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LTX-2.3 Audio-Conditioned Video
Create one audio-conditioned video with LTX-2.3. Upload a reference image and audio track, then replace the cactus example prompt with your subject, action, setting and camera movement. For a first run, keep 1280 × 720, 24 FPS, 9 seconds and the supplied sampling settings. Supply enough audio after Audio start to cover Duration. Audio start is measured in seconds; negative values count from the end of the track. Duration controls both audio trimming and video frame calculation: integer(Duration × FPS + 1). The extra frame can make video playback slightly longer than the audio excerpt. Use positive dimensions, duration and frame rate. The source latent layout declares spatial steps of 32 pixels at the initial half resolution and a temporal step of 8 frames; use compatible settings when changing output shape or timing. The workflow generates at half the requested dimensions, upscales the video latent by 2× and runs a refinement pass. Width and height also set reference framing: center crops to the requested aspect ratio, while disabled stretches the image. Reference longer edge independently resizes the prepared image before compression and both image-conditioning passes; its default is 1536 pixels. Uploaded transparency is not used as an alpha mask: only the image loader's RGB image output is connected. Image strength controls reference influence in initial generation; Refinement image strength independently controls its reapplication after upscaling. Text-to-video mode disables image conditioning in both passes while retaining audio conditioning. A valid reference image remains required because its loader and preparation stages stay connected. Enhance prompt is enabled by default and rewrites your scene prompt using text only. Describe relevant image and audio context yourself. Disable it to use your original prompt directly. Advanced prompt settings include text sampling, its independent seed and temperature, token-selection controls, the generation limit, template and optional model-supported thinking mode. The fixed Gemma LoRA strength applies to prompt enhancement separately from the video distillation strength. Initial generation and refinement have independent seeds, CFG, samplers and manual noise schedules. Schedule entries determine sampling intervals; there is no separate Steps control. Keep the supplied schedules and distillation strength for a first run. Seed -1 randomizes each run; fix both video seeds and the sampled enhancement seed, or disable enhancement, when controlling those sources of variation. At CFG 1, the negative prompt has no classifier-free guidance effect in that pass. The delivered video includes audio decoded from the audio latent after both passes, rather than a direct copy of the uploaded file. Exact waveform preservation and precise lip synchronization are not guaranteed. Advanced output settings select container, codec, encoding quality and bit depth; keep their defaults for a first run. Ten-bit video requires compatible encoding and playback.
FLUX.2 Klein single-image edit
Generate an edited image from one uploaded source image and written instructions with FLUX.2 Klein 9B Distilled. For a first run, upload your source image, then describe what to change and which details to retain. The supplied instruction replaces the background with a coastal cliff; replace it if that edit does not suit your image. Start with 1 megapixel, one image per run, 4 steps, guidance scale 1 and Euler sampling. Seed -1 chooses a new random seed each run; use a fixed seed when comparing instruction or control changes. Output size approximately follows the source image's aspect ratio at the chosen megapixel count. This setting resizes the reference and determines the generated canvas dimensions. Resize method controls interpolation of the uploaded reference before encoding; it is available alongside output size. Advanced controls expose image count, seed, steps, guidance scale and sampler. Larger sizes and batches require more memory. The uploaded image supplies reference conditioning. Generation starts from a fresh latent rather than directly denoising the upload, so exact preservation of identity, pose or unchanged regions is not guaranteed. There is no mask, edit-strength slider or separate negative prompt. Text conditioning for the negative branch is zeroed automatically, while the image reference is supplied to both conditioning branches. Uploaded transparency is not preserved because only the loader's RGB image output is connected. This tool accepts one source image. Combining a main image with a second visual reference belongs to the separate multi-reference edit tool.
