Merge pull request #123 from Kosinkadink/sd3-changes

SD3 ControlNet support + New ComfyUI Compatibility

adv_control/control_reference.py

@@ -218,7 +218,7 @@ def create_combo(reference_type: str, style_fidelity: float, ref_weight: float,
 
 
 class ReferencePreprocWrapper(AbstractPreprocWrapper):
-    error_msg = error_msg = "Invalid use of Reference Preprocess output. The output of RGB SparseCtrl preprocessor is NOT a usual image, but a latent pretending to be an image - you must connect the output directly to an Apply Advanced ControlNet node. It cannot be used for anything else that accepts IMAGE input."
+    error_msg = error_msg = "Invalid use of Reference Preprocess output. The output of Reference preprocessor is NOT a usual image, but a latent pretending to be an image - you must connect the output directly to an Apply Advanced ControlNet node. It cannot be used for anything else that accepts IMAGE input."
     def __init__(self, condhint: Tensor):
         super().__init__(condhint)
 
@@ -228,10 +228,12 @@ class ReferenceAdvanced(ControlBase, AdvancedControlBase):
 
     def __init__(self, ref_opts: ReferenceOptions, timestep_keyframes: TimestepKeyframeGroup, device=None):
         super().__init__(device)
-        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite())
+        AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite(), allow_condhint_latents=True)
+        # TODO: allow vae_optional to be used instead of preprocessor
+        #require_vae=True
         self.ref_opts = ref_opts
         self.order = 0
-        self.latent_format = None
+        self.model_latent_format = None
         self.model_sampling_current = None
         self.should_apply_attn_effective_strength = False
         self.should_apply_adain_effective_strength = False
@@ -288,9 +290,9 @@ def should_run(self):
 
     def pre_run_advanced(self, model, percent_to_timestep_function):
         AdvancedControlBase.pre_run_advanced(self, model, percent_to_timestep_function)
-        if type(self.cond_hint_original) == ReferencePreprocWrapper:
+        if isinstance(self.cond_hint_original, AbstractPreprocWrapper):
             self.cond_hint_original = self.cond_hint_original.condhint
-        self.latent_format = model.latent_format # LatentFormat object, used to process_in latent cond_hint
+        self.model_latent_format = model.latent_format # LatentFormat object, used to process_in latent cond_hint
         self.model_sampling_current = model.model_sampling
         # SDXL is more sensitive to style_fidelity according to sd-webui-controlnet comments
         if type(model).__name__ == "SDXL":
@@ -328,7 +330,7 @@ def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int):
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to_extend(self.cond_hint, x_noisy.shape[0], batched_number, except_one=False)
         # noise cond_hint based on sigma (current step)
-        self.cond_hint = self.latent_format.process_in(self.cond_hint)
+        self.cond_hint = self.model_latent_format.process_in(self.cond_hint)
         self.cond_hint = ref_noise_latents(self.cond_hint, sigma=t, noise=None)
         timestep = self.model_sampling_current.timestep(t)
         self.should_apply_attn_effective_strength = not (math.isclose(self.strength, 1.0) and math.isclose(self._current_timestep_keyframe.strength, 1.0) and math.isclose(self.ref_opts.attn_strength, 1.0))
@@ -343,8 +345,8 @@ def get_control_advanced(self, x_noisy: Tensor, t, cond, batched_number: int):
 
     def cleanup_advanced(self):
         super().cleanup_advanced()
-        del self.latent_format
-        self.latent_format = None
+        del self.model_latent_format
+        self.model_latent_format = None
         del self.model_sampling_current
         self.model_sampling_current = None
         self.should_apply_attn_effective_strength = False

adv_control/control_sparsectrl.py

@@ -23,13 +23,13 @@
 from comfy.ldm.modules.attention import SpatialTransformer
 from comfy.ldm.modules.attention import attention_basic, attention_pytorch, attention_split, attention_sub_quad, default
 from comfy.ldm.modules.attention import FeedForward, SpatialTransformer
-from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential, ResBlock, Downsample
+from comfy.ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential
 from comfy.model_patcher import ModelPatcher
 import comfy.ops
 import comfy.model_management
 
 from .logger import logger
-from .utils import (BIGMAX, TimestepKeyframeGroup, disable_weight_init_clean_groupnorm,
+from .utils import (BIGMAX, AbstractPreprocWrapper, disable_weight_init_clean_groupnorm,
                     prepare_mask_batch, broadcast_image_to_extend, extend_to_batch_size)
 
