Compare commits
3
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Rick Staa
|
4e883d8e94 | ||
|
ryanontheinside
|
c98a0494a1 | ||
|
ryanontheinside
|
702d9ed2f4 |
@@ -0,0 +1,295 @@
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+375
@@ -0,0 +1,375 @@
|
||||
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"21": {
|
||||
"inputs": {
|
||||
"backend": "inductor",
|
||||
"fullgraph": false,
|
||||
"mode": "reduce-overhead",
|
||||
"controlnet": [
|
||||
"20",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "TorchCompileLoadControlNet",
|
||||
"_meta": {
|
||||
"title": "TorchCompileLoadControlNet"
|
||||
}
|
||||
},
|
||||
"26": {
|
||||
"inputs": {
|
||||
"image": "example.png"
|
||||
},
|
||||
"class_type": "LoadImage",
|
||||
"_meta": {
|
||||
"title": "Load Image"
|
||||
}
|
||||
},
|
||||
"36": {
|
||||
"inputs": {
|
||||
"clip_name": "CLIPText/model.fp16.safetensors",
|
||||
"type": "stable_diffusion",
|
||||
"device": "default"
|
||||
},
|
||||
"class_type": "CLIPLoader",
|
||||
"_meta": {
|
||||
"title": "Load CLIP"
|
||||
}
|
||||
},
|
||||
"37": {
|
||||
"inputs": {
|
||||
"conditioning_to": [
|
||||
"9",
|
||||
1
|
||||
],
|
||||
"conditioning_from": [
|
||||
"19",
|
||||
1
|
||||
]
|
||||
},
|
||||
"class_type": "ConditioningConcat",
|
||||
"_meta": {
|
||||
"title": "Conditioning (Concat)"
|
||||
}
|
||||
},
|
||||
"38": {
|
||||
"inputs": {
|
||||
"conditioning_to": [
|
||||
"9",
|
||||
0
|
||||
],
|
||||
"conditioning_from": [
|
||||
"19",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "ConditioningConcat",
|
||||
"_meta": {
|
||||
"title": "Conditioning (Concat)"
|
||||
}
|
||||
},
|
||||
"39": {
|
||||
"inputs": {
|
||||
"static_image_mode": false,
|
||||
"max_num_faces": 1,
|
||||
"refine_landmarks": false,
|
||||
"min_detection_confidence": 0.3500000000000001,
|
||||
"min_tracking_confidence": 0.3500000000000001,
|
||||
"draw_mesh": true,
|
||||
"draw_contours": true,
|
||||
"mesh_color": "green",
|
||||
"mesh_thickness": 2,
|
||||
"image": [
|
||||
"26",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "FaceMeshNode",
|
||||
"_meta": {
|
||||
"title": "Face Mesh"
|
||||
}
|
||||
},
|
||||
"41": {
|
||||
"inputs": {
|
||||
"num_steps": 3
|
||||
},
|
||||
"class_type": "StreamBatchSampler",
|
||||
"_meta": {
|
||||
"title": "Stream Batch Sampler"
|
||||
}
|
||||
},
|
||||
"42": {
|
||||
"inputs": {
|
||||
"add_noise": true,
|
||||
"noise_seed": 977567268563782,
|
||||
"cfg": 1,
|
||||
"model": [
|
||||
"45",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"38",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"37",
|
||||
0
|
||||
],
|
||||
"sampler": [
|
||||
"41",
|
||||
0
|
||||
],
|
||||
"sigmas": [
|
||||
"44",
|
||||
0
|
||||
],
|
||||
"latent_image": [
|
||||
"43",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "SamplerCustom",
|
||||
"_meta": {
|
||||
"title": "SamplerCustom"
|
||||
}
|
||||
},
|
||||
"43": {
|
||||
"inputs": {
|
||||
"buffer_size": 3,
|
||||
"latent": [
|
||||
"16",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "StreamFrameBuffer",
|
||||
"_meta": {
|
||||
"title": "Stream Frame Buffer"
|
||||
}
|
||||
},
|
||||
"44": {
|
||||
"inputs": {
|
||||
"t_index_list": "0,16,32",
|
||||
"num_inference_steps": 50,
|
||||
"model": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "StreamScheduler",
|
||||
"_meta": {
|
||||
"title": "Stream Scheduler"
|
||||
}
|
||||
},
|
||||
"45": {
|
||||
"inputs": {
|
||||
"use_feature_injection": true,
|
||||
"feature_injection_strength": 0.8,
|
||||
"feature_similarity_threshold": 0.98,
|
||||
"feature_cache_interval": 4,
|
||||
"feature_bank_max_frames": 4,
|
||||
"model": [
|
||||
"3",
|
||||
0
|
||||
]
|
||||
},
|
||||
"class_type": "FeatureBankAttentionProcessor",
|
||||
"_meta": {
|
||||
"title": "Feature Bank Attention Processor"
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,15 @@
|
||||
from ..src.batched_denoising.batched_denoising import StreamBatchSampler, StreamScheduler, StreamFrameBuffer
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"StreamBatchSampler": StreamBatchSampler,
|
||||
"StreamScheduler": StreamScheduler,
|
||||
"StreamFrameBuffer": StreamFrameBuffer,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"StreamBatchSampler": "Stream Batch Sampler",
|
||||
"StreamScheduler": "Stream Scheduler",
|
||||
"StreamFrameBuffer": "Stream Frame Buffer",
|
||||
}
|
||||
|
||||
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
|
||||
@@ -0,0 +1,287 @@
|
||||
import torch
|
||||
import comfy.model_management
|
||||
import comfy.samplers
|
||||
|
||||
class StreamBatchSampler:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"num_steps": ("INT", {
|
||||
"default": 4,
|
||||
"min": 1,
|
||||
"max": 10,
|
||||
"step": 1,
|
||||
"tooltip": "Number of denoising steps. Should match the frame buffer size."
