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SeedVR2 image upscale
Upscale and restore an uploaded still image with SeedVR2, returning the restored image directly. For a first run, upload an image, leave the target width at 1080 pixels and guidance at 1.01, then run. Compare the result with your original: restoration can reinterpret fine details. The target-width control accepts 1–4320 pixels. The seed defaults to -1 for a fresh random seed each run. Choose a fixed seed from 0 to 5000 when comparing guidance or other settings. Guidance accepts 0.01–2. GPU-memory conservation is enabled by default and can be toggled. Processing batch size defaults to 1; keep that starting value for the single uploaded image. It does not select the number of output variants. The fixed seedvr2_ema_3b_fp16.safetensors model and the SeedVR2 custom node must be available in the runtime. This Pipe accepts still images and has no video input or encoding mode. Uploaded transparency is not preserved because the loader's separate alpha-mask output is not connected.
veryBadImageNegative
veryBadImageNegativev1.3 uses a new training atlas and performs well when used in AOM3 and viewer-mixv1.7 .
Grok video extension
Continue an existing video with a prompted 2–10 second extension using Grok. For a first run, upload an MP4 clip lasting 2–15 seconds, replace the example space-scene prompt with what should happen next in your footage, and keep the default six-second extension. Describe the action, setting, and camera movement that should follow the clip’s ending. Extension length requests 2–10 seconds in whole-second increments; it does not set the source clip’s length or total output length. The Pipe returns the video supplied by Grok, with no separate concatenation stage. Advanced output encoding controls offer file type, codec, and CRF settings; keep their defaults for a first run. The Grok partner API requires runtime authentication. Leave Seed at -1 for randomization on each run. Seed is a rerun control, and a fixed value does not make Grok’s results reproducible.
Whole-body pose map
Create a rendered whole-body pose map from an image of one person using SDPose. Upload your source image, leave the longer edge at 1024 pixels and all drawing regions enabled, then run to view the output image. The input is resized with its aspect ratio preserved before pose extraction. Choose whether to draw the body, hands, face, feet and head. Head drawing is separate from facial keypoints. Line width changes the skeleton's thickness; face point size applies when Draw face is enabled. The confidence threshold ranges from 0 to 1: raising it omits less-confident keypoints, while lowering it can display more uncertain detections. These controls affect the rendered map rather than pose extraction. Longer edge sets the resized image's longer dimension in pixels; use a positive value. Resampling method selects interpolation before detection. Area is suited to downscaling and Lanczos to upscaling. Advanced settings expose the extractor's processing batch size, defaulting to 16; this does not request multiple output variations. This tool uses the fixed sdpose_wholebody_fp16.safetensors checkpoint and returns a pose visualization through the image preview output, without a separate keypoint-data output. The extractor requires bounding boxes for multi-person detection, and this workflow supplies none, so use a single-person image.
Gemini Omni Flash text to video
Generate a 3–10 second video with audio using Google's Gemini Omni Flash model. The source node specifies 720p output at 24 FPS. For a first run, keep the example video prompt, portrait aspect ratio (9:16), three-second duration and default advanced settings, then run the Pipe. No image, video or audio upload is required. To create your own clip, describe the scene, action, visual style, camera movement and desired sound. Use 9:16 for portrait or 16:9 for landscape. Enter duration as 3s through 10s in whole-second increments. These controls append instructions to the scene prompt rather than cropping or trimming the result; avoid conflicting duration or framing instructions in your prompt. Advanced generation settings expose temperature and top-p sampling controls. Seed -1 randomizes the request seed each run. Seed affects whether the generation node reruns, but results remain non-deterministic even when a seed is repeated. Advanced video output settings expose file type, codec and CRF for output transport. Keep their defaults for the first run; these settings do not change the generation model or scene instructions. Generation requires access to the ComfyUI partner API. The workflow sends the assembled prompt to the Gemini provider and makes a provider-priced generation call. This Pipe uses text input only and returns the generated video; the model's separate text output is not selected.
Video frame interpolation
Add intermediate frames to an uploaded video for smoother playback or slow motion, returning one video at the source frame dimensions. For a first run, upload a source video, keep Frame multiplier at 2 and Keep original speed enabled, and use the default film_net_fp16.safetensors interpolation model. Leave the advanced bit depth and encoding settings at their defaults. With Keep original speed enabled, output FPS is the source FPS multiplied by Frame multiplier, giving smoother playback at approximately the original duration. Disable it to retain the source FPS and play the additional frames as slow motion. The multiplier supports integers from 2 to 16; larger values create more frames and increase processing work. Output FPS is calculated automatically, and there is no resizing control. The advanced Interpolation model control selects from the source loader's available runtime models. Models must be available in the runtime's frame_interpolation folder; the default is film_net_fp16.safetensors. Source audio is passed through unchanged. It is not slowed or stretched, so slow-motion output may have mismatched audio and video durations. Advanced controls select 8-bit or 10-bit video, the output container (Auto, MP4, MKV or WebM), and codec (Auto, H.264 or AV1). Start with 8-bit and automatic format and codec selection. The source describes 10-bit as reducing gradient banding, but some players and downstream tools may not support it. Explicit container and codec choices must be supported together by the runtime and your playback software.
