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Action figure image edit
Create an articulated toy or action-figure style image from an uploaded image. Describe the edit in the positive prompt using "action the [thing]", such as "action the scene"; optionally add exclusions in the negative prompt. Fast mode selects the Lightning LoRA branch with four sampling steps by default; disabling it selects standard sampling. Advanced controls adjust edit strength, LoRA strengths, seed, sampler, scheduler, and each mode's steps and guidance.
Product placement video
Create a product-placement video from three product photos and one composition reference. Nano Banana generates a new start frame from all four images; Gemini examines it and writes a motion prompt; Kling animates it. Every run includes composition generation. This tool does not accept an existing finished start frame or return the intermediate still as a separate output. For a first run, upload all four required images: products are images 1–3, and the composition reference is image 4. Edit Placement prompt to describe how the products should appear in that scene. Its default assumes products held in a hand in a fanned arrangement; revise it for other compositions. In Motion instructions, describe desired subject movement and camera motion. These instructions go to Gemini, whose response becomes Kling's actual prompt automatically. Keep the initial 1K start frame, 5-second duration, 1080p video, and automatic output encoding settings. Start frame aspect ratio determines the composition shape and the resulting video's framing. Auto follows an input image's aspect ratio, but the supplied source does not identify which of the four takes precedence. Choose an explicit ratio when framing matters. Kling's own aspect-ratio field is ignored with the generated start frame connected. Start frame resolution and Video resolution are independent. The three model selectors act independently. Image model offers Nano Banana 2 (Gemini 3.1 Flash Image) and Nano Banana 2 Lite. Both retain all four image bindings and share aspect-ratio and thinking-level controls. For Start frame resolution, enter 1K, 2K, or 4K with the full model; set 1K before switching to Lite, which supports only 1K. Thinking level offers MINIMAL or HIGH. Image temperature controls randomness from 0 to 2, and Image nucleus sampling controls the sampling threshold from 0 to 1; lower values favor more focused choices. Keep their supplied defaults initially. Image generation instructions changes foundational behavior; use Placement prompt for ordinary scene changes and preserve image production. Motion writer model offers gemini-2.5-pro, gemini-2.5-flash, gemini-3-pro-preview, gemini-3-1-pro, and gemini-3-1-flash-lite. Motion writer instructions optionally changes its foundational behavior. Leave that field empty initially; any resulting response is consumed directly as the video prompt. Video model offers kling-v3 and kling-3.0-turbo. kling-v3 supports 4k, 1080p, and 720p; Turbo supports 1080p and 720p, so switch away from 4k before selecting Turbo. Generate audio controls kling-v3. Turbo always generates native audio and ignores this toggle. The retained mode generates a single shot lasting 3–15 seconds; storyboard controls are not exposed. Negative prompt describes unwanted elements for video generation and does not affect the still composition. A seed of -1 randomizes each run. Fixed image and motion-writer seeds request best-effort repeatability without guaranteeing identical results. Kling's seed controls re-running rather than reproducible video generation. File type and Codec affect the returned video's encoding; keep auto initially. Inspect product identity, packaging, label text, pose, and motion before using the result: prompt requests are not guarantees of exact reproduction. The upload loaders' alpha-mask outputs are not connected, so uploaded transparency is not preserved through a separate mask path. Text and thinking-image outputs from image generation are not returned. For a still composition without motion writing or animation, use the separate Product placement photo tool.
Hentai Manga Style Concept
This is a embedding i trained on just hentai images. Therefore you will get a lot of open mouths, ahegao, sweating, saliva etc.. from just using the embedding.
Character Expression Sheet
Create a labeled sheet of 12 expressions from a character image, or render one selected preset or custom expression. Start with a character image and set its longest-side size. Batch mode is on by default and creates angry, anxious, confident, confused, contemptuous, disgusted, fearful, happy, sad, shy, sleepy, and surprised expressions in one sheet. Turn batch mode off to render one selected preset or enter a custom expression instruction. Expression strength and seed are available as advanced controls.
Topaz video upscale
Enhance an uploaded video with Topaz Starlight Precise 2.5 and return one video at FullHD (1080p) or 4K (2160p). For a first run, upload your source video, keep FullHD (1080p), leave Compression level at Low, and leave File type and Codec on auto. Run the Pipe to receive the enhanced video. A source video is required; no sample video is supplied as the upload default. Output resolution selects the Topaz enhancement resolution. Compression level selects Topaz's CQP setting from Low, Mid and High. In Video encoding, you can choose MP4, MKV or WebM and H.264 or AV1 instead of automatic selection. Manual file type and codec combinations must be compatible and supported by the runtime. These output encoding choices are separate from Topaz's compression setting. This Pipe uses Starlight Precise 2.5 with frame interpolation disabled. Other enhancement models, creative-model tuning, target frame rate and slow-motion settings are not offered because their required mode-specific bindings are absent from this configuration. Processing sends the uploaded video to the Topaz partner API, requires runtime authentication, and uses provider-priced processing.
