{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-omerbt--tokenflow","slug":"omerbt--tokenflow","name":"TokenFlow","type":"repo","url":"https://diffusion-tokenflow.github.io","page_url":"https://unfragile.ai/omerbt--tokenflow","categories":["image-generation"],"tags":["iclr2024","stable-diffusion","text-to-image","text-to-video","tokenflow","video-editing"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-omerbt--tokenflow__cap_0","uri":"capability://data.processing.analysis.video.to.latent.space.encoding.with.ddim.inversion","name":"video-to-latent-space-encoding-with-ddim-inversion","description":"Converts source video frames into latent representations using Stable Diffusion's VAE encoder, then applies DDIM inversion to compute noise maps that can deterministically reconstruct original frames. This preprocessing stage extracts temporal sequences as latent codes and inverts them through the diffusion process, enabling frame-by-frame consistency tracking during editing. The inversion produces both latent tensors (for editing) and an inverted video reconstruction (for quality validation before proceeding to editing).","intents":["I need to convert my video into a latent space representation that preserves temporal structure for consistent editing","I want to verify that my video can be accurately reconstructed before attempting edits","I need to extract noise maps from my video so I can apply text-guided diffusion edits while maintaining spatial layout"],"best_for":["video editors building consistent frame-by-frame editing pipelines","researchers prototyping diffusion-based video synthesis","teams implementing structure-preserving video-to-video translation"],"limitations":["DDIM inversion quality depends on number of inversion steps (typically 50-100); fewer steps = faster but lower reconstruction fidelity","VAE encoding introduces quantization artifacts inherent to Stable Diffusion's 8x downsampling factor","Requires storing full latent tensors on disk; a 1-minute 512x512 video at 30fps generates ~2-3GB of latent data","Inversion is deterministic but sensitive to prompt accuracy; poor inversion prompts cause temporal inconsistencies in downstream edits"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support (GPU strongly recommended; CPU inversion is prohibitively slow)","Stable Diffusion model weights (1.5 or 2.1 supported)","Input video file (MP4, MOV, or AVI; max resolution 768x768 recommended)"],"input_types":["video file (MP4, MOV, AVI)","text prompt describing video content (for DDIM inversion guidance)"],"output_types":["latent tensor files (PyTorch .pt format)","inverted video file (MP4) for reconstruction quality assessment","metadata JSON with frame count, latent dimensions, and inversion parameters"],"categories":["data-processing-analysis","video-preprocessing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_1","uri":"capability://data.processing.analysis.inter.frame.correspondence.based.feature.propagation","name":"inter-frame-correspondence-based-feature-propagation","description":"Propagates diffusion features across video frames by computing optical flow or patch-based correspondences between consecutive frames, then using these correspondences to enforce consistency in the diffusion feature space during editing. During the reverse diffusion process, features extracted from one frame are warped and injected into neighboring frames based on computed motion vectors, ensuring that semantic edits (e.g., 'change dog to cat') apply consistently across the temporal sequence without flickering or temporal artifacts.","intents":["I want to apply a text edit to a video while ensuring all frames change consistently without temporal flickering","I need to maintain spatial layout and motion dynamics while changing the semantic content of my video","I want to propagate diffusion features across frames so that edits respect the original video's structure and motion"],"best_for":["video editors performing semantic edits (object replacement, style transfer) across entire videos","content creators needing flicker-free video transformations","researchers studying temporal consistency in diffusion-based video synthesis"],"limitations":["Feature propagation introduces ~50-100ms latency per diffusion step due to optical flow computation and feature warping","Correspondence estimation fails on fast motion, occlusions, or scenes with large displacements (>50 pixels); requires fallback to frame-independent editing","Requires storing intermediate diffusion features in memory; a 10-second 512x512 video at 30fps with 50 diffusion steps consumes ~8-12GB VRAM","Propagation quality degrades with video length; temporal drift accumulates over 100+ frames without periodic re-anchoring"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support (GPU required; CPU propagation is impractical)","Optical flow model (RAFT or similar) or patch-matching library","Preprocessed latent representations from video-to-latent-space-encoding capability"],"input_types":["latent tensor sequences (from preprocessing)","diffusion feature maps (intermediate activations from UNet during reverse diffusion)","optical flow or correspondence maps (computed from original video frames)"],"output_types":["propagated feature maps (same shape as input, with temporal consistency enforced)","correspondence visualization (optional, for