From b77dc6f2236745658bdc45bf27eb3bbefb0c3cfa Mon Sep 17 00:00:00 2001 From: shootthesound <110498357+shootthesound@users.noreply.github.com> Date: Sat, 27 Dec 2025 21:29:51 +0000 Subject: [PATCH] Initial Commit --- README.md | 199 + __init__.py | 27 + examples/20241210133335-d9f5de77.jpg | Bin 0 -> 253261 bytes examples/Car-Racing-Example.json | 11686 +++++++++++++++++++++++++ examples/Single-Shot-Example.json | 1311 +++ nodes.py | 597 ++ 6 files changed, 13820 insertions(+) create mode 100644 README.md create mode 100644 __init__.py create mode 100644 examples/20241210133335-d9f5de77.jpg create mode 100644 examples/Car-Racing-Example.json create mode 100644 examples/Single-Shot-Example.json create mode 100644 nodes.py diff --git a/README.md b/README.md new file mode 100644 index 0000000..860189e --- /dev/null +++ b/README.md @@ -0,0 +1,199 @@ +# comfyUI-LongLook + +**Consistent motion and prompt adherence for Wan 2.2 video generation.** + +**TL;DR:** +- **Chunked generation (best use case)**: Stable motion provides clean anchors AND makes the next chunk far more likely to correctly continue the direction of a given action +- **Single generation**: Smooths motion reversal and "ping-pong" in 81+ frame generations + +See Demo Workflows in Folder and [demo clip here](https://youtu.be/wZgoklsVplc) + +If you find this useful: + + + Buy Me A Coffee + + +## What It Does + +### The Core: FreeLong Spectral Blending + +Based on [FreeLong (NeurIPS 2024)](https://arxiv.org/abs/2407.19918), this implements frequency-aware attention blending that dramatically improves motion consistency: + +| Without FreeLong | With FreeLong | +|------------------|---------------| +| Motion reverses mid-video ("ping-pong") | Stable directional motion throughout | +| Subject drifts between "scenes" | Consistent subject appearance | +| Prompt partially ignored for motion | Motion follows prompt intent | +| 2-3 distinct "scenes" per generation | Single coherent sequence | + +**How it works:** +- **Global stream**: Full-sequence attention captures overall motion direction +- **Local stream**: Windowed attention preserves sharp details +- **Spectral blend**: FFT combines low frequencies (motion) from global + high frequencies (detail) from local + +This is essentially **motion-level prompt enforcement** - the model maintains its initial motion intent rather than "forgetting" mid-generation. + +### The Bonus: Unlimited Length via Chunking + +FreeLong's stable motion interpretation is what makes reliable chunk chaining possible: + +``` +[Chunk 1: 81 frames] → last frame → [Chunk 2: 81 frames] → last frame → [Chunk 3...] → ∞ +``` + +**Why this works:** + +Without FreeLong, chunk boundaries can often fail because: +- Motion may have reversed mid-chunk → last frame has wrong direction +- Subject drifted → last frame doesn't match earlier frames +- Ambiguous anchor → next chunk likely misinterprets intended motion + +With FreeLong, each chunk has: +- Consistent motion direction throughout → last frame shows correct direction +- Stable subject interpretation → clean visual handoff +- Clear motion cues → next chunk far more likely to correctly continue + +**Two-sided benefit:** FreeLong provides clean anchor frames AND makes the next generation far more likely to correctly interpret the motion direction. Clear input signals lead to better continuation. + +**Additional chunking benefits:** +- Different prompts per chunk (scene evolution) +- Constant VRAM (no scaling with length) +- Fresh anchor resets any accumulated drift + + + +Wan 2.2's attention mechanism struggles with temporal coherence: +- Native context is 81 frames (~5 seconds) +- Motion often reverses or drifts within that window +- Extending beyond 81 frames compounds these issues + +## Installation + +1. Clone into ComfyUI custom_nodes: +```bash +cd ComfyUI/custom_nodes +git clone https://github.com/shootthesound/comfyUI-LongLook.git +``` + +2. Restart ComfyUI + +3. Load example workflow from `examples/Car-Racing-Example.json` and Single-Shot-Example.json + +## Quick Start + +### Single Generation (Improved Motion) +Just add `WanFreeLong` before your sampler: +``` +Load Model → WanFreeLong → KSampler → VAE Decode +``` +Even without