AloeVera's SuperNegative embeddings
0.VERY IMPORTANT -> All the versions of the embeddings are available on the huggingface link. (Civitai has upload limitations). Also click on "Show more" down below for all the examples/comparisons + tips. ☺️
Multi-keyframe video
Create one silent video by generating five prompted transitions between six ordered keyframe images with Wan 2.2 and its paired four-step Lightx2v LoRAs. The generated frame batches are joined in sequence. For a first run, keep all six keyframes enabled and upload an image to each slot. Describe the subject, motion and camera action for each adjacent pair in its transition prompt. Replace transition 1's cat example and fill the other four prompts. Leave size, frame counts and output encoding at their defaults. The defaults generate five 25-frame segments at 720 × 720, joined into 125 frames played at 24 FPS: approximately 5.21 seconds. In advanced controls, each transition has its own frame count and dimensions. Use frame counts of 4n + 1, such as 25, 49 or 81. If changing size, set identical widths and identical heights across all five transitions, using multiples of 16; mismatched dimensions can prevent frame joining. Playback FPS changes how fast the existing frames play, without generating more frames. Duration is the sum of all five frame counts divided by FPS. To omit an endpoint image, disable its keyframe node. Every enabled keyframe needs an upload. Disabling an interior keyframe removes the preceding transition's end-image condition and the following transition's start-image condition. It does not remove either transition or shorten the sequence. A transition with both endpoints disabled still generates from its prompt. All five prompts remain independently editable in these modes. Each transition has an independent initial noise seed. Use -1 for a new random seed each run, or a fixed seed to reuse that transition's noise. Negative prompts are independently connected, but the packaged CFG value of 1 does not apply conventional negative-prompt guidance. The two sampling stages use a coordinated four-step schedule with a handoff after step two; retain the packaged preset for normal use. Video output offers automatic, MP4, MKV or WebM file selection and automatic, H.264 or AV1 encoding. Leave automatic selection and CRF -1 for the first run; these controls affect encoding rather than generated content. The graph joins full frame batches without trimming shared endpoints or blending seams. Repeated boundary frames or visible seams between independently generated transitions may occur. The result has no audio or transparency; uploaded alpha masks are not connected.
Runway image generation
Generate images with Runway from a text prompt and an optional reference image. For a first run, replace the example prompt with your desired subject, setting, composition, and visual style. Keep Output shape at 1024:1024 for a square image. Reference image is enabled initially: upload one image, or bypass that stage to generate from text alone. Run to preview the generated result in Sweet Tea. With a reference image enabled, describe the result you want that image to guide. Only one reference image is accepted, and no image transformation strength control is exposed. Other output shape values must be supported by the Runway node; the draft does not enumerate them. Running requires configured ComfyUI partner API access and sufficient account credits. Generation sends the prompt and any enabled reference image to Runway and uses a paid provider call. The output stage returns images for preview without permanently saving them through ComfyUI.
Causal Forcing image to video
Animate an uploaded starting image into one silent video with Causal Forcing Framewise. For a first run, upload an image and replace the example motion prompt with a description of your subject's movement and the desired camera behavior. Choose width and height in multiples of 16 with an aspect ratio similar to the image. Start with 832 × 480, duration 5, batch size 1 and playback at 16 FPS. The image is used as the starting-frame input; uploaded transparency is not preserved through the connected RGB image output. Duration is measured on a fixed 16 FPS generation basis. The graph calculates min(duration × 16 + 1, 81) and uses its integer output as the requested frame count. Use positive duration values up to 5 seconds; larger values do not add frames. At 16 FPS, 81 frames play for about 5.06 seconds. Playback FPS changes how quickly the generated frames play, independently of their count: higher FPS shortens playback and lower FPS lengthens it. Seed -1 requests fresh random variation each run. A fixed seed reuses the sampling noise. CFG defaults to 1, where the negative prompt has no effect. For experiments with CFG above 1, describe unwanted appearance or motion in the negative prompt. Changing guidance departs from the supplied checkpoint preset. Advanced sampling controls expose steps, noise schedule and denoise fraction. Begin with the source configuration: 4 steps, simple schedule and denoise 1. Denoise controls the sampling schedule rather than directly specifying how closely to preserve the uploaded image. The fixed model is causal_forcing-framewise.safetensors with the Wan VAE and text encoder; its autoregressive sampler is fixed to one frame per block, matching the Framewise mode. Keep batch size 1 for a single sequence. The graph has one video assembly path and does not return separate videos for batch members. File type and codec control output encoding and default to auto. No audio input is connected, so the result is silent.
CyberRealistic Negative - Pony v2.0
Join The Tinkerer on Whop. Membership gets you early releases, private tools and a bunch of extra stuff.