Qwen illustration
Generate illustrations from text using Qwen-Image with the fixed Alvdansen Illustration 1.0 LoRA. Describe the subject, composition, colors and desired style in Image prompt, or keep the included example for a first run. Optionally list unwanted elements in Negative prompt; it can remain empty. No uploaded image is required or supported. For a first run, keep the 1024 × 1024 dimensions, image count 1 and illustration strength 1. The advanced sampling defaults are 45 steps, prompt guidance (CFG) 3.5, Euler sampler, Simple scheduler, denoising amount 1 and sampling shift 7. Width and height are pixels and must be multiples of 16. Image count sets the batch size; larger dimensions and batches increase resource use. The output returns every decoded image in the batch through Sweet Tea's image preview transport. Illustration style is enabled by default. Reduce its strength for less LoRA influence, or disable that stage to generate with the base Qwen-Image model. Illustration strength has no effect while the stage is disabled; prompts, output dimensions and sampling controls still apply. Seed -1 selects fresh starting noise each run. Use a fixed seed when comparing changes with the same starting noise. Advanced controls adjust denoising steps, prompt guidance, the sampling algorithm and noise schedule. Keep denoising amount at 1 initially: lowering it does not preserve an input image because generation starts from an empty latent. The model, text encoder, VAE and illustration LoRA are fixed in this tool.
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.
Corneo's Power (Chainsaw Man) TI Embedding
This is a textual inversion embedding for Power from Chainsaw Man. Chainsaw Man is an incredible series that you should try out if you haven't already.
Vidu Q2 start and end frame video
Generate a short video guided by a start image, an end image, and a description of the motion between them. For a first run, upload both endpoint images and replace the example prompt with the action, camera movement, and scene transition you want. Both images are required; there is no start-frame-only mode. Keep the defaults of Vidu Q2 Pro Fast, 5 seconds, 720p, and small movement initially. Prompts support up to 2000 characters. Duration supports 2–8 seconds in whole-second increments. Resolution offers 720p or 1080p. Movement amplitude sets object movement to small, medium, large, or automatic; describe camera movement in the prompt. Match the requested action to the selected duration. The model selector also offers Vidu Q2 Pro and Turbo. Seed -1 randomizes each run; a fixed seed controls generation randomization. The Pipe returns one generated video. 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 a first run. Generation sends the endpoint images and prompt to Vidu through a paid provider call. Endpoint images guide a generative transition rather than a deterministic blend.
Character turnaround
Generate one image containing multiple views of a character. For a first run, upload a character reference in Source image, keep the default prompt, 9:16 aspect ratio and 2K resolution, then run. The default requests four close-up face views on a white background. To generate without a reference, bypass Source image and add the character's appearance to the prompt; leaving the upload empty alone does not select text-only mode. For full-body framing, change the prompt to: “A 360 turnaround full body view of the subject, straight in 4 different angles. Front view, half turn, side profile, back view. Full-body shots, all white background.” Set Aspect ratio to 16:9, matching the source full-body branch. Headshot and full-body framing are prompt choices within the same generator; there is no separate preset switch. Aspect ratio controls the complete image, not individual view panels. Auto follows the reference image's ratio when supplied; choose an explicit ratio when generating without a reference if the output shape matters. Resolution offers 1K, 2K and 4K; 2K and 4K use Gemini's native upscaler. Model, Response format and System instructions remain independently editable. System instructions set the provider's foundational behavior. IMAGE+TEXT requests an accompanying provider text response, but this Pipe returns only the image. Seed -1 requests a new random seed each run. Fixed seeds offer best-effort repeatability rather than deterministic results. This tool performs one generation, rather than separately generating and stitching headshot and full-body panels. Requested view counts, pose accuracy and identity consistency are generation instructions rather than guarantees.
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.
Wan 2.2 speech-driven video
Generate a speech-driven video from a reference image, a speech recording and a scene prompt. Separated vocals condition generation; the trimmed original recording supplies the soundtrack. Additional outputs preview the prepared reference, separated vocals and instruments. First run: upload both required files, replace the example positive prompt to match your reference and desired motion, and retain the source defaults: 960 × 528 fill/crop framing, 49 frames per chunk, 5 steps, CFG 1, uni_pc sampling, simple scheduling and 16 FPS. The negative prompt describes unwanted content and also feeds negative-attention guidance. Seeds of -1 randomize each run; use fixed seeds when you want to retain the noise choices. Width, height and framing prepare the reference and determine generation dimensions. Use positive dimensions divisible by 16. Stretch, crop and pad offer different ways to fit the reference; keep-proportion mode can change the resulting dimensions. Frames per chunk follows the 1 + 4n convention, such as 49. It does not choose the number of chunks: the graph generates an initial segment and one continuation, while five further source examples remain bypassed and are not selectable here. Advanced settings retain independent initial and continuation denoising, generation batch size, three stacked LoRA strengths, sampling shift and negative-attention guidance strength, mixing and clipping. Start with their authored defaults. Batch size 1 is the single-video recipe, not a chunk-count setting. The Lightning HIGH and LOW LoRAs are applied in series, followed by Lightx2v; they are not alternative model modes. FPS changes playback timing without generating more frames or retiming audio. The starting frame offset defaults to 2 and changes the final frame selection without trimming the soundtrack. Both controls can affect synchronization. Audio trimming remains fixed at source positions 0–6; the supplied evidence does not establish those positions' units or boundary behavior. Output duration is not automatically matched to the recording. The video output defaults to MP4 with NVENC H.264 and requires a compatible encoding runtime. Model and encoder selections retain the authored recipe. Uploaded transparency is not preserved through the RGB reference-image path.
