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Recraft Crisp Upscale

Upload a source image and run to receive the Recraft Crisp Upscale result. Prepare a file no larger than 5 MB, with width and height each no larger than 2048 pixels, as specified by the source template. This tool has one fixed upscaling mode and exposes no prompt, scale factor or enhancement settings. Uploaded transparency is not preserved because only the image loader's RGB output reaches the upscaler. The image is sent to Recraft for processing through a paid provider call.

Not Just Another Pretty Face

Any resemblance to actual persons, living or dead, is purely coincidental. This work is a product of fiction and any similarities to real individuals are entirely unintentional.

TextualInversionSD 1.5

ACE-Step1024x10248GB VRAM

Gemini image refinement and generation

Refine or edit an uploaded image with Gemini, or generate an image from a prompt, at a target resolution of 1K, 2K or 4K. For a first refinement run, upload a Source image, leave that stage enabled, and use the default prompt and 4K resolution. The prompt requests detail refinement while preserving text and composition. Replace it with specific editing instructions and describe what should remain unchanged. For text-only generation, bypass Source image and replace the editing prompt with a description of the image you want. Auto aspect ratio follows the reference image when supplied; choose an explicit ratio to specify the proportions when generating without a reference. Resolution selects the target output size; the source describes 2K and 4K as using Gemini's native upscaler. Model selects Gemini 3 Pro Image or Nano Banana 2 (Gemini 3.1 Flash Image). Seed -1 randomizes each run; a fixed seed requests best-effort repeatability, without guaranteeing identical results. System instructions set foundational behavior and default to prioritizing image creation. Response format requests IMAGE or IMAGE+TEXT, but this Pipe exposes only the generated image. Refinement is generative: text, textures and composition can change despite preservation instructions. Uploaded transparency is not preserved by the connected RGB image path. The generated image is returned through Sweet Tea's preview transport rather than a permanent SaveImage stage. Prompts and any reference image are sent to Gemini through a paid provider call.

Replace video background

Replace a video's background with a supplied image using Bria and receive one processed video. For a first run, upload the foreground video under Source video and a replacement image under Background image. Keep Background image enabled and leave File type and Codec on Auto, then run the Pipe. Background image is optional and bypassable. Bypassing it omits the replacement image; the supplied provider definition does not specify the resulting appearance. This Pipe has no replacement background video input. Uploaded image transparency is not carried through the connected RGB image output. Under Background replacement, Seed defaults to -1 to randomize on each run. Seed controls provider re-execution; results remain non-deterministic even with a fixed seed. Under Video output, File type offers Auto, MP4, MKV and WebM; Codec offers Auto, H.264 and AV1. Keep Auto for a first run, or choose a compatible container and codec together. The preserved hidden CRF control lacks documented semantics in the supplied evidence.

Subject and product composition

Generate a scene featuring a subject and product from two reference images using Gemini. Upload a subject image and a product image. In the scene prompt, image 1 means the subject and image 2 means the product. For a first run, keep the supplied prompt to request the subject holding the product with its logo visible, leave the other defaults in place, and run the Pipe. Edit the prompt to describe the interaction, setting, lighting, style and details to retain. Choose the output aspect ratio and resolution. Auto follows the reference aspect ratio; the product reference is resized and padded to match the subject image dimensions before both images are sent to Gemini. The 2K and 4K settings use Gemini's native upscaler. In Product reference preparation, padding selects white or black borders around the resized product, and interpolation selects its pixel resampling method. These controls affect the reference image rather than the generated scene background or final image size. Model selects either the supplied Gemini Pro image model or Nano Banana 2 (Gemini 3.1 Flash Image). Keep the supplied Generation instructions initially; edit them to change the provider's general behavior alongside the scene prompt. Provider response offers IMAGE or IMAGE+TEXT, but this Pipe returns only generated images through its image preview output; accompanying provider text is not returned. Use seed -1 for a new random seed each run. A fixed seed requests best-effort repeatability but does not guarantee identical results. Subject identity, product details and logo fidelity are requests rather than guarantees. Uploaded alpha masks are not connected, so uploaded transparency is not preserved through the reference path. Both reference images and the generation instructions are sent to Gemini for a provider-priced generation request.

