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Copy pathnodes_latent.py
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506 lines (437 loc) · 17.2 KB
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import comfy.utils
import comfy_extras.nodes_post_processing
import torch
import nodes
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
import logging
import math
def reshape_latent_to(target_shape, latent, repeat_batch=True):
if latent.shape[1:] != target_shape[1:]:
latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
if repeat_batch:
return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
else:
return latent
class LatentAdd(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentAdd",
search_aliases=["combine latents", "sum latents"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples1"),
io.Latent.Input("samples2"),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples1, samples2) -> io.NodeOutput:
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
samples_out["samples"] = s1 + s2
return io.NodeOutput(samples_out)
class LatentSubtract(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentSubtract",
search_aliases=["difference latent", "remove features"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples1"),
io.Latent.Input("samples2"),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples1, samples2) -> io.NodeOutput:
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
samples_out["samples"] = s1 - s2
return io.NodeOutput(samples_out)
class LatentMultiply(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentMultiply",
search_aliases=["scale latent", "amplify latent", "latent gain"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples"),
io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples, multiplier) -> io.NodeOutput:
samples_out = samples.copy()
s1 = samples["samples"]
samples_out["samples"] = s1 * multiplier
return io.NodeOutput(samples_out)
class LatentInterpolate(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentInterpolate",
search_aliases=["blend latent", "mix latent", "lerp latent", "transition"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples1"),
io.Latent.Input("samples2"),
io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples1, samples2, ratio) -> io.NodeOutput:
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2)
m1 = torch.linalg.vector_norm(s1, dim=(1))
m2 = torch.linalg.vector_norm(s2, dim=(1))
s1 = torch.nan_to_num(s1 / m1)
s2 = torch.nan_to_num(s2 / m2)
t = (s1 * ratio + s2 * (1.0 - ratio))
mt = torch.linalg.vector_norm(t, dim=(1))
st = torch.nan_to_num(t / mt)
samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
return io.NodeOutput(samples_out)
class LatentConcat(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentConcat",
search_aliases=["join latents", "stitch latents"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples1"),
io.Latent.Input("samples2"),
io.Combo.Input("dim", options=["x", "-x", "y", "-y", "t", "-t"]),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples1, samples2, dim) -> io.NodeOutput:
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = comfy.utils.repeat_to_batch_size(s2, s1.shape[0])
if "-" in dim:
c = (s2, s1)
else:
c = (s1, s2)
if "x" in dim:
dim = -1
elif "y" in dim:
dim = -2
elif "t" in dim:
dim = -3
samples_out["samples"] = torch.cat(c, dim=dim)
return io.NodeOutput(samples_out)
class LatentCut(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentCut",
search_aliases=["crop latent", "slice latent", "extract region"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples"),
io.Combo.Input("dim", options=["x", "y", "t"]),
io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
io.Int.Input("amount", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples, dim, index, amount) -> io.NodeOutput:
samples_out = samples.copy()
s1 = samples["samples"]
if "x" in dim:
dim = s1.ndim - 1
elif "y" in dim:
dim = s1.ndim - 2
elif "t" in dim:
dim = s1.ndim - 3
if index >= 0:
index = min(index, s1.shape[dim] - 1)
amount = min(s1.shape[dim] - index, amount)
else:
index = max(index, -s1.shape[dim])
amount = min(-index, amount)
samples_out["samples"] = torch.narrow(s1, dim, index, amount)
return io.NodeOutput(samples_out)
class LatentCutToBatch(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentCutToBatch",
search_aliases=["slice to batch", "split latent", "tile latent"],
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples"),
io.Combo.Input("dim", options=["t", "x", "y"]),
io.Int.Input("slice_size", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples, dim, slice_size) -> io.NodeOutput:
samples_out = samples.copy()
s1 = samples["samples"]
if "x" in dim:
dim = s1.ndim - 1
elif "y" in dim:
dim = s1.ndim - 2
elif "t" in dim:
dim = s1.ndim - 3
if dim < 2:
return io.NodeOutput(samples)
s = s1.movedim(dim, 1)
if s.shape[1] < slice_size:
slice_size = s.shape[1]
elif s.shape[1] % slice_size != 0:
s = s[:, :math.floor(s.shape[1] / slice_size) * slice_size]
new_shape = [-1, slice_size] + list(s.shape[2:])
samples_out["samples"] = s.reshape(new_shape).movedim(1, dim)
return io.NodeOutput(samples_out)
class LatentBatch(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentBatch",
search_aliases=["combine latents", "merge latents", "join latents"],
display_name="Batch Latents (DEPRECATED)",
category="model/latent/batch",
is_deprecated=True,
inputs=[
io.Latent.Input("samples1"),
io.Latent.Input("samples2"),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples1, samples2) -> io.NodeOutput:
samples_out = samples1.copy()
s1 = samples1["samples"]
s2 = samples2["samples"]
s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
s = torch.cat((s1, s2), dim=0)
samples_out["samples"] = s
samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
return io.NodeOutput(samples_out)
class LatentBatchSeedBehavior(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentBatchSeedBehavior",
