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Wan 2.1 VACE 14B1024x10248GB VRAM

Wan 2.6 Reference-to-video

Generate one video featuring two referenced characters using Wan 2.6. Upload one reference video for each character. Both uploads are required and feed the same generation stage. Describe your scene and motion in the scene prompt, using character1 and character2 to identify the corresponding uploaded characters. English and Chinese prompts are supported. You can describe desired dialogue and sound, but exact delivery is not guaranteed. For a first run, replace the example battle prompt with your scene and keep the default 720p landscape size and 5-second duration. Choose multi for multiple shots with cuts or single for one continuous shot, and write matching shot instructions in your prompt. Available durations are 5 and 10 seconds; keep the requested actions and shots practical for the chosen duration. Use the negative prompt for unwanted elements, or leave it empty. Output size selects resolution and aspect ratio together. AI watermark requests the provider's watermark. Seed -1 randomizes each run; a fixed seed controls generation randomization. Advanced video output settings select the file container and codec; leave both on auto for the first run. The result is returned to Sweet Tea as one video. This tool exposes no FPS control. Generation sends the references and prompt to the Wan provider and uses a paid provider call.

NewBie Exp0.1 Anime Image Generator

Generate anime illustrations from structured character tags and a prose scene caption using the fixed NewBie Image Exp0.1 model stack. Both text fields are inserted into a fixed positive prompt template; a separate negative prompt supplies negative conditioning. The result is an image or image batch returned through Sweet Tea's preview output, without permanent server-side saving. For a first run, keep the supplied character example and negative prompt, 1024 × 1536 dimensions, batch size 1, 20 steps, CFG 5.5, res_multistep sampler, simple scheduler, denoising amount 1 and sampling shift 6. Run the tool to generate the portrait example. No uploaded media is required. For another scene, edit both the structured prompt and scene caption so they describe the same subject, action, composition, background and lighting. Follow the example's XML-style tags and retain its character_1 structure and $character_1$ reference to match the fixed template. The template's character identifier and bounding box are fixed. Caption citation markers and Markdown are literal prompt text; the tool does not fetch references or render that formatting. The supplied negative prompt is encoded as text, not executed as an instruction. Width and height are pixels in multiples of 16. Batch size sets how many images are returned; larger dimensions and batches increase memory demand. Seed -1 chooses a new random seed each run. Use a fixed seed when comparing prompt or sampling changes. Advanced sampling settings expose steps, prompt guidance (CFG), sampler, scheduler, denoising amount and model sampling shift. Steps set the denoising iteration count; CFG changes conditioning strength. Sampler chooses the denoising algorithm, while scheduler and sampling shift affect its noise schedule. Denoising amount changes the sampled noise range; keep 1 for the supplied setup. Lowering it does not enable image-to-image generation. This tool generates from an empty latent. It has no image upload, caption generation, image-to-image mode or optional processing stages. Model selection and prompt assembly remain fixed.

Reskin a sprite and remove its background

Reimagine a sprite using an object description and a style reference, then remove the generated background. Upload both required images: the sprite whose composition and outline should guide the result, and an image showing the desired visual style. Replace the example object description with what you want to render, such as “copper gas lamp.” Gemini receives the sprite as image 1 and the style reference as image 2. For a first run, keep the supplied model, square aspect ratio, 1K resolution and random seed (-1). The default prompt requests the sprite's composition and outline, applies your object description and the reference style, and asks for a black background. Recraft background removal always processes the generated image before the result is returned. Outline preservation is a request to the model, not a guarantee of exact geometry or pixel alignment. Uploaded transparency is not carried through the reference-image paths. Choose either supplied Gemini model to change the generation model. Aspect ratio offers auto and the listed fixed ratios; auto follows the input image's aspect ratio, while another ratio can change framing. Resolution offers 1K, 2K and 4K; 2K and 4K use Gemini's native upscaler. Use -1 for a new random seed each run, or a fixed seed to request repeatability. Identical results are not guaranteed. For custom instructions, edit the advanced composition and style prompt fragments. The prompt joins composition instructions, object description and style instructions in that order without an added separator; preserve spaces or punctuation at their boundaries. The system instructions control Gemini's interpretation of the request. Editing these instructions does not bypass background removal; this interface has no removal bypass.

