Support Z Image alipai controlnets.

These are not actual controlnets so put it in the models/model_patches
folder and use the ModelPatchLoader + QwenImageDiffsynthControlnet node to
use it.

Author: comfyanonymous

comfy/ldm/lumina/controlnet.py

@@ -0,0 +1,115 @@
+import torch
+from torch import nn
+import math
+
+from .model import JointTransformerBlock
+
+class ZImageControlTransformerBlock(JointTransformerBlock):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        multiple_of: int,
+        ffn_dim_multiplier: float,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+        block_id=0,
+        operation_settings=None,
+    ):
+        super().__init__(layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, modulation, z_image_modulation=True, operation_settings=operation_settings)
+        self.block_id = block_id
+        if block_id == 0:
+            self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, c, x, **kwargs):
+        if self.block_id == 0:
+            c = self.before_proj(c) + x
+        c = super().forward(c, **kwargs)
+        c_skip = self.after_proj(c)
+        return c_skip, c
+
+class ZImage_Control(torch.nn.Module):
+    def __init__(
+        self,
+        dim: int = 3840,
+        n_heads: int = 30,
+        n_kv_heads: int = 30,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: float = (8.0 / 3.0),
+        norm_eps: float = 1e-5,
+        qk_norm: bool = True,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs
+    ):
+        super().__init__()
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+
+        self.additional_in_dim = 0
+        self.control_in_dim = 16
+        n_refiner_layers = 2
+        self.n_control_layers = 6
+        self.control_layers = nn.ModuleList(
+            [
+                ZImageControlTransformerBlock(
+                    i,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    block_id=i,
+                    operation_settings=operation_settings,
+                )
+                for i in range(self.n_control_layers)
+            ]
+        )
+
+        all_x_embedder = {}
+        patch_size = 2
+        f_patch_size = 1
+        x_embedder = operations.Linear(f_patch_size * patch_size * patch_size * self.control_in_dim, dim, bias=True, device=device, dtype=dtype)
+        all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
+
+        self.control_all_x_embedder = nn.ModuleDict(all_x_embedder)
+        self.control_noise_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                    z_image_modulation=True,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+
+    def forward(self, cap_feats, control_context, x_freqs_cis, adaln_input):
+        patch_size = 2
+        f_patch_size = 1
+        pH = pW = patch_size
+        device = control_context.device
+        B, C, H, W = control_context.shape
+        control_context = self.control_all_x_embedder[f"{patch_size}-{f_patch_size}"](control_context.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2))
+
+        x_attn_mask = None
+        for layer in self.control_noise_refiner:
+            control_context = layer(control_context, x_attn_mask, x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input)
+        return control_context
+
+    def forward_control_block(self, layer_id, control_context, x, x_attn_mask, x_freqs_cis, adaln_input):
+        return self.control_layers[layer_id](control_context, x, x_mask=x_attn_mask, freqs_cis=x_freqs_cis[:control_context.shape[0], :control_context.shape[1]], adaln_input=adaln_input)

comfy/ldm/lumina/model.py

@@ -568,7 +568,7 @@ def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwar
         ).execute(x, timesteps, context, num_tokens, attention_mask, **kwargs)
 
     # def forward(self, x, t, cap_feats, cap_mask):
-    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+    def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, transformer_options={}, **kwargs):
         t = 1.0 - timesteps
         cap_feats = context
         cap_mask = attention_mask
@@ -585,16 +585,24 @@ def _forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwa
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
+        patches = transformer_options.get("patches", {})
         transformer_options = kwargs.get("transformer_options", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
-        freqs_cis = freqs_cis.to(x.device)
-
-        for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
-
-        x = self.final_layer(x, adaln_input)
-        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
-
-        return -x
+        img, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
+        freqs_cis = freqs_cis.to(img.device)
+
+        for i, layer in enumerate(self.layers):
+            img = layer(img, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            if "double_block" in patches:
+                for p in patches["double_block"]:
+                    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})
+                    if "img" in out:
+                        img[:, cap_size[0]:] = out["img"]
+                    if "txt" in out:
+                        img[:, :cap_size[0]] = out["txt"]
+
+        img = self.final_layer(img, adaln_input)
+        img = self.unpatchify(img, img_size, cap_size, return_tensor=x_is_tensor)[:, :, :h, :w]
+
+        return -img
 

comfy_extras/nodes_model_patch.py

@@ -6,6 +6,7 @@
 import comfy.model_management
 import comfy.ldm.common_dit
 import comfy.latent_formats
+import comfy.ldm.lumina.controlnet
 
 
 class BlockWiseControlBlock(torch.nn.Module):
@@ -189,6 +190,35 @@ def _process_layer_features(
 