 
@@ -85,7 +85,8 @@ def forward(self, x: Tensor, hint: Tensor, timesteps, context, y=None, **kwargs)
         x = torch.zeros_like(x)
         guided_hint = self.input_hint_block(hint, emb, context)
 
-        outs = []
+        out_output = []
+        out_middle = []
 
         hs = []
         if self.num_classes is not None:
@@ -100,12 +101,12 @@ def forward(self, x: Tensor, hint: Tensor, timesteps, context, y=None, **kwargs)
                 guided_hint = None
             else:
                 h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
+            out_output.append(zero_conv(h, emb, context))
 
         h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
+        out_middle.append(self.middle_block_out(h, emb, context))
 
-        return outs
+        return {"middle": out_middle, "output": out_output}
 
 
 class SparseModelPatcher(ModelPatcher):
@@ -154,36 +155,10 @@ def clone(self):
             self.object_patches_backup = n.object_patches_backup
 
 
-class PreprocSparseRGBWrapper:
-    error_msg = "Invalid use of RGB SparseCtrl output. The output of RGB SparseCtrl preprocessor is NOT a usual image, but a latent pretending to be an image - you must connect the output directly to an Apply ControlNet node (advanced or otherwise). It cannot be used for anything else that accepts IMAGE input."
+class PreprocSparseRGBWrapper(AbstractPreprocWrapper):
+    error_msg = error_msg = "Invalid use of RGB SparseCtrl output. The output of RGB SparseCtrl preprocessor is NOT a usual image, but a latent pretending to be an image - you must connect the output directly to an Apply ControlNet node (advanced or otherwise). It cannot be used for anything else that accepts IMAGE input."
     def __init__(self, condhint: Tensor):
-        self.condhint = condhint
-
-    def movedim(self, *args, **kwargs):
-        return self
-
-    def __getattr__(self, *args, **kwargs):
-        raise AttributeError(self.error_msg)
-
-    def __setattr__(self, name, value):
-        if name != "condhint":
-            raise AttributeError(self.error_msg)
-        super().__setattr__(name, value)
-
-    def __iter__(self, *args, **kwargs):
-        raise AttributeError(self.error_msg)
-
-    def __next__(self, *args, **kwargs):
-        raise AttributeError(self.error_msg)
-
-    def __len__(self, *args, **kwargs):
-        raise AttributeError(self.error_msg)
-
-    def __getitem__(self, *args, **kwargs):
-        raise AttributeError(self.error_msg)
-
-    def __setitem__(self, *args, **kwargs):
-        raise AttributeError(self.error_msg)
+        super().__init__(condhint)
 
 
 class SparseContextAware:

adv_control/control_svd.py

@@ -311,7 +311,8 @@ def forward(self, x, hint, timesteps, context, y=None, **kwargs):
 
         guided_hint = self.input_hint_block(hint, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
 
-        outs = []
+        out_output = []
+        out_middle = []
 
         hs = []
         if self.num_classes is not None:
@@ -326,12 +327,12 @@ def forward(self, x, hint, timesteps, context, y=None, **kwargs):
                 guided_hint = None
             else:
                 h = module(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
-            outs.append(zero_conv(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator))
+            out_output.append(zero_conv(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator))
 
         h = self.middle_block(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
-        outs.append(self.middle_block_out(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator))
+        out_middle.append(self.middle_block_out(h, emb, context, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator))
 
-        return outs
+        return {"middle": out_middle, "output": out_output}
 