Luma text to video
Generate one video from a scene description through Luma, with optional camera motion concepts. Enter a text prompt describing the subject, setting, action, lighting, and movement. This tool has no image input. Generation sends the request to Luma and requires API access and sufficient account credits. For a first run, replace the example prompt and keep Ray 2, 16:9, 720p, and 5s. Leave both camera stages bypassed and run the tool to receive the video. Enable Loop to request a looping clip. For camera concepts, enable Camera motion in the advanced controls. Its initial choices are zoom_in and orbit_left; change the four slots as needed, using None for unused slots. Enable Additional camera motion as well when you want to combine up to four more concepts with the primary stage. Its slots initially contain None. Bypassed stages do not add their own selected concepts. The model affects supported output settings: Ray 1.6 ignores duration and resolution. Re-run seed controls whether the API generation runs again, not reproducibility. Keep -1 to randomize each run; identical seeds do not reproduce the same video. Video output returns the generated clip through Sweet Tea. Keep File type and Codec at Auto for the first run, or use the advanced output controls to choose a container and codec. These settings affect output encoding, rather than the scene generated by Luma.
花想容/Chinese style/古风/中国風です Lora
推荐的质量词: masterpiece, best quality, score7, score8, highres, extremely detailed 8k, illustration, great quality, amazing quality, very aesthetic, absurdres, Chinese style,
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 .
LongCat Image Edit
Create an edited image with LongCat Image Edit by providing a source image and describing the desired change. Set output resolution in megapixels while retaining the source aspect ratio. Advanced controls include edit strength, positive and negative guidance, sampling settings, and seed. An optional negative prompt describes elements to avoid.
JoyAI Image Edit
Generate edited versions of a reference image using natural-language instructions. For a first run, upload the image you want to edit, replace the example instruction with your requested change, and state what should remain unchanged. For example: “Change the background to a glacial scene; keep the bottle and its label.” Leave Output resolution at 1 megapixel, Variations at 1, and the sampling defaults unchanged, then run. Output resolution sets the approximate total pixel count. The reference is resized before conditioning, and its resized dimensions determine the generated image dimensions, aligned to multiples of 16. Resize method changes how the reference is resampled. Increasing resolution or the number of variations increases memory use. This is a generated edit, not a masked, pixel-preserving retouch: changes can extend beyond the requested region. Uploaded transparency is not preserved because only the loader's RGB IMAGE output is connected. Negative prompt describes unwanted content and can remain empty. Both positive and negative conditioning receive the resized reference. Variations generates multiple edited images in one batch. Seed -1 selects a new random seed each run; use a fixed seed when comparing control changes. Advanced sampling controls include Steps, CFG, Sampler and Scheduler. Start with 40 steps, CFG 4, Euler and Normal. Denoise controls the sampling schedule fraction; retain 1 initially. Since generation starts from an empty latent, lowering denoise is not a direct reference-preservation control. Guidance normalization exposes independent strength and timing settings; begin with strength 1 and Normalize before CFG enabled. The Pipe returns the edited image batch through Sweet Tea's image output. It does not return a separate side-by-side comparison. The original save node has been replaced, so its file-format and permanent-save settings do not describe this selected output.
