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myself-badhand
This embedding focuses on correcting issues related to the number and shape of fingers, which may result in a change in the model's style.
HTRP Mesugaki/Gyaru
This isn't a character model; it's a concept. When combined with LORA, the character becomes a fusion of gyaru and mesugaki.
Z-Image Turbo edge-guided generation
Generate images from a prompt while following a reference image's Canny edges. Each run returns generated images and a preview of the reference edge map. For a first run, upload a reference with the composition and outlines you want, then enter a prompt describing the desired subject, appearance, lighting and style. Keep control strength at 1, image count at 1, and the source sampling setup: 8 steps, CFG 1, res_multistep sampler, simple scheduler and denoise amount 1. Seed -1 provides fresh randomization; use a fixed seed to compare changes with the same initial noise. Inspect the returned edge preview to understand the structural guidance. The normalized low and high Canny thresholds default to 0.1 and 0.32. Lower thresholds retain more faint detail; higher thresholds suppress weaker edges. Keep the low threshold below the high threshold. Control strength scales the edge guidance influence; reducing it toward 0 loosens that influence. Output dimensions are derived from the processed reference, with no independent width or height controls. Resize reference is bypassed by default. Enable it to choose a longest side in pixels, initially 1024, while retaining the reference aspect ratio. The resize method affects the image used for edge extraction. Resizing changes both the guidance map and the dimensions supplied to generation. Increase image count for a batch of variations; the edge preview remains one reference map. Advanced sampling controls include steps, CFG, sampler, scheduler, denoise amount and sampling shift. Their source defaults provide the initial setup, and changes affect sampling behavior. The reference is structural guidance, not an encoded starting image: its original colors, textures and transparency are not preserved. Lower denoise does not turn this into pixel-preserving image editing. Negative conditioning is zeroed internally, so there is no editable negative prompt. This Pipe implements Canny guidance only.
Inflate an image subject
Give a subject in an uploaded image an inflated appearance using Qwen Image Edit 2511 and the INFL8 LoRA. This Pipe returns one edited image. For a first run, upload your source image and replace the example Edit instruction with a subject visible in it, such as “inflate the chair.” Keep Inflation effect enabled, LoRA strength 1, denoise 1, 40 steps, CFG 4, Euler sampler and simple scheduler. Leave the negative prompt empty unless you have attributes to discourage. Seed -1 requests a fresh random variation each run; use a fixed seed to compare settings with consistent starting noise. The source image is automatically scaled and supplies both image conditioning and the starting latent. There are no independent output-size controls. Denoise ranges from 0 to 1: lower values retain more starting-image structure but may weaken the edit. LoRA strength changes INFL8's influence rather than specifying an exact physical expansion. Bypass Inflation effect to use the same image-editing path without INFL8. LoRA strength has no effect in this mode, and inflation behavior depends on the base model and instruction. The negative prompt remains available to discourage unwanted attributes. Sampling controls select the step count, prompt guidance, denoising algorithm and noise schedule. Advanced guidance normalization controls adjust its strength and whether normalization occurs before CFG combination; the default false setting uses post-CFG attenuation, while true uses unclamped normalization that can amplify the prediction. Sampling shift adjusts the model's noise schedule and defaults to 3.1. Keep these source defaults for your first run. This is a generative whole-image edit without a mask input, so surrounding details may also change. Uploaded transparency is not preserved. The result is returned through Sweet Tea's image preview output; the workflow does not automatically save it through ComfyUI SaveImage.
Chromatic aberration
Create an image with chromatic-aberration color fringing from an uploaded source image. For a first run, upload your image and run with the saved settings: Amount 31 and Pattern Linear. These settings apply horizontal color separation rather than a neutral preview. Set Amount to 0 to preview the loaded image without the effect, then change the amount or pattern and run again. Amount uses a unitless 0 to 100 scale, not pixels or a percentage. Higher values increase the displacement of red and blue samples in opposite directions, while green stays at its original position. The same amount produces different displacement across patterns. Choose Linear for a uniform horizontal shift. Radial produces separation along directions radiating from the image center, increasing toward the edges. Barrel increases separation with the square of distance from the center; it separates color channels rather than warping the whole image. Swirl shifts perpendicular to the radial direction, producing separation around the center. Diagonal shifts along both image axes in normalized image coordinates, so its apparent angle depends on the image proportions. If Pattern appears as a text field, enter Linear, Radial, Barrel, Swirl or Diagonal exactly. Radial, Barrel and Swirl account for the source aspect ratio and have no displacement at the center. Amount 0 disables every pattern. Strong settings sample farther from each pixel and can produce pronounced edge artifacts. Output dimensions follow the source image. Uploaded transparency is not preserved: the image loader's separate alpha mask is unused, even though the shader copies its input texture's alpha. This tool applies only chromatic aberration; the other image adjustments in this family are separate tools.
