nodes/banodoco-steerable-motion
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updated to work with latest ComfyUI, added Load Images node to batch load images (sorted), added timestep_keyframe output on weight nodes to simplify node connections if not chaining timestep_keyframes together

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This commit is contained in:
Jedrzej Kosinski
2023-08-26 12:43:05 -05:00
parent 354a8ce10c
commit 25b60aa491
2 changed files with 88 additions and 23 deletions
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control.py
+19 -12
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@@ -14,7 +14,8 @@ sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "co
from comfy.cldm import cldm
from comfy.t2i_adapter import adapter
from comfy.sd import ControlBase, ModelPatcher, broadcast_image_to, ControlLora
from comfy.sd import ModelPatcher
from comfy.controlnet import ControlBase, broadcast_image_to, ControlLora
import comfy.utils as utils
import comfy.model_management as model_management
import comfy.model_detection as model_detection
@@ -61,7 +62,7 @@ class LatentKeyframeGroup:
class TimestepKeyframe:
def __init__(self,
start_percent: float,
start_percent: float = 0.0,
control_net_weights: ControlNetWeightsType = None,
t2i_adapter_weights: T2IAdapterWeightsType = None,
latent_keyframes: LatentKeyframeGroup = None) -> None:
@@ -105,6 +106,12 @@ class TimestepKeyframeGroup:
def is_empty(self) -> bool:
return len(self.keyframes) == 0
@classmethod
def default(cls, keyframe: TimestepKeyframe) -> 'TimestepKeyframeGroup':
group = cls()
group.keyframes[0] = keyframe
return group
# Copied from comfy.sd, weights modified
@@ -113,7 +120,6 @@ class ControlNetAdvanced(ControlBase):
super().__init__(device)
self.control_model = control_model
self.control_model_wrapped = ModelPatcher(self.control_model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
self.timestep_keyframes = timestep_keyframes if timestep_keyframes else TimestepKeyframeGroup()
self.weights = self.timestep_keyframes.keyframes[0].control_net_weights if self.timestep_keyframes.keyframes[0].control_net_weights else [1.0]*13
@@ -166,16 +172,17 @@ class ControlNetAdvanced(ControlBase):
if self.global_average_pooling:
x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
# get batch indeces to zero out, AKA latents that should not be influenced by ControlNet
indeces_to_zero = set(range(x.size()[0]//2))
for keyframe in current_timestep_keyframe.latent_keyframes:
if keyframe.batch_index in indeces_to_zero:
indeces_to_zero.remove(keyframe.batch_index)
if current_timestep_keyframe.latent_keyframes is not None:
# get batch indeces to zero out, AKA latents that should not be influenced by ControlNet
indeces_to_zero = set(range(x.size()[0]//2))
for keyframe in current_timestep_keyframe.latent_keyframes:
if keyframe.batch_index in indeces_to_zero:
indeces_to_zero.remove(keyframe.batch_index)
# zero them out by multiplying by zero
for batch_index in indeces_to_zero:
x[batch_index] *= 0.0
x[(x.size()[0]//2) + batch_index] *= 0.0
# zero them out by multiplying by zero
for batch_index in indeces_to_zero:
x[batch_index] *= 0.0
x[(x.size()[0]//2) + batch_index] *= 0.0
x *= self.strength * self.weights[i]
if x.dtype != output_dtype and not autocast_enabled: