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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.
Negative Embedding for Deliberate
A negative prompt embedding for Deliberate V2. Use in your negative prompt to make the image look better. Do not use as a regular prompt.
Luma UNI-1 image edit
Edit an uploaded image with written instructions using Luma UNI-1. Upload a source image, replace the example grassland instruction with your desired changes, and describe any subjects or details you want to keep. For a first run, leave Model at uni-1-max, Style at auto and Web references off. Run the Pipe to receive an edited image preview. Choose uni-1 or uni-1-max as the editing model. Edit instructions must contain 1–6000 characters. Auto style follows your prompt. Manga applies a manga/anime aesthetic and requires a portrait aspect ratio of 2:3, 9:16, 1:2 or 1:3. There is no separate aspect-ratio control, so prepare a suitable portrait source before choosing manga. Enable Web references to let Luma search the web for visual references before generating. Leave Seed at -1 to randomize on each run. Seed controls re-execution, not reproducibility: even a fixed seed does not reproduce the same image. This Pipe sends the source image and editing request to the Luma partner API, requires configured provider access and uses provider-priced generation. It supplies one source image, with no additional reference-image or mask input. Uploaded alpha transparency is not preserved by the connected image path.
Nano Banana 2: Image edit
Edit one uploaded source image with written instructions using Nano Banana 2. Describe the changes you want and the details that should stay unchanged. For a first run, upload your image, replace the example instructions with edits suited to it, and keep aspect ratio at auto and resolution at 1K. Auto matches the source image's aspect ratio; select a specific ratio to request a different composition. The standard Nano Banana 2 model supports 1K, 2K and 4K output targets. Nano Banana 2 Lite supports only 1K, so set resolution to 1K before switching to Lite. Thinking level offers MINIMAL and HIGH. Temperature controls generation randomness, and nucleus sampling controls the breadth of sampling choices. Keep their existing defaults for a first run. Seed -1 randomizes each run; a fixed seed requests best-effort consistency, but identical results are not guaranteed, especially when other settings change. System instructions guide how the model interprets your request; the default directs it to produce an image. IMAGE requests image-only generation, while IMAGE+TEXT allows accompanying text. This Pipe delivers only the final image in either mode; generated text and intermediate thinking images are not connected to the output. The source loader supplies RGB image data without its alpha mask, so uploaded transparency is not preserved. Instructions to retain details express the desired edit rather than guaranteeing that those details remain identical.
Yoruichi Shihouin (Bleach) TI Embedding by Corneo
This is a textual inversion embedding for Yoruichi Shihouin from Bleach. Use corneoyoruichi to activate.
Yor Forger - Thorn Princess version - TI Embedding by Corneo
This is a textual inversion embedding for Yor Forger / Yor Briar from Spy x Family in her Thorn Princess assassin outfit.
pony_negative for PianoMix
Even if you don't specify all the troublesome prompts, you can get a certain result by specifying one negative prompt.
Lens Text to Image
Create one image or a batch from a text prompt using the fixed Lens model, Lens text encoder, and Flux 2 VAE. No uploaded image is required. For a first run, describe your subject, composition, lighting, and style in Positive prompt, or use the supplied flower example. Leave Negative prompt empty unless you have specific exclusions. Keep the square, 2-megapixel output and supplied sampling defaults: 20 steps, CFG 5, Euler, and the simple schedule. Seed -1 chooses fresh starting noise on each run; use a nonnegative seed to select a fixed starting noise pattern. Increase Image count to generate multiple images together. Aspect ratio and Target megapixels jointly determine the output dimensions. Dimensions are aligned to multiples of eight and shared by the latent canvas and model sampling setup. Megapixels is an approximate pixel budget rather than a fixed width or height. Larger sizes and batches require more memory. CFG controls prompt guidance, including the influence of negative conditioning. Advanced controls select the sampling algorithm, distribute noise levels across the requested steps, and adjust CFG normalization and resolution-dependent sampling shifts. Pre-CFG normalization applies normalization before CFG combination without clamping and can amplify predictions; the default post-CFG mode only attenuates them. Base shift and Maximum shift work together with the selected output dimensions. Leave Add noise enabled and Denoise at 1 for ordinary text-to-image generation. Lower Denoise uses a shortened noise schedule on an empty latent canvas; it does not preserve or transform an existing image. Disabling Add noise removes the seeded starting-noise initialization, so the seed no longer controls that initialization. All generated images are returned through one batch image output using Sweet Tea's output transport. The workflow does not permanently save images through the original SaveImage node. The model stack and dimension plumbing are fixed; this Pipe does not accept an input image.