Wan 2.1 Camera Motion
Animate an uploaded image into a silent video using Wan 2.1 Fun Camera 14B, a scene prompt and explicit camera-motion conditioning. For a first run, upload your source image, replace the example jazz-bar prompt with a description of its scene, subject action, lighting and style, and choose a camera movement. Keep camera speed and sampling settings at their defaults. The default output is 512 × 512 pixels with 81 frames at 16 FPS, approximately 5.1 seconds. Use dimensions in multiples of 16 and frame counts of 4n + 1, such as 49, 81 or 121. Available movements are Static, directional pans, zoom in or out, and clockwise or counterclockwise rotation. Camera speed is a relative conditioning parameter, not a physical speed. Align camera instructions in your prompt with the chosen movement. Static requests a stationary camera; subjects can still move. Generated motion is model-guided and does not guarantee a geometric reconstruction of the scene. Frames determines how many frames are generated. FPS sets playback rate: raising FPS shortens playback without adding frames. Width, height and frame count are supplied by Camera and output shape. The uploaded image supplies both the starting image and visual-reference conditioning and is required in this graph. Reference crop changes only visual-reference encoding. Uploaded transparency is not retained through the RGB image path. Advanced controls include negative prompt, seed, sampling steps, prompt guidance, sampler, scheduler, sampling shift and denoise. Use seed -1 for a new random seed each run, or a fixed seed when comparing changes. Denoise is a sampling-schedule fraction rather than a direct image-preservation strength. Optional normalized focal and principal-point parameters adjust camera intrinsics; retain their defaults initially. Keep batch size at 1 for one clip: this Pipe has one assembled video output rather than separate outputs for each latent sample. Video playback and encoding controls provide FPS, 8-bit or 10-bit video, container, codec and CRF. Retain automatic container and codec selection for the first run. Ten-bit output requires compatible encoding and playback support. CRF changes encoding quality rather than generated content. No audio is connected, so the output is silent.
Bernini-R: Video Edit
Generate an edited video with consistent relighting using Bernini-R. Ideal for portrait and product relighting, e-commerce catalog photography, and creating consistent lighting across photo sets.
Qwen Image Edit 2511 · Material replacement
Create an edited image that changes an object's material using written instructions and an optional material reference. For a first run, upload a furniture photo as Source image and a fur texture as Material reference. In Edit instructions, write: "Replace the leather upholstery in image 1 with the fur material shown in image 2." Adapt the surface and material to your uploads. Leave Lightning mode off and keep the sampling defaults: 40 steps, CFG 4, Euler, simple scheduler and denoise 1. Run the Pipe to receive the edited image. Source image is required and is image 1 in the prompt. Material reference is image 2 and is enabled by default: upload it or explicitly bypass its node. To edit using a text description alone, bypass Material reference and describe the desired material directly without mentioning image 2. The source is automatically scaled before conditioning and latent encoding; there is no independent output-size control. Denoise ranges from 0 to 1: lower values retain more source structure but may weaken the material change. Seed -1 requests fresh randomness each run; use a fixed seed to compare edits with the same sampling settings. Lightning mode enables the four-step LoRA and selects separate defaults of 4 steps and CFG 1. Only the selected mode's steps and CFG apply. Lightning LoRA strength applies only when Lightning mode is on. Denoise, seed, sampler and scheduler are shared by both modes. The negative prompt describes details to discourage; at CFG 1, including Lightning's default, negative conditioning has no guidance effect. Advanced settings also expose guidance normalization, noise schedule shift and independent positive and negative reference-latent methods. Normalization and shift affect both modes. Normalize before CFG changes normalization from post-CFG attenuation to pre-CFG rescaling that can amplify guidance. Keep normalization strength 1, Normalize before CFG off, shift 3.1 and both reference methods at index_timestep_zero for the first run. This is a generative edit without a region mask, so other areas may change and material matching is not guaranteed. Uploaded transparency is not preserved by the connected RGB image path.
Boogu image edit
Create edited images from one uploaded reference and a natural language instruction. Upload your source image, enter the change you want and run with the defaults: 1 megapixel, one variant, 25 steps and instruction guidance (CFG) 3.5. “Remove the hat” is an example instruction; choose wording appropriate to your image. The negative prompt can describe unwanted details, or remain empty. Output size sets a target pixel budget in millions while retaining the source aspect ratio. Bypass Output size to derive dimensions from the source image instead. This stage controls output dimensions; the original upload still feeds editing conditioning. Resampling method affects only the image used to derive dimensions. Variants sets the number of edited images returned in a batch. Larger dimensions and batches require more memory. Advanced controls include guidance, seed, sampling steps, sampler and scheduler. Seed -1 selects fresh randomness each run; use a fixed seed when comparing settings with Add noise enabled. Leave Add noise enabled and Noise schedule fraction at 1 for a first run. The workflow starts from an empty latent conditioned on your image and instruction, so lowering the schedule fraction does not directly preserve source pixels. Schedule adjustment changes the sampling configuration used to calculate noise levels; the sampler itself receives the original model. This is a generative editor without a connected editing mask. Instructions to retain details do not guarantee that all other content stays unchanged. Uploaded transparency is not retained because only LoadImage’s IMAGE output is connected. The fixed model files are boogu_image_edit_fp8_scaled.safetensors, qwen3vl_8b_fp8_scaled.safetensors and ae.safetensors. Supplied dependency evidence marks all three model files and the Sweet Tea output node pack as missing in the inspected environment; their source locations are supplied. The selected result is the decoded edited-image batch returned through Sweet Tea’s image output.
