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Wan 2.7 video edit
Edit a source video using text instructions and two reference images, and return one edited video. Upload the video and both reference images. Reference image 1 connects to image1; reference image 2 connects to image2. Use those names in your instructions to explain how each reference should affect the edit. Both image uploads are required by this saved Pipe. For a first run with the default instructions, upload a clip of two people playing chess, a bear as reference image 1 and a dog as reference image 2. For other footage, rewrite the instructions to describe the changes and details to retain. Requests to preserve the scene or movement guide the model; they do not guarantee unchanged pixels or motion. Start with the defaults: 720P, 16:9 and duration auto. Choose 720P or 1080P and the aspect ratio for your intended output. Duration auto matches the input video duration; choosing 2–10 seconds truncates from the beginning of the source video. Audio mode auto lets the model decide whether to regenerate audio based on your instructions. Include desired sounds in the instructions when using auto. Choose origin to preserve the input audio; requests for new sounds should then not be expected to take effect. Seed -1 randomizes each run. A nonnegative seed controls generation variation. Enable Watermark to request the provider's AI-generated watermark. Under Video encoding, leave file type and codec on auto for automatic selection. Supported file types are mp4, mkv and webm; supported codecs are h264 and av1. This Pipe uses the Wan 2.7 partner API and requires configured provider access. Uploaded video, reference images and instructions are sent to the provider, and generation uses a provider-priced API call.
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
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Bria: Remove image background
Remove an uploaded image’s background using Bria RMBG 2.0 and preview the resulting image in Sweet Tea. For a first run, upload your source image and run the Pipe with the default settings. No prompt or sampling setup is required. The workflow uses a ComfyUI partner API node and requires the corresponding authenticated service access. Advanced controls let you enable the provider’s moderation mode, which is disabled by default, and control provider re-runs with the re-run seed. Leave the seed at -1 to request a fresh run each time. The seed does not make results reproducible; provider results are non-deterministic. Existing uploaded transparency is not preserved through the source connection: only the loader’s image output is sent to Bria, and its separate alpha mask is not connected. The selected output returns an image preview rather than permanently saving an image through the workflow.
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.
SD 3.5 Image Generator
Generate images from text prompts using Stable Diffusion 3.5. Describe your subject and scene in the positive prompt, and use the negative prompt for elements to exclude. Set the image width, height and batch size. Advanced sampling controls include seed, steps, guidance, sampler, scheduler and denoise strength. The model is fixed.
LTX-2.3 first and last frame video
Generate a video with audio between two endpoint images, guided by a motion and audio prompt, using the fixed LTX-2.3 distilled model. For a first run, upload both a first frame and a last frame. Replace the example prompt with the action and camera movement connecting your images, and include desired music, sound effects, or dialogue in the same prompt. Keep the defaults of 1280 × 720, approximately 5 seconds, 25 FPS, and endpoint strengths of 0.7. Run the tool and review the returned video with generated audio. Both images are required; there is no exposed silent-output mode or audio upload. Width and height resize both images, and the first resized image determines the video canvas. Use matching aspect ratios and compatible compositions to reduce unwanted cropping. Each image has independent advanced fitting and resampling settings: center fitting preserves aspect ratio by cropping, while disabled fitting stretches to the requested dimensions. Area resampling suits downscaling, lanczos suits upscaling, and nearest-exact suits hard pixel edges. Uploaded transparency is not retained because only the loaders' RGB image outputs are connected. Endpoint strengths independently control each image's conditioning influence. The first guide is fixed at the beginning and the last guide at the end; these guide positions are not exposed controls. Endpoint guidance does not guarantee exact copied frames. Advanced preprocessing controls apply compression independently to each resized image before conditioning; both default to 25 on a 0–100 scale. Use positive dimensions of at least 64 pixels. The downstream latent schema declares a sizing step of 32, although the retained source height is 720; exact spatial alignment behavior is not established by the supplied evidence. Duration is requested in positive whole seconds. FPS controls generation conditioning, audio timing, and playback; use 1–120. The graph requests duration × FPS + 1 frames for video and audio, and this count must stay at or below the audio latent limit of 1000. Temporal alignment can change the final duration. Larger canvases and longer clips require more memory and computation. Keep CFG at 1 for the first run. At this value, negative conditioning does not contribute through standard classifier-free guidance. When changing CFG, review the negative prompt's scene-specific exclusions and replace them to suit your images. Seed -1 randomizes each run; a fixed seed reuses the initial generation noise. Ancestral noise defaults to zero; its separate noise scale becomes relevant when ancestral noise is enabled. The advanced sampling schedule contains nine comma-separated noise levels defining eight intervals. Editing it changes the sampling trajectory and interval count; there is no independent steps control. Keep the supplied distilled-model schedule for a first run. Advanced output settings offer 8-bit or 10-bit video, container selection, and codec selection. Use 8-bit and automatic container and codec selection initially. The source notes that 10-bit can retain smoother gradients, but some players and downstream tools may not support it. Explicit container and codec choices must be compatible. Encoding choices do not disable generated audio.