 
 TEMPORAL_TRANSFORMER_BLOCKS = {

adv_control/nodes.py

@@ -4,8 +4,8 @@
 import folder_paths
 from comfy.model_patcher import ModelPatcher
 
-from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet
-from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, BIGMAX
+from .control import load_controlnet, convert_to_advanced, is_advanced_controlnet, is_sd3_advanced_controlnet
+from .utils import ControlWeights, LatentKeyframeGroup, TimestepKeyframeGroup, AbstractPreprocWrapper, BIGMAX
 from .nodes_weight import (DefaultWeights, ScaledSoftMaskedUniversalWeights, ScaledSoftUniversalWeights, SoftControlNetWeights, CustomControlNetWeights,
     SoftT2IAdapterWeights, CustomT2IAdapterWeights)
 from .nodes_keyframes import (LatentKeyframeGroupNode, LatentKeyframeInterpolationNode, LatentKeyframeBatchedGroupNode, LatentKeyframeNode,
@@ -89,6 +89,7 @@ def INPUT_TYPES(s):
                 "latent_kf_override": ("LATENT_KEYFRAME", ),
                 "weights_override": ("CONTROL_NET_WEIGHTS", ),
                 "model_optional": ("MODEL",),
+                "vae_optional": ("VAE",),
             }
         }
 
@@ -99,7 +100,7 @@ def INPUT_TYPES(s):
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝"
 
     def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent,
-                         mask_optional: Tensor=None, model_optional: ModelPatcher=None,
+                         mask_optional: Tensor=None, model_optional: ModelPatcher=None, vae_optional=None,
                          timestep_kf: TimestepKeyframeGroup=None, latent_kf_override: LatentKeyframeGroup=None,
                          weights_override: ControlWeights=None):
         if strength == 0:
@@ -121,7 +122,7 @@ def apply_controlnet(self, positive, negative, control_net, image, strength, sta
                     c_net = cnets[prev_cnet]
                 else:
                     # copy, convert to advanced if needed, and set cond
-                    c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent))
+                    c_net = convert_to_advanced(control_net.copy()).set_cond_hint(control_hint, strength, (start_percent, end_percent), vae_optional)
                     if is_advanced_controlnet(c_net):
                         # disarm node check
                         c_net.disarm()
@@ -130,6 +131,17 @@ def apply_controlnet(self, positive, negative, control_net, image, strength, sta
                             if not model_optional:
                                 raise Exception(f"Type '{type(c_net).__name__}' requires model_optional input, but got None.")
                             c_net.patch_model(model=model_optional)
+                        # if vae required, verify vae is passed in
+                        if c_net.require_vae:
+                            # if controlnet can accept preprocced condhint latents and is the case, ignore vae requirement
+                            if c_net.allow_condhint_latents and isinstance(control_hint, AbstractPreprocWrapper):
+                                pass
+                            elif not vae_optional:
+                                # make sure SD3 ControlNet will get a special message instead of generic type mention
+                                if is_sd3_advanced_controlnet:
+                                    raise Exception(f"SD3 ControlNet requires vae_optional input, but got None.")
+                                else:
+                                    raise Exception(f"Type '{type(c_net).__name__}' requires vae_optional input, but got None.")
                         # apply optional parameters and overrides, if provided
                         if timestep_kf is not None:
                             c_net.set_timestep_keyframes(timestep_kf)

adv_control/nodes_weight.py

@@ -198,6 +198,7 @@ def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [weight_00, weight_01, weight_02, weight_03]
         weights = get_properly_arranged_t2i_weights(weights)
+        weights.reverse()  # to account for recent ComfyUI changes
         weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
         return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
@@ -229,5 +230,6 @@ def load_weights(self, weight_00, weight_01, weight_02, weight_03, flip_weights,
                      uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
         weights = [weight_00, weight_01, weight_02, weight_03]
         weights = get_properly_arranged_t2i_weights(weights)
+        weights.reverse()  # to account for recent ComfyUI changes
         weights = ControlWeights.t2iadapter(weights, flip_weights=flip_weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)
         return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))