debugging)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_10","uri":"capability://data.processing.analysis.latent.space.video.decoding.with.vae.decoder","name":"latent-space-video-decoding-with-vae-decoder","description":"Decodes edited latent tensors back to pixel-space video frames using the Stable Diffusion VAE decoder, converting 4-channel latent representations (8x downsampled) to 3-channel RGB video frames at the original resolution. The decoder is applied frame-by-frame to edited latents, producing the final edited video output. This stage is the inverse of the VAE encoding step in preprocessing, enabling the full latent-space editing pipeline to produce viewable video output.","intents":["I want to convert edited latent tensors back to video frames for viewing and export","I need to decode latents to RGB video at the original resolution","I want to assess the quality of edited latents by visualizing them as video"],"best_for":["users completing the editing pipeline and generating final video output","researchers visualizing intermediate latent representations","teams validating editing results before export"],"limitations":["VAE decoding introduces quantization artifacts and color shifts due to the lossy VAE compression (8x downsampling factor)","Decoding is computationally expensive; decoding a 1-minute 512x512 video at 30fps requires ~5-10 minutes on a single GPU","No built-in upsampling; decoded video is limited to the resolution of the latent space (typically 64x64 or 128x128 latents → 512x512 or 1024x1024 pixels)","Decoder output quality depends on latent quality; poor edits in latent space produce poor decoded video"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Stable Diffusion VAE decoder module","Edited latent tensors from editing stage"],"input_types":["latent tensors (4-channel, 8x downsampled resolution)"],"output_types":["RGB video frames (3-channel, original resolution)","video file (MP4 or similar format)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_11","uri":"capability://data.processing.analysis.optical.flow.based.motion.estimation.for.correspondence","name":"optical-flow-based-motion-estimation-for-correspondence","description":"Estimates optical flow between consecutive video frames to compute inter-frame correspondences, which are used to guide feature propagation during editing. The optical flow maps represent pixel-level motion vectors between frames, enabling the system to warp features from one frame to the next while respecting the underlying motion. This correspondence estimation is a prerequisite for the feature propagation mechanism, ensuring that edits follow the original video's motion dynamics.","intents":["I want to compute motion correspondences between video frames for feature propagation","I need to estimate optical flow to guide temporal consistency during editing","I want to understand the motion structure of my video to inform editing decisions"],"best_for":["video editors implementing feature propagation-based editing","researchers studying motion estimation in video synthesis","teams building motion-aware video processing pipelines"],"limitations":["Optical flow estimation fails on fast motion, occlusions, or scenes with large displacements (>50 pixels per frame); requires fallback strategies","Flow estimation is computationally expensive; computing flow for a 1-minute 512x512 video at 30fps requires ~10-20 minutes on a single GPU","Flow maps are noisy and may contain artifacts at occlusion boundaries; no built-in filtering or smoothing","Flow estimation assumes brightness constancy, which fails on videos with lighting changes or reflections"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Optical flow model (RAFT, PWCNet, or similar)","Original video frames (for flow computation)"],"input_types":["consecutive video frames (RGB, any resolution)"],"output_types":["optical flow maps (2-channel, representing x and y motion vectors)","flow visualization (optional, for debugging)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_12","uri":"capability://data.processing.analysis.batch.processing.and.frame.sequence.management","name":"batch-processing-and-frame-sequence-management","description":"Manages video frame sequences as batches during preprocessing and editing, enabling efficient processing of multiple frames in parallel on GPU. The system handles frame extraction, batching, and sequence management, allowing users to process videos of arbitrary length by chunking them into manageable batches. Batch processing reduces per-frame overhead and enables GPU parallelization, improving throughput compared to frame-by-frame processing.","intents":["I want to process long videos efficiently by batching frames on GPU","I need to manage frame sequences without loading entire videos into memory","I want to parallelize video processing across multiple frames"],"best_for":["users processing long videos (>1 minute) with limited GPU memory","teams building high-throughput video processing pipelines","researchers studying batch processing efficiency in video synthesis"],"limitations":["Batch size is limited by GPU memory; typical batch sizes are 4-16 frames for 512x512 resolution","Batching introduces temporal boundary artifacts; frames at batch boundaries may have inconsistent features","No adaptive batching; users must manually tune batch size for their hardware","Batch processing assumes frames are independent (except for feature propagation); cannot handle frame-dependent operations like optical flow across batch boundaries"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Sufficient GPU memory for batch size (typically 8-16GB for batch size 8 at 512x512)"],"input_types":["video frames (as tensors or file paths)","batch size (integer, typically 4-16)"],"output_types":["batched frame tensors","batch metadata (frame indices, batch boundaries)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_2","uri":"capability://image.visual.plug.and.play.pnp.feature.and.attention.injection","name":"plug-and-play-pnp-feature-and-attention-injection","description":"Implements feature and attention injection at configurable diffusion timestep thresholds, allowing selective replacement of UNet features and cross-attention maps with values from the inverted source video. During the reverse diffusion process, features are injected at early timesteps (high noise) to preserve structure and at later timesteps (low noise) to allow text-guided semantic changes. This technique balances fidelity to the original video structure with adherence to the target text prompt through threshold-based switching.","intents":["I want to edit my video with a new text prompt while preserving the original structure and layout","I need to control the balance between text fidelity and structure preservation by adjusting injection thresholds","I want a general-purpose editing technique that works with any text prompt without requiring additional guidance"],"best_for":["video editors performing general-purpose semantic edits (style changes, object replacement)","users who want intuitive control over structure vs. text trade-off via single threshold parameter","teams prototyping video editing pipelines without domain-specific constraints"],"limitations":["Threshold selection is empirical and video-dependent; no principled method for choosing optimal injection timestep (typically 0.3-0.7 of total steps)","Aggressive feature injection at early timesteps can suppress text guidance, resulting in edits that ignore the target prompt","Attention injection assumes cross-attention maps are spatially aligned with video frames; fails on videos with extreme aspect ratios or unusual compositions","No built-in mechanism to handle occlusions or disocclusions; injected features may cause artifacts at frame boundaries"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Stable Diffusion model (1.5 or 2.1)","Preprocessed latent representations and inverted features from preprocessing stage","Configuration file specifying injection threshold and other hyperparameters"],"input_types":["latent tensors (from preprocessing)","inverted UNet features (cached from inversion stage)","cross-attention maps (from source video inversion)","target text prompt","injection threshold (float, 0.0-1.0)"],"output_types":["edited latent tensors (with injected features)","edited video frames (decoded from edited latents via VAE decoder)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_3","uri":"capability://image.visual.sdedit.noise.level.controlled.diffusion.editing","name":"sdedit-noise-level-controlled-diffusion-editing","description":"Implements SDEdit-style editing by controlling the noise level (number of diffusion steps) applied to the source video before running the reverse diffusion process with a new text prompt. Lower noise levels preserve more of the original video structure; higher noise levels allow more dramatic semantic changes. The technique works by adding Gaussian noise to the inverted latents for a specified number of steps, then denoising with the target text prompt, effectively interpolating between structure preservation and text fidelity.","intents":["I want to make subtle edits to my video that preserve most of the original content","I need to control the intensity of edits by adjusting a single noise level parameter","I want a simple, interpretable editing technique that doesn't require threshold tuning"],"best_for":["users performing subtle, localized edits (e.g., changing colors, minor object modifications)","scenarios where structure preservation is critical and text fidelity is secondary","quick prototyping where simplicity is valued over fine-grained control"],"limitations":["Noise level is a coarse control mechanism; small changes in noise steps can cause large, unpredictable changes in output","High noise levels (>50% of total steps) often result in edits that ignore the target prompt entirely, defaulting to random generation","No mechanism to selectively edit regions; noise is applied uniformly across all frames, affecting both foreground and background equally","Temporal consistency depends entirely on feature propagation; SDEdit alone does not enforce frame-to-frame coherence"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Stable Diffusion model (1.5 or 2.1)","Preprocessed latent representations from preprocessing stage","Configuration file specifying noise level (number of diffusion steps to add)"],"input_types":["latent tensors (from preprocessing)","target text prompt","noise level (integer, typically 10-50 steps out of 50-100 total)"],"output_types":["edited latent tensors","edited video frames (decoded via VAE decoder)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_4","uri":"capability://image.visual.controlnet.guided.structural.editing.with.edge.detection","name":"controlnet-guided-structural-editing-with-edge-detection","description":"Integrates ControlNet guidance into the diffusion editing pipeline by extracting edge maps from the source video and using them as structural constraints during the reverse diffusion process. The edge detection (typically Canny or similar) creates a structural skeleton of the original video, which is fed to a ControlNet model alongside the text prompt. This ensures that edited frames maintain the same spatial structure and object boundaries as the original, even when applying dramatic semantic changes.","intents":["I want to edit my video while strictly preserving object boundaries and spatial structure","I need to apply dramatic semantic changes (e.g., 'dog to cat') while maintaining the original composition","I want to use structural guidance to prevent the model from hallucinating new objects or distorting layouts"],"best_for":["video editors requiring strict structural preservation (e.g., architectural visualization, product photography)","scenarios with dramatic semantic changes where structure guidance prevents hallucination","teams with access to ControlNet models and comfortable with multi-model pipelines"],"limitations":["Requires loading an additional ControlNet model (~1.5GB VRAM), increasing memory footprint by 30-50%","Edge detection quality varies with video content; low-contrast or transparent objects produce weak edge maps, reducing structural guidance effectiveness","ControlNet conditioning adds ~20-30% latency per diffusion step due to additional forward passes","Edge maps are static per frame; cannot adapt to dynamic structural changes (e.g., objects entering/leaving frame), causing artifacts at occlusion boundaries"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Stable Diffusion model (1.5 or 2.1)","ControlNet model weights (canny-edge variant)","Edge detection library (OpenCV or similar)","Preprocessed latent representations from preprocessing stage"],"input_types":["latent tensors (from preprocessing)","original video frames (for edge detection)","target text prompt","edge detection parameters (threshold values, kernel size)"],"output_types":["edge maps (grayscale images, one per frame)","edited latent tensors (with ControlNet guidance)","edited video frames (decoded via VAE decoder)"],"categories":["image-visual","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_5","uri":"capability://data.processing.analysis.temporal.consistency.validation.and.reconstruction.quality.assessment","name":"temporal-consistency-validation-and-reconstruction-quality-assessment","description":"Generates an inverted video reconstruction during preprocessing to enable visual assessment of temporal consistency and reconstruction fidelity before proceeding to editing. The inverted video is created by decoding the DDIM-inverted latent tensors back to pixel space using the VAE decoder, producing a frame-by-frame comparison against the original. Users can inspect this reconstruction to identify temporal artifacts, flickering, or structural degradation that would propagate through downstream editing steps.","intents":["I want to verify that my video preprocessing is high-quality before spending time on editing","I need to identify temporal inconsistencies or reconstruction artifacts early in the pipeline","I want to assess whether my inversion prompt is accurate enough for successful editing"],"best_for":["video editors validating preprocessing quality before committing to editing workflows","researchers debugging temporal consistency issues in diffusion-based video synthesis","teams implementing quality gates in automated video editing pipelines"],"limitations":["Inverted video is a lossy reconstruction; perfect pixel-level matching is impossible due to VAE quantization and DDIM approximation error","Visual inspection is subjective; no automated metrics provided for quantifying reconstruction quality (LPIPS, SSIM, or temporal consistency scores must be computed separately)","Generating inverted video adds ~10-20% overhead to preprocessing time (requires additional VAE decoder passes)","Inverted video quality does not guarantee editing quality; poor inversion can still produce good edits if feature propagation compensates"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Stable Diffusion VAE decoder","Preprocessed latent tensors from DDIM inversion stage"],"input_types":["latent tensors (from DDIM inversion)","original video frames (for visual comparison)"],"output_types":["inverted video file (MP4 or similar format)","optional: frame-by-frame difference maps (for detailed artifact analysis)"],"categories":["data-processing-analysis","video-generation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_6","uri":"capability://automation.workflow.yaml.based.configuration.management.for.editing.workflows","name":"yaml-based-configuration-management-for-editing-workflows","description":"Provides YAML configuration files (e.g., config_pnp.yaml, config_sdedit.yaml) that specify all editing parameters including technique selection, hyperparameters (thresholds, noise levels, step counts), model paths, and I/O specifications. The configuration system decouples parameter tuning from code, enabling users to experiment with different editing strategies by modifying YAML files without touching Python code. Each editing technique has a dedicated config template with documented parameters and sensible defaults.","intents":["I want to experiment with different editing parameters without modifying code","I need to reproduce editing results by version-controlling configuration files","I want to switch between editing techniques (PnP, SDEdit, ControlNet) by changing a config file"],"best_for":["non-technical users or content creators who prefer configuration over coding","teams managing multiple editing workflows with different parameter sets","researchers conducting hyperparameter studies or ablation experiments"],"limitations":["YAML configuration is static; no support for dynamic parameter scheduling or adaptive thresholds based on video content","Parameter validation is minimal; invalid configurations may fail silently or produce cryptic errors during execution","No built-in parameter search or optimization; users must manually tune hyperparameters through trial-and-error","Configuration files are technique-specific; switching between PnP and SDEdit requires loading different config files with different parameter names"],"requires":["Python 3.8+","PyYAML library","Text editor for modifying YAML files"],"input_types":["YAML configuration file","command-line arguments (optional, for overriding config values)"],"output_types":["parsed configuration dictionary (used by editing scripts)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_7","uri":"capability://automation.workflow.multi.technique.editing.pipeline.orchestration","name":"multi-technique-editing-pipeline-orchestration","description":"Orchestrates a three-stage pipeline (preprocessing → technique selection → editing) with separate entry points for each editing technique (run_tokenflow_pnp.py, run_tokenflow_sdedit.py, run_tokenflow_controlnet.py). The pipeline manages data flow between stages, handles intermediate file I/O (latent tensors, inverted videos, configuration files), and provides a unified command-line interface for executing end-to-end workflows. Users specify the editing technique via configuration or command-line arguments, and the pipeline automatically routes to the appropriate editing implementation.","intents":["I want to run a complete video editing workflow from raw video to edited output without manual stage management","I need to switch between editing techniques without rewriting pipeline code","I want to integrate TokenFlow into larger video processing systems with clear input/output contracts"],"best_for":["video editing teams building end-to-end workflows","researchers comparing different editing techniques on the same video","systems integrators embedding TokenFlow into larger video processing pipelines"],"limitations":["Pipeline assumes sequential execution; no support for parallel processing of multiple videos or techniques","Intermediate files (latents, inverted videos) must be stored on disk; no in-memory pipelining, causing I/O bottlenecks for large videos","Error handling is minimal; failures in one stage (e.g., preprocessing) do not provide clear recovery paths","Pipeline is tightly coupled to specific file formats and directory structures; adapting to custom I/O requires code modification"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","All dependencies for selected editing technique (Stable Diffusion, ControlNet if applicable)","Sufficient disk space for intermediate files (~2-3GB per minute of video)"],"input_types":["source video file (MP4, MOV, AVI)","inversion prompt (text description of video content)","target editing prompt (text description of desired edits)","configuration file (YAML) specifying editing technique and parameters"],"output_types":["edited video file (MP4)","intermediate files: latent tensors, inverted video, feature maps (optional)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_8","uri":"capability://tool.use.integration.command.line.interface.with.argument.parsing","name":"command-line-interface-with-argument-parsing","description":"Provides a command-line interface (CLI) for executing preprocessing and editing stages with arguments for specifying input/output paths, prompts, and technique-specific parameters. The CLI uses Python argparse to parse command-line arguments, with sensible defaults for common parameters and validation for required arguments. Users can invoke preprocessing (preprocess.py) and editing (run_tokenflow_*.py) scripts directly from the terminal, making TokenFlow accessible to non-Python developers and enabling integration with shell scripts or workflow automation tools.","intents":["I want to run TokenFlow from the command line without writing Python code","I need to integrate TokenFlow into shell scripts or batch processing workflows","I want to automate video editing tasks by invoking TokenFlow from external tools or CI/CD pipelines"],"best_for":["command-line users and system administrators","teams building shell-based video processing pipelines","researchers automating large-scale video editing experiments"],"limitations":["CLI argument parsing is basic; no support for complex parameter structures (e.g., nested configurations) without additional parsing logic","Error messages are generic; users must inspect logs to debug failures","No interactive mode; users cannot adjust parameters mid-execution or inspect intermediate results without restarting","Argument names and defaults vary between preprocessing and editing scripts, requiring users to memorize different interfaces"],"requires":["Python 3.8+ with argparse library","Shell environment (bash, zsh, cmd, etc.)","All dependencies for TokenFlow (PyTorch, Stable Diffusion, etc.)"],"input_types":["command-line arguments (strings, integers, floats)","file paths (video, configuration files)"],"output_types":["console output (status messages, progress bars)","edited video file and intermediate files"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-omerbt--tokenflow__cap_9","uri":"capability://tool.use.integration.stable.diffusion.model.integration.with.multiple.versions","name":"stable-diffusion-model-integration-with-multiple-versions","description":"Integrates Stable Diffusion 1.5 and 2.1 models as the core diffusion backbone, using the pre-trained UNet, VAE, and text encoder from these models without requiring fine-tuning or additional training. The integration abstracts model loading, device management (CPU/GPU), and inference through a unified interface, allowing users to specify which Stable Diffusion version to use via configuration. The system leverages the pre-trained text-to-image capabilities of these models for video editing without modifying model weights.","intents":["I want to use pre-trained Stable Diffusion models for video editing without fine-tuning","I need to choose between different Stable Diffusion versions (1.5 vs 2.1) based on quality or speed trade-offs","I want to leverage existing Stable Diffusion model weights without retraining"],"best_for":["users with access to Stable Diffusion model weights (via Hugging Face or local files)","teams wanting to avoid fine-tuning overhead and use pre-trained models directly","researchers studying how pre-trained text-to-image models generalize to video editing"],"limitations":["Model selection is limited to Stable Diffusion 1.5 and 2.1; newer models (SDXL, Stable Diffusion 3) are not supported","Model loading requires downloading ~4-7GB of weights; no built-in caching or incremental loading","Text encoder is frozen; no fine-tuning of text understanding for domain-specific vocabularies","Model inference speed is limited by Stable Diffusion's architecture; no quantization or distillation for faster inference"],"requires":["Python 3.8+","PyTorch 1.13+ with CUDA support","Hugging Face transformers library","Stable Diffusion model weights (1.5 or 2.1) accessible via Hugging Face or local file path","API key or authentication for downloading models from Hugging Face (if using remote models)"],"input_types":["model identifier (string, e.g., 'runwayml/stable-diffusion-v1-5')","model path (local file path to model weights)"],"output_types":["loaded UNet, VAE, and text encoder modules (PyTorch nn.Module objects)"],"categories":["tool-use-integration","image-visual"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.13+ with CUDA support (GPU strongly recommended; CPU inversion is prohibitively slow)","Stable Diffusion model weights (1.5 or 2.1 supported)","Input video file (MP4, MOV, or AVI; max resolution 768x768 recommended)","PyTorch 1.13+ with CUDA support (GPU required; CPU propagation is impractical)","Optical flow model (RAFT or similar) or patch-matching library","Preprocessed latent representations from video-to-latent-space-encoding capability","PyTorch 1.13+ with CUDA support","Stable Diffusion VAE decoder module","Edited latent tensors from editing stage"],"failure_modes":["DDIM inversion quality depends on number of inversion steps (typically 50-100); fewer steps = faster but lower reconstruction fidelity","VAE encoding introduces quantization artifacts inherent to Stable Diffusion's 8x downsampling factor","Requires storing full latent tensors on disk; a 1-minute 512x512 video at 30fps generates ~2-3GB of latent data","Inversion is deterministic but sensitive to prompt accuracy; poor inversion prompts cause temporal inconsistencies in downstream edits","Feature propagation introduces ~50-100ms latency per diffusion step due to optical flow computation and feature warping","Correspondence estimation fails on fast motion, occlusions, or scenes with large displacements (>50 pixels); requires fallback to frame-independent editing","Requires storing intermediate diffusion features in memory; a 10-second 512x512 video at 30fps with 50 diffusion steps consumes ~8-12GB VRAM","Propagation quality degrades with video length; temporal drift accumulates over 100+ frames without periodic re-anchoring","VAE decoding introduces quantization artifacts and color shifts due to the lossy VAE compression (8x downsampling factor)","Decoding is computationally expensive; decoding a 1-minute 512x512 video at 30fps requires ~5-10 minutes on a single GPU","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4725054387720857,"quality":0.5,"ecosystem":0.5800000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:44.860Z","last_commit":"2025-02-03T15:34:18Z"},"community":{"stars":1712,"forks":142,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=omerbt--tokenflow","compare_url":"https://unfragile.ai/compare?artifact=omerbt--tokenflow"}},"signature":"onxzG9djzHJLdgJHKpXjOnKFA5P5qy+KCP/b8X/Tawe2Hh6PluqxPcEYaoJHSeh8giMv113GCHt67Xj8k1S/DA==","signedAt":"2026-06-19T21:01:29.112Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/omerbt--tokenflow","artifact":"https://unfragile.ai/omerbt--tokenflow","verify":"https://unfragile.ai/api/v1/verify?slug=omerbt--tokenflow","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}