Topaz Bloom 2 image enhancement

Enhance an uploaded image with the workflow's fixed Topaz Bloom 2 preset and preview the enhanced result. For a first run, upload your source image and run the Pipe. The image upload is required and is the only editable control. The source configuration uses an empty prompt; its accompanying guidance says this lets the model create a prompt automatically. Prompt, seed, model selection and enhancement settings are fixed in this Pipe. No alternate modes or optional stages are exposed. Uploaded transparency is not preserved. Output scale and repeatable results are not established by the supplied source evidence. The selected output is an image preview; the original save node's PNG encoding settings do not apply to this output.

Create images from two references

Generate or edit an image with Gemini using two reference images and a written instruction. Upload both references, then explain which subjects, details or style to take from each. Both uploads are required and are sent together as context for one image request, rather than processed into separate outputs. For a first run, upload character and clothing references, describe how to combine them in a casual fashion pose, and keep the supplied 1:1 aspect ratio and 1K resolution. The references have no fixed character or clothing roles; you can request other compositions or edits. Choose an explicit aspect ratio for the output shape. Auto asks Gemini to match the input image proportions; an explicit ratio avoids ambiguity when the references differ. Resolution selects 1K, 2K or 4K; 2K and 4K use Gemini's native upscaler. The model selector offers Gemini 3 Pro Image Preview and Nano Banana 2 (Gemini 3.1 Flash Image). System instructions set foundational behavior; the supplied instructions prioritize interpreting the prompt visually and producing an image. Keep IMAGE for image-only responses, or select IMAGE+TEXT to request accompanying text from Gemini. This Pipe returns only generated images, so accompanying text is not delivered. Use seed -1 for a new random seed each run. Fixed seeds request best-effort consistency, not identical results. Reference details are interpreted by the model, so exact identity or garment reproduction is not guaranteed. Uploaded alpha masks are not connected, so reference transparency is not preserved. This workflow sends the prompt and reference images to Gemini through a paid provider request. Generated images are returned to Sweet Tea through an output transport that does not permanently save them through the workflow.

Wan 2.1 video outpainting

Expand an uploaded video's canvas with generated surroundings using Wan 2.1 VACE, and export an H.264 MP4 with source audio when available. For a first run, upload a continuous scene and enter an outpainting prompt describing both the existing scene and the surroundings to add. Keep both aspect-ratio controls on Horizontal and retain the other defaults. If you change the canvas shape, set Aspect ratio and Continuation aspect ratio to the same choice: their generated batches must have matching dimensions. Both stages use the fixed I2V720p/HQ resolution preset. The input is resampled to 24 frames per second. Initial source size and Continuation source size set the original content's size in megapixels before padding to the target canvas. Smaller source frames can leave more space for generated surroundings; use matching sizes across stages for consistent framing. Each stage has its own mask-feathering and VACE control-strength settings. Control strength adjusts guidance from the padded source frames and masks. The workflow generates an initial 81-frame batch followed by three fixed continuation iterations. Each continuation requests 81 generated frames, carries the previous generated batch's last frame as a reference, and advances the source slice by 81 frames. The continuation source slices request up to frame index 405, approximately 16.9 seconds at 24 FPS. Supply enough continuous footage to cover those requests. This tool does not adapt its iteration count to the uploaded clip's length. The source's value labeled Length in Seconds controls loop arithmetic rather than exact playback duration and is not an editable duration control. Continuation slices request more conditioning frames than each generated batch; the supplied evidence does not establish precisely how excess conditioning frames are handled. Both stages share the prompt, negative prompt, seed, and CausVid LoRA. Seed -1 randomizes each run; a fixed seed reuses the shared noise seed. Initial and continuation sampling controls are independent. The supplied recipe uses CausVid strength 1, three Euler steps, normal scheduling, CFG 1, and denoise 1 in both stages. Keep these defaults for the first run; changes to LoRA strength affect both stages. Compression controls H.264 CRF: lower values retain more detail and generally create larger files. Trim to audio is enabled by default and can shorten the encoded video to the source audio's length. Disable it to retain generated frames beyond shorter audio. Playback remains fixed at 24 FPS. Outpainting synthesizes content and can alter the original region or show transitions between batches. Scene cuts can conflict with the previous-frame continuation reference. That reference remains part of the workflow and has no supported disable switch.