chunking, you get more consistent motion within 81 frames. + +### Chained Generation (Unlimited Length) +``` +[Chunk 1] +Image → WanImageToVideo → FreeLong → Sampler → VAE Decode + ↓ + [Last Frame] + ↓ +[Chunk 2] +FreeLong → WanContinuationConditioning → Sampler → VAE Decode + ↑ ↓ + [New Prompt] [Last Frame] + ↓ +[Chunk 3...] [Repeat] +``` + +## Nodes + +### WanFreeLong (Spectral Blend) +Patches Wan model for FreeLong spectral blending. + +| Parameter | Default | Description | +|-----------|---------|-------------| +| enabled | true | Toggle for A/B testing | +| blend_strength | 0.7 | Spectral blend strength (0=off, 1=full) | +| low_freq_ratio | 0.5 | Portion of spectrum from global stream | +| local_window_frames | 33 | Video frames for detail window (~40% of total recommended) | +| blend_start_block | 0 | First transformer block to apply | +| blend_end_block | -1 | Last block (-1 = all) | + +**Optimal settings**: `blend_strength=0.8`, `low_freq_ratio=0.8`, `local_window_frames=33` for 81-frame videos + +### WanContinuationConditioning +Creates i2v conditioning from previous chunk's last frame. + +| Parameter | Default | Description | +|-----------|---------|-------------| +| width | 512 | Output video width | +| height | 512 | Output video height | +| video_frames | 81 | Frames to generate | + +**Inputs**: `positive`, `negative`, `anchor_images` (from previous VAE decode), `vae` + +## Workflows + +### Basic Continuation (`anchor_blend_workflow.json`) +Two chunks, shared prompts. Extends a single scene. + +### Multi-Prompt (`multi_prompt_workflow.json`) +Different prompt per chunk for scene evolution: +- Chunk 1: "car driving on highway" +- Chunk 2: "car enters city streets" +- Chunk 3: "car parks at destination" + +Same car, smooth motion, continuous narrative. + +## Parameter Tuning + +| Goal | Adjustment | +|------|------------| +| More motion consistency | Increase `blend_strength` (0.7-0.9) | +| More detail/sharpness | Decrease `blend_strength` (0.4-0.6) | +| Smoother motion | Increase `low_freq_ratio` (0.5-0.7) | +| More dynamic motion | Decrease `low_freq_ratio` (0.3-0.4) | +| Less morphing | Increase `local_window_frames` (49-65) | + +## Technical Details + +### FreeLong Implementation +- Dual-stream processing at transformer block level +- 50% overlapping windows with cosine crossfade +- Complementary FFT filters (low + high = 1.0) +- RoPE embeddings properly sliced per window +- Float32 FFT operations for numerical stability + +### Continuation Conditioning +- Last frame extracted from decoded video (float32, no 8-bit quantization) +- Re-encoded as i2v conditioning via VAE +- Proper mask format for Wan's conditioning system + +### Memory Efficiency +- Each chunk processes independently +- Only last frame passed between chunks +- No VRAM accumulation across chains +- Unlimited chunk count +- 8GB+ VRAM compatible + +## Experimental: Beyond 81 Frames + +FreeLong was designed to extend beyond training length. You may be able to generate 97, 113, or more frames in a single pass. Trade-offs: +- VRAM scales with frame count +- Quality may degrade at extreme lengths +- Chunking approach is proven stable + +## Requirements + +- ComfyUI +- Wan 2.2 models (i2v recommended) +- VideoHelperSuite (for video output) + +## Credits + +- FreeLong paper: [arxiv.org/abs/2407.19918](https://arxiv.org/abs/2407.19918) +- Wan 2.2: Alibaba +- Implementation: Collaborative human-AI development + +## License + +MIT diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..068a2f5 --- /dev/null +++ b/__init__.py @@ -0,0 +1,27 @@ +""" +comfyUI-LongLook: FreeLong for Wan 2.2 Video Generation + +Implements FreeLong (NeurIPS 2024) spectral blending for: +1. Better motion consistency within each generation +2. Reliable chunk chaining for unlimited length videos +""" + +from .nodes import ( + WanContinuationConditioning, + WanFreeLong, +) + +NODE_CLASS_MAPPINGS = { + "WanContinuationConditioning": WanContinuationConditioning, + "WanFreeLong": WanFreeLong, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "WanContinuationConditioning": "Wan Continuation Conditioning", + "WanFreeLong": "Wan FreeLong", +} + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] + +WEB_DIRECTORY = None +__version__ = "3.0.0" diff --git a/examples/20241210133335-d9f5de77.jpg b/examples/20241210133335-d9f5de77.