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("SAMPLER",)
|
||||
FUNCTION = "update"
|
||||
CATEGORY = "StreamPack/sampling"
|
||||
DESCRIPTION = "Implements batched denoising for faster inference by processing multiple frames in parallel at different denoising steps. Also adds temportal consistency to the denoising process."
|
||||
|
||||
def __init__(self):
|
||||
self.num_steps = None
|
||||
self.frame_buffer = []
|
||||
self.x_t_latent_buffer = None
|
||||
self.stock_noise = None
|
||||
self.is_txt2img_mode = False
|
||||
|
||||
# Initialize all buffers
|
||||
self.zeros_reference = None
|
||||
self.random_noise_buffer = None
|
||||
self.sigmas_view_buffer = None
|
||||
self.expanded_stock_noise = None
|
||||
self.working_buffer = None
|
||||
self.output_buffer = None
|
||||
|
||||
def sample(self, model, noise, sigmas, extra_args=None, callback=None, disable=None):
|
||||
"""Sample with staggered batch denoising steps - Optimized version"""
|
||||
extra_args = {} if extra_args is None else extra_args
|
||||
|
||||
# Get number of frames in batch and available sigmas
|
||||
batch_size = noise.shape[0]
|
||||
num_sigmas = len(sigmas) - 1 # Subtract 1 because last sigma is the target (0.0)
|
||||
|
||||
# Reuse zeros buffer for txt2img detection
|
||||
if self.zeros_reference is None:
|
||||
self.zeros_reference = torch.zeros(1, device=noise.device, dtype=noise.dtype)
|
||||
|
||||
# Check if noise tensor is all zeros
|
||||
self.is_txt2img_mode = torch.abs(noise).sum() < 1e-5
|
||||
|
||||
if self.is_txt2img_mode:
|
||||
# If txt2img mode, reuse the noise tensor directly
|
||||
if self.random_noise_buffer is None or self.random_noise_buffer.shape != noise.shape:
|
||||
self.random_noise_buffer = torch.empty_like(noise)
|
||||
|
||||
# Generate random noise in-place
|
||||
self.random_noise_buffer.normal_()
|
||||
x = self.random_noise_buffer # Use pre-allocated buffer
|
||||
else:
|
||||
# If not txt2img, we'll still need to add noise later
|
||||
x = noise
|
||||
|
||||
# Verify batch size matches number of timesteps
|
||||
if batch_size != num_sigmas:
|
||||
raise ValueError(f"Batch size ({batch_size}) must match number of timesteps ({num_sigmas})")
|
||||
|
||||
# Pre-compute alpha and beta terms
|
||||
alpha_prod_t = (sigmas[:-1] / sigmas[0]).view(-1, 1, 1, 1) # [B,1,1,1]
|
||||
beta_prod_t = (1 - alpha_prod_t)
|
||||
|
||||
# Initialize stock noise with reuse
|
||||
if self.stock_noise is None or self.stock_noise.shape != noise[0].shape:
|
||||
self.stock_noise = torch.empty_like(noise[0])
|
||||
self.stock_noise.normal_() # Generate random noise in-place
|
||||
|
||||
# Pre-allocate and reuse view buffer for sigmas
|
||||
if self.sigmas_view_buffer is None or self.sigmas_view_buffer.shape[0] != len(sigmas)-1:
|
||||
self.sigmas_view_buffer = torch.empty((len(sigmas)-1, 1, 1, 1),
|
||||
device=sigmas.device,
|
||||
dtype=sigmas.dtype)
|
||||
# In-place copy of sigmas view
|
||||
self.sigmas_view_buffer.copy_(sigmas[:-1].view(-1, 1, 1, 1))
|
||||
|
||||
# Eliminate unsqueeze allocation by pre-expanding stock noise
|
||||
if self.expanded_stock_noise is None or self.expanded_stock_noise.shape[0] != batch_size:
|
