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Wan 2.2 first–last frame video
Generate a silent video guided by start and end images and a motion prompt using Wan 2.2 14B. For a first run, upload a Start image and an End image, replace the saved ice-kitten-to-beast prompt with a description of your scene and transition, and keep the generation and encoding defaults. The default result is 640 × 640 pixels with 81 frames at 16 FPS, approximately 5.1 seconds. The saved negative prompt is Chinese text discouraging visual artifacts, blur, overexposure, static imagery, anatomical errors and clutter; edit it as needed. Each endpoint can be disabled independently. Upload an image for every enabled endpoint; disable its node when omitting it. With only one endpoint enabled, only that boundary is image-conditioned. With both disabled, generation uses the text conditioning without endpoint images. The image loaders supply RGB images only; uploaded alpha masks are unused and transparency preservation is not supported. Width and height use increments of 16 pixels. Frame count follows 1, 5, 9 and so on. Duration equals frame count divided by FPS; changing FPS changes playback timing without adding generated frames. Keep batch size 1 for a single clip: this Pipe exposes one video output, not separate batch-member outputs. Advanced controls include negative text, seed, independent guidance for the initial and refinement stages, sampling algorithms, schedules and model sampling shifts. Seed -1 randomizes the initial noise choice; use a fixed nonnegative seed to hold that choice constant. The default high-noise stage executes steps 0–10 and passes its noisy latent to the low-noise stage, which continues from step 10 through the end of the same 20-step schedule. Refinement runs as part of the complete generation process. If editing schedules, keep both total step counts equal, match their schedulers, and keep the initial handoff step equal to the refinement start step within that schedule. The saved samplers are Euler, guidance is 4 for each stage, and sampling shifts are approximately 8. This variant uses no acceleration LoRAs; the family's four-step Lightx2v variant is a separate configured tool. Output controls select container, codec and CRF. Leave these automatic for the first run; CRF -1 selects automatic quality. Video creation supports 8-bit and 10-bit; the source notes that 10-bit can reduce gradient banding but has player and downstream compatibility limitations. No audio is connected. The graph uses the Wan 2.2 high- and low-noise 14B model files, UMT5 text encoder and Wan 2.1 VAE named in the package source.
Krea 2 Turbo text to image
Generate an image or batch of images from text with Krea 2 Turbo. For a first run, replace the example positive prompt with your subject, composition, lighting and medium. Keep square 1 MP output, one image, prompt refinement enabled, style LoRA disabled and the supplied 8-step Euler sampling settings, then run the Pipe to receive the image batch. The runtime needs krea2_turbo_fp8_scaled.safetensors, qwen3vl_4b_fp8_scaled.safetensors and qwen_image_vae.safetensors, plus the Sweet Tea image output node. Using the default style additionally requires krea2_darkbrush.safetensors. Model selection for the base generator, text encoder and VAE is fixed in this Pipe. Prompt refinement uses the local text model and fixed instructions to expand or polish your description before image conditioning. Disable refinement to use your original wording. Refinement length, thinking mode and built-in template settings apply only when refinement is enabled. With refinement sampling on, temperature, token filters, repetition and presence penalties, and the refinement seed control sampled wording. Turning sampling off disables those sampling controls. Image noise and sampled refinement have separate seeds. Use -1 for a new random seed each run. To compare runs with stable wording and image noise, fix both seeds and retain the other settings, or disable refinement and fix the image seed. Enable style LoRA to apply the selected adapter and append the style trigger to the original or refined prompt. The default adapter is Darkbrush and the default phrase is 'monochrome ink wash style'. Adapter selection and trigger text are independent: update both when choosing another installed compatible LoRA. LoRA strength controls adapter influence only when style LoRA is enabled. Disabling style removes both the adapter and its appended phrase. Aspect ratio and megapixels jointly determine dimensions, rounded to multiples of eight. Image count sets the output batch size; larger dimensions and batches require more memory. Advanced sampling controls change image generation, with Euler, simple scheduling, 8 steps and CFG 1 supplied as defaults. Keep denoise at 1 for ordinary generation from the empty latent. This Pipe has no uploaded image input or editable negative prompt; its negative conditioning is zeroed.