ACE-Step1024x10248GB VRAM

Boogu 0.1 image edit · Int8

Create edited images from one uploaded reference and natural-language instructions using the fixed Boogu Image Edit convrot Int8 model. Describe both the changes you want and anything that should stay unchanged, such as the subject, clothing or layout. The results appear as an edited-image batch in Sweet Tea. For a first run, upload your source image, replace the example editing instruction, leave Negative prompt blank, and keep Output megapixels at 1, Variants at 1 and the default sampling settings. Negative prompt describes features to discourage. Output size measures a resized copy of the source to derive output dimensions at the requested megapixel count while retaining its aspect ratio. The original uploaded image remains the editing reference. Bypass Output size to derive dimensions directly from the uploaded image instead; Output megapixels then has no effect. Final dimensions may be rounded by latent sizing. Variants controls how many edited images are generated from the same reference and instruction. Larger resolutions and batches require more memory. Seed -1 randomizes each run; use a fixed seed to reuse the initial noise with otherwise unchanged settings. Advanced controls include CFG, steps, sampler and scheduler. Sampling schedule fraction uses only part of the noise schedule when below 1; it is not an image-preservation strength. Keep Add noise enabled and the schedule fraction at 1 for the intended operation. Schedule shift affects the scheduler's noise-level calculation, while the sampler receives the original loaded model. Keep the source defaults initially. This tool generates from an empty latent conditioned on the reference, rather than directly denoising the uploaded pixels. Exact identity and layout preservation are not guaranteed. Only the loader's image output is connected; the uploaded alpha mask is unused, so uploaded transparency is not preserved. The selected output uses Sweet Tea preview transport rather than the original file-saving stage; the original PNG export settings do not apply.

ACE-Step music remix

Remix an uploaded music track with ACE-Step using style tags and lyrics, then export the result as MP3. For a first run, upload the audio excerpt you want to remix, replace the example style tags and lyrics with your intended direction, and keep the sampling defaults, including remix strength 0.3. Describe genre, instruments, mood, tempo and vocal character with comma-separated tags. Enter intended sung words with section labels and line breaks. Lyrics and tags guide generation; they do not guarantee exact words or performance. Remix strength ranges from 0 to 1: lower values retain more source structure, while higher values permit larger changes. Lyrics strength adjusts lyric conditioning, and CFG controls overall text guidance. The uploaded audio supplies the starting latent and temporal extent. Prepare your excerpt before uploading: this tool has no trimming, independent duration control or separate text-to-music mode. Negative conditioning is zeroed internally, with no editable negative prompt. Use seed -1 for random variations or a fixed nonnegative seed to compare settings. Steps, sampler and scheduler control denoising. Advanced latent tonemap multiplier and sampling shift controls change internal guidance and model sampling; retain their source defaults of 1 and 5 for a first run. Sampling shift is not an audio timing offset. MP3 quality offers V0 variable bitrate, 128k and 320k. This controls file encoding rather than the generated composition. For multilingual lyrics, the supplied source guidance calls for manual romanization and language markers such as [zh], [ja] or [ko]; automatic transliteration is absent. The workflow requires ace_step_v1_3.5b.safetensors in the ComfyUI checkpoints model directory. The supplied model source is https://huggingface.co/Comfy-Org/ACE-Step_ComfyUI_repackaged/resolve/main/all_in_one/ace_step_v1_3.5b.safetensors?download=true. The supplied dependency catalog has not resolved this checkpoint.

Anima masked image generation

Generate images from a prompt using Anima's LLLite image-and-mask guidance. Each run returns the generated image batch and a preview of the guidance mask. Before your first run, prepare a Painter PNG asset reference accessible to the execution runtime. Painted mask is a required text reference, not a PNG upload or an interactive painting editor. This Pipe does not create that asset for you. Enter the reference, upload a source image, replace the example prompt with your desired result, and start with the 1024 × 1024 output, one variant and Turbo enabled. Inspect the returned mask preview alongside the generated images. To work without a source image, disable the Source image stage. Painter then uses its blank-canvas width, height and background color; the Painter asset reference is still required. Leaving the upload empty while the stage remains enabled does not select blank-canvas mode. Canvas dimensions and generated output dimensions are independent controls. Prepare compatible image, mask and output sizes before running; the disconnected source resize node does not resize uploads. Turbo mode selects the Turbo LoRA and its own steps and CFG controls, initially 8 steps and CFG 1. Disabling Turbo selects the model without that LoRA and the standard controls, initially 30 steps and CFG 4. Both modes retain image-and-mask guidance. Turbo LoRA strength affects the Turbo branch only. Control strength scales the inpainting patch's influence, while Guidance start and Guidance end specify fractions of sampling from 0 to 1; keep start at or below end. Advanced controls include the negative prompt, separate steps and CFG for each mode, Turbo LoRA strength, sampler, scheduler and denoise. Seed -1 requests fresh random noise; a fixed seed reuses the initial noise. Width and height specify generated pixels in multiples of 8, and Variants sets the image count. Generation starts from an empty latent rather than an encoded source image. There is no final compositing stage to restore pixels outside the mask, so exact preservation of unedited regions and uploaded transparency is unsupported. Lowering denoise does not provide source-image preservation. The runtime needs anima-base-v1.0.safetensors in diffusion_models, anima-lllite-inpainting-v2.safetensors in model_patches, qwen_3_06b_base.safetensors in text_encoders, and qwen_image_vae.safetensors in vae. Turbo mode additionally selects anima-turbo-lora-v0.2.safetensors in loras. Source installation references are the circlestone-labs/Anima, Comfy-Org/Anima-LLLite and circlestone-labs/Anima-Official-LoRAs repositories on Hugging Face. The runtime must also provide the workflow's node implementations and Sweet Tea image output transport.

Anima1024x10248GB VRAM

Kandinsky 5 Lite text to image

Generate an image or image batch from text with the fixed Kandinsky 5 Lite model. Describe the subject, composition, lighting, colors, and style in Positive prompt. Negative prompt can describe unwanted elements or remain blank. No uploaded image is required or supported. For a first run, use the supplied hiker prompt or replace it, keep Width and Height at 1024 pixels and Image count at 1, and leave the advanced settings at 50 steps, prompt guidance 3.5, Euler sampler, Simple scheduler, Schedule shift 3, and Denoise strength 1. Run the tool to receive the generated image batch. The output transport replaces ComfyUI SaveImage and does not permanently save images through that node. Width and Height use increments of 8; adjust them together to set the aspect ratio. Image count controls how many images are generated together. Larger dimensions and batches increase resource use. Advanced Sampling controls change randomness, sampling effort, prompt guidance, and the algorithm and noise schedule used to generate images. Seed -1 selects fresh randomness on each run; use a fixed seed when comparing settings. Schedule shift changes the model's timestep distribution. Denoise strength remains at 1 for the supplied setup because sampling starts from an empty latent. Reducing it does not provide image preservation or an image-to-image mode. The setup uses kandinsky5lite_t2i.safetensors, qwen_2.5_vl_7b_fp8_scaled.safetensors, clip_l.safetensors, and ae.safetensors, plus the Sweet Tea output node pack. These assets must be available in the execution environment. Model and encoder choices are fixed in this tool.

Hoshimachi Suisei 星街すいせい / Hololive

sui1, 1girl, solo, side ponytail, hoshimachi suisei, fingerless gloves, single thighhigh, jewelry, thigh strap, bracelet, buttons, plaid dress, blue choker, blue belt, plaid skirt, mini crown, grey skirt, blue ascot,...

Edit video with Runway Aleph 2

Edit existing footage with a text description using Runway Aleph 2. For a first run, upload a source video lasting 2–30 seconds at 30 fps or lower. Replace the example surfing prompt with an English description of what should appear in the result, using 1–1000 characters. Describe the desired changes to subjects, objects, surroundings, lighting, or visual style, then run the tool and inspect the returned video. Leave Seed at -1 to randomize each run, or enter a fixed seed to reuse the generation seed. Public figure moderation offers auto and low settings for recognizable public figures; the template defaults to low. Advanced Video output controls select the output container, codec, and encoding quality. Leave their defaults for a first run. These controls change output encoding, not the requested scene edit. This tool supports text guidance only; the source template's image-guidance and keyframe branches are disconnected from generation. There are no separate output-size, duration, or frame-rate controls. Inspect the result for unintended changes, since the graph does not enforce preservation of untouched details. The source template specifies a logged-in ComfyUI comfy.org API account for the Runway partner node.

TemporalDiff Motion Module

This model was created by CiaraRowles , posted to Civitai.com with permission. Basic Getting Started Tutorial available at the Civitai Education Hub !

Atomix Wan I2V Workflow — Wan 2.2

A Simple Wan I2V workflow

Image to imageWan 2.1

LTX-Video 2.31024x10248GB VRAM

LTX 2.3 video with generated audio

Generate a video with audio from a text description using LTX 2.3. No image, video, or audio upload is required. For a first run, replace the example scene and sound prompt with your subject, setting, action, camera movement, dialogue, and sounds. Review the negative prompt, choose the requested width and height in pixels, and enter a positive duration in whole seconds. Keep the advanced sampling and encoding defaults for the first run. The source defaults request a 900 × 1600 portrait video lasting approximately 20 seconds at 24 FPS. Initial sampling jointly generates video and audio at half the requested spatial size. A fixed 2× latent upscaler feeds a second sampling stage, whose video is decoded for the final result. The returned soundtrack is decoded from the first stage; refinement settings do not replace it. Actual dimensions and duration can differ from the request because of latent spatial and frame-count constraints. Playback FPS controls playback, audio timing, and duration-to-frame conversion. Conditioning FPS is a separate control used by both sampling stages: change both fields together and keep them equal. Use playback FPS within the downstream video's supported 1–120 range. Each sampling stage has its own seed, CFG, sampler, and editable manual noise schedule in advanced settings. Seed -1 randomizes that stage; a fixed nonnegative seed reuses its noise. To reuse both stages' noise, set both seeds. The schedules define sampling intervals directly, so there is no separate steps control. Negative conditioning is connected to both stages, and its effect depends on their CFG settings. The optional fixed distillation LoRA starts disabled, matching the source. Enable its stage to make the LoRA strength operative. The base quantized model, text encoders, VAEs, 2× upscaler, and memory patches are fixed implementation choices. Output settings select the container, codec, bit depth, and compression quality. Start with MP4, automatic codec, 8-bit precision, and automatic CRF (-1). Container and codec must be compatible; 10-bit output also requires compatible encoding and playback. The source's 8 GB hardware claim is not a verified runtime guarantee. Dependency evidence is incomplete: the inventory lists an older upscaler than node 23 selects, and node 26's first text encoder filename has a doubled .safetensors suffix. The graph preserves those source model selections.

Portrait relighting

Generate a relit portrait from a reference image and written lighting directions. For a first run, upload a portrait, keep the default dual rim prompt, leave aspect ratio on auto and resolution at 1K, then run. The prompt requests identity preservation, but this is generative editing: likeness, details and composition can change. Use one editable prompt to explore the five lighting recipes in the source workflow. With a reference, begin with “Maintain the identity of input image 1, modify the scene lighting and shadows to match the following lighting style:” and append one of these recipes: - Dual rim: no front key, twin hard lights from behind left and right, subject silhouetted at center, glowing edge separation on both sides, dramatic dark fill. - Overhead: hard top-down overhead key, harsh light directly above, deep under-eye and jaw shadows, pronounced brow ridge highlight, strong nose shadow pointing straight down, theatrical and sculpted. - Split: hard side key at 90 degrees, one half of the face fully lit, the other half near-black, sharp nose shadow line, minimal fill. - Rembrandt: side key slightly above, deep falloff, subtle rim separation. - Motivated practical: off-axis kicker mimicking an environmental source, strong directional spill, naturalistic shadow logic, atmospheric fill. These are editable prompt recipes within one tool, rather than separate operations or simultaneous outputs. Change the directions freely to request another lighting arrangement. Bypass Source image to generate a new portrait without a reference. In that mode, replace the identity-preservation wording with a description of the subject and desired scene, and choose an explicit aspect ratio. Auto matches the reference aspect ratio when an image is supplied. Resolution offers 1K, 2K and 4K; the node uses Gemini's native upscaler for 2K and 4K. Model selects between the two source-supported Gemini image models. Seed defaults to a fresh random value per run; a fixed value requests best-effort consistency, not deterministic reproduction. Response mode controls whether the provider also generates text, but this Pipe delivers the image only because the source text output is unconnected. Generation instructions provide advanced behavioral guidance; retain instructions to produce an image when editing them.

1024x10248GB VRAM