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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.
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.
Kling reference image to video
Generate one video from a reference image and a motion prompt. Upload one image, then replace the example prompt with the desired action, scene and camera movement. For a first run, keep kling-v3-omni, five seconds, 16:9 framing and 720p resolution. Generated audio is enabled by default. Choose landscape (16:9), portrait (9:16) or square (1:1) framing, a duration from 3 to 15 seconds, and 720p, 1080p or 4k resolution. The Model control also offers kling-video-o1; turn Generate audio off when selecting it because generated audio is supported only by kling-v3-omni. Execution uses the ComfyUI partner API, sends the reference image and prompt to Kling, and incurs provider charges. Model, duration, resolution and audio choices affect the charge. The image guides the subject and appearance; this Pipe has no separate first-frame or end-frame input. Include desired content and things to avoid in the same prompt. Storyboard mode remains disabled because this interface has no editable segment prompts or durations. Seed defaults to -1 for randomization. It controls provider re-execution rather than reproducibility: results remain non-deterministic with a fixed seed. The Pipe returns one video for preview. Advanced Video output settings provide file type and codec choices, both defaulting to automatic selection.
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.
【FLUX】IMG to IMG — v6.2
A few modifications to standardize my workflows.
Flux 1.1 Pro Ultra image generator
Create a single image with Flux 1.1 Pro Ultra. For a first run, edit the prompt, choose an aspect ratio, and leave reference image guidance disabled. The default produces a square image without an uploaded input. To guide generation with an existing image, enable Reference image and upload an image. Reference strength controls its influence: higher values make the result closer to the reference. This setting has no effect while Reference image is disabled. Enable Raw mode for less processed, more natural-looking images. Prompt expansion lets the provider modify the prompt for more creative generation; with it enabled, the same seed will not produce exactly the same result. Seed defaults to a new random value each run. This workflow uses a hosted API node and requires configured API access and sufficient credits. Output size is controlled by aspect ratio; the source does not expose explicit pixel dimensions.
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.
Gender Slider
Don't let the Model or Lora push a gender on you. Male, female, and every point in between. You decide. This isn't to push any agenda, just giving you the tools to get the image you want, despite the biases that...
Bad-Hands-5
Check my EXCLUSIVE models on Mage.Space : AniMage PXL • AniReal PXL • Lucid Dream • AniMage SD1.5 • Realistic Portrait SDXL - Pony : AniVerse PXL • AniMerge PXL • AniToon PXL • AniMics PXL • AniVerse XL SD1.5 :...
Bridal Style
This is a Style which brings out a beautiful bridal aesthetic. Simply call Style-Bridal in your prompt and your female characters will be ready for their wedding day!
Seedance first and last frame video
Generate one video between two supplied endpoint images, guided by a motion prompt. For a first run, upload both the first frame and the last frame, then replace the example wizard prompt with the action, transition and camera movement that should connect your images. Both images are required; there is no single-image mode. Start with the existing defaults of 10 seconds, 1080p and a square aspect ratio, or choose proportions suited to your images. Duration supports 3–12 seconds. Resolution offers 480p, 720p and 1080p; aspect ratio includes an adaptive provider mode. Choose among the supplied Seedance Pro and Lite models. Generate audio applies only to seedance-1-5-pro-251215 and is ignored by the other models. Fixed camera adds a request to keep the camera still, without guaranteeing the result; avoid conflicting camera movement in your prompt. Seed -1 selects a fresh random seed each run; a specific seed supplies that value to generation. AI watermark requests an “AI generated” watermark. The advanced Video output section offers file type, codec and encoding quality controls. Keep their defaults for a first run. The generated video is returned to Sweet Tea through the output transport. Generation uses the ByteDance partner API and requires configured API access, authentication and sufficient credits. The uploaded images feed the image sockets only; their alpha masks are not passed to generation.
Seedream 5.0 Lite image generation
Create one image or a related image set from a text prompt with Seedream 5.0 Lite. For a first run, enter a prompt describing the subject, composition, lighting and style, or try the supplied fashion-poster example with layered plant shadows. Keep Output size at 2048x2048 (1:1) and Image-set mode at disabled, then run to generate one image. Choose an output size preset, or select Custom to use Width and Height in pixels. Custom width supports 1024–6240 pixels and height supports 1024–4992 pixels, both in increments of 2. These controls apply only with Custom selected. For related scenes or variations, describe the desired set in the prompt, select auto under Image-set mode, and raise Maximum images above 1. The model decides how many images to generate within that limit, so the maximum is not a guaranteed count. Disabled mode generates one image and ignores Maximum images. Seed -1 randomizes each run; a fixed seed controls generation randomness. Enable AI watermark to add an “AI generated” watermark. Require complete results is enabled by default and aborts if any requested images are missing or return an error. Disable it to permit partial results when available. This tool accepts text only and has no reference-image input or separate negative prompt. Generation sends the prompt to ByteDance's paid provider service.