Flux Klein crop inpaint
Replace a selected region with a Flux 2 Klein generated crop and blend it onto the original RGB image. First run: upload the image, provide a manual mask through the image input's mask workflow, and replace the example Edit instruction with your desired change. Leave Use mask enabled, Automatic mask disabled and Use native crop size enabled. Set Object to select to an object present in your image: SAM2's image output feeds the crop even in manual-mask mode. Run and inspect the edited image, processed mask and input crop preview. The processed mask preview is before cutoff and final feathering, not the final blending mask. For text-detected selection, keep Use mask enabled, enable Automatic mask and name the object to select. Detection threshold, blur, offset and inversion adjust this selection. Disable Use mask for whole-image image-to-image processing. Neither mask switch bypasses SAM2 processing. The crop supplies both the starting latent and a latent reference. Denoise controls change strength throughout that crop; the selected mask controls cropping and final compositing, not a diffusion noise mask. Crop padding adds surrounding context in source-image pixels. Mask coverage adjusts the resized mask, Mask cutoff thresholds it, and Composite edge applies final expansion and feathering. Inverting a processed or composite mask does not change the earlier crop selection. Native crop size uses the crop dimensions as resize targets, with processing dimensions subject to the resize stage's divisibility setting. Disabling it stretches the crop to a square whose width and height equal Sampling resolution in pixels; this is not a maximum-side limit. The graph resizes the generated image back to the crop dimensions but does not explicitly route a correspondingly restored processed mask into compositing. Inspect alignment in this mode. Large native crops can increase memory use. Advanced controls include seed, sampling algorithm, schedule, steps, Flux guidance and resolution-dependent sampling shifts. Seed -1 requests fresh randomization; a fixed seed reuses the noise choice. Desaturation reduces generated-crop saturation before blending, with a selectable grayscale conversion method. The source suggests 0.05–0.20 for saturation mismatch, but this does not guarantee brightness or color matching. Uploaded transparency is not preserved as output alpha.
Hamunaptra Style
This is a Style embedding which turns everything in your image towards an Ancient Egyptian style! Think Pyramids, the river Nile, and Pharaohs! Simply throw Style-Hamunaptra on to the end of your prompt (or the...
FLUX.2 Klein Base 9B text to image
Create an image or a batch of images from text using the fixed FLUX.2 Klein Base 9B model. Describe the subject, composition, lighting and style in the positive prompt; optionally list unwanted elements in the negative prompt. No uploaded image is required. For a first run, keep the 1024 × 1024 size, one image, 20 steps, CFG 5 and Euler sampler. The supplied motorcycle prompt is a ready-to-run example. Width and height jointly set the latent image size and the resolution-aware sampling schedule; use multiples of 16. Larger dimensions and batches increase memory requirements. Advanced controls let you tune guidance, steps, sampler and batch size. Higher CFG increases the influence of prompt conditioning and can change the visual balance; more steps increase sampling work without guaranteeing a better result. Seed -1 randomizes each run; use a fixed seed to reuse the initial noise while comparing other settings. The runtime requires flux-2-klein-base-9b-fp8.safetensors, qwen_3_8b_fp8mixed.safetensors and full_encoder_small_decoder.safetensors. The source graph uses the Base model with its supplied sampling settings; it does not provide an image-editing mode or a selectable distilled-model preset.
Recreate an image with realistic detail
The reference image supplies a generated scene description, rather than direct image conditioning. A style prefix and detail suffix surround that description, and the resulting positive prompt drives both Grok generation and Z-Image Turbo refinement. The default style is black and white photography. Refinement denoise controls how much the generated image changes. Before refinement, the image is resized to the chosen base megapixel count, enlarged fourfold with RealESRGAN, and reduced by half, producing approximately four times the base pixel count. Grok resolution affects the initial generation; base megapixels determines the final refinement size. The negative prompt affects only refinement and becomes useful when refinement CFG is increased above its default of 1.