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6ce4e8265a9aff7d2fa173c4d19f9cd89ec81fe6 GIT binary patch literal 253261 zcmeFa2RK~Y_b`0M=v{PCM(>Q?iB1r`M#z{Mj5dreT0%q+J&069M6^Vrq>(5=B)SNZ zf=Kir2%>yvNW1qYzyI&P@AJOj_k54@Wag~dYwfkxUVH6**4}4Z*?ze_BJ80Xf^h`^ zLqidO6aWAcU>^huKtKrpVS^Cu++9F;|2NnJgoVD*5P+~0gaCkncO3YU24M#9o(_I6 zrwDiIUISrOkl8$V$MgJt*F<4m&@f|+n|lBbi}i*nO2A=4CI}?PCjjg3E(}P+rDc_+ 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+logger.setLevel(logging.INFO) + + +class WanContinuationConditioning: + """ + Simple video continuation conditioning for Wan 2.2. + + Takes the last frame from a previous video chunk and creates i2v conditioning + for generating the next chunk. Equivalent to WanImageToVideo but designed + for easy chaining in continuation workflows. + + Usage: Connect decoded images from chunk 1 → this node → KSampler for chunk 2 + """ + + @classmethod + def INPUT_TYPES(cls) -> Dict[str, Any]: + return { + "required": { + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "anchor_images": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16}), + "height": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 16}), + "video_length": ("INT", { + "default": 81, + "min": 1, + "max": 1024, + "step": 4, + "tooltip": "Output video length in frames. Must match your sampler settings." + }), + } + } + + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "modify_conditioning" + CATEGORY = "video/wan" + DESCRIPTION = "Creates i2v conditioning from last frame for video continuation" + + def modify_conditioning( + self, + positive, + negative, + anchor_images: torch.Tensor, + vae, + width: int, + height: int, + video_length: int + ) -> Tuple[list, list, Dict[str, torch.Tensor]]: + """ + Modify conditioning to use anchor frames for continuation. + """ + import comfy.utils + + # Calculate latent dimensions using Wan's formula + latent_frames = ((video_length - 1) // 4) + 1 + latent_h = height // 8 + latent_w = width // 8 + + # Get the LAST frame from anchor images (single frame continuation like standard i2v) + frames_to_encode = anchor_images[-1:] # Always use just the last frame + + # Resize to target dimensions if needed + if frames_to_encode.shape[1] != height or frames_to_encode.shape[2] != width: + frames_to_encode = comfy.utils.common_upscale( + frames_to_encode.movedim(-1, 1), + width, height, "bilinear", "center" + ).movedim(1, -1) + + # Create full video tensor for VAE encoding (matches WanImageToVideo exactly) + # Gray (0.5) for frames to generate, actual image for first frame + full_video = torch.ones( + video_length, height, width, 3, + device=frames_to_encode.device, dtype=frames_to_encode.dtype + ) * 0.5 + + # Place the single anchor frame at the beginning + full_video[:1] = frames_to_encode[:, :, :, :3] + + # VAE encode the entire video (anchor frames + gray neutral frames) + concat_latent_image = vae.encode(full_video) + if concat_latent_image.ndim == 4: + concat_latent_image = concat_latent_image.unsqueeze(0) + + encoded_latent_frames = concat_latent_image.shape[2] + latent_channels = concat_latent_image.shape[1] + + # Calculate how many latent frames to preserve (1 video frame = 1 latent frame) + # Formula: ((1 - 1) // 4) + 1 = 1 + anchor_latent_frames = 1 + + # Create concat_mask using LATENT frames (like WanImageToVideo does - simpler, no reshape) + # Shape: [1, 1, latent_frames, h, w] + # Wan convention: 0.0 = preserve (don't denoise), 1.0 = generate (full denoise) + concat_mask = torch.ones( + 1, 1, encoded_latent_frames, + concat_latent_image.shape[-2], + concat_latent_image.shape[-1], + device=frames_to_encode.device, dtype=frames_to_encode.dtype + ) + + # Preserve anchor latent frames + concat_mask[:, :, :anchor_latent_frames] = 0.0 + + # Move to CPU for conditioning storage + concat_latent_image = concat_latent_image.cpu() + concat_mask = concat_mask.cpu() + + # Modify conditioning with our anchor data + # This overwrites any existing concat_latent_image/concat_mask from WanImageToVideo + positive_out = node_helpers.conditioning_set_values(positive, { + "concat_latent_image": concat_latent_image, + "concat_mask": concat_mask + }) + negative_out = node_helpers.conditioning_set_values(negative, { + "concat_latent_image": concat_latent_image, + "concat_mask": concat_mask + }) + + # Create empty latent for sampling + latent = torch.zeros( + 1, latent_channels, latent_frames, latent_h, latent_w, + device=comfy.model_management.intermediate_device() + ) + + return (positive_out, negative_out, {"samples": latent}) + + +# ============================================================================ +# FreeLong Helper Functions +# ============================================================================ + +def freelong_freq_filter(x: torch.Tensor, threshold: float, filter_type: str = "low") -> torch.Tensor: + """ + Apply frequency filtering along the temporal (frame) dimension. + + Uses complementary filters that sum to 1.0 at all frequencies. + + Args: + x: Tensor of shape [batch, frames, spatial, dim] or [batch, seq, dim] + threshold: Cutoff frequency ratio (0.0 to 1.0) + filter_type: "low" for low-pass, "high" for high-pass + + Returns: + Filtered tensor + """ + # cuFFT doesn't support half precision for non-power-of-two sizes + # Convert to float32 for FFT, then back to original dtype + orig_dtype = x.dtype + x_float = x.float() + + # FFT along frame/sequence dimension + freq = torch.fft.rfft(x_float, dim=1) + + # Create frequency mask (in float32) + # Use smooth cosine transition that sums to 1.0 with complement + num_freq = freq.shape[1] + cutoff = int(num_freq * threshold) + + # Width of transition zone (at least 1, at most 25% of spectrum) + transition_width = max(1, min(num_freq // 4, cutoff // 2)) + + # Create smooth low-pass mask using cosine transition + # This ensures low + high = 1.0 at all frequencies + mask = torch.ones(num_freq, device=x.device, dtype=torch.float32) + + # Transition zone centered around cutoff + transition_start = max(0, cutoff - transition_width // 2) + transition_end = min(num_freq, cutoff + transition_width // 2) + + if transition_end > transition_start: + # Cosine transition from 1 to 0 for low-pass + transition_idx = torch.arange(transition_start, transition_end, device=x.device, dtype=torch.float32) + transition_values = 0.5 * (1 + torch.cos(torch.pi * (transition_idx - transition_start) / (transition_end - transition_start))) + mask[transition_start:transition_end] = transition_values + + # Set values outside transition + mask[transition_end:] = 0.0 + + if filter_type == "high": + # High-pass is complement of low-pass: high = 1 - low + mask = 1.0 - mask + + # Apply mask (keep in float32) + mask = mask.view(1, -1, *([1] * (len(freq.shape) - 2))) + filtered_freq = freq * mask + + # Inverse FFT and convert back to original dtype + result = torch.fft.irfft(filtered_freq, n=x.shape[1], dim=1) + return result.to(orig_dtype) + + +def freelong_spectral_blend( + global_features: torch.Tensor, + local_features: torch.Tensor, + low_freq_ratio: float = 0.25, + blend_strength: float = 1.0 +) -> torch.Tensor: + """ + Blend global and local features using spectral decomposition. + Low frequencies from global (structure/motion direction), + High frequencies from local (details/sharpness). + + Args: + global_features: Features from full-sequence attention + local_features: Features from windowed attention + low_freq_ratio: Ratio of frequencies to take from global (default 0.25) + blend_strength: How much to apply the blend (0=passthrough, 1=full blend) + + Returns: + Blended features + """ + if blend_strength == 0.0: + return local_features + + # Get low-freq from global, high-freq from local + global_low = freelong_freq_filter(global_features, low_freq_ratio, "low") + local_high = freelong_freq_filter(local_features, low_freq_ratio, "high") + + # Blend with strength control + blended = global_low + local_high + + # Mix with original based on blend_strength + if blend_strength < 1.0: + return local_features * (1.0 - blend_strength) + blended * blend_strength + + return blended + + +# ============================================================================ +# FreeLong Model Patcher +# ============================================================================ + +class WanFreeLong: + """ + FreeLong model patcher for Wan 2.2 