HappyHorse video edit
Edit a source video with text instructions and a reference image using HappyHorse 1.0, and return one edited video. For a first run, upload a 3–15 second source clip and a reference image. Adapt the example instruction to identify the subject in your clip and explain how the reference should be used. The instructions also support appearance changes or style transfer. Both uploads are required in this Pipe. Choose 720P or 1080P and the desired aspect ratio. The provider describes output duration as 3–15 seconds, matching the input within that range; clips longer than 15 seconds are truncated. This Pipe has no duration or FPS control. Use seed -1 for a new random seed each run, or enter a fixed seed to control generation variation. Enable Watermark to request the provider's AI-generated mark. Under Video output, leave file type and codec on auto for a first run, or choose the available container and codec settings. These affect encoding of the returned video. Execution requires configured access to the HappyHorse partner API. The source video, reference image and editing instructions are sent to the provider, and generation uses a provider-priced API call.
Resize image by scale
Upload an image and set the scale factor to resize both dimensions while keeping its aspect ratio. The default factor of 2 doubles its width and height; values below 1 reduce it. Run with the default Lanczos method for a first result. The resampling method changes how pixels are interpolated. Lanczos and bicubic suit smooth photographic resizing; nearest-exact keeps hard pixel edges. This is conventional resizing, so enlarging does not generate new scene detail. The source connects only the loader's image output, so uploaded transparency is not preserved.
Replace video background
Replace an uploaded video's background with a selected solid color using Bria, and return the processed video as a preview result. For a first run, upload your source video, leave Background color on Black, keep Seed at -1 and leave the advanced File type and Codec controls on auto. Run the Pipe to view the resulting video. Choose another background color to change the solid fill behind the foreground subject. Seed -1 requests a fresh re-run seed each run. A fixed seed does not make Bria's results reproducible: results remain non-deterministic regardless of seed. For explicit output encoding, enter auto, mp4, mkv or webm for File type, and auto, h264 or av1 for Codec. Explicit selections must be compatible with each other; keep both on auto unless you need a particular encoding. The Bria stage sends the uploaded video to an external provider and uses a paid provider operation. This Pipe offers solid backgrounds only, with no transparent-background mode. Audio preservation is not established by the supplied source evidence.
Moody Base + Turbo Image Generator
Generate an image or batch from text using the source's fixed Moody Base/Turbo checkpoint pair. A Base pass establishes composition, the latent is resized, Turbo refines it, and tiled diffusion produces the enlarged result. The Pipe returns only the finished image collection, without permanently saving images through the source SaveImage node. For a first run, replace the positive prompt with your subject, composition, lighting and style, and review the negative prompt. Keep the 640 × 960 starting size, refinement scale of 1.7, enlargement factor of 2 and supplied sampling defaults. No uploaded image is required. Final dimensions depend on both scale factors and latent-size rounding; final enlargement applies to the decoded Turbo result. Scale factors below 1 reduce dimensions. Larger dimensions, scales and image counts increase memory and processing requirements. The negative prompt affects the Base pass only. Turbo refinement, tiled enlargement and face refinement use zeroed negative conditioning. Enlargement normally reuses the main positive prompt. Enable Use separate enlargement prompt and edit Enlargement prompt to replace it for tiled redraw. Higher enlargement denoise permits more changes to existing details and content. Advanced sampling controls belong to separate stages. Base defaults use a 17-step schedule starting at 0 and stopping at 12, retaining noise for refinement. Turbo defaults use an 11-step schedule beginning at 5 and completing the schedule. These schedule lengths are not the number of iterations each pass executes. When changing them, review the corresponding start and end boundaries; keep the Turbo starting boundary below its schedule length. Each stage has its own seed. -1 randomizes that stage on each run; use explicit seeds for all active stages when retaining their random choices. Enlargement uses automatically calculated tile dimensions that depend on the decoded image size and enlargement factor. Tile batch size controls how many redraw tiles are processed together, separate from Image count, and can change results. Tile padding controls redraw context, while the automatic dimension formulas retain a fixed 96-pixel allowance. Seam repair starts at None. Select a repair mode before adjusting its denoise, blur or padding; Seam band width applies to Band Pass repair. Face refinement is optional and initially bypassed. Enable it to detect and redraw faces, then replace the portrait-specific Face prompt for your subject. Face denoise controls redraw strength. Detection threshold and minimum size determine which detections are accepted; crop context, region expansion and feathering control the processed regions and their blending. Detection may miss small or obscured faces. Face sampling and cycle controls apply only when this stage is enabled. Skin contrast finishing is optional and initially bypassed. Enable it to apply the fixed skin contrast model, then adjust Skin contrast strength. With Normal blending, 0 keeps the image before finishing and 1 uses the full finishing result; strength has no effect while finishing is bypassed. Other blend modes can alter the image even with finishing bypassed because they combine two identical inputs differently. The source's manual mask detailer and native editor bridge remain hidden and bypassed. This Pipe does not provide a live paint-mask session or portable manual mask refinement. Optional finishing stages process the generated image and still run the upstream generation and enlargement stages. Model selections are fixed to the source's Moody checkpoints; stock Z-Image catalog entries do not establish that those specific files are installed.
NegativeMutation
Original Hugging Face Repository Simply uploaded by me, all credit goes to . If you like this embedding, please consider taking the time to give the repository a like and browsing their other work on HuggingFace.