Extend video with Vidu
Generate a video continuation from an uploaded clip with Vidu Q2. For a first run, upload the required source video, describe what should happen next, and keep Q2 Pro, a 4-second extension, and 720p resolution. The continuation prompt can include motion or camera direction and accepts up to 2,000 characters. Leave it blank to request a continuation without text guidance. Access to the authenticated Vidu partner API is required. Choose Q2 Pro or Q2 Turbo, an extension duration of 1–7 seconds, and 720p or 1080p output. Duration specifies the generated continuation rather than the uploaded clip's length. Keep seed -1 for a new random seed each run, or enter a fixed seed; identical provider results are not guaranteed. End frame guidance is enabled by default under advanced settings. It derives an ending target from the end of the uploaded video, which can be useful when requesting a return toward that image. Bypass the stage to request extension without an ending image. There is no separate end-frame upload, and a seamless loop is not guaranteed. Advanced video output settings provide automatic or explicit file type and codec selection. Keep both on auto initially. These settings affect output encoding rather than the generated scene. The selected result is Vidu's returned video; this graph has no separate concatenation or source-audio restoration stage, so do not assume that the original clip or its audio is preserved in the result.
ChronoEdit image editing
Create one prompt-guided edited image from an uploaded source using ChronoEdit 14B. The source supplies both visual conditioning and the starting frame. Internally, the workflow generates and decodes a five-frame sequence, then returns only the fifth image; it does not export a video. For a first run, upload the image to edit, replace the example advertising prompt with a description of your desired result, leave the output at 720 × 720 pixels, and keep fast mode off. The negative prompt discourages unwanted attributes; its supplied Chinese text can be edited or replaced with exclusions appropriate to your scene. Standard mode uses the supplied defaults of 20 steps and CFG 4. Fast mode selects the fixed ChronoEdit distillation LoRA and its separate defaults of 4 steps and CFG 1. Standard and fast step counts and guidance affect only their respective modes. LoRA strength affects only fast mode. The mode name describes this distilled configuration; actual runtime depends on the execution environment. Width and height set the output canvas in pixels, in multiples of 16. Changing the aspect ratio can change source framing. Reference crop controls only the visual-conditioning branch: none avoids a center crop, while center uses the middle of the source. It does not crop the separately connected starting-frame branch. In advanced Sampling, use seed -1 for a new random seed each run or a fixed seed to compare changes with the same sampling noise. Sampler, scheduler and denoise affect both modes. Denoise uses a fraction of the noise schedule and is not a direct source-preservation slider in this temporal setup. Keep the supplied sampling values for a first run. Advanced positional encoding and model sampling shift also affect both modes. They adjust model encoding and its noise schedule, rather than directly controlling canvas size, image translation or frame selection. The sequence length and selection of the fifth image remain fixed. The uploaded alpha mask is unused, so source transparency is not preserved. The output is a generated edit, and the source framing or details can change.
Z-Image: Text to image
Generate square images from text using the fixed Z-Image base model and Qwen text encoder. Generated images are returned through Sweet Tea's image preview output. For a first run, replace the example positive prompt with your subject, setting, composition, lighting and visual style. Leave the negative prompt empty unless you want to discourage specific elements. Keep image count at 1, steps at 25, prompt guidance (CFG) at 4, sampler at res_multistep, scheduler at simple and denoising strength at 1, then run the Pipe. No source image is required. The saved resolution setting is square at approximately 1 megapixel, around 1024 × 1024 pixels. Resolution and aspect ratio are fixed in this interface. Image count creates a batch of images; larger batches require more memory. Seed -1 randomizes each run. Use a fixed seed to compare prompt or sampling changes with the same initial noise. Steps control sampling work, and CFG controls prompt guidance; excessive guidance can harm the image. Sampler and scheduler offer alternative sampling behavior. Change one setting at a time when comparing results. Denoising strength ranges from 0 to 1. Keep 1 for the saved empty-latent generation setup; lower values shorten the noise schedule and do not provide image-to-image editing. Advanced noise schedule shift adjusts the model's schedule and starts at 3. The model, text encoder and VAE are fixed to z_image_bf16.safetensors, qwen_3_4b.safetensors and ae.safetensors. This Pipe provides text-to-image generation, with no image upload or selectable model mode. Its preview output does not perform the original workflow's permanent PNG-save operation.
Text-to-Image · Flux.1 Schnell Full: Text to Image
Generate images quickly with Flux.1 Schnell full version. Uses Apache2.0 license, requires only 4 steps to generate images while maintaining good image quality.
