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Video to pose animation
Turn a source video into a rendered whole-body pose animation using SDPose. The output is one pose video, rather than an overlay on the original footage or a downloadable keypoint dataset. It uses the source frame rate and includes source audio when available. For a first run, upload a clip with one clearly visible person, keep the default 1024-pixel longer edge and drawing settings, and run the Pipe. The required checkpoint is sdpose_wholebody_fp16.safetensors. Frames are resized while preserving aspect ratio before pose extraction; Longer edge sets both detection resolution and rendered frame dimensions. Use Draw body, Draw hands, Draw face, Draw feet and Draw head to choose the rendered features. These switches change visualization, not what the model extracts. Confidence threshold controls which keypoints are drawn: higher values suppress uncertain points, while lower values include more points. Line width and Face point size are measured in output pixels; Face point size applies when Draw face is enabled. Resampling method controls frame resizing before detection. Area is suited to downscaling and Lanczos to upscaling. Processing batch size controls how many frames the extractor processes together; lower it if memory is limited. It does not change video duration. Frame rate is inherited from the source and cannot be independently adjusted here. This workflow supplies no bounding boxes, which the extractor requires for multi-person detection. Use single-person footage rather than expecting reliable multi-person capture. Advanced output controls select the container, codec, encoding quality and video bit depth. Keep File type and Codec on automatic, Quality (CRF) at -1 and Bit depth at 8 for a first run. Explicit container and codec choices must be compatible. The 10-bit option requires compatible encoding and playback and does not improve pose detection.
OmniGen2: Text to Image
Generate a single image or a batch from text with OmniGen2. No uploaded image is required; the diffusion model, Qwen text encoder, and VAE are fixed by this workflow. For a first run, enter the subject, composition, lighting, and style in Positive prompt. Negative prompt describes unwanted features. Leave the sampling defaults in place and generate one 1024 × 1024 image. Width and height are measured in pixels and must be multiples of 16; together they determine the aspect ratio. Images per run sets the batch size. Larger dimensions and batches increase memory use. Advanced settings expose guidance, seed, and sampling controls. Guidance defaults to 5. Guidance mode selects the source dual guider's regular or nested combination. Negative prompt feeds both its secondary and negative branches; there is no separate secondary prompt. Secondary guidance defaults to 2 and is not an independent negative-prompt strength in regular mode. Seed -1 chooses new random noise for each run. Use a fixed nonnegative seed when comparing settings. Sampling defaults are 20 steps, Euler sampler, and the simple scheduler. Steps sets the sampling step count, Sampler chooses the sampling algorithm, and Scheduler sets the distribution of noise levels. Denoise selects the fraction of the schedule used and defaults to 1 for the full schedule. Sampling begins from an empty latent. Lowering Denoise does not enable image editing, and this tool has no reference-image input. All decoded images in the batch are returned through Sweet Tea's preview output. The workflow requires omnigen2_fp16.safetensors, qwen_2.5_vl_fp16.safetensors, and ae.safetensors; model selection is not exposed.
SDXL image remix
Remix an uploaded image using a text prompt and the fixed waiNSFWIllustrious_v130.safetensors checkpoint, with optional LoRAs, tiled enlargement and subject refinement. The saved result includes every enabled finishing stage. For a first run, upload a source image, replace the example positive prompt with your desired subject, appearance and style, and review the negative prompt for unwanted content. Keep automatic resizing enabled and the default 1024 × 1024 generation dimensions. Leave LoRAs, upscaling and subject refinement off initially. Denoise starts at 0.75: lower values retain more of the source, while higher values allow stronger reinterpretation. Width and height apply to base generation when automatic resizing is enabled; source-size handling with resizing disabled is determined by the toolkit. Seed -1 requests a fresh random seed each run. Use a fixed seed when comparing prompts or settings. Steps, CFG, sampler and scheduler control base sampling. The checkpoint and baked VAE are fixed; CLIP skip is available under advanced model settings. Each of the three LoRA slots has an independent enable switch, installed LoRA name and strength. Select a LoRA compatible with the fixed Illustrious checkpoint and enable its slot. Multiple enabled slots combine in the same generation. Optional upscaling runs after base generation and before subject refinement. It uses the fixed 4x_NMKD-Siax_200k.pth model; the upscale factor multiplies the base image dimensions. Upscale denoise controls changes during enlargement. Tile width, height and padding are in pixels. Seam correction starts at None; its related settings apply when a supported seam mode is selected. Prompt cleanup, tile-processing mode, uniform tiles and tiled decoding are also available. Upscale sampler and scheduler start at Pipeline. Upscale steps retains the source value 0, whose exact fallback behavior is not established here; retain it initially and consult the installed toolkit before changing it. Optional subject refinement runs last, targets face by default and has its own denoise control. Enter only a subject category supported by the installed toolkit. Its advanced detection, crop, feather and mask controls preserve the source defaults. Several have undocumented types or units, including boolean guide size, integer noise mask 5 and detection threshold 1024. Consult the matching UmeAiRT Toolkit documentation before changing these values; no normalized confidence range or pixel interpretation is established for them. Requires ComfyUI-UmeAiRT-Toolkit (https://gitlab.com/UmeAiRT-Studio/ComfyUI-UmeAiRT-Toolkit.git) and waiNSFWIllustrious_v130.safetensors in ComfyUI/models/checkpoints/. Enabling upscaling additionally requires 4x_NMKD-Siax_200k.pth in ComfyUI/models/upscale_models/. Optional LoRAs belong in ComfyUI/models/loras/. The source describes subject-refinement detector and segmentation assets under models/ultralytics/bbox/, models/sams/ and models/ultralytics/segm/, with automatic downloads handled by the toolkit; exact asset names are not supplied. This Pipe requires an uploaded image and exposes one final remixed-image output. It does not expose checkpoint replacement, a separate text-to-image mode or standalone finishing of an upload without upstream generation.