Image grid

Combine two to four uploaded images into one collage. With all four inputs enabled and the default directions, the first pair forms the top row and the second pair forms the bottom row. For a first run, upload all four images and keep the defaults for a 2048 × 2048 output without gaps. Top-left and bottom-left images are required. Top-right and bottom-right are independently optional: disable their input stages when omitting them. With default directions, disabling both right inputs produces two images stacked vertically; disabling only one produces rows with different image counts. Open the advanced layout stages to change each pair's direction and the direction used to join the completed pairs. Each direction places the second image or pair relative to the first: right, down, left or up. Input position labels describe the default grid; changing directions changes their final positions. Size matching is enabled independently for each pair and for the final assembly. Gap widths and colors are also independent at those three joins. Gap widths are measured in pixels before the final resize, so resizing changes their final displayed size. A pair's direction, size matching and gap controls have no effect when its optional second image is disabled. Set the final width and height in pixels. Center cropping preserves proportions but can cut off collage content when its aspect ratio differs from the requested dimensions. Select disabled cropping to stretch instead, or set one dimension to zero to calculate it from the other while preserving aspect ratio. Keep at least one dimension greater than zero. Resampling controls pixel interpolation during this final resize. The result is one image returned through the Pipe output. Uploaded alpha masks are not connected, so uploaded transparency is not preserved.

Seedream 4 reference image generation

Create or edit images with Seedream 4 from one reference image and a text prompt, with an optional related-image mode. For a first run, upload your reference image and replace the example prompt with the result or edits you want. Describe which reference details should remain. Keep Output size at 2048x2048 (1:1) and Related images disabled to request one square image, then run the Pipe. This workflow requires one upload and does not provide additional reference inputs. Uploaded transparency is not preserved as an alpha mask. Choose an output size preset, or select Custom to use Width and Height in pixels. Custom width accepts even values from 1024 to 6240; custom height accepts even values from 1024 to 4992. These fields affect generation only when Custom is selected. For related scenes or variations, describe them in the prompt, set Related images to auto, and increase Maximum images. The model decides how many images to generate; Maximum images is a ceiling rather than an exact count. Input and generated images together cannot exceed 15, so with this one reference image keep Maximum images at 14 or below. Seed -1 randomizes each run; a fixed seed controls generation randomization. Enable Watermark to add an “AI generated” mark. Fail on partial results is enabled by default and aborts when requested images are missing or return an error; disable it to accept available partial results. Generation sends your reference image and prompt to ByteDance and uses a paid provider call.

ACE-Step song generator

Generate an MP3 song from music style tags and lyrics. Style tags describe the genre, instruments, vocals, tempo, and mood. Lyrics provide the sung text, with lyric strength available for tuning their influence. Choose the duration and number of variations; the default duration is 120 seconds. Advanced controls expose sampling, latent guidance, and MP3 encoding quality. Negative conditioning is automatically zeroed. For multilingual lyrics, the source notes require manual romanization and language markers such as [zh], [ja], or [ko].

