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Amazing Embeddings - fcNegative + fcPortrait suite
fcNeg is a Negative Embedding. You just install it in the embedding folder and then type fcNeg in the negative prompt. I don't know why it shows two trigger words. Ignore "fcneg-neg"
ACE-Step music generator
Describe genre, instruments, mood, and tempo in the music prompt. Supply lyrics and section markers for vocals, or retain [instrumental] and arrangement cues for instrumental music. Lyrics strength controls lyric conditioning and remains available in either mode. The workflow uses zeroed negative conditioning, so there is no separate negative prompt. The source note advises manually transliterating multilingual lyrics and adding language markers such as [zh], [ja], or [ko].
HappyHorse reference to video
Generate one video from two reference images and a prompt describing the subjects, action and camera movement. Upload both reference images, then replace the supplied car-and-driver prompt with a description matching your uploads. For a first run, use an object or scene as Reference image 1 and a person as Reference image 2, following the example's arrangement. Start with 5 seconds, 720P and 16:9, and describe an action that fits the chosen duration. The images feed reference slots 1 and 2; they are not designated start and end frames. The example prompt uses @image1 and @image2, while the source's prompt guidance recommends identifiers such as character1 and character2 for reference characters. Clearly describe each subject and its role in words when adapting the prompt. Choose a duration from 3–15 seconds, resolution of 720P or 1080P, and aspect ratio of 16:9, 9:16, 1:1, 4:3 or 3:4. Select HappyHorse 1.0 or 1.1; the default is 1.0. Seed -1 randomizes each run, while a fixed seed requests the same generation seed. Enable Watermark to request an AI-generated watermark. Generation sends the references and prompt to the provider API, requires configured provider access and incurs provider charges. The Pipe returns one video for preview with automatic format and codec selection. The example's requests for consistent details and smooth motion are creative instructions, not guarantees.
Qwen Image canvas outpainting
Extend an uploaded image on selected sides with prompted scene continuation using Qwen Image and inpainting ControlNet. The output is a resized, composited image with an automatically created padding mask. For a first run, upload an image and replace the example scene continuation prompt with content that fits its subject, lighting and style. The defaults add 200 pixels on the left, top and bottom and none on the right, before resizing the padded canvas to a longest edge of 1536 pixels. Set at least one extension amount above zero. Keep Lightning enabled at strength 1, with 4 steps, CFG 1, denoise 1, Euler and Simple for the initial run. Seed -1 randomizes each run; use a fixed seed to compare settings. Each extension is measured in original-image pixels before resizing. The padded image and its padding mask share the longest-edge target and resize together, retaining aspect ratio. Use a positive target size. The original image is also resampled, so preservation outside the generated region refers to the resized working image rather than the original file's exact pixels. No separate mask upload is needed. The region is derived from directional padding; uploaded alpha and loader edit masks are unused, and output transparency is not preserved. Boundary feathering acts before resizing. Mask expansion then grows or contracts the region in resized-mask pixels: positive values create overlap into the original image, while negative values contract it. Tapered corners affect nonzero expansion. Blur radius controls the Gaussian neighborhood and sigma independently controls its spread. The processed mask guides ControlNet and the final blend; softened boundaries mix original and generated pixels. The padded canvas is encoded and sampled without a separate latent noise mask. Final compositing determines where the generated result is visible, retaining the resized source outside the processed mask. Denoise controls how much of the encoded padded canvas is changed during sampling; lower values retain more initial structure. Control strength adjusts image-and-mask guidance. Guidance start and end are sampling-progress fractions from 0 to 1; keep start below end. The default interval covers the full sampling process. A negative prompt can discourage unwanted content, but has little or no effect at CFG 1. Sampler, scheduler and model-specific sampling shift are independent controls; retain Euler, Simple and shift 3.1 for the first run. Lightning is an optional adapter and starts enabled. Its toggle does not update steps or CFG. When disabling it, manually use the source baseline of 20 steps and CFG 2.5; when enabling it again at strength 1, return to 4 steps and CFG 1. For replacing a selected region inside an existing canvas using an uploaded edit mask, use the separate masked inpainting tool.
Recraft image generation from style references
Generate one to six images from a text prompt using a Recraft style created from four uploaded reference images. All four references are required and contribute to the shared style; they guide visual treatment rather than providing an image-editing canvas. For a first run, upload four references with a consistent look, replace the example image prompt with your subject and composition, and keep Output size at 1024x1024 pixels and Image count at 1. Choose realistic_image for a photographic base or digital_illustration for an illustrated base. Output size must be a Recraft-supported width-by-height value. Use Negative prompt to describe unwanted elements, or leave it blank. Image count supports 1–6 images with the same prompt and reference style. Leave Seed at -1 for a randomized re-run seed. A fixed seed controls re-run behavior but does not make Recraft results reproducible; generation remains nondeterministic. This tool requires access to the Recraft partner API. Reference images and generation inputs are sent to the service, and style creation and image generation use provider-priced calls. Style creation remains part of the connected generation path. Reusing a manually supplied style ID is not an available mode in this Pipe.