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.
LongCat Image: Text to image
Generate an image or image batch from text with the fixed LongCat Image model setup. No uploaded image is required or accepted. For a first run, replace the positive prompt with your subject, composition, lighting and style. Review the negative prompt for conflicting exclusions: the supplied positive example requests hard lighting while the negative example excludes harsh lighting. Remove unwanted exclusions, or leave the negative prompt blank. Use square output at 1 megapixel, one image, 20 steps and the supplied guidance and sampling defaults, then run the Pipe. Aspect ratio and megapixels jointly determine output dimensions. At square proportions, 1 megapixel is approximately 1024 × 1024 pixels. Text such as “8K” in a prompt does not set resolution. Image count controls the batch size; larger batches and higher resolutions require more memory and processing. Advanced sampling controls include seed, steps, sampler, scheduler and sampler CFG. Seed -1 requests fresh random noise each run; a fixed seed lets you compare settings using the same initial noise. Positive and negative guidance are separate values attached to their respective conditioning branches, and neither is sampler CFG. Their source defaults are both 4. Guidance normalization exposes a separate strength setting, defaulting to 1. Normalize before CFG is off by default, using post-CFG attenuation only. Turning it on moves normalization before CFG combination without clamping and permits amplification. Keep denoise at 1 for full generation from the empty latent. Lower denoise values do not enable image editing. The diffusion model, text encoder and VAE are fixed in this Pipe; its selectable controls change text conditioning, output size, batch size and sampling rather than switching to an uploaded-image mode.
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.
Kling motion transfer
Animate a reference character image using movement and expression from a motion reference video, producing one video through Kling motion control. For a first run, upload a character image and a 3–30-second motion clip, preferably with similar aspect ratios. Leave Character orientation set to video, Keep original sound enabled, generation mode set to pro and model set to kling-v2-6. Replace the example studio-setting prompt with your desired setting, lighting and visual details, then run the Pipe. Character orientation determines which reference supplies the character's facing direction. In video mode, movement, expression, camera motion and orientation follow the reference video; the clip must be 3–30 seconds. In image mode, movement and expression still follow the video, while orientation follows the image and camera details come from the prompt; the clip must be 3–10 seconds. Prepare a clip within the selected mode's duration limit before uploading. Disable Keep original sound if you do not want reference-video audio retained. Generation mode offers pro and std, and Kling model offers kling-v2-6 and kling-v3. The supplied source does not explain their comparative behavior. This stage sends the references and prompt to the Kling provider and uses a provider-priced generation call. The advanced Video output section provides file type, codec and CRF controls. Keep auto for file type and codec and -1 for CRF on a first run. The supplied evidence does not specify container/codec compatibility or codec-specific CRF behavior. The selected output returns one video through Sweet Tea rather than using the original workflow's SaveVideo stage.
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.
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.
Qwen Image Layer Decomposition
Turn a source image into a batch of generated layer images, guided by an overall image description. Upload your image and replace the example description “guitar” with a description of its objects, appearance and arrangement, including relevant partly occluded elements. The description guides the whole decomposition; there are no individual layer prompts or controls assigning objects to particular layers. For a first run, keep the maximum input dimension at 640 pixels, request two layers and use one decomposition per run. Leave the negative prompt empty unless you want to discourage particular attributes. Keep the sampling defaults: 20 steps, CFG 2.5, Euler sampler, simple scheduler, denoise 1 and sampling shift 1. The maximum input dimension sets the reference image’s longest dimension in pixels; the other dimension follows its aspect ratio. Output dimensions derive from this resized reference rather than independent width and height controls. Resize method changes the interpolation applied before reference encoding. More layers, larger images or more decompositions per run increase processing demands. Multiple decompositions use the same source and description, and their generated layers are returned together as an image batch. Use seed -1 for randomization on each run, or a fixed seed to compare settings with the same sampling noise. CFG controls text-conditioning influence. Sampler and scheduler select the denoising algorithm and noise schedule; sampling shift separately adjusts the model sampling schedule. Denoise controls sampling from an empty layered latent, so lowering it is not a source-image preservation strength. Results are generated image layers rather than an editable layered document. Sweet Tea returns the output batch through its image preview transport; this workflow does not permanently save images through ComfyUI SaveImage. The source loader’s RGB image output supplies the reference; uploaded alpha masks are not connected or preserved as input masks. The model, text encoder and layered VAE remain fixed to the source configuration.
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.
