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Topaz image enhancement
Enhance an uploaded image with the workflow’s saved Topaz configuration. For your first run, upload a source image and run the Pipe. The enhanced image appears in the result preview. The saved configuration includes “Wonder 3.5.” Enhancement settings are fixed in this interface; there are no selectable enhancement modes, prompt controls or output-size controls. The resulting dimensions and degree of alteration depend on that configuration, and a specific upscale factor is not established. Uploaded transparency is not preserved through the source image connection. The selected output is an image preview; the original save node’s PNG format, bit depth and color-space settings do not apply to this output.
Kimberly Nobody (age slider)
Follow me to make sure you see new styles , poses and Nobodys when I post them. Things move fast on this site, it's easy to miss.
PMRF image enhancer
Upload a source image to produce a PMRF-enhanced image. For a first run, keep the source defaults of 2× scale, 25 steps, and Lanczos interpolation. Scale controls enlargement; use 1× to process at the original size. Larger scales increase output dimensions and resource requirements. Steps controls the processing iteration count and trades additional computation for a different restoration result. Interpolation selects the resizing method. Seed lets you vary the result; the default randomizes on each run. This workflow has no prompt or restoration-strength control. Uploaded transparency is not preserved because only the loader's RGB image output is connected.
Kling image generation
Generate one or more images from text using Kling, with controls for model, aspect ratio, and image count. For a first run, replace the supplied surreal portrait prompt with a description of your subject, scene, composition, and style, or use the example as supplied. Leave the negative prompt empty unless you want to discourage particular content or visual characteristics. Keep kling-v3, 16:9, and one image, then run the Pipe. Generated images appear together in the result preview. Choose kling-v3, kling-v2, or kling-v1-5 to change the generation model. Aspect ratio sets the image's width-to-height proportions; 16:9 produces a landscape composition. Image count supports 1–9 images per run. This Pipe supports text-driven generation only. It has no connected reference image or upload input, so the hidden reference settings provide no usable reference-image mode. Keep seed at -1 to randomize it each run. Seed controls whether the provider node re-runs; a fixed seed does not make image content reproducible. Generation sends your prompts to the external Kling provider and uses provider-priced calls.
Beauty In Evil - By HailoKnight
“When the people of the world all know beauty as beauty, there arises the recognition of ugliness. When they all know the good as good, there arises the recognition of evil.”
Flux 2 two-image design transfer
Generate a new image with Flux 2 using a design reference, a scene reference, and a prompt describing the transfer. For a first run, upload the design as Reference image 1 and the objects or scene as Reference image 2. Both uploads are required. Keep the supplied transfer prompt or describe precisely how each reference should contribute. Start with 1 output megapixel, 1 image, and Turbo mode off, then run to receive the generated image. Both references provide conditioning; generation starts from an empty latent. The result is a newly generated interpretation, so original pixels and object details are not guaranteed to remain unchanged. Output dimensions are derived from the resized first reference, following its aspect ratio subject to latent dimension rounding, even when the second reference supplies the scene. Output megapixels also controls the first reference's processing resolution. Second reference megapixels affects its conditioning resolution independently and does not determine output dimensions. Each reference has its own resize interpolation control. Turbo mode switches model adaptation and sampling budget together. Off uses the base model and Standard steps, initially 20. On uses the supplied Flux2TurboComfyv2 LoRA and Turbo steps, initially 8. Turbo strength affects only Turbo mode and defaults to 1. Enter step counts from 1 to 4096; only the active mode's step count is used. Guidance and sampler apply in both modes. Use seed -1 for fresh randomness each run, or a fixed seed to compare settings with the same initial noise. Image count sets the generated batch size. Larger resolutions and batches increase memory and processing requirements. Uploaded transparency is not retained through either reference path. The tool has one main prompt and no separate negative-prompt input.
