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facok
@@ -0,0 +1,3 @@
|
||||
from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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@@ -0,0 +1,820 @@
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@@ -0,0 +1,275 @@
|
||||
import os
|
||||
import torch
|
||||
import numpy as np
|
||||
import comfy.model_management as mm
|
||||
import comfy.utils
|
||||
import latent_preview
|
||||
from typing import Any, List, Tuple, Optional, Union, Dict
|
||||
|
||||
def modulate(x, shift, scale):
|
||||
"""Modulate layer implementation for HunyuanVideo"""
|
||||
try:
|
||||
# Ensure consistent data types
|
||||
shift = shift.to(dtype=x.dtype, device=x.device)
|
||||
scale = scale.to(dtype=x.dtype, device=x.device)
|
||||
|
||||
# Reshape shift and scale to match x dimensions
|
||||
B = x.shape[0] # batch size
|
||||
|
||||
# Adjust shift and scale based on x dimensions
|
||||
if len(x.shape) == 3: # [B, L, D]
|
||||
shift = shift.view(B, 1, -1) # [B, 1, D]
|
||||
scale = scale.view(B, 1, -1) # [B, 1, D]
|
||||
# Expand to match x length
|
||||
shift = shift.expand(-1, x.shape[1], -1) # [B, L, D]
|
||||
scale = scale.expand(-1, x.shape[1], -1) # [B, L, D]
|
||||
elif len(x.shape) == 5: # [B, C, T, H, W]
|
||||
shift = shift.view(B, -1, 1, 1, 1) # [B, C, 1, 1, 1]
|
||||
scale = scale.view(B, -1, 1, 1, 1) # [B, C, 1, 1, 1]
|
||||
# Expand to match x temporal and spatial dimensions
|
||||
shift = shift.expand(-1, -1, x.shape[2], x.shape[3], x.shape[4]) # [B, C, T, H, W]
|
||||
scale = scale.expand(-1, -1, x.shape[2], x.shape[3], x.shape[4]) # [B, C, T, H, W]
|
||||
else:
|
||||
raise ValueError(f"Unsupported input shape: {x.shape}")
|
||||
|
||||
# Step-by-step calculation to reduce memory usage
|
||||
result = x.mul_(1 + scale) # in-place operation
|
||||
result.add_(shift) # in-place operation
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Modulation failed: {str(e)}")
|
||||
|
||||
class TeaCacheHunyuanVideoSampler:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"noise": ("NOISE",),
|
||||
"guider": ("GUIDER",),
|
||||
"sampler": ("SAMPLER",),
|
||||
"sigmas": ("SIGMAS",),
|
||||
"latent_image": ("LATENT",),
|
||||
"speedup": (["Original (1x)", "Fast (1.6x)", "Faster (2.1x)"], {
|
||||
"default": "Fast (1.6x)",
|
||||
"tooltip": "Control TeaCache speed/quality trade-off:\nOriginal: Base quality\nFast: 1.6x speedup\nFaster: 2.1x speedup"
|
||||
}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT", "LATENT")
|
||||
RETURN_NAMES = ("output", "denoised_output")
|
||||
FUNCTION = "sample"
|
||||
CATEGORY = "sampling/custom_sampling"
|
||||
|
||||
def teacache_forward(self, transformer, x, timestep, context=None, y=None, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
|
||||
"""TeaCache forward implementation"""
|
||||
should_calc = True
|
||||
|
||||
if transformer.enable_teacache:
|
||||
try:
|
||||
# Get input dimensions
|
||||
B, C, T, H, W = x.shape
|
||||
|
||||
# Ensure consistent input dtypes
|
||||
try:
|
||||
x = x.to(dtype=transformer.dtype)
|
||||
timestep = timestep.to(dtype=transformer.dtype)
|
||||
if context is not None:
|
||||
context = context.to(dtype=transformer.dtype)
|
||||
if guidance is not None:
|
||||
guidance = guidance.to(dtype=transformer.dtype)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to convert input dtypes: {str(e)}")
|
||||
|
||||
# Prepare modulation vectors
|
||||
try:
|
||||
# HunyuanVideo uses timestep_embedding for time step encoding
|
