Cleanup HunyuanDit controlnets.

Use the: ControlNetApply SD3 and HunyuanDiT node.

comfy/controlnet.py

@@ -1,4 +1,24 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Comfy
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+
 import torch
+from enum import Enum
 import math
 import os
 import logging
@@ -33,6 +53,10 @@ def broadcast_image_to(tensor, target_batch_size, batched_number):
     else:
         return torch.cat([tensor] * batched_number, dim=0)
 
+class StrengthType(Enum):
+    CONSTANT = 1
+    LINEAR_UP = 2
+
 class ControlBase:
     def __init__(self, device=None):
         self.cond_hint_original = None
@@ -51,6 +75,8 @@ class ControlBase:
             device = comfy.model_management.get_torch_device()
         self.device = device
         self.previous_controlnet = None
+        self.extra_conds = []
+        self.strength_type = StrengthType.CONSTANT
 
     def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
         self.cond_hint_original = cond_hint
@@ -93,6 +119,8 @@ class ControlBase:
         c.latent_format = self.latent_format
         c.extra_args = self.extra_args.copy()
         c.vae = self.vae
+        c.extra_conds = self.extra_conds.copy()
+        c.strength_type = self.strength_type
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
@@ -113,7 +141,10 @@ class ControlBase:
 
                     if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once
                         applied_to.add(x)
-                        x *= self.strength
+                        if self.strength_type == StrengthType.CONSTANT:
+                            x *= self.strength
+                        elif self.strength_type == StrengthType.LINEAR_UP:
+                            x *= (self.strength ** float(len(control_output) - i))
 
                     if x.dtype != output_dtype:
                         x = x.to(output_dtype)
@@ -142,7 +173,7 @@ class ControlBase:
 
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None, extra_conds=[], strength_type=StrengthType.CONSTANT):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
@@ -154,6 +185,8 @@ class ControlNet(ControlBase):
         self.model_sampling_current = None
         self.manual_cast_dtype = manual_cast_dtype
         self.latent_format = latent_format
+        self.extra_conds += extra_conds
+        self.strength_type = strength_type
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -192,7 +225,7 @@ class ControlNet(ControlBase):
 
         context = cond.get('crossattn_controlnet', cond['c_crossattn'])
         extra = self.extra_args.copy()
-        for c in ["y", "guidance"]: #TODO
+        for c in self.extra_conds:
             temp = cond.get(c, None)
             if temp is not None:
                 extra[c] = temp.to(dtype)
@@ -382,109 +415,15 @@ def load_controlnet_mmdit(sd):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
-class ControlNetWarperHunyuanDiT(ControlNet): 
-    def get_control(self, x_noisy, t, cond, batched_number):
-        control_prev = None
-        if self.previous_controlnet is not None:
-            control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number)
-
-        if self.timestep_range is not None:
-            if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
-                if control_prev is not None:
-                    return control_prev
-                else:
-                    return None
-
-        dtype = self.control_model.dtype
-        if self.manual_cast_dtype is not None:
-            dtype = self.manual_cast_dtype
-
-        output_dtype = x_noisy.dtype
-        if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
-            if self.cond_hint is not None:
-                del self.cond_hint
-            self.cond_hint = None
-            compression_ratio = self.compression_ratio
-            if self.vae is not None:
-                compression_ratio *= self.vae.downscale_ratio
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
-            if self.vae is not None:
-                loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
-                self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
-                comfy.model_management.load_models_gpu(loaded_models)
-            if self.latent_format is not None:
-                self.cond_hint = self.latent_format.process_in(self.cond_hint)
-            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
-        if x_noisy.shape[0] != self.cond_hint.shape[0]:
-            self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
-
-        def get_tensor(name):
-            if name in cond:
-                if isinstance(cond[name], torch.Tensor):
-                    return cond[name].to(dtype)
-                else:
-                    return cond[name]
-            else:
-                return None
-            
-        encoder_hidden_states = get_tensor('c_crossattn')
-        text_embedding_mask = get_tensor('text_embedding_mask')
-        encoder_hidden_states_t5 = get_tensor('encoder_hidden_states_t5')
-        text_embedding_mask_t5 = get_tensor('text_embedding_mask_t5')
-        image_meta_size = get_tensor('image_meta_size')
-        style = get_tensor('style')
-        cos_cis_img = get_tensor('cos_cis_img')
-        sin_cis_img = get_tensor('sin_cis_img')
-        
-        timestep = self.model_sampling_current.timestep(t)
-        x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
-
-        control = self.control_model(
-                x=x_noisy.to(dtype), 
-                t=timestep.float(),  
-                condition=self.cond_hint, 
-                encoder_hidden_states=encoder_hidden_states,
-                text_embedding_mask=text_embedding_mask,
-                encoder_hidden_states_t5=encoder_hidden_states_t5,
-                text_embedding_mask_t5=text_embedding_mask_t5,
-                image_meta_size=image_meta_size,
-                style=style,
-                cos_cis_img=cos_cis_img,
-                sin_cis_img=sin_cis_img,
-                **self.extra_args
-            )
-        return self.control_merge(control, control_prev, output_dtype)
-
-    def copy(self):
-        c = ControlNetWarperHunyuanDiT(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
-        c.control_model = self.control_model
-        c.control_model_wrapped = self.control_model_wrapped
-        self.copy_to(c)
-        return c
-
 def load_controlnet_hunyuandit(controlnet_data):
-
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(controlnet_data)
-    supported_inference_dtypes = [torch.float16, torch.float32]
-
-    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
-    load_device = comfy.model_management.get_torch_device()
-    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
-    if manual_cast_dtype is not None:
-        operations = comfy.ops.manual_cast
-    else:
-        operations = comfy.ops.disable_weight_init
 