Wan 2.1 video outpainting
Expand a video's borders with prompt-guided content using Wan 2.1 VACE, producing a silent generated video. For a first run, upload a source video and enter a scene prompt describing its content, the surroundings to add and any desired motion. Keep the initial padding and generation settings, then adjust the padding on each edge to suit your composition. Padding is measured in source-frame pixels and creates the control canvas and outpainting mask; output width and height independently set the generated resolution. Choose output dimensions of at least 16 pixels in multiples of 16. Choose a positive frame count of 4n + 1, such as the default 81. These requirements are documented here because the dimension and frame-count fields have broad numeric ranges. Output FPS sets playback speed independently of source FPS. The default 81 frames at 16 FPS lasts about 5.1 seconds. Source audio is not carried into the result. Frame count is selected separately from the uploaded video's length; this tool does not promise to preserve the original duration or reproduce source pixels exactly. Advanced controls include unwanted-content prompting, VACE control strength, mask feathering and sampling settings. Seed -1 selects randomization; use a fixed nonnegative seed to reuse sampling noise with otherwise matching settings. Keep generation batch size at 1 for a single-video first run. The fixed CausVid LoRA is enabled by default, paired with 3 sampling steps and CFG 1. Its model and text encoder strengths are separately adjustable; text encoder strength only has an effect if the LoRA contains applicable weights. To use the supported bypass mode, bypass the CausVid stage and change sampling to the documented 20 steps and CFG 6. Bypassing does not change those sampling settings automatically. Video encoding offers automatic or explicit container and codec selection, plus CRF quality control; -1 retains the transport's default quality. Video creation defaults to 8-bit. The source describes optional 10-bit creation as retaining smoother gradients, with possible player or downstream compatibility limitations.
Flux Kontext multi-reference editing
Create an edited image from a primary image and up to two additional references using Flux.1 Kontext Pro. Each run also returns a preview of the assembled reference image sent to generation. For a first run, upload the required Primary image and replace the example Edit prompt with your desired change. Identify the referenced subjects and state what should remain unchanged. Both additional references are enabled initially: upload an image for each enabled reference, or disable any unused reference. Disable both for single-image editing; either additional reference can also be used independently. Keep the default layout initially. The second reference is placed above the primary image, and the third is placed to the right of that assembly. Advanced layout controls set each reference's position, joining-edge size matching, gap in pixels, and gap color. Third reference placement is relative to the primary image alone when the second reference is disabled. These controls change the assembled input, not the generated image's aspect ratio. Set Aspect ratio to the desired output width-to-height ratio, between 1:4 and 4:1. Generation controls include guidance strength and step count; their source defaults are 3 and 50. Seed -1 randomizes each run; a fixed seed reuses the seed setting. Prompt expansion lets the provider elaborate your instructions, but prevents exact repeatability even with a fixed seed. The reference preview accompanies generation; it is not a separate preview-only mode. This workflow uses a partner API and requires configured access and sufficient credits. Uploaded transparency is not preserved because only the image outputs, not the alpha masks, enter the reference assembly.
Wan 2.2 controlled video · Standard
Generate a silent video guided by uploaded control footage and a scene-and-motion prompt using Wan 2.2 14B Fun Control. This standard branch uses high-noise and low-noise models across a coordinated 20-step schedule; the separate four-step sibling uses Lightx2v LoRAs. For a first run, upload a control video and replace the saved scene prompt. Leave Canny edge extraction disabled if the footage already contains prepared control frames. For ordinary footage, enable Canny to extract edges before conditioning generation. Its low and high thresholds apply only while enabled; keep the low threshold below the high threshold. Reference-image conditioning is disabled initially. To use it, upload an image and enable Reference image. You can leave it disabled to generate with the prompt and control video alone. The reference upload supplies the loader's RGB image output, not its alpha mask. Defaults generate 640 × 640 pixels and 81 frames at 16 FPS, approximately 5.1 seconds. Width and height use increments of 16; frame count follows 1 + 4n. Playback FPS changes duration independently of frame count. The result does not inherit source audio or source FPS. Keep batch size at 1 for a first run; this Pipe returns through one video output rather than separate named outputs for batch items. Advanced controls include the negative prompt, random seed, independent prompt guidance for each generation stage, samplers, schedulers and model schedule shifts. Seed -1 randomizes initial noise; a fixed nonnegative seed reuses initial noise with otherwise unchanged settings. Preserve the saved sampling setup initially. If editing schedules, keep both total-step values equal, use coordinated schedulers, and make Initial end step equal Refinement start step within that schedule. The default handoff is step 10 of 20. Refinement continues the initial latent without adding new noise. Video output offers automatic or explicit container and codec choices, plus CRF encoding quality. CRF -1 uses the transport default. Video construction defaults to 8-bit; 10-bit is available but may have player or downstream compatibility limitations. The output is newly generated footage guided by the control frames, rather than a copy of the uploaded clip.
CyberRealistic Positive (Pony)
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