UpscalingWan 2.21024x10248GB VRAM

Edit a photo and animate it

Edit a reference photo with FLUX Kontext, then animate the edited image with Wan 2.2. Each run returns an animated video and an edited starting-image preview. For a first run, upload a photo with a clearly visible subject. Replace or adapt Image prompt to describe the appearance, setting and visual style you want: the saved example requests a navy business suit in a modern office. Keep Motion prompt at its saved small-movement request, or describe the subject movement and camera behavior you want. Then run the Pipe and review both outputs. Image prompt controls the Kontext edit; Motion prompt controls the subsequent animation. In the advanced section, Video negative prompt discourages unwanted video content and movement. Its saved text combines Chinese visual-quality exclusions with English exclusions for fast movement, mouth movement, talking, visible teeth and camera zooms. Remove conflicting exclusions when requesting those actions. The negative prompt does not affect the image edit. Every run performs the image edit before animation. This interface has no separate animation-only or image-only mode. Video dimensions and seed come from the image-generation pipeline. Size, seed, duration, frame rate, sampling, Lightning acceleration, LoRA and enhancement settings remain at their saved workflow configurations and cannot be adjusted here. The workflow uses ComfyUI-UmeAiRT-Toolkit with the FLUX/Kontext and WAN_2.2/I2V model bundles. The broader source README describes additional configurations and toggles; those are not selectable modes in this Pipe.

HunyuanVideo1024x10248GB VRAM

Capybara: Image to Video

Create one silent video from an uploaded image using the fixed Capybara v0.1 model. The image supplies both the resized start frame and a separate visual reference. For a first run, upload an image and replace the example positive prompt with scene details, subject motion and camera movement appropriate to your image. Keep 640 × 640 pixels, 81 frames and 24 FPS for approximately 3.4 seconds of playback. Use positive dimensions divisible by 16 and frame counts of 4n + 1, such as 49 or 81. Larger dimensions and more frames increase generation work. The start image is resized to fit the output dimensions and padded when its aspect ratio differs. Padding color selects white or black borders; resize interpolation controls resampling. Reference crop selects center or none for the separate visual-reference encoding and does not change start-frame padding. Only the uploaded RGB image is connected; uploaded transparency is not preserved. Advanced controls include the negative prompt, seed, CFG, sampling settings and image preparation. Seed -1 randomizes each run; a fixed nonnegative seed reuses the initial noise. CFG controls the contrast between positive and negative conditioning. Begin with the existing sampling defaults: 20 steps, simple scheduler, Euler sampler, denoise 1 and model shift 7. Denoise changes the sampling schedule rather than directly controlling source-image preservation. Model shift changes the sampling model's noise-time behavior. FPS changes playback duration without changing the generated frame count. Choose 8-bit video for compatibility or 10-bit for smoother gradients where encoding and playback support it. File type and codec control the returned video's container and encoding; begin with auto for both, and use compatible combinations when selecting them manually. One video is returned through Sweet Tea's preview output, without a server-side save stage. No audio is connected. Batch size remains one in the normal interface because decoded frames are assembled into a single video rather than exported as separate variation clips.

Create a miniature architectural scene

Create an image of a miniature architectural model in a setting you describe. Upload a building photo or rendering; this Pipe generates a conceptual blueprint, then supplies both that blueprint and the original building image to the scene generator. It returns only the final scene image. Use the separate architectural blueprint tool for a standalone sheet. For a first run, upload your building image, edit the scene prompt to match its appearance, and keep the default 16:9 framing and 1K resolution. The supplied scene prompt requests a purple house on an architect’s desk with a blueprint displayed behind it. Describe the model, materials, surroundings, lighting, and camera view you want. Mention the blueprint in the scene prompt if you want it depicted. Scene aspect ratio and resolution control the final image. Auto framing asks the provider to match an input reference. Under Blueprint reference, edit the drawing views, annotations, and sheet layout for the intermediate image. Blueprint framing and resolution are independent of the final scene settings, but changes to that reference can influence the final composition. Both generation stages run on every request. Each stage has an independent model selector offering gemini-3-pro-image-preview and Nano Banana 2 (Gemini 3.1 Flash Image). Optional generation instructions guide each generator’s foundational behavior separately from its visual prompt; retain their defaults for a first run. Resolution choices are 1K, 2K, and 4K, with the provider’s native upscaler used for 2K and 4K. Each seed defaults to -1, which randomizes that stage on every run. Fixed seeds request best-effort consistency rather than identical results. Fixing only the scene seed does not stabilize an independently randomized blueprint reference. The blueprint and miniature scene are generated concept imagery. Geometry, dimensions, structural details, and annotations may be invented; use them for presentation rather than measured construction documentation. Higher resolution does not establish architectural accuracy.