Seedance image to video
Animate a starting image into one video using a motion prompt and ByteDance Seedance. Upload the image to use as the first frame, then describe the subject's action and desired camera movement. Generation sends the image and prompt to the ByteDance API and requires configured API access and sufficient credits. The result is returned through Sweet Tea's video output. For a first run, replace the example prompt to match your image and keep Seedance 1.0 Pro, 480p, adaptive aspect ratio and a duration of 5 seconds. Duration accepts whole seconds from 3 to 12. Resolution and aspect ratio set the requested output shape. Choose among the available Seedance models. Generate audio applies only to seedance-1-5-pro-251215; other models ignore it. Fixed camera adds a stationary-camera instruction to the prompt without guaranteeing the effect, so keep your motion prompt consistent with that request. Seed -1 randomizes each run; a specified seed controls generation randomness. AI watermark requests an “AI generated” mark on the video. Advanced Video output settings select the returned file container and codec. Leave both on auto and Quality (CRF) on -1 for default output encoding, or choose from the exposed encoding controls.
HiDream E1 image editor
Generate an edited image from an uploaded source and a written instruction using HiDream E1. The source supplies visual conditioning, and the instruction describes the requested transformation. The generated image is returned as a Pipe preview using the fixed HiDream E1 model, associated text encoders and VAE. For a first run, upload your image, replace the example instruction with the change you want, and keep 768 × 768 pixels, 28 steps, text guidance 5, image guidance 2 and regular guidance combination. Use positive width and height values divisible by 8. Center fitting crops the source to the chosen aspect ratio before resizing; disabled fitting retains the full image but can stretch it when aspect ratios differ. Resize method selects the interpolation used for this preprocessing. Text guidance and image guidance control separate conditioning contributions. In regular mode, text guidance scales the difference between instruction-conditioned and image-conditioned predictions, while image guidance scales the difference between image-conditioned and negative predictions. Nested mode changes how the two guidance values combine. The negative prompt describes unwanted content or qualities and supplies text to both the image-conditioned branch and the separate negative branch. Advanced controls include seed, steps, sampler, scheduler and denoise. Seed -1 selects a new random seed each run; use a fixed seed to compare settings with consistent initial sampling noise. Steps sets sampling iterations, sampler selects the algorithm, and scheduler sets the distribution of noise levels. Denoise selects the fraction of the noise schedule used. Sampling starts from an empty latent rather than the encoded source, so reducing denoise is not a conventional source-preservation strength control. This is a whole-image generative edit with no supplied editing mask and no guarantee that untouched details remain identical. Uploaded transparency is not preserved through the connected RGB image path.
4× video upscale
Upscale an uploaded video to four times its source width and height using RealESRGAN x4plus. Upload a source video and run with the defaults: 8-bit output with automatic container and codec selection. Source audio, when present, and the source frame rate feed the reconstructed video. Advanced controls offer 8-bit or 10-bit video, containers auto/mp4/mkv/webm, and codecs auto/h264/av1. Manual container, codec and bit-depth choices must be compatible. 10-bit output can reduce gradient banding but requires compatible encoding and playback; it does not recover precision missing from the source. Each frame is upscaled independently. There is no temporal consistency stage, prompt or diffusion refinement, and no target-resolution resize control. Larger or longer videos require more processing and memory. The encoding quality control retains its source default because tuning semantics are not established in the supplied evidence.