||||
self.expanded_stock_noise = self.stock_noise.expand(batch_size, *self.stock_noise.shape)
|
||||
|
||||
# Apply noise with pre-allocated buffers - no new memory allocation
|
||||
if not self.is_txt2img_mode: # Already handled txt2img case above
|
||||
# If we need a working buffer separate from noise input:
|
||||
if id(x) == id(noise): # They're the same object, need a separate buffer
|
||||
if self.working_buffer is None or self.working_buffer.shape != noise.shape:
|
||||
self.working_buffer = torch.empty_like(noise)
|
||||
x = self.working_buffer
|
||||
# Add noise to input
|
||||
torch.add(noise, self.expanded_stock_noise * self.sigmas_view_buffer, out=x)
|
||||
|
||||
# Initialize and manage latent buffer with memory optimization
|
||||
if (self.x_t_latent_buffer is None or self.is_txt2img_mode) and num_sigmas > 1:
|
||||
# Pre-allocate or resize as needed
|
||||
if self.x_t_latent_buffer is None or self.x_t_latent_buffer.shape != x[0].shape:
|
||||
self.x_t_latent_buffer = torch.empty_like(x[0])
|
||||
self.x_t_latent_buffer.copy_(x[0])
|
||||
|
||||
# Use buffer for first frame to maintain temporal consistency
|
||||
if num_sigmas > 1:
|
||||
x[0].copy_(self.x_t_latent_buffer)
|
||||
|
||||
with torch.no_grad():
|
||||
# Process all frames in parallel
|
||||
sigma_batch = sigmas[:-1]
|
||||
|
||||
denoised_batch = model(x, sigma_batch, **extra_args)
|
||||
|
||||
# Update buffer with intermediate results
|
||||
if num_sigmas > 1:
|
||||
# Store result from first frame as buffer for next iteration
|
||||
self.x_t_latent_buffer.copy_(denoised_batch[0]) # In-place update
|
||||
|
||||
# Pre-allocate output buffer
|
||||
if self.output_buffer is None or self.output_buffer.shape != (1, *denoised_batch[-1].shape):
|
||||
self.output_buffer = torch.empty(1, *denoised_batch[-1].shape,
|
||||
device=denoised_batch.device,
|
||||
dtype=denoised_batch.dtype)
|
||||
# Copy the result directly to pre-allocated buffer
|
||||
self.output_buffer[0].copy_(denoised_batch[-1])
|
||||
x_0_pred_out = self.output_buffer
|
||||
else:
|
||||
x_0_pred_out = denoised_batch
|
||||
self.x_t_latent_buffer = None
|
||||
|
||||
# Call callback if provided
|
||||
if callback is not None:
|
||||
callback({'x': x_0_pred_out, 'i': 0, 'sigma': sigmas[0], 'sigma_hat': sigmas[0], 'denoised': denoised_batch[-1:]})
|
||||
|
||||
return x_0_pred_out
|
||||
|
||||
def update(self, num_steps=4):
|
||||
"""Create sampler with specified settings"""
|
||||
self.num_steps = num_steps
|
||||
sampler = comfy.samplers.KSAMPLER(self.sample)
|
||||
return (sampler,)
|
||||
|
||||
|
||||
class StreamScheduler:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"model": ("MODEL",),
|
||||
"t_index_list": ("STRING", {
|
||||
"default": "0,16,32,49",
|
||||
"tooltip": "Comma-separated list of timesteps to actually use for denoising. Examples: '32,45' for img2img or '0,16,32,45' for txt2img"
|
||||
}),
|
||||
"num_inference_steps": ("INT", {
|
||||
"default": 50,
|
||||
"min": 1,
|
||||
"max": 1000,
|
||||
"step": 1,
|
||||
"tooltip": "Total number of timesteps in schedule. StreamDiffusion uses 50 by default. Only timesteps specified in t_index_list are actually used."