Image upscale and refinement
Refine an uploaded image with prompt-guided diffusion, enlarge it with tiled refinement, and sharpen the final image. The preset Illustrious anime checkpoint, micro-detail LoRA and AnimeSharp upscaler target illustrated imagery. Generative refinement can change details, textures and anatomy. For a first run, upload an illustration, keep the default prompts, 2× upscale factor and refinement strengths, then run. Both diffusion stages share the positive and negative prompts. Add a description of your subject to the positive prompt for specific guidance. The default positive prompt includes addmicrodetails. Negative terms discourage traits; they do not guarantee removal. Initial denoise controls changes before enlargement. Tile denoise controls changes during the enlarged redraw. Lower values retain more incoming structure; higher values allow stronger reinterpretation. Each stage has independent seed, steps, CFG, sampler and scheduler controls. Seeds default to -1 for fresh randomization. Set both seeds to fixed nonnegative values when comparing settings with repeatable noise. Upscale factor multiplies image width and height; tile width and height specify processing regions in pixels rather than output dimensions. Tile pattern changes redraw order. Tile mask blur softens redraw boundaries, and padding adds surrounding context. Larger tiles, padding and tile batches require more memory. Tile batch size affects the main redraw, not seam correction. Uniform tiles keeps boundary tiles at the specified dimensions; disabling it allows smaller boundary tiles. Tiled decoding applies to the enlarged refinement stage, not the initial decoder. Seam correction is enabled by default and performs additional generative processing. Its default denoise is 1, so seam regions can change substantially. Lower seam denoise to retain more existing detail, or disable seam correction to skip that processing. Seam band width applies only to Band Pass mode; seam mask blur and padding control blending and surrounding context. The advanced detail LoRA controls independently adjust diffusion-model influence in both passes and positive-prompt encoder influence. Negative conditioning uses the encoder without this LoRA. Set both LoRA strengths to zero to disable its influence. The checkpoint, LoRA file and upscaler model are preset rather than selectable. Final sharpening has amount, radius and sigma controls. Reduce amount if edges develop halos, or bypass sharpening for the unsharpened upscale. The result is one final image; intermediate images are not saved. Uploaded transparency is not preserved.
HiDream I1 Fast image generator
Generate an image or batch of images from a text description using the fixed HiDream I1 Fast FP8 model. No uploaded media is required. For a first run, replace the example prompt with your subject, composition, lighting and style. Keep the 1024 × 1024 dimensions and batch size 1, then run to receive the generated image. Width and height are in pixels and must be multiples of 16. Change both to choose an aspect ratio; increase batch size to receive several images together. Larger dimensions and batches require more memory. Seed -1 randomizes each run. Set a specific seed to compare prompt or sampling changes with the same starting noise. Advanced sampling defaults are 16 steps, CFG 1, LCM sampler, normal scheduler, sampling shift 3 and denoising strength 1. The negative prompt has no effect at CFG 1. Raising CFG above 1 applies negative guidance and changes prompt influence. Sampler, scheduler, steps and sampling shift offer alternative sampling configurations; the template defaults provide a starting point. Denoising strength below 1 uses only part of the noise schedule. Because generation starts from an empty latent, it does not preserve a reference image. The diffusion model, supporting text encoders and VAE are fixed. This tool provides text-to-image generation, without image uploads or model selection.
FLUX.1 Krea image generation
Create images from text using the fixed FLUX.1 Krea dev model, CLIP-L and T5 text encoders, and VAE. No uploaded image is required. For a first run, describe your subject, composition, lighting, and style in the positive prompt. Keep the default 1024 × 1024 size, one image, 20 steps, Euler sampler, and Simple scheduler. The supplied portrait prompt is an example; replace it for another subject. Width and height are measured in pixels and must be multiples of 16. Image count generates a batch with the same prompt and dimensions. Larger dimensions and image counts require more memory and computation. Open Sampling for advanced controls. Seed defaults to -1, which randomizes each run. Use a fixed seed when comparing prompt or sampling changes with other settings unchanged. Steps controls the number of denoising steps; sampler selects the sampling algorithm, and scheduler distributes noise levels across those steps. CFG defaults to 1; higher values amplify the difference between positive conditioning and zeroed negative conditioning. This graph has no editable negative prompt or separate FLUX guidance control. Denoise controls the fraction of the sampling schedule used, from 0 to 1. Keep its default of 1 for full generation. The workflow always starts from an empty latent, so reducing denoise does not enable image editing or preserve an existing image. Model and encoder selection are fixed in this tool.
Add music to a video
Generate a soundtrack from your video's content and receive a video combining that music with the original audio. For a first run, upload a video containing an audio track and lasting no more than six minutes. Leave Music prompt empty to let Sonilo rely entirely on the video, or describe the soundtrack you want. Run with the existing gain and export defaults, then listen to the result and adjust the two audio levels as needed. Original audio volume and Music volume independently adjust the tracks in decibels before they are added together. Both default to +1 dB; 0 dB leaves a track unchanged, positive values raise its level and negative values lower it. The input must still contain audio even when its level is reduced. Adding tracks can increase the combined level, so lower their gains if the mix sounds overloaded. The result uses the source video's frames and frame rate. Advanced Video output controls select the container, codec and encoder CRF; leave these at their defaults for a first run. The graph also saves generated music separately before the Music volume adjustment, without the original audio. Advanced Music export controls select FLAC, MP3 or Opus; the supplied MP3 configuration offers V0, 128k and 320k quality settings. Sonilo currently ignores seed, so this tool does not offer reproducible generation through a seed setting.