         return embedding
 
+def z_image_convert(sd):
+    replace_keys = {".attention.to_out.0.bias": ".attention.out.bias",
+                    ".attention.norm_k.weight": ".attention.k_norm.weight",
+                    ".attention.norm_q.weight": ".attention.q_norm.weight",
+                    ".attention.to_out.0.weight": ".attention.out.weight"
+                    }
+
+    out_sd = {}
+    for k in sorted(sd.keys()):
+        w = sd[k]
+
+        k_out = k
+        if k_out.endswith(".attention.to_k.weight"):
+            cc = [w]
+            continue
+        if k_out.endswith(".attention.to_q.weight"):
+            cc = [w] + cc
+            continue
+        if k_out.endswith(".attention.to_v.weight"):
+            cc = cc + [w]
+            w = torch.cat(cc, dim=0)
+            k_out = k_out.replace(".attention.to_v.weight", ".attention.qkv.weight")
+
+        for r, rr in replace_keys.items():
+            k_out = k_out.replace(r, rr)
+        out_sd[k_out] = w
+
+    return out_sd
+
 class ModelPatchLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -211,6 +241,9 @@ def load_model_patch(self, name):
         elif 'feature_embedder.mid_layer_norm.bias' in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"feature_embedder.": ""}, filter_keys=True)
             model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+        elif 'control_all_x_embedder.2-1.weight' in sd: # alipai z image fun controlnet
+            sd = z_image_convert(sd)
+            model = comfy.ldm.lumina.controlnet.ZImage_Control(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
 
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
@@ -263,6 +296,70 @@ def to(self, device_or_dtype):
     def models(self):
         return [self.model_patch]
 
+class ZImageControlPatch:
+    def __init__(self, model_patch, vae, image, strength):
+        self.model_patch = model_patch
+        self.vae = vae
+        self.image = image
+        self.strength = strength
+        self.encoded_image = self.encode_latent_cond(image)
+        self.encoded_image_size = (image.shape[1], image.shape[2])
+        self.temp_data = None
+
+    def encode_latent_cond(self, image):
+        latent_image = comfy.latent_formats.Flux().process_in(self.vae.encode(image))
+        return latent_image
+
+    def __call__(self, kwargs):
+        x = kwargs.get("x")
+        img = kwargs.get("img")
+        txt = kwargs.get("txt")
+        pe = kwargs.get("pe")
+        vec = kwargs.get("vec")
+        block_index = kwargs.get("block_index")
+        spacial_compression = self.vae.spacial_compression_encode()
+        if self.encoded_image is None or self.encoded_image_size != (x.shape[-2] * spacial_compression, x.shape[-1] * spacial_compression):
+            image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
+            loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+            self.encoded_image = self.encode_latent_cond(image_scaled.movedim(1, -1))
+            self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
+            comfy.model_management.load_models_gpu(loaded_models)
+            print("encode")
+
+        cnet_index = (block_index // 5)
+        cnet_index_float = (block_index / 5)
+
+        kwargs.pop("img")  # we do ops in place
+        kwargs.pop("txt")
+
+        cnet_blocks = self.model_patch.model.n_control_layers
+        if cnet_index_float > (cnet_blocks - 1):
+            self.temp_data = None
+            return kwargs
+
+        if self.temp_data is None or self.temp_data[0] > cnet_index:
+            self.temp_data = (-1, (None, self.model_patch.model(txt, self.encoded_image.to(img.dtype), pe, vec)))
+
+        while self.temp_data[0] < cnet_index and (self.temp_data[0] + 1) < cnet_blocks:
+            next_layer = self.temp_data[0] + 1
+            self.temp_data = (next_layer, self.model_patch.model.forward_control_block(next_layer, self.temp_data[1][1], img[:, :self.temp_data[1][1].shape[1]], None, pe, vec))
+
+        if cnet_index_float == self.temp_data[0]:
+            img[:, :self.temp_data[1][0].shape[1]] += (self.temp_data[1][0] * self.strength)
+            if cnet_blocks == self.temp_data[0] + 1:
+                self.temp_data = None
+
+        return kwargs
+
+    def to(self, device_or_dtype):
+        if isinstance(device_or_dtype, torch.device):
+            self.encoded_image = self.encoded_image.to(device_or_dtype)
+            self.temp_data = None
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
 class QwenImageDiffsynthControlnet:
     @classmethod
     def INPUT_TYPES(s):
@@ -289,7 +386,10 @@ def diffsynth_controlnet(self, model, model_patch, vae, image, strength, mask=No
                 mask = mask.unsqueeze(2)
             mask = 1.0 - mask
 
-        model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
+        if isinstance(model_patch.model, comfy.ldm.lumina.controlnet.ZImage_Control):
+            model_patched.set_model_double_block_patch(ZImageControlPatch(model_patch, vae, image, strength))
+        else:
+            model_patched.set_model_double_block_patch(DiffSynthCnetPatch(model_patch, vae, image, strength, mask))
         return (model_patched,)