video generation. + + Implements SpectralBlend temporal attention to extend video generation + beyond the training context window while maintaining motion consistency. + + Based on "FreeLong: Training-Free Long Video Generation with + SpectralBlend Temporal Attention" (NeurIPS 2024) + + How it works: + 1. Hooks into Wan's transformer blocks + 2. Runs attention twice: global (full sequence) and local (windowed) + 3. Blends results using FFT: low-freq from global, high-freq from local + 4. This preserves motion direction (low-freq) while keeping details (high-freq) + """ + + @classmethod + def INPUT_TYPES(cls) -> Dict[str, Any]: + return { + "required": { + "model": ("MODEL",), + "enabled": ("BOOLEAN", { + "default": True, + "tooltip": "Turn FreeLong on or off. Useful for comparing results with and without." + }), + "blend_strength": ("FLOAT", { + "default": 0.7, + "min": 0.0, + "max": 1.0, + "step": 0.1, + "tooltip": "How strongly FreeLong affects the video. Higher = more consistent motion but slightly softer. Lower = sharper but may drift. 0.7 is optimal for most cases." + }), + "low_freq_ratio": ("FLOAT", { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.05, + "tooltip": "Balance between motion consistency and fine detail. Higher = smoother, more consistent motion. Lower = more dynamic but may drift. 0.5 is a good balance." + }), + "local_window_frames": ("INT", { + "default": 33, + "min": 9, + "max": 241, + "step": 4, + "tooltip": "How many video frames the detail-preservation looks at. Smaller = sharper details but may cause morphing. Larger = smoother transitions. 33 works well for 81-frame videos. For longer videos, try ~40% of total frames." + }), + "blend_start_block": ("INT", { + "default": 0, + "min": -1, + "max": 40, + "tooltip": "Advanced: Which model layer to start applying FreeLong. 0 = from the beginning. Most users should leave this at 0." + }), + "blend_end_block": ("INT", { + "default": -1, + "min": -1, + "max": 40, + "tooltip": "Advanced: Which model layer to stop applying FreeLong. -1 = apply to all layers. Most users should leave this at -1." + }), + } + } + + RETURN_TYPES = ("MODEL",) + RETURN_NAMES = ("model",) + FUNCTION = "patch_model" + CATEGORY = "video/wan" + DESCRIPTION = "Apply FreeLong spectral blending for better motion consistency in long videos" + + def patch_model( + self, + model, + enabled: bool, + blend_strength: float, + low_freq_ratio: float, + local_window_frames: int, + blend_start_block: int, + blend_end_block: int + ): + """ + Patch the model to use FreeLong spectral blending in temporal attention. + """ + # Convert video frames to latent frames + local_window_size = ((local_window_frames - 1) // 4) + 1 + # If disabled, return model unchanged (no patches) + if not enabled: + logger.info("[FreeLong] DISABLED - Model returned without patching") + return (model,) + + logger.info("=" * 60) + logger.info("[FreeLong] APPLYING SPECTRAL BLENDING PATCHES") + logger.info("=" * 60) + logger.info(f" Blend Strength: {blend_strength:.2f}") + logger.info(f" Low Freq Ratio: {low_freq_ratio:.2f}") + logger.info(f" Local Window: {local_window_frames} video frames → {local_window_size} latent frames") + logger.info(f" Block Range: {blend_start_block} to {blend_end_block if blend_end_block != -1 else 'ALL'}") + + model = model.clone() + + # Store settings for the patch function + freelong_settings = { + "blend_strength": blend_strength, + "low_freq_ratio": low_freq_ratio, + "local_window_size": local_window_size, + "blend_start_block": blend_start_block, + "blend_end_block": blend_end_block, + "first_run": True, # Track first execution to log details once + "blocks_patched": set(), # Track which blocks executed + } + + def freelong_block_patch(block_args, extra_args): + """ + Patch function that wraps transformer blocks with FreeLong blending. + + Implements dual-stream processing: + 1. Global stream: Full sequence attention (original behavior) + 2. Local stream: Windowed attention (process temporal chunks) + 3. Spectral blend: Low-freq from global, high-freq from local + """ + original_block = extra_args["original_block"] + transformer_options = block_args.get("transformer_options", {}) + block_index = transformer_options.get("block_index", 