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("SIGMAS",)
|
||||
FUNCTION = "update"
|
||||
CATEGORY = "StreamPack/sampling"
|
||||
DESCRIPTION = "Implements StreamDiffusion's efficient timestep selection. Use in conjunction with StreamBatchSampler."
|
||||
|
||||
def update(self, model, t_index_list="32,45", num_inference_steps=50):
|
||||
# Get model's sampling parameters
|
||||
model_sampling = model.get_model_object("model_sampling")
|
||||
|
||||
# Parse timestep list
|
||||
try:
|
||||
t_index_list = [int(t.strip()) for t in t_index_list.split(",")]
|
||||
except ValueError as e:
|
||||
|
||||
t_index_list = [32, 45]
|
||||
|
||||
# Create full schedule using normal scheduler
|
||||
full_sigmas = comfy.samplers.normal_scheduler(model_sampling, num_inference_steps)
|
||||
|
||||
# Select only the sigmas at our desired indices, but in reverse order
|
||||
# This ensures we go from high noise to low noise
|
||||
selected_sigmas = []
|
||||
for t in sorted(t_index_list, reverse=True): # Sort in reverse to go from high noise to low
|
||||
if t < 0 or t >= num_inference_steps:
|
||||
|
||||
continue
|
||||
selected_sigmas.append(float(full_sigmas[t]))
|
||||
|
||||
# Add final sigma
|
||||
selected_sigmas.append(0.0)
|
||||
|
||||
# Convert to tensor and move to appropriate device
|
||||
selected_sigmas = torch.FloatTensor(selected_sigmas).to(comfy.model_management.get_torch_device())
|
||||
return (selected_sigmas,)
|
||||
|
||||
|
||||
class StreamFrameBuffer:
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {
|
||||
"required": {
|
||||
"latent": ("LATENT",),
|
||||
"buffer_size": ("INT", {
|
||||
"default": 4,
|
||||
"min": 1,
|
||||
"max": 10,
|
||||
"step": 1,
|
||||
"tooltip": "Number of frames to buffer before starting batch processing. Should match number of denoising steps."
|
||||
}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("LATENT",)
|
||||
FUNCTION = "update"
|
||||
CATEGORY = "StreamPack/sampling"
|
||||
DESCRIPTION = "Accumulates frames to enable staggered batch denoising like StreamDiffusion. Use in conjunction with StreamBatchSampler"
|
||||
|
||||
|
||||
def __init__(self):
|
||||
self.frame_buffer = None # Tensor of shape [buffer_size, C, H, W]
|
||||
self.buffer_size = None
|
||||
self.buffer_pos = 0 # Current position
|
||||
self.is_initialized = False # Track buffer initialization
|
||||
self.is_txt2img_mode = False
|
||||
|
||||
def update(self, latent, buffer_size=4):
|
||||
"""Add new frame to buffer and return batch when ready"""
|
||||
self.buffer_size = buffer_size
|
||||
|
||||
# Extract latent tensor from input and remove batch dimension if present
|
||||
x = latent["samples"]
|
||||
|
||||
# Check if this is a txt2img (zeros tensor) or img2img mode
|
||||
# In ComfyUI, EmptyLatentImage returns a zeros tensor with shape [batch_size, 4, h//8, w//8]
|
||||
# We consider it txt2img mode if the tensor contains all zeros
|
||||
is_txt2img_mode = torch.sum(torch.abs(x)) < 1e-6
|
||||
self.is_txt2img_mode = is_txt2img_mode
|
||||
|
||||
# If it's a batch with size 1, remove the batch dimension for our buffer
|
||||
if x.dim() == 4 and x.shape[0] == 1: # [1,C,H,W]
|
||||
x = x.squeeze(0) # Remove batch dimension -> [C,H,W]
|
||||
|
||||
# Initialize or resize frame_buffer as a tensor
|
||||
if not self.is_initialized or self.frame_buffer.shape[0] != self.buffer_size or \
|
||||
self.frame_buffer.shape[1:] != x.shape:
|
||||
# Pre-allocate buffer with correct shape
|
||||
self.frame_buffer = torch.zeros(
|
||||
(self.buffer_size, *x.shape),
|
||||
device=x.device,
|
||||
dtype=x.dtype
|
||||
)
|
||||
if self.is_txt2img_mode or not self.is_initialized:
|
||||
# Optimization: Use broadcasting to fill buffer with copies
|
||||
self.frame_buffer[:] = x.unsqueeze(0) # Broadcast x to [buffer_size, C, H, W]
|
||||
|
||||
self.is_initialized = True
|
||||
self.buffer_pos = 0
|
||||
else:
|
||||
# Add new frame to buffer using ring buffer logic
|
||||
self.frame_buffer[self.buffer_pos] = x # In-place update
|
||||
self.buffer_pos = (self.buffer_pos + 1) % self.buffer_size # Circular increment
|
||||
|
||||
# Optimization: frame_buffer is already a tensor batch, no need to stack
|
||||
batch = self.frame_buffer
|
||||
|
||||
|
||||
# Return as latent dict with preserved dimensions
|
||||
result = {"samples": batch}
|
||||
|
||||
# Preserve height and width if present in input
|
||||
if "height" in latent:
|
||||
result["height"] = latent["height"]
|
||||
if "width" in latent:
|
||||
result["width"] = latent["width"]
|
||||
|
||||
return (result,)
|
||||
Reference in New Issue
Block a user