||||
time_emb = comfy.ldm.flux.layers.timestep_embedding(timestep, 256, time_factor=1.0).to(x.dtype)
|
||||
vec = transformer.time_in(time_emb) # [B, hidden_size]
|
||||
|
||||
# text modulation - HunyuanVideo uses vector_in to process y instead of context
|
||||
if y is not None:
|
||||
if not hasattr(transformer, 'params') or not hasattr(transformer.params, 'vec_in_dim'):
|
||||
raise AttributeError("Transformer missing required attributes: params.vec_in_dim")
|
||||
vec = vec + transformer.vector_in(y[:, :transformer.params.vec_in_dim])
|
||||
|
||||
# guidance modulation
|
||||
if guidance is not None and transformer.params.guidance_embed:
|
||||
guidance_emb = comfy.ldm.flux.layers.timestep_embedding(guidance, 256).to(x.dtype)
|
||||
guidance_vec = transformer.guidance_in(guidance_emb)
|
||||
vec = vec + guidance_vec
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to prepare modulation vector: {str(e)}")
|
||||
|
||||
# Embed image
|
||||
try:
|
||||
img = transformer.img_in(x)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to embed image: {str(e)}")
|
||||
|
||||
if transformer.enable_teacache:
|
||||
try:
|
||||
# Use in-place operation to reduce memory usage
|
||||
inp = img.clone()
|
||||
vec_ = vec.clone()
|
||||
|
||||
# Get modulation parameters
|
||||
modulation_output = transformer.double_blocks[0].img_mod(vec_)
|
||||
|
||||
# Process modulation output
|
||||
if isinstance(modulation_output, tuple):
|
||||
if len(modulation_output) >= 2:
|
||||
mod_shift = modulation_output[0]
|
||||
mod_scale = modulation_output[1]
|
||||
if hasattr(mod_shift, 'shift') and hasattr(mod_scale, 'scale'):
|
||||
img_mod1_shift = mod_shift.shift
|
||||
img_mod1_scale = mod_scale.scale
|
||||
else:
|
||||
img_mod1_shift = mod_shift
|
||||
img_mod1_scale = mod_scale
|
||||
else:
|
||||
raise ValueError(f"Tuple too short, expected at least 2 elements, got {len(modulation_output)}")
|
||||
elif hasattr(modulation_output, 'shift') and hasattr(modulation_output, 'scale'):
|
||||
img_mod1_shift = modulation_output.shift
|
||||
img_mod1_scale = modulation_output.scale
|
||||
elif hasattr(modulation_output, 'chunk'):
|
||||
chunks = modulation_output.chunk(6, dim=-1)
|
||||
img_mod1_shift = chunks[0]
|
||||
img_mod1_scale = chunks[1]
|
||||
else:
|
||||
raise ValueError(f"Unsupported modulation output format: {type(modulation_output)}")
|
||||
|
||||
# Ensure get is tensor
|
||||
if not isinstance(img_mod1_shift, torch.Tensor) or not isinstance(img_mod1_scale, torch.Tensor):
|
||||
raise ValueError(f"Failed to get tensor values for shift and scale")
|
||||
|
||||
# Apply normalization and modulation
|
||||
normed_inp = transformer.double_blocks[0].img_norm1(inp)
|
||||
del inp # Release memory
|
||||
|
||||
modulated_inp = modulate(normed_inp, shift=img_mod1_shift, scale=img_mod1_scale)
|
||||
del normed_inp # Release memory
|
||||
|
||||
# Calculate relative L1 distance and decide whether to calculate
|
||||
if transformer.cnt == 0 or transformer.cnt == transformer.num_steps-1:
|
||||
should_calc = True
|
||||
transformer.accumulated_rel_l1_distance = 0
|
||||
else:
|
||||
try:
|
||||
coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02]
|
||||
rescale_func = np.poly1d(coefficients)
|
||||
rel_l1 = ((modulated_inp-transformer.previous_modulated_input).abs().mean() /
|
||||
transformer.previous_modulated_input.abs().mean()).cpu().item()
|
||||
transformer.accumulated_rel_l1_distance += rescale_func(rel_l1)
|
||||
|
||||
