x = auto_grad_checkpoint(block, x, y, t0, y_lens, (self.h, self.w), **kwargs)  # (N, T, D) #support grad checkpoint
+
+        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, img_hw=None, aspect_ratio=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
+        img_hw: height|width conditioning
+        aspect_ratio: aspect ratio conditioning
+        """
+        ## size/ar from cond with fallback based on the latent image shape.
+        bs = x.shape[0]
+        data_info = {}
+        if img_hw is None:
+            data_info["img_hw"] = torch.tensor(
+                [[x.shape[2]*8, x.shape[3]*8]],
+                dtype=self.dtype,
+                device=x.device
+            ).repeat(bs, 1)
+        else:
+            data_info["img_hw"] = img_hw.to(dtype=x.dtype, device=x.device)
+        if aspect_ratio is None or True:
+            data_info["aspect_ratio"] = torch.tensor(
+                [[x.shape[2]/x.shape[3]]],
+                dtype=self.dtype,
+                device=x.device
+            ).repeat(bs, 1)
+        else:
+            data_info["aspect_ratio"] = aspect_ratio.to(dtype=x.dtype, device=x.device)
+
+        ## 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.to(self.dtype),
+            t = timesteps.to(self.dtype),
+            y = context.to(self.dtype),
+            data_info=data_info,
+        )
+
+        ## only return EPS
+        out = out.to(torch.float)
+        eps, rest = 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]
+        assert self.h * self.w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
+        return imgs