Detail Tweaker LoRA (细节调整LoRA)

This is a LoRA for enhancing/diminishing detail while keeping the overall style/character; it works well with all kinds of base models (incl anime & realistic models)/style LoRA/character LoRA, etc.

Animate an image with Vidu Q1

Turn a starting image and motion prompt into a five-second, 1080p video with Vidu Q1. For a first run, upload your starting image, replace the example leopard prompt with action and camera instructions suited to that image, and leave Motion amount on auto. Generation uses the Vidu partner API and requires configured API access and sufficient credits. Motion amount requests the amplitude of object movement: auto lets the service choose, while small, medium, and large express an explicit preference. Describe camera movement and atmosphere in the prompt. Duration is fixed at five seconds and resolution at 1080p in this workflow; frame-rate wording in the prompt does not configure actual FPS. Seed -1 randomizes each run. A positive integer supplies a fixed generation seed without guaranteeing identical results; the provider treats 0 as random as well. The Video output advanced section offers automatic or explicit container and codec selection, plus CRF encoding quality. Leave File type and Codec on auto and Quality (CRF) on -1 for automatic settings. These controls affect output encoding rather than the generated action. The result is returned as a video through Sweet Tea.

Vidu Q11024x10248GB VRAM

Grok image generation

Describe the image you want and generate one or more images with Grok. For a first run, use the supplied portrait prompt with the default model, square aspect ratio, 1K resolution, and one image. Change aspect ratio to set the composition and resolution to choose the output size tier; request 1–10 images for variations. The model selector offers the three Grok models present in the source. This workflow accepts text only and has no source-image editing input. Seed controls whether the provider node reruns, but the provider's results remain nondeterministic even with a fixed seed. The default seed randomizes on each run.

Hades style textual inversion

Textual Inversion trained on art from the Hades game, tested on Anything V3 model. Put this TI in the embeddings folder, type "Hades-8500" in prompt to activate. Also I recommend to use words "realistic", "cartoon",...

TextualInversionSD 1.5

Stable Diffusion 3.5 Large1024x10248GB VRAM

SD3.5 Large Canny-guided images

Generate new images from a prompt while following edges extracted from a reference image. Outputs include the generated image batch and one reference edge-map preview. For a first run, upload a reference image and replace the example prompt with your desired subject, colors, setting, and style. Keep output and reference dimensions at 1024 × 1024, Canny thresholds at 0.3 and 0.6, control strength at 0.66, and denoise at 1. The fixed SD3.5 Large checkpoint and matching Canny ControlNet are used together. Control strength sets how strongly reference edges influence generation. Set it to 0 for generation without edge influence; the reference upload is still required and its edge map is still returned. Guidance start and end are fractions of the denoising schedule from 0 to 1. Keep start at or below end. Reference resizing and generated output dimensions are independent. Match their aspect ratios for consistent framing. Center crop can remove content near the reference borders; disabled cropping resizes to the requested shape and can stretch the image. Reference interpolation changes resizing before edge detection, not generated output dimensions. Inspect the edge preview when changing crop, interpolation, or thresholds. Keep the low threshold below the high threshold; lower thresholds retain more faint detail, while higher thresholds select stronger contours. Variations sets the number of generated images using the same prompt and edge map. Sampling offers seed, steps, prompt guidance scale (CFG), sampler, scheduler, and denoise. Seed -1 selects a fresh random seed each run; use a fixed seed to compare settings with the same initial noise. Begin with the supplied sampling defaults. Denoise 1 uses the full generation schedule; lower values shorten denoising from the empty latent. The reference supplies edges rather than an initial image latent, so this tool does not preserve uploaded pixels. Uploaded transparency is not carried through the connected image path. Negative conditioning is zeroed internally; there is no editable negative prompt.