if transformer.accumulated_rel_l1_distance < transformer.rel_l1_thresh:
|
||||
should_calc = False
|
||||
else:
|
||||
should_calc = True
|
||||
transformer.accumulated_rel_l1_distance = 0
|
||||
except Exception as e:
|
||||
should_calc = True
|
||||
|
||||
transformer.previous_modulated_input = modulated_inp
|
||||
transformer.cnt += 1
|
||||
|
||||
except Exception as e:
|
||||
should_calc = True
|
||||
|
||||
except Exception as e:
|
||||
should_calc = True
|
||||
|
||||
# If calculation is needed, call the original forward function
|
||||
if should_calc:
|
||||
try:
|
||||
out = transformer.original_forward(x, timestep, context, y, guidance,
|
||||
attention_mask=attention_mask,
|
||||
control=control,
|
||||
transformer_options=transformer_options,
|
||||
**kwargs)
|
||||
transformer.previous_residual = out
|
||||
return out
|
||||
except Exception as e:
|
||||
raise
|
||||
else:
|
||||
# If calculation is not needed, return the previous result
|
||||
return transformer.previous_residual
|
||||
|
||||
def sample(self, noise, guider, sampler, sigmas, latent_image, speedup):
|
||||
"""Sampling implementation"""
|
||||
device = mm.get_torch_device()
|
||||
|
||||
# Convert options to specific thresholds
|
||||
thresh_map = {
|
||||
"Original (1x)": 0.0,
|
||||
"Fast (1.6x)": 0.1,
|
||||
"Faster (2.1x)": 0.15
|
||||
}
|
||||
actual_thresh = thresh_map[speedup]
|
||||
|
||||
try:
|
||||
# Get transformer
|
||||
transformer = guider.model_patcher.model.diffusion_model
|
||||
|
||||
# Initialize TeaCache state
|
||||
transformer.enable_teacache = True
|
||||
transformer.cnt = 0
|
||||
transformer.num_steps = len(sigmas) - 1
|
||||
transformer.rel_l1_thresh = actual_thresh
|
||||
transformer.accumulated_rel_l1_distance = 0
|
||||
transformer.previous_modulated_input = None
|
||||
transformer.previous_residual = None
|
||||
|
||||
latent = latent_image
|
||||
latent_image = latent["samples"]
|
||||
latent = latent.copy()
|
||||
|
||||
noise_mask = None
|
||||
if "noise_mask" in latent:
|
||||
noise_mask = latent["noise_mask"]
|
||||
|
||||
# Save original forward function
|
||||
transformer.original_forward = transformer.forward
|
||||
|
||||
# Replace with TeaCache forward
|
||||
transformer.forward = lambda *args, **kwargs: self.teacache_forward(transformer, *args, **kwargs)
|
||||
|
||||
try:
|
||||
x0_output = {}
|
||||
callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
|
||||
|
||||
disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
|
||||
samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas,
|
||||
denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar,
|
||||
seed=noise.seed)
|
||||
samples = samples.to(mm.intermediate_device())
|
||||
|
||||
finally:
|
||||
# Restore original forward function
|
||||
transformer.forward = transformer.original_forward
|
||||
delattr(transformer, 'original_forward')
|
||||
transformer.enable_teacache = False
|
||||
|
||||
out = latent.copy()
|
||||
out["samples"] = samples
|
||||
if "x0" in x0_output:
|
||||
out_denoised = latent.copy()
|
||||
out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
|
||||
else:
|
||||
out_denoised = out
|
||||
|
||||
return (out, out_denoised)
|
||||
|
||||
except Exception as e:
|
||||
raise
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"TeaCacheHunyuanVideoSampler": TeaCacheHunyuanVideoSampler
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"TeaCacheHunyuanVideoSampler": "TeaCache HunyuanVideo Sampler"
|
||||
}
|
||||
Reference in New Issue
Block a user