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fixed ksampler for video models

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Fill
2025-10-10 16:42:34 -07:00
parent f9122dae89
commit e4298b1b48
2 changed files with 35 additions and 9 deletions
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nodes/ksamplers/FL_KsamplerPlus.py
+34 -8
View File
@@ -131,7 +131,18 @@ class FL_KsamplerPlus:
if use_sliced_conditioning:
batch_size = 1
b, c, h, w = latent_image["samples"].shape
# Handle variable tensor dimensions (4D or 5D)
latent_shape = latent_image["samples"].shape
if len(latent_shape) == 5:
# 5D tensor: [batch, frames, channels, height, width]
b, f, c, h, w = latent_shape
logging.info(f"Processing 5D latent tensor with shape: {latent_shape}")
elif len(latent_shape) == 4:
# 4D tensor: [batch, channels, height, width]
b, c, h, w = latent_shape
else:
raise ValueError(f"Unexpected latent tensor shape: {latent_shape}. Expected 4D or 5D tensor.")
base_slice_height = h // y_slices
base_slice_width = w // x_slices
overlap_height = int(base_slice_height * overlap)
@@ -145,7 +156,11 @@ class FL_KsamplerPlus:
x_start = max(0, x * base_slice_width - overlap_width)
x_end = min(w, (x + 1) * base_slice_width + overlap_width)
section = latent_image["samples"][:, :, y_start:y_end, x_start:x_end].to(device=device)
# Handle both 4D and 5D tensor slicing
if len(latent_shape) == 5:
section = latent_image["samples"][:, :, :, y_start:y_end, x_start:x_end].to(device=device)
else:
section = latent_image["samples"][:, :, y_start:y_end, x_start:x_end].to(device=device)
if use_sliced_conditioning:
region = (x_start * 8, y_start * 8, x_end * 8, y_end * 8)
@@ -188,8 +203,12 @@ class FL_KsamplerPlus:
# Initialize samples tensor if it hasn't been initialized yet
if samples is None:
processed_channels = processed_sections[0].shape[1]
samples = torch.zeros((b, processed_channels, h, w), device=device)
if len(latent_shape) == 5:
processed_channels = processed_sections[0].shape[2]
samples = torch.zeros((b, f, processed_channels, h, w), device=device)
else:
processed_channels = processed_sections[0].shape[1]
samples = torch.zeros((b, processed_channels, h, w), device=device)
for (_, y_start, y_end, x_start, x_end, _, _), processed_section in zip(batch_sections,
processed_sections):
@@ -207,10 +226,17 @@ class FL_KsamplerPlus:
processed_section = processed_section.to(device=device)
blend_mask = blend_mask.to(device=device)
samples[:, :, y_start:y_end, x_start:x_end] = (
samples[:, :, y_start:y_end, x_start:x_end] * (1 - blend_mask) +
processed_section * blend_mask
)
# Handle both 4D and 5D tensor blending
if len(latent_shape) == 5:
samples[:, :, :, y_start:y_end, x_start:x_end] = (
samples[:, :, :, y_start:y_end, x_start:x_end] * (1 - blend_mask) +
processed_section * blend_mask
)
else:
samples[:, :, y_start:y_end, x_start:x_end] = (
samples[:, :, y_start:y_end, x_start:x_end] * (1 - blend_mask) +
processed_section * blend_mask
)
if device.type == 'cuda':
torch.cuda.empty_cache()
pyproject.toml
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "comfyui_fill-nodes"
description = "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection."
version = "1.9.5"
version = "1.9.6"
license = "LICENSE"
dependencies = ["diffusers", "librosa", "sounddevice", "glitch_this", "PyOpenGL", "glfw", "scipy>=1.13.1", "requests", "aiohttp", "moviepy", "matplotlib", "reportlab", "openai", "PyPDF2", "pdf2image", "PyMuPDF", "reportlab", "PyPDF2", "ollama", "kornia", "opencv-python", "gdown", "open_clip_torch", "google-genai"]