     control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=load_device, dtype=unet_dtype)
-    missing, unexpected = control_model.load_state_dict(controlnet_data)
+    control_model = controlnet_load_state_dict(control_model, controlnet_data)
-
-    if len(missing) > 0:
-        logging.warning("missing controlnet keys: {}".format(missing))
-
-    if len(unexpected) > 0:
-        logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
     latent_format = comfy.latent_formats.SDXL()
-    control = ControlNetWarperHunyuanDiT(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    extra_conds = ['text_embedding_mask', 'encoder_hidden_states_t5', 'text_embedding_mask_t5', 'image_meta_size', 'style', 'cos_cis_img', 'sin_cis_img']
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.LINEAR_UP)
     return control
 
 def load_controlnet(ckpt_path, model=None):

comfy/ldm/hydit/controlnet.py

@@ -16,28 +16,11 @@ from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
 from .poolers import AttentionPool
 
 import comfy.latent_formats
-from .models import HunYuanDiTBlock
+from .models import HunYuanDiTBlock, calc_rope
 
 from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
 
 
-def zero_module(module):
-    for p in module.parameters():
-        nn.init.zeros_(p)
-    return module
-
-
-def calc_rope(x, patch_size, head_size):
-    th = (x.shape[2] + (patch_size // 2)) // patch_size
-    tw = (x.shape[3] + (patch_size // 2)) // patch_size
-    base_size = 512 // 8 // patch_size
-    start, stop = get_fill_resize_and_crop((th, tw), base_size)
-    sub_args = [start, stop, (th, tw)]
-    # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
-    rope = get_2d_rotary_pos_embed(head_size, *sub_args)
-    return rope
-
-
 class HunYuanControlNet(nn.Module):
     """
     HunYuanDiT: Diffusion model with a Transformer backbone.
@@ -213,35 +196,32 @@ class HunYuanControlNet(nn.Module):
         )
 
         # Input zero linear for the first block
-        self.before_proj = zero_module(
+        self.before_proj = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
-            nn.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
-        )
 
