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Wan 2.1 image-to-video with optional end frame
Generate a silent video from a starting image and a text prompt using Wan 2.1 Fun InP 1.3B, optionally guiding the ending with a second image. For a first run, upload a start image and replace the example positive prompt with a description of your subject, scene, movement, and camera behavior. End frame is enabled by default: either upload an end image or bypass End frame to use the start image alone. The start image remains required in both modes because it also supplies visual conditioning. Images guide generation; an exact transition or exact endpoint match is not guaranteed. Begin with the supplied 480 × 768 dimensions, 81 frames, 16 playback FPS, and 20 sampling steps. This gives approximately 5.1 seconds of video. Width and height are pixels in multiples of 16; frame counts follow 4n + 1, such as 49, 81, or 121. Choose an input aspect ratio close to the output dimensions. Larger dimensions and more frames increase processing and memory requirements. FPS sets playback speed without adding or interpolating frames; duration is frame count divided by FPS. Keep Batch size at 1 initially: larger batches do not have separate clip output paths. The negative prompt describes unwanted appearance and motion; the supplied Chinese text discourages artifacts, poor anatomy, static imagery, and clutter. In Sampling, seed -1 chooses fresh random noise for each run; use a fixed seed to compare settings. Keep the supplied sampler, scheduler, guidance scale, and denoise fraction initially. Denoise is not a direct start-image preservation strength. Reference crop affects visual conditioning from the start image, independently of output dimensions. Skip-layer guidance is enabled by default and can be bypassed for comparison; its strength, start/end fractions, rescaling, and layer selections apply only when enabled. Noise schedule shift and the independent attention multipliers expose additional model tuning. Their effects are separate from playback timing; retain the packaged settings for the first run. Video playback also offers 8-bit or 10-bit depth. The supplied 8-bit setting favors compatibility; 10-bit supports smoother gradients but may be unsupported by some players or downstream tools. The output controls offer automatic encoding or an explicit file type (MP4, MKV, WebM) and codec (H.264, AV1). Automatic format and codec are the defaults. No audio input is connected, so the result is silent.
FLUX.1 Schnell text to image
Create images from text using the fixed FLUX.1 Schnell FP8 checkpoint. No source image is required. The runtime needs flux1-schnell-fp8.safetensors in its checkpoints directory. For a first run, replace the example prompt with your subject, setting and visual style. Keep 1024 × 1024 pixels, one image, four steps, CFG 1, Euler sampler, simple scheduler and denoise strength 1. Run the tool to receive the generated image batch through Sweet Tea's preview output; this graph does not permanently save files with SaveImage. Width and height must be multiples of 16. Adjust them together to choose the aspect ratio. Image count determines the number of images returned per run. Larger dimensions and batches require more memory. Seed -1 chooses fresh noise each run; a fixed seed lets you compare changes with the same starting noise. Advanced sampling controls expose steps, CFG, sampler, scheduler and denoise strength. Start with the source defaults. The optional negative prompt feeds negative conditioning but has no effect at CFG 1. Raising CFG allows that conditioning to influence sampling and is not a guaranteed improvement for this Schnell setup. This tool starts from an empty latent and supports text-to-image generation only. Lower denoise strength changes sampling of that empty latent; it does not provide image editing. The checkpoint is fixed, with no model-selection or source-image mode.
SDXL text to image with refinement
Generate one image or a batch from text using SDXL 1.0 Base followed by SDXL 1.0 Refiner. No source image is required. The fixed checkpoints sd_xl_base_1.0.safetensors and sd_xl_refiner_1.0.safetensors must be available to the runtime. For a first run, describe your scene in Positive prompt and copy that description into Refiner positive prompt. Enter unwanted features in both negative prompts, then run with the default 1024 × 1024 dimensions and Image count of 1. All four prompt fields are independent: editing a base prompt does not update its refiner counterpart. Keep their intent aligned, or deliberately change the refiner wording to emphasize finishing details. Width and Height are in pixels and use multiples of 8. Image count generates a batch using the same prompts. Larger dimensions and batches require more memory. In advanced settings, Seed controls the initial base noise: -1 selects a new seed each run, while a fixed seed lets you compare changes using the same initial noise. Guidance and sampler choices are independent for Base and Refiner. The default schedule has 25 total steps: Base performs the first 20, passes its partially denoised latent onward, and Refiner completes the remaining five without adding fresh noise. Refinement is integral to this output path and cannot be bypassed through this interface. Advanced schedule controls must be adjusted together. Keep Base schedule steps equal to Refiner schedule steps, use matching schedulers, and set Base handoff step equal to Refiner handoff step below the total schedule length. The schedule-step fields describe the full schedule for each stage, not how many steps that stage executes. Begin with the coordinated defaults before changing the schedule.