Z-Image upscale and refine
Upload an image to produce one resized, RealESRGAN-upscaled and Z-Image Turbo diffusion-refined image. The selected result includes the entire processing chain; there is no separate upscale-only output or refinement bypass. For a first run, upload your source image and describe its subject, composition and desired details in the positive prompt. Keep the source defaults: 1 megapixel working size, 0.5 final scaling, 0.33 refinement strength, five sampling steps and prompt guidance (CFG) 1. The default prompt, “masterpiece, 8k,” is a minimal starting point; its text does not determine resolution. Working size sets the pixel budget before the fixed 4× RealESRGAN upscaler. Final scaling then multiplies the upscaled width and height before diffusion refinement. Approximate output megapixels equal working megapixels × (4 × final scaling)². The defaults therefore produce approximately 4 megapixels. Aspect ratio follows the upload, subject to image and latent rounding. This is not a fixed enlargement relative to the original upload; the working-size stage can also shrink a large source. Refinement strength controls how much diffusion can reinterpret the resized image. Lower values maintain more source structure; higher values allow stronger changes. Use a prompt that describes the image and the changes you want. Refinement can alter details rather than merely recover them. The negative prompt has no effect at CFG 1. It becomes useful for describing unwanted attributes when experimenting with guidance above 1. Steps, sampler and scheduler change refinement behavior. Advanced model sampling shift is a separate noise-schedule control, retained at the source default of 3 for the first run. The model setup is fixed to Z-Image Turbo with the Qwen text encoder, ae VAE and RealESRGAN_x4plus upscaler. Seed -1 randomizes each run. Use a fixed seed to compare settings with the same noise initialization. The Pipe uses the loader's RGB image output and does not preserve uploaded transparency.
Create a HeyGen talking-head video
Generate one talking-head video with a HeyGen avatar speaking your written script. For a first run, replace the demo script, keep Annie Lounge Standing Side and (avatar's default voice), and leave resolution at 720p, aspect ratio at auto, and speech speed at 1. Avatar and voice are text fields for supported HeyGen selections; this Pipe does not supply selection lists. Custom avatar and voice ID fields accept existing HeyGen identifiers. Leave them empty when using the regular selections. These fields do not create or train an avatar or voice. Choose 720p or 1080p resolution and an automatic, landscape, portrait, square, 4:5, or 5:4 aspect ratio. Background color accepts a HeyGen-supported color value; leave it empty for the provider's default behavior. Speech speed is a multiplier from 0.5 to 1.5: 1 is normal speed, lower values slow speech, and higher values accelerate it. The advanced Video output section offers automatic, MP4, MKV, or WebM file type and automatic, H.264, or AV1 codec selection. Leave both on auto for a first run. Seed -1 randomizes each run; a fixed seed does not guarantee identical provider-generated video. This retained workflow uses the automatic avatar engine and script speech only. It has no audio upload, reference-image input, separate language selector, or selectable alternative engine or speech branch.
Playground AI's Playground v2.5 1024px
This repository contains a model that generates highly aesthetic images of resolution 1024x1024, as well as portrait and landscape aspect ratios.
Wan Animate 2: Motion transfer
Animate a reference character using motion from a driving video, with text control over appearance and background. For a first run, upload a character image and a driving video with similar subject framing. Replace the appearance example with a description of your character and desired background, and replace the motion example with the action shown in your video. Leave the sampling defaults and context windows off initially, then generate the animation. The default requests one 81-frame segment at 482 × 854 pixels, using the driving video's frame rate and audio when present. Width and height resize the driving frames; the reference image is resized to the resulting dimensions. Both use center cropping for aspect-ratio mismatches. Set one dimension to 0 to derive it from the other using the driving video's aspect ratio; do not set both to 0. The reference upload's alpha mask is not connected, so uploaded transparency is not preserved. Frame count sets the requested segment length. Motion strength and reference strength independently control their respective conditioning. Motion guidance start and end specify the sampling interval for pose guidance, with 0 to 1 covering the full process. Starting frame offsets motion conditioning, but the driving video's first frame still supplies its vision conditioning and the attached audio is not offset. Audio synchronization is therefore not guaranteed when changing the starting frame or removing the first generated frame. Optional context windows switch to temporal-window sampling. Window length, overlap, schedule, blending and FreeNoise apply only when enabled. Stride applies only to uniform schedules, and closed loop only to looped schedules; this toggle does not guarantee a seamless video loop. The schedule and blending fields retain source names without an enumerated list of alternatives, so use the defaults unless you know another value supported by the installed node. Advanced sampling retains the source's Lightx2v distillation LoRA at strength 1, six steps, lcm sampler, simple scheduler, CFG 1 and sampling shift 5. Seed -1 randomizes noise; a fixed nonnegative seed reuses its initialization. Denoise, added noise, latent batch size and LoRA strength remain available for advanced changes. Batch size defaults to 1; this Pipe returns one video rather than separate outputs for batch members. Output container and codec default to automatic selection, and CRF -1 uses the transport default. The delivered result is the generated animation. The source's side-by-side comparison is auxiliary, and its disconnected continuation stage does not extend this output.
Exploded view round trip
Create a combined video that animates from a recreated source image to a destination frame and back. Upload a source image and describe separated components in the destination prompt for an exploded view. Tune each direction's motion prompt, duration, camera and seed independently; keep both video stages' resolution and aspect ratio matched. Each clip defaults to four seconds, giving approximately eight seconds total. The combined video uses the outward clip's frame rate and audio only. Starting and destination frame settings remain independent.