Character motion transfer
Animate a reference character using motion poses from a source video, producing one animation assembled from five sequential generated segments. Provide a character reference image and a motion video file path accessible to the execution runtime. The image supplies appearance, reference pose and an automatic caption; the video supplies motion poses. Source audio and uploaded image transparency are not used. For a first run, use a reference showing the character's body clearly and a motion clip with one clearly visible performer. Edit both dancing prompt additions to describe your intended action. They surround the automatic reference-image caption, which remains part of the positive prompt. Keep the default six sampling steps, guidance settings, 81 frames per segment and five-frame overlap. The default output is a silent H.265 MP4. Width and height control both input resizes. The default requested portrait size is 450 by 800 pixels; actual dimensions are adjusted for divisibility by 16. Both inputs use aspect-preserving cropping, with independent reference and motion crop positions. Keep the character and relevant limbs inside each crop. Frame rate is shared between motion-source resampling and final playback. Enter a positive FPS value. Frame stride keeps every nth resampled source frame; values above 1 advance the action faster at the unchanged playback FPS. Skip initial frames selects a later starting action. Source frame limit caps loaded motion frames, with 0 meaning no cap; it does not change the number of generated segments. The graph always generates an initial segment and four continuations rather than automatically matching the full source duration. Keep frames per segment in the 4n+1 pattern, such as 81, and no greater than 256. Keep overlap below the segment length and no greater than 64. Provide enough sampled motion frames for the initial segment. Each continuation defaults to repeating the last available pose when its motion window runs out; its short-source behavior can instead require enough frames by selecting error. Reference strength and its sampling start/end fractions apply across all segments. Body, hand and face pose components can be enabled independently for the motion source and reference image. Pose strength, pose CFG and sampling start/end fractions are independent for each segment. Pose CFG has no effect while the corresponding sampler CFG is 1. The negative prompt is shared, and its influence depends on sampler CFG. Steps is shared by all five samplers. Seeds, CFG, schedule shift, scheduler and denoise are separate per-segment controls. Start with the source defaults and matching segment settings; earlier generated frames become context for later segments, so an early change can affect the remaining video. The fixed Lightx2v distillation LoRA is part of the six-step setup and has an adjustable strength. Advanced continuation context controls adjust the encoded generated-frame multiplier and noise augmentation for each later segment. Automatic caption controls affect the positive prompt used by every segment. Caption seed matters when caption sampling is enabled; the token limit is a ceiling, and lowering it can truncate the caption. Seed -1 requests fresh randomization for the caption or the corresponding video segment. Video output controls change encoding rather than generated motion. File type and codec are selectable; choose compatible combinations supported by the runtime. The source defaults are MP4, H.265, 10-bit yuv420p10le and sRGB. CRF defaults to 22; for codecs using CRF, lower values generally retain more detail and produce larger files. Bitrate is expressed in Mbps, with 0 leaving a positive bitrate unspecified. Keep pixel format and bit depth consistent with the codec. HDR encoding choices do not establish HDR detail in the generated frames.
ZIB + ZIT image generator and upscaler
Generate a final image batch from text using the fixed Moody ZIB composition model and Moody ZIT detail model, followed by tiled enlargement. No upload is required. For a first run, replace Positive prompt with your subject, composition, lighting and style. Keep the 640 × 960 starting canvas, Image count of 1 and supplied sampling settings. Leave Face detailing and Contrast finishing disabled. The example prompts favor cool, pale portrait styling; replace the tile and face examples when enabling those options for another subject. Detail enlargement defaults to 1.7× before the second diffusion pass. Final enlargement defaults to 2× during tiled refinement and supports 0.05–4 in increments of 0.05. Final dimensions are approximately the starting dimensions multiplied by both factors, subject to latent rounding. Factors below 1 reduce dimensions. Larger canvases, factors and image counts increase memory use and processing time. This Pipe always generates its starting image and has no uploaded-image input for standalone upscaling. Positive prompt guides both generation passes and normally tile refinement. Enable Use separate tile prompt to substitute Tile prompt for the tiled stage. Negative prompt affects only composition; later diffusion stages use zeroed negative conditioning. Tile denoise controls how strongly the enlarged image is repainted. Composition steps defines a full 17-step schedule, with Composition start step 0 and Composition stop step 12. The partial pass retains noise before latent enlargement. Detail steps defines a separate 11-step schedule starting at Detail start step 5; Detail stop step 999 allows it to finish. Start and stop indices belong to each stage's own schedule. Keep schedule lengths, boundaries and noise settings consistent when changing them. Latent resize method changes interpolation between these passes. Composition sampling shift affects the first model; Detail and tile sampling shift affects both subsequent stages. Composition, detail, tile and optional face sampling have independent seeds. -1 randomizes each active stage on each run. Fix all active stage seeds to reuse their randomization; other control changes can still change the result. Tile dimensions are calculated automatically from the detail-pass image dimensions and Final enlargement. The formulas retain a fixed 96-pixel term even if Tile padding changes, so preserve the padding default initially. Tiles per batch controls tile processing rather than output image count, and applies to redraw rather than seam correction. Seam correction starts at None; seam denoise, band width, blur and padding apply when a correction mode is selected. Tiled decoding controls VAE decoding separately from image redraw tiling. Enable Face detailing to detect and repaint faces after enlargement. Face prompt is independent of Positive prompt. Face denoise controls how strongly faces change and may affect identity or expression. Face crop sizing, detection, segmentation, noise masks, sampling and encoding/decoding controls apply only while this stage is enabled. Guide size works with its bounding-box or crop basis and maximum size. Keep Face inpainting model mode false for the fixed supplied model, and leave Face wildcard text empty unless using supported Impact Pack syntax. Enable Contrast finishing to apply the fixed 1× skin-contrast model, then adjust Contrast strength and Blend mode. With normal blending, strength 0 keeps the pre-finish image and 1 uses the finish. When finishing is disabled, normal blending leaves the image unchanged regardless of strength. Other blend modes can alter the image even while finishing is disabled. The model pair and source LoRA configurations are fixed. Manual masked detailing is unavailable because its mask depends on a live ComfyUI editor session rather than a portable Pipe input. The retained manual bridge and regional detailer remain bypassed without enable toggles; their archived settings do not affect the output. Only final images are returned.
