nodes/Kosinkadink-ComfyUI-Advanced-ControlNet
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Added sd3 support from upcoming ComfyUI update

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Jedrzej Kosinski
2024-06-27 18:23:10 -05:00
parent 96d957e9ec
commit 4454c47707
1 changed files with 16 additions and 9 deletions
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adv_control/control.py
+16 -9
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@@ -21,8 +21,8 @@ from .logger import logger
class ControlNetAdvanced(ControlNet, AdvancedControlBase):
def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
def __init__(self, control_model, timestep_keyframes: TimestepKeyframeGroup, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None):
super().__init__(control_model=control_model, global_average_pooling=global_average_pooling, compression_ratio=compression_ratio, latent_format=latent_format, device=device, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controlnet())
def get_universal_weights(self) -> ControlWeights:
@@ -52,18 +52,28 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
output_dtype = x_noisy.dtype
# make cond_hint appropriate dimensions
# TODO: change this to not require cond_hint upscaling every step when self.sub_idxs are present
if self.sub_idxs is not None or self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
if self.sub_idxs is not None or 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
# if self.cond_hint_original length greater or equal to real latent count, subdivide it before scaling
if self.sub_idxs is not None:
actual_cond_hint_orig = self.cond_hint_original
if self.cond_hint_original.size(0) < self.full_latent_length:
actual_cond_hint_orig = extend_to_batch_size(tensor=actual_cond_hint_orig, batch_size=self.full_latent_length)
self.cond_hint = comfy.utils.common_upscale(actual_cond_hint_orig[self.sub_idxs], x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
self.cond_hint = comfy.utils.common_upscale(actual_cond_hint_orig[self.sub_idxs], x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, 'nearest-exact', "center")
else:
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, 'nearest-exact', "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_extend(self.cond_hint, x_noisy.shape[0], batched_number)
@@ -71,10 +81,7 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
self.prepare_mask_cond_hint(x_noisy=x_noisy, t=t, cond=cond, batched_number=batched_number, dtype=dtype)
context = cond.get('crossattn_controlnet', cond['c_crossattn'])
# uses 'y' in new ComfyUI update
y = cond.get('y', None)
if y is None: # TODO: remove this in the future since no longer used by newest ComfyUI
y = cond.get('c_adm', None)
if y is not None:
y = y.to(dtype)
timestep = self.model_sampling_current.timestep(t)
@@ -92,7 +99,7 @@ class ControlNetAdvanced(ControlNet, AdvancedControlBase):
@staticmethod
def from_vanilla(v: ControlNet, timestep_keyframe: TimestepKeyframeGroup=None) -> 'ControlNetAdvanced':
return ControlNetAdvanced(control_model=v.control_model, timestep_keyframes=timestep_keyframe,
global_average_pooling=v.global_average_pooling, device=v.device, load_device=v.load_device, manual_cast_dtype=v.manual_cast_dtype)
global_average_pooling=v.global_average_pooling, compression_ratio=v.compression_ratio, latent_format=v.latent_format, device=v.device, load_device=v.load_device, manual_cast_dtype=v.manual_cast_dtype)
class T2IAdapterAdvanced(T2IAdapter, AdvancedControlBase):