0) + total_blocks = transformer_options.get("total_blocks", 40) + + # Determine if this block should use FreeLong + start = freelong_settings["blend_start_block"] + end = freelong_settings["blend_end_block"] + if end == -1: + end = total_blocks + + # For blocks outside our range, just run original + if block_index < start or block_index >= end: + return original_block(block_args) + + # Skip if blend_strength is 0 + strength = freelong_settings["blend_strength"] + if strength == 0.0: + return original_block(block_args) + + # Track that this block executed (for logging summary) + freelong_settings["blocks_patched"].add(block_index) + + try: + x = block_args["img"] # [batch, seq_len, dim] + batch_size, seq_len, hidden_dim = x.shape + + window_size = freelong_settings["local_window_size"] + low_ratio = freelong_settings["low_freq_ratio"] + + # Log details on first execution + if freelong_settings["first_run"]: + freelong_settings["first_run"] = False + logger.info("") + logger.info("[FreeLong] DUAL-STREAM PROCESSING STARTING") + logger.info(f" Input shape: batch={batch_size}, seq_len={seq_len}, hidden_dim={hidden_dim}") + logger.info(f" Window size: {window_size} latent frames") + + # ============ GLOBAL STREAM ============ + # Full sequence attention - preserves global motion patterns + global_out = original_block(block_args) + global_x = global_out["img"] + + # ============ LOCAL STREAM ============ + # Process sequence in temporal windows for local detail preservation + # + # Wan's sequence is flattened [frames * spatial, dim] + # We need to estimate the temporal structure + # For 81 frame video: 21 latent frames, so spatial = seq_len / 21 + + # Try to infer frame count from sequence length + # Common Wan resolutions and their spatial token counts: + # 1280x720 -> 160x90/4 = 40x22.5 -> ~900 tokens per frame (after 2x2 patch) + # 848x480 -> 106x60/4 = 26.5x15 -> ~400 tokens per frame + # We'll try common latent frame counts: 21, 17, 13, 9, 5 + + num_frames = None + for candidate_frames in [21, 17, 13, 9, 5]: + if seq_len % candidate_frames == 0: + num_frames = candidate_frames + break + + if num_frames is None or num_frames <= window_size: + # Can't determine temporal structure or window covers all frames + # Fall back to just using global output with spectral smoothing + if freelong_settings.get("log_fallback", True): + freelong_settings["log_fallback"] = False + if num_frames is None: + logger.warning(f"[FreeLong] Could not determine temporal structure (seq_len={seq_len})") + else: + logger.warning(f"[FreeLong] Window size >= frames ({window_size} >= {num_frames})") + logger.warning("[FreeLong] Using fallback: spectral smoothing without windowing") + blended_x = freelong_spectral_blend(global_x, global_x, low_ratio, strength) + return {"img": blended_x} + + spatial_size = seq_len // num_frames + + # Log temporal structure detection on first run + if freelong_settings.get("log_structure", True): + freelong_settings["log_structure"] = False + logger.info(f" Detected temporal structure: {num_frames} frames × {spatial_size} spatial tokens") + + # Reshape to [batch, frames, spatial, dim] + x_temporal = x.view(batch_size, num_frames, spatial_size, hidden_dim) + + # Process in OVERLAPPING windows with crossfade blending + # This prevents hard discontinuities at window boundaries + + # Get RoPE embeddings - need to slice these for each window too + freqs = block_args.get("pe") # RoPE positional embeddings + + # freqs shape is typically [batch, rope_dim, seq_len] or [1, seq_len, rope_dim] + # We need to figure out which dim is the sequence dim + freqs_seq_dim = None + if freqs is not None: + for dim_idx, dim_size in enumerate(freqs.shape): + if dim_size == seq_len: + freqs_seq_dim = dim_idx + break + + # Use 50% overlap between windows for smooth blending + overlap = window_size // 2 + stride = window_size - overlap + + # Calculate number of windows + num_windows = 0 + temp_start = 0 + while temp_start < num_frames: + num_windows += 1 + temp_start += stride + if temp_start >= num_frames: + break + + # Log windowing setup on first run + if freelong_settings.get("log_windows", True): + freelong_settings["log_windows"] = False + logger.info(f" Window