Wan 2.1 camera-controlled image to video
Animate an uploaded still image into a prompt-guided video with selectable camera movement using Wan 2.1 Fun Camera 1.3B. For a first run, upload a source image, replace the landscape example prompt with a description of your scene and desired subject action, and choose a camera movement. Keep the remaining defaults initially: Zoom In, camera speed 1, 512 × 512 pixels, 81 frames, 16 FPS and generation batch size 1. These settings produce about 5.1 seconds of silent video. The source image supplies starting-frame conditioning and a separate visual reference; its alpha mask is not connected, so transparency is not preserved. Camera choices include Static, directional pans, zooms and clockwise or counterclockwise rotation. Camera speed changes camera conditioning. Width and height use multiples of 16; frame count follows 1, 5, 9 and so on. These shape controls drive both camera and video conditioning. Playback FPS changes duration without changing the generated frame count. Static camera conditioning still permits subject motion described by the prompt. Camera controls condition generation rather than guaranteeing an exact rendered trajectory. In advanced settings, edit the negative prompt to describe unwanted results. Image guidance can be bypassed while keeping the uploaded image connected as the starting frame. Reference crop applies only to image guidance and has no effect when that stage is bypassed. Camera focal-length and optical-center controls are available in Camera and video shape; their source uses a 0–1 scale. Sampling controls include seed, steps, prompt guidance, sampler, scheduler, denoising amount and sampling shift. Seed -1 selects random noise; use a fixed nonnegative seed to reuse the initial noise with the same settings. Denoising amount is not a direct image-preservation slider. The saved sampling settings are 20 steps, CFG 6, uni_pc, simple scheduler, denoising amount 1 and shift 8. Video playback and export expose FPS, 8- or 10-bit depth, container, codec and CRF. Start with automatic container and codec and CRF -1. Some players or downstream tools may not support 10-bit video. Generation batch size controls latent batching, not separate exported files; the selected output assembles decoded images into one video, so use 1 for a single clip. This tool has no connected audio input and returns the video output rather than an animated WebP or individual frame export.
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.
Wan image to video
Animate an uploaded image into one video using Wan 2.5 or Wan 2.6, with optional generated audio. Upload the image you want to animate and replace the example prompt with instructions matching its subjects and setting. Describe the scene, subject motion, and camera movement in English or Chinese. For a first run, keep Wan 2.5, 720P, and 5 seconds. The result appears in Sweet Tea's video output. Choose 480P, 720P, or 1080P and a duration of 5, 10, or 15 seconds. A 15-second video requires Wan 2.6. The negative prompt describes elements to avoid and may be left blank. AI prompt extension is enabled by default and lets the provider enhance your instructions; disable it to submit your wording without that enhancement. Generated audio is enabled by default and can be turned off. This Pipe has no uploaded audio input. Watermarking is optional and disabled by default. Use seed -1 to randomize each run, or enter a fixed generation seed; identical provider results are not guaranteed. Advanced video output controls select the file type and codec. Leave both on auto for the first run and choose compatible settings when changing them. Uploaded transparency is not preserved: only the loader's RGB image output feeds generation. Generation sends the image and prompt to the Wan API and uses a provider-priced generation call.