LTX image to video
Animate an uploaded starting image into a short, silent video using LTX-Video 2B v0.9.5. For a first run, upload an image and replace the fox example in the positive prompt with a description of your subject, intended movement and camera behavior. Keep the default 768 × 512 dimensions, 97 frames, image strength 0.15, 30 sampling steps, CFG 3 and Euler sampler. At 24 FPS playback, the returned clip lasts about 4.04 seconds. Leave batch size at 1 and output container, codec and CRF at their source defaults. Width and height use multiples of 32 with a minimum of 64 pixels. Frame counts are 9, 17, 25 and so on in increments of 8. Image strength controls starting-image conditioning. The negative prompt describes unwanted visual or motion characteristics. Larger dimensions and frame counts increase processing and memory requirements. Playback FPS changes how quickly the generated frames play and sets duration without adding frames. Conditioning FPS supplies a separate frame-rate cue during generation. Their source defaults are 24 and 25 FPS respectively; set them equal when you want the two settings aligned. Sampling controls include text guidance, initial noise, seed, steps, algorithm and the LTX schedule's base and maximum shifts. Keep their defaults for a first run. Seed -1 requests fresh sampling noise each run; use a fixed seed to compare changes with the same initial noise. Disabling initial noise removes that seed's effect on initial noise selection. Video playback settings offer 8-bit or 10-bit output. The source describes 10-bit as retaining smoother gradients, but player and encoder support varies. Video output settings select automatic encoding or an explicit MP4, MKV or WebM container and H.264 or AV1 codec. Use compatible container and codec choices; CRF interpretation depends on the codec. This graph returns one assembled video. Increasing latent batch size does not create separate video outputs. Only the image loader's IMAGE output is connected; its alpha mask is unused, so uploaded transparency is not preserved. There is no audio input or separate end-frame control.
LTX-2.3 image to video with audio
Animate a source image into one video with generated audio. Upload an image and replace the example prompt with directions for the scene, action, camera movement, dialogue and sounds. The tool generates video and audio together, performs fixed 2× spatial upscaling on the video latent, then refines both streams before returning the video with its soundtrack. For a first run, keep the requested 1280 × 720 size, nominal 5-second duration and 25 FPS. Leave Text-to-video mode and prompt enhancement disabled, and keep the sampling and encoding defaults. The image is center-cropped to the requested aspect ratio; Image fit can instead stretch it. It is then resized to a fixed 1,536-pixel longer edge and compressed before image conditioning. Uploaded alpha is not used as a transparency channel. Width and height also determine the initial half-resolution generation size. Use positive dimensions and duration, and a whole-number frame rate from 1 to 120. Requested frame count is duration × FPS + 1; keep this at or below the audio stage's 1,000-frame limit. Model alignment can affect actual dimensions and duration, so these settings are requests rather than exact output trimming. Image strength controls initial source-image conditioning. Refinement image strength separately controls conditioning reapplied after upscaling. Text-to-video mode bypasses both conditioning stages, so neither strength affects that mode. An uploaded image is still required because its processing path remains connected. If prompt enhancement is enabled, the image can still influence the expanded prompt in Text-to-video mode. Prompt enhancement expands your instructions before video prompt encoding. Leave it disabled to use your written prompt directly. Enhancement tuning and its text LoRA strength apply to expansion; temperature, token filters, penalties and the enhancement seed belong to sampled text generation. Thinking mode only applies if supported by the loaded text model. Initial generation and refinement have independent seeds, CFG guidance, samplers and noise schedules. Use -1 for randomized seeds; fix both generation seeds when comparing settings, and also fix the enhancement seed if using sampled expansion. Negative-prompt influence depends on each stage's CFG and can be limited at the default of 1. Keep the distilled LoRA strength and manual schedules at their supplied values for the first run. Advanced output settings choose bit depth, container, codec and CRF. Start with 8-bit assembly, automatic container and codec selection, and CRF -1. Explicit encoding choices must be compatible with each other and your player; 10-bit output can have compatibility limitations. Audio is generated from the prompt rather than supplied through an audio upload. The model, text encoder, audio decoder and spatial upscaler are fixed parts of this tool.
Wan 2.6 image to video
Animate a source image into a short Wan 2.6 video, with optional automatically generated audio. For a first run, upload the required source image and replace the sample car prompt with motion, camera movement, and visual style suited to your image. Prompts support English and Chinese. Adjust the car-specific negative prompt to describe unwanted features for your subject. Start with the default 720P resolution and 5-second duration, then run the Pipe to receive the video in Sweet Tea. Resolution choices are 480P, 720P, and 1080P. Duration choices are 5, 10, and 15 seconds. References to 4K and 60fps in the sample prompt are creative text, not output settings; this Pipe has no FPS control. Prompt enhancement is enabled by default and allows AI assistance to expand your prompt. Shot type selects single for one continuous shot or multi for multiple shots with cuts; this choice only takes effect while prompt enhancement is enabled. Generated audio is enabled by default. Turn it off when you do not want automatic audio. This Pipe does not accept an uploaded audio track. Seed -1 selects a fresh random seed each run; a nonnegative seed controls generation randomization. Watermark enables the provider's AI-generated watermark. Advanced video output settings let you choose file type and codec; both default to auto. Generation runs through the Wan partner API using the fixed wan2.6-i2v model. The source image uses LoadImage's RGB image output; uploaded transparency is not retained.
MCNL (Multi Concept NSFW Lora) [Qwen Image]
MCNL is meant to be a convinient way to uncensor Qwen with many concepts included. Please keep in mind that loras that are trained on one concept only will generally have better quality than a multi purpose one like...
Yoruichi Shihouin (Bleach) TI Embedding by Corneo
This is a textual inversion embedding for Yoruichi Shihouin from Bleach. Use corneoyoruichi to activate.
Recraft creative upscale
Upload an image to produce a Recraft creative upscale result. For your first run, choose a source image no larger than 5 MB, with width and height each no greater than 2048 pixels, then run the Pipe. The resulting image appears in Sweet Tea's output viewer; this workflow does not permanently save it through a SaveImage node. The image is sent to Recraft for a provider-priced upscale operation. This Pipe offers no prompt, scale factor, tuning controls or alternate modes. Uploaded transparency is not preserved because only the loader's RGB image output is connected.