processing: {num_windows} windows with {overlap} frame overlap") + logger.info(f" Overlap strategy: 50% crossfade blending between windows") + + # Accumulator for blended output and weights + local_x_temporal = torch.zeros(batch_size, num_frames, spatial_size, hidden_dim, + device=x.device, dtype=x.dtype) + weight_accumulator = torch.zeros(batch_size, num_frames, 1, 1, + device=x.device, dtype=x.dtype) + + # Process overlapping windows + window_idx = 0 + start_frame = 0 + while start_frame < num_frames: + end_frame = min(start_frame + window_size, num_frames) + window_frames = end_frame - start_frame + + # Extract window + window_x = x_temporal[:, start_frame:end_frame, :, :] + + # Flatten for block processing + window_flat = window_x.reshape(batch_size, window_frames * spatial_size, hidden_dim) + + # Create modified block_args for this window + window_args = dict(block_args) + window_args["img"] = window_flat + + # Slice RoPE embeddings to match window + if freqs is not None and freqs_seq_dim is not None: + start_token = start_frame * spatial_size + end_token = end_frame * spatial_size + slices = [slice(None)] * len(freqs.shape) + slices[freqs_seq_dim] = slice(start_token, end_token) + window_args["pe"] = freqs[tuple(slices)] + + # Run block on window + window_out = original_block(window_args) + window_result = window_out["img"] + + # Reshape back to temporal + window_result = window_result.view(batch_size, window_frames, spatial_size, hidden_dim) + + # Create blending weights - ramp up at start, ramp down at end + # This creates smooth crossfades in overlapping regions + weights = torch.ones(window_frames, device=x.device, dtype=x.dtype) + + if start_frame > 0 and overlap > 0: + # Ramp up at the start (we're overlapping with previous window) + ramp_len = min(overlap, window_frames) + ramp = torch.linspace(0, 1, ramp_len + 1, device=x.device, dtype=x.dtype)[1:] + weights[:ramp_len] = ramp + + if end_frame < num_frames and overlap > 0: + # Ramp down at the end (next window will overlap with us) + ramp_len = min(overlap, window_frames) + ramp = torch.linspace(1, 0, ramp_len + 1, device=x.device, dtype=x.dtype)[:-1] + weights[-ramp_len:] = weights[-ramp_len:] * ramp + + weights = weights.view(1, window_frames, 1, 1) + + # Accumulate weighted results + local_x_temporal[:, start_frame:end_frame] += window_result * weights + weight_accumulator[:, start_frame:end_frame] += weights + + # Move to next window position + start_frame += stride + if start_frame >= num_frames: + break + window_idx += 1 + + # Normalize by accumulated weights + weight_accumulator = weight_accumulator.clamp(min=1e-8) + local_x_temporal = local_x_temporal / weight_accumulator + local_x = local_x_temporal.reshape(batch_size, seq_len, hidden_dim) + + # ============ SPECTRAL BLEND ============ + # Low frequencies from global (motion direction/consistency) + # High frequencies from local (sharp details) + if freelong_settings.get("log_blend", True): + freelong_settings["log_blend"] = False + logger.info("") + logger.info("[FreeLong] SPECTRAL BLENDING") + logger.info(f" Low-freq (global): {low_ratio:.0%} of spectrum (motion consistency)") + logger.info(f" High-freq (local): {1-low_ratio:.0%} of spectrum (sharp details)") + logger.info(f" Blend strength: {strength:.0%}") + + blended_x = freelong_spectral_blend(global_x, local_x, low_ratio, strength) + + return {"img": blended_x} + + except Exception as e: + # If anything fails, fall back to original block + logger.error(f"[FreeLong] Error in block {block_index}: {e}", exc_info=True) + return original_block(block_args) + + # Wan 2.2 typically has 40 blocks + num_blocks = 40 + + # Register the patch for transformer blocks + for i in range(num_blocks): + model.set_model_patch_replace( + freelong_block_patch, + "dit", + "double_block", + i + ) + + actual_end = blend_end_block if blend_end_block != -1 else num_blocks + blocks_to_patch = actual_end - blend_start_block + logger.info(f" Registered patches for {blocks_to_patch} transformer blocks ({blend_start_block} to {actual_end-1})") + logger.info("=" * 60) + logger.info("[FreeLong] Patching complete. FreeLong will activate during sampling.") + logger.info("=" * 60) + + return (model,)