Create an architectural blueprint
Generate a conceptual architectural blueprint image from a building photo or rendering with Gemini. For a first run, upload one building image that clearly shows its form, keep the default blueprint prompt, and use the default 16:9 aspect ratio and 1K resolution. The prompt requests plan, elevation, and section views. Edit it to request different views, annotations, sheet layouts, or drawing styles. Aspect ratio controls the blueprint sheet's proportions; auto follows the uploaded image's aspect ratio. Resolution selects a 1K, 2K, or 4K target, with Gemini's native upscaler used for 2K and 4K. Seed -1 randomizes each run. A fixed seed requests best-effort consistency, but identical results are not guaranteed. Model selects Gemini 3 Pro Image Preview or Nano Banana 2 (Gemini 3.1 Flash Image). Changing the model can change the generated interpretation. Generation instructions are optional foundational instructions, separate from the drawing request; keep their default for a first run. This tool requires an uploaded building image and returns only the generated blueprint image. It does not create the family's miniature scene or return a text response. Unseen interiors, dimensions, annotations, and structural details may be invented. Use the result for concept presentation rather than measured construction documentation; higher resolution does not establish architectural accuracy.
Hunyuan Video 1.5 Image to Video
Animate an uploaded image into a silent video with Hunyuan Video 1.5. For a first run, upload a starting image and enter a motion prompt describing the subject's action, atmosphere and camera movement. Leave the negative prompt empty unless you want to discourage particular content or artifacts. Keep the initial settings of 1280 × 720, 121 frames, batch size 1 and 24 FPS to return a clip lasting about 5.04 seconds. The image feeds both starting-frame conditioning and a separate visual-reference encoder. Visual reference crop offers center or none and affects only that encoder; it does not set output dimensions. Uploaded alpha transparency is not preserved by this route. Width and height set generated dimensions in pixels. Frame count changes how many frames are generated; playback FPS changes their speed and duration without adding frames. Duration is frame count divided by FPS. The frame-count control advances in increments of four from one. Batch size controls latent initialization; the Pipe returns one video rather than separate clips for batch members. Advanced controls retain the source's sampling choices: 20 steps, simple scheduler, euler sampler, CFG 6, denoise 1 and sampling shift 7. Seed -1 selects fresh random noise; a fixed nonnegative seed reuses initial noise. Denoise changes the sampling schedule rather than directly specifying how much of the uploaded image to preserve. The optional sampling cache is bypassed by default and can be enabled with its saved reuse settings. Choose the returned video's container and codec in Video output. Encoding quality uses -1 for the transport default and affects compression rather than generation. Video bit depth defaults to 8; 10-bit may retain smoother gradients but has player and downstream compatibility limitations. This Pipe returns the base-video route. The source's disabled 1080p refinement branch is not included, and there is no audio input.
Clutter
Fill your background with a lot of stuff with a quick prompt. Gives any scene that "lived in" look and feel.
Seedream layer separation
Separate an uploaded image into requested visual layers with generated transparency. Upload a source image and describe which elements should belong together, such as a character, background, lettering, or decorative graphics. The connected image outputs are combined with their corresponding generated masks and returned as one image batch. For a first run, replace the example layer instruction with elements present in your image, keep output resolution at 1K, and leave Crop layers off. The instruction requests layer organization; it does not guarantee a fixed number of output images. Enable Crop layers to request cropped layers, or leave it off to request the uncropped layout for compositing. Output resolution offers auto, 1K, 1.5K, and 2K; independent width and height controls are unavailable. Prompt optimization offers the provider's standard and fast modes. Watermark enables the provider's watermark option. Seed -1 requests a fresh randomized seed on each run; a fixed seed is supplied to the provider without guaranteeing identical results. Transparency comes from the generated separation masks. The source image's alpha-mask output is not connected, so uploaded transparency is not preserved as an input mask. Results are returned through Sweet Tea's image output; this graph does not permanently save PNG files.
Grok image edit
Edit a source image with a written instruction using Grok. For a first run, upload one source image, describe the changes you want and the features to retain, and keep the supplied defaults: grok-imagine-image, 1K resolution, one result and request seed -1. The example instruction requests anime styling while retaining identity. The output returns the edited images. Model selects among grok-imagine-image, grok-imagine-image-quality and grok-imagine-image-pro. Resolution selects a 1K or 2K output request. Image count requests 1 to 10 edited results from the same reference and instruction. Request seed controls whether the editing request reruns. Keep -1 for randomization or change a nonnegative seed to trigger another request. Results remain nondeterministic even with a fixed seed. Aspect ratio remains automatic. The source allows manual aspect ratios only with multiple connected reference images, while this tool connects one. Increasing image count changes the number of results, not the number of references. A source image is required; this tool has no text-only mode, mask-based editing input or denoise-strength control. Uploaded alpha transparency is not preserved because only the loader's RGB image output is connected.
Minimax H3 INT8/INT6/INT4 ConvRot
Minimax H3 INT8 / INT6 / INT4 ConvRot