         # Output zero linear for the every block
         self.after_proj_list = nn.ModuleList(
             [
-                zero_module(
+
-                    nn.Linear(
+                    operations.Linear(
                         self.hidden_size, self.hidden_size, dtype=dtype, device=device
                     )
-                )
                 for _ in range(len(self.blocks))
             ]
         )
 
     def forward(
         self,
-        x: torch.Tensor,
+        x,
-        t: torch.Tensor = None,
+        hint,
-        condition=None,
+        timesteps,
-        encoder_hidden_states: Optional[torch.Tensor] = None,
+        context,#encoder_hidden_states=None,
         text_embedding_mask=None,
         encoder_hidden_states_t5=None,
         text_embedding_mask_t5=None,
         image_meta_size=None,
         style=None,
-        control_weight=1.0,
+        return_dict=False,
-        transformer_options=None,
         **kwarg,
     ):
         """
@@ -270,10 +250,11 @@ class HunYuanControlNet(nn.Module):
         return_dict: bool
             Whether to return a dictionary.
         """
+        condition = hint
         if condition.shape[0] == 1:
             condition = torch.repeat_interleave(condition, x.shape[0], dim=0)
 
-        text_states = encoder_hidden_states  # 2,77,1024
+        text_states = context  # 2,77,1024
         text_states_t5 = encoder_hidden_states_t5  # 2,256,2048
         text_states_mask = text_embedding_mask.bool()  # 2,77
         text_states_t5_mask = text_embedding_mask_t5.bool()  # 2,256
@@ -304,7 +285,7 @@ class HunYuanControlNet(nn.Module):
         )  # (cos_cis_img, sin_cis_img)
 
         # ========================= Build time and image embedding =========================
-        t = self.t_embedder(t, dtype=self.dtype)
+        t = self.t_embedder(timesteps, dtype=self.dtype)
         x = self.x_embedder(x)
 
         # ========================= Concatenate all extra vectors =========================
@@ -337,12 +318,4 @@ class HunYuanControlNet(nn.Module):
             x = block(x, c, text_states, freqs_cis_img)
             controls.append(self.after_proj_list[layer](x))  # zero linear for output
 
-        control_weights = [1.0 * (control_weight ** float(19 - i)) for i in range(19)]
-        assert len(control_weights) == len(
-            controls
-        ), "control_weights and controls should have the same length"
-        controls = [
-            control * weight for control, weight in zip(controls, control_weights)
-        ]
-
         return {"output": controls}

comfy_extras/nodes_hunyuan.py

@@ -20,57 +20,6 @@ class CLIPTextEncodeHunyuanDiT:
         return ([[cond, output]], )
 
 
-class ControlNetApplyAdvancedHunYuan:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "control_net": ("CONTROL_NET", ),
-                             "image": ("IMAGE", ),
-                             "vae": ("VAE", ),
-                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-                             "control_weight": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.001}),
-                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
-                             }}
-
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING")
-    RETURN_NAMES = ("positive", "negative")
-    FUNCTION = "apply_controlnet"
-
-    CATEGORY = "conditioning/controlnet"
-
-    def apply_controlnet(self, positive, negative, control_net, image, strength, control_weight, start_percent, end_percent, vae=None):
-        if strength == 0:
-            return (positive, negative)
-
-        control_hint = image.movedim(-1,1)
-        cnets = {}
-
-        out = []
-        for conditioning in [positive, negative]:
-            c = []
-            for t in conditioning:
-                d = t[1].copy()
-
-                prev_cnet = d.get('control', None)
-                if prev_cnet in cnets:
-                    c_net = cnets[prev_cnet]
-                else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
-                    c_net.set_extra_arg('control_weight', control_weight)
-                    
-                    c_net.set_previous_controlnet(prev_cnet)
-                    cnets[prev_cnet] = c_net
-
-                d['control'] = c_net
-                d['control_apply_to_uncond'] = False
-                n = [t[0], d]
-                c.append(n)
-            out.append(c)
-        return (out[0], out[1])
-    
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
-    "ControlNetApplyAdvancedHunYuan": ControlNetApplyAdvancedHunYuan,
 }

comfy_extras/nodes_sd3.py

@@ -100,3 +100,8 @@ NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
     "ControlNetApplySD3": ControlNetApplySD3,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    # Sampling
+    "ControlNetApplySD3": "ControlNetApply SD3 and HunyuanDiT",
+}