category="model/latent/advanced",
inputs=[
io.Latent.Input("samples"),
io.Combo.Input("seed_behavior", options=["random", "fixed"], default="fixed"),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples, seed_behavior) -> io.NodeOutput:
samples_out = samples.copy()
latent = samples["samples"]
if seed_behavior == "random":
if 'batch_index' in samples_out:
samples_out.pop('batch_index')
elif seed_behavior == "fixed":
batch_number = samples_out.get("batch_index", [0])[0]
samples_out["batch_index"] = [batch_number] * latent.shape[0]
return io.NodeOutput(samples_out)
class LatentApplyOperation(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentApplyOperation",
search_aliases=["transform latent"],
category="model/latent/advanced/operations",
is_experimental=True,
inputs=[
io.Latent.Input("samples"),
io.LatentOperation.Input("operation"),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, samples, operation) -> io.NodeOutput:
samples_out = samples.copy()
s1 = samples["samples"]
samples_out["samples"] = operation(latent=s1)
return io.NodeOutput(samples_out)
class LatentApplyOperationCFG(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentApplyOperationCFG",
category="model/latent/advanced/operations",
is_experimental=True,
inputs=[
io.Model.Input("model"),
io.LatentOperation.Input("operation"),
],
outputs=[
io.Model.Output(),
],
)
@classmethod
def execute(cls, model, operation) -> io.NodeOutput:
m = model.clone()
def pre_cfg_function(args):
conds_out = args["conds_out"]
if len(conds_out) == 2:
conds_out[0] = operation(latent=(conds_out[0] - conds_out[1])) + conds_out[1]
else:
conds_out[0] = operation(latent=conds_out[0])
return conds_out
m.set_model_sampler_pre_cfg_function(pre_cfg_function)
return io.NodeOutput(m)
class LatentOperationTonemapReinhard(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentOperationTonemapReinhard",
search_aliases=["hdr latent"],
category="model/latent/advanced/operations",
is_experimental=True,
inputs=[
io.Float.Input("multiplier", default=1.0, min=0.0, max=100.0, step=0.01),
],
outputs=[
io.LatentOperation.Output(),
],
)
@classmethod
def execute(cls, multiplier) -> io.NodeOutput:
def tonemap_reinhard(latent, **kwargs):
latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
normalized_latent = latent / latent_vector_magnitude
dims = list(range(1, latent_vector_magnitude.ndim))
mean = torch.mean(latent_vector_magnitude, dim=dims, keepdim=True)
std = torch.std(latent_vector_magnitude, dim=dims, keepdim=True)
top = (std * 5 + mean) * multiplier
#reinhard
latent_vector_magnitude *= (1.0 / top)
new_magnitude = latent_vector_magnitude / (latent_vector_magnitude + 1.0)
new_magnitude *= top
return normalized_latent * new_magnitude
return io.NodeOutput(tonemap_reinhard)
class LatentOperationSharpen(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LatentOperationSharpen",
category="model/latent/advanced/operations",
is_experimental=True,
inputs=[
io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1, advanced=True),
io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1, advanced=True),
io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01, advanced=True),
],
outputs=[
io.LatentOperation.Output(),
],
)
@classmethod
def execute(cls, sharpen_radius, sigma, alpha) -> io.NodeOutput:
def sharpen(latent, **kwargs):
luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
normalized_latent = latent / luminance
channels = latent.shape[1]
kernel_size = sharpen_radius * 2 + 1
kernel = comfy_extras.nodes_post_processing.gaussian_kernel(kernel_size, sigma, device=luminance.device)
center = kernel_size // 2
kernel *= alpha * -10
kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
padded_image = torch.nn.functional.pad(normalized_latent, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
return luminance * sharpened
return io.NodeOutput(sharpen)
class ReplaceVideoLatentFrames(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="ReplaceVideoLatentFrames",
display_name="Replace Video Latent Frames",
category="model/latent/batch",
inputs=[
io.Latent.Input("destination", tooltip="The destination latent where frames will be replaced."),
io.Latent.Input("source", optional=True, tooltip="The source latent providing frames to insert into the destination latent. If not provided, the destination latent is returned unchanged."),
io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1, tooltip="The starting latent frame index in the destination latent where the source latent frames will be placed. Negative values count from the end."),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, destination, index, source=None) -> io.NodeOutput:
if source is None:
return io.NodeOutput(destination)
dest_frames = destination["samples"].shape[2]
source_frames = source["samples"].shape[2]
if index < 0:
index = dest_frames + index
if index > dest_frames:
logging.warning(f"ReplaceVideoLatentFrames: Index {index} is out of bounds for destination latent frames {dest_frames}.")
return io.NodeOutput(destination)
if index + source_frames > dest_frames:
logging.warning(f"ReplaceVideoLatentFrames: Source latent frames {source_frames} do not fit within destination latent frames {dest_frames} at the specified index {index}.")
return io.NodeOutput(destination)
s = source.copy()
s_source = source["samples"]
s_destination = destination["samples"].clone()
s_destination[:, :, index:index + s_source.shape[2]] = s_source
s["samples"] = s_destination
return io.NodeOutput(s)
class LatentExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
LatentAdd,
LatentSubtract,
LatentMultiply,
LatentInterpolate,
LatentConcat,
LatentCut,
LatentCutToBatch,
LatentBatch,
LatentBatchSeedBehavior,
LatentApplyOperation,
LatentApplyOperationCFG,
LatentOperationTonemapReinhard,
LatentOperationSharpen,
ReplaceVideoLatentFrames
]
async def comfy_entrypoint() -> LatentExtension:
return LatentExtension()
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