HappyHorse image to video
Animate an uploaded first-frame image into one 3–15 second video using a motion prompt. The image determines the output aspect ratio. For a first run, upload your image, replace the example prompt with movement and camera directions appropriate to it, and keep HappyHorse 1.1, 720P resolution, and the 5-second duration. Prompts support English and Chinese. The uploaded image is required; the example prompt refers to a woman and horse and should be adapted to your subject. Choose 720P or 1080P output and a duration from 3 to 15 seconds in whole-second increments. HappyHorse 1.0 is also available. Seed -1 requests a fresh random seed each run; a fixed seed controls generation randomization. Enable Watermark to add the provider's AI-generated watermark. Generation sends the image and prompt to the HappyHorse partner API, requires configured API access, and uses provider-priced generation. The result is returned as one video for preview. Advanced video output settings offer automatic, MP4, MKV, or WebM file type and automatic, H.264, or AV1 codec selection; keep both on auto for the first run.
PonyScores
Embedding created by merging score tokens " score9, score8up, score7up, score6up, score5up, score4up " for Pony based models.
Style Asian Less
This tool doesn't specifically race-swap asian to white-caucasian, it just removes asian so your prompts can be more effective with whatever other race or culture you're trying to portray. This is due mostly to heavy...
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.
WanMove motion-guided image to video
Create a motion-guided video from an uploaded image, a scene-and-action prompt, and editable motion paths. For a first run, upload an image and replace the skiing example prompt with a description of its scene and desired action. Set path 1's start and end positions to guide the intended movement. Review paths 2 and 3 in Advanced: both are enabled by default, and each can be bypassed independently if unnecessary. Keep the initial 832 × 480 size, 81 frames and 16 FPS for an approximately five-second clip, and leave Turbo off to use the standard settings. The image is resized with Lanczos and center-cropped by default. The resized image supplies the start image and optional visual-reference encoding; its dimensions determine the video size. Use positive multiples of 16 when changing width or height. Uploaded transparency is not used, and the selected output has no connected audio. Each path creates a bundle of tracks. X runs from left (0) to right (1), and Y from top (0) to bottom (1) on that path's canvas. Curved path enables a Bezier control point defined by Curve X and Curve Y; the control point shapes the curve rather than specifying a required waypoint. Motion timing controls progression along the path. Track count sets the number of tracks, and Track spread sets their normalized spacing perpendicular to movement. Motion strength controls track-conditioning influence; paths guide generation rather than guaranteeing exact object trajectories. Video frame count and each path's frame count are independent. When changing length, update every enabled path to match. Video lengths follow 1 plus a multiple of 4, while path generators support at most 1024 frames. Frame rate changes playback duration, not the number of generated frames. Track canvases retain the source's 720 × 480 defaults, which differ from the 832 × 480 output. Canvas dimensions are independent controls: keep enabled path canvases consistent, and do not assume that changing output size updates them. Turbo mode selects the fixed Lightx2v LoRA together with its separate sampling settings. Standard defaults are 20 steps and CFG 6; Turbo defaults are 6 steps and CFG 1. LoRA strength affects only Turbo generation. The negative prompt applies in both modes; its supplied Chinese text lists unwanted artifacts and traits. Seed -1 randomizes the seed; use a fixed nonnegative seed to reuse it with otherwise matching settings. Sampler, scheduler, denoise and sampling shift remain available in Advanced. Keep Generation batch size at 1 for a single clip: the selected output assembles decoded images into one video rather than delivering separate files. Visual reference can be bypassed without removing the start image. Its center/none crop setting affects visual encoding and is separate from resize crop. Video encoding offers container, codec and CRF controls; auto container/codec and CRF -1 retain transport defaults. Bit depth defaults to 8; 10-bit may lack player support. The selected output is the generated video, without the source's disabled track-visualization diagnostic. The workflow uses fixed WanMove diffusion, UMT5 text-encoder, Wan 2.1 VAE, CLIP vision and Lightx2v LoRA assets identified in the source metadata. Those assets must be available to the runtime; this review does not establish execution results or generation speed.
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.
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].
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.
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.
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.