ACE-Step 1.5 song generator
Generate an MP3 song from a musical description, lyrics and musical settings using ACE-Step 1.5 Turbo. Describe the genre, instruments, mood and arrangement in Music description, and enter the words to sing in Lyrics. No uploaded audio is required or supported. For a first run, use the supplied rock description and section-marked lyrics with 190 BPM, E minor, English (en), time-signature value 4 and Batch size 1. Keep both duration fields at 120 seconds, then run the tool and listen to the MP3 result. To make your own song, replace the description and lyrics; section markers such as [Verse 1] and [Chorus] describe the intended structure. Output duration sets the generated audio length in seconds. Song duration is a separate conditioning and audio-planning hint. When changing length, set both to the same number; they do not synchronize automatically. Batch size requests multiple audio latents with the same length and song conditioning; its default is one. Choose Language to match the lyrics. Language has a finite list of language codes, with en for English. Time signature accepts 2, 3, 4 or 6. Musical key starts at E minor; the source does not enumerate accepted alternatives, so other entries are not verified by this interface. Audio planning is enabled by default and uses an additional language-model stage to generate audio codes. Turning it off disables code planning while retaining the text-to-audio generation path; it does not enable an audio-reference input. Planning seed, Planning guidance and the temperature, top-p, top-k and min-p controls apply to planning. For top-p, use probability values between 0 and 1 despite the source field's broader numeric range. Zero disables the top-k and min-p filters. Planning seed and the audio diffusion Seed control separate random stages. Both default to -1, which randomizes on each run. Use explicit seeds for both stages when you want to retain their random choices. Advanced Audio diffusion controls include Steps, CFG, Sampler, Scheduler and Denoising strength. Start with the supplied eight steps, CFG 1, euler sampler, simple scheduler and denoising strength 1. Generation starts from an empty audio latent; reducing denoising strength does not preserve uploaded audio. Noise schedule shift is a separate model-level adjustment, defaulting to 3. Negative conditioning is zeroed internally, so there is no editable negative prompt. MP3 quality offers V0 variable-bitrate encoding and fixed bitrates of 128k or 320k, affecting compression and file size. The selected output is compressed MP3 rather than a lossless audio master.
Corneo's Mercy (Overwatch) Embedding
This is a textual inversion embedding for Mercy (Angela Ziegler) from Overwatch. Use corneomercy to activate.
Image restyle round-trip video
Create one silent round-trip video from an uploaded image and a description of its transformed appearance. Gemini generates the restyled image and separate motion prompts; Wan 2.2 generates the outward and return segments, which are concatenated in that order. First run: upload a source image, replace the Restyle prompt with the appearance you want, and leave the remaining defaults. The supplied prompt requests a back-facing anime illustration. With batch sizes of 1, the default 81 outward frames plus 81 return frames produce 162 frames, or 6.75 seconds at 24 FPS. The video size starts at a 1-megapixel source-image budget and follows that image's aspect ratio. Adjust the two frame counts independently using their supported increments of four from 1. Playback FPS changes how quickly the generated frames play; it does not generate more frames. Restyle aspect ratio affects the Gemini image, while Restyled image megapixels controls its separate resize budget. Neither sets the final video dimensions. For motion or camera changes, edit the advanced motion instructions. These tell Gemini how to write each segment's positive prompt. The comparison image always has the original on the left and the restyled image on the right: outward instructions should describe left to right, and return instructions right to left. Retain the request for prompt-only text. Negative prompts apply separately to each video segment. Image generation, each motion-prompt response, and each video segment have independent seeds. -1 randomizes. Fixed Gemini seeds request repeatability but do not guarantee identical responses. Holding a video seed steady alone does not hold the generated image or motion text steady. Keep batch sizes at 1 for one round trip. Larger batches feed all outward frames followed by all return frames into the same video; they do not create separate paired exports. The return segment is newly generated, not a reversed copy. Endpoint frames are not explicitly removed at the segment join or loop boundary, so a seamless loop is not guaranteed. Uploaded alpha transparency is not preserved, and no audio is connected. The source uses fixed high- and low-noise Wan 2.2 models with Lightx2v four-step LoRAs. Advanced sampling controls remain available independently. For the first run, retain the coordinated defaults: both stages use a four-step schedule, CFG 1, Euler/Simple, shift 5, and LoRA strength 1; initial sampling stops at step 2 and refinement resumes at step 2 without new noise. These controls form a coordinated setup rather than independent quality presets. Leave output container, codec, and compression quality on Auto initially. Final 10-bit output is optional, but both intermediate segment videos are fixed at 8-bit, so it cannot restore precision lost upstream and may have player compatibility limitations.
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...
PixelDiT image generator
Generate one image from text using the fixed PixelDiT 1300M model, Gemma text encoder, and pixel-space decoding. No source image is required. For a first run, replace the positive prompt with your subject, setting, composition, lighting, and desired appearance. Keep or edit the prefilled negative prompt to describe features to discourage. Start with the square, 1-megapixel default and the supplied sampling settings: 30 steps, CFG 4, er_sde sampler, simple scheduler, and denoise 1. Run the Pipe to obtain the generated image. Aspect ratio sets the image shape; megapixels sets the approximate total pixel count in millions. Width and height are calculated together. A square image at 1 MP is approximately 1024 × 1024. Prompt text such as “8k” does not set actual output resolution. Larger images require more resources. Advanced sampling controls include seed, steps, CFG, sampler, scheduler, and denoise. Seed -1 requests fresh randomization on each run; a fixed nonnegative seed reuses the noise initialization. Steps set the sampling step count. CFG controls prompt guidance; excessive values can harm the result. Sampler selects the sampling algorithm, while scheduler sets the progression of noise levels. Changing these controls can change the generated appearance. Keep denoise at 1 for full generation from the empty latent. Lower values sample a shorter noise range. This is text-to-image generation: there is no uploaded image to edit or preserve. Image count is fixed at one, and model selection is fixed. The execution environment needs pixeldit_1300m_1024px_bf16.safetensors, gemma_2_2b_it_elm_bf16.safetensors, and the Sweet Tea output nodes.
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.
