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Apoc, Lost, Decay and Ruin - What a perfect day to end the world (Apocalypse Embed Series for 2.1)
As a fan of post apocalyptic series like The Mountain Man or Arisen , and of course great video games like The Last of Us and Project Zomboid, I really wanted an easy way to recreate that "world's gone to hell and we...
ElevenLabs text to speech
Choose a voice and enter the text to speak. Tune speech speed, stability, similarity, and style to shape the delivery. Leave the language code empty for automatic detection. The final output is an MP3; synthesis encoding and final MP3 quality are separate settings. Seed-based reproducibility is not guaranteed.
SimpleNegative | Negative TI
I created 'SimpleNegative' because there are too many repetitive responses influenced by 'EasyNegative,' and it's becoming boring. Please place it in NegativePrompt in the same way.
Photo to geometry maps
Upload one perspective photo to produce two geometry-map images from a shared MoGe-2 estimate. For a first run, upload your image, keep the defaults, and run the Pipe. The first image shows OpenGL normals and the second shows DirectX normals, with inference detail set to 9, automatic camera field of view, and invalid-region filtering enabled. Choose each map independently: OpenGL normals, DirectX normals, depth, colored depth, or the predicted validity mask. OpenGL normals use green = +Y; DirectX normals use green = −Y. Both outputs use the same geometry estimate and inference settings. In Geometry estimation, inference detail ranges from 0 (fastest) to 9 (most detail), as described by the source. Enter the camera's horizontal field of view in degrees when known, or leave 0 to recover it automatically from predicted points. This controls the focal length used to unproject depth into 3D. Invalid-region filtering marks predicted sky and invalid pixels as invalid in points and depth; disable it to retain raw predictions. The predicted mask remains selectable either way. Uploaded transparency is not used as a mask. Automatic resizing checks image width: images wider than 2048 pixels are resized to a 2048-pixel longer edge. Images whose width is at most 2048 remain unchanged, including tall images. Disable automatic resizing to use the original image size. Inference batch size specifies images per inference call, rather than output variations; this Pipe supplies one photo. Force projection is enabled by default, but the supplied source does not explain its effect on the maps. These images are estimates from visible photo content, not guaranteed accurate measurements. This Pipe returns image maps and does not export a mesh or GLB. It requires the fixed moge_2_vitl_normal_fp16.safetensors checkpoint under models/geometry_estimation/.
Flux Kontext image editor
Create one edited image with Flux.1 Kontext Pro using an uploaded image and written instructions. Upload your source image, keep Second image disabled, and try the supplied pop art instruction with the default square output. Describe the subject or region to change and anything you want preserved. The Pipe returns one generated image. To combine two images as editing context, enable Second image and upload another image. Explain in your instruction how each image should be used. Reference layout places the second image relative to the source, matches their adjoining dimensions, and sets the gap and its color. These settings apply only when Second image is enabled. Both images are stitched into a single model input; they are not separate model reference sockets. No second upload is needed while Second image is disabled. Output aspect ratio sets the generated image shape independently of the reference layout. Enter a width:height ratio between 1:4 and 4:1. Guidance controls prompt guidance strength, and Steps sets the number of generation steps; the supplied defaults are 3 and 50. Seed -1 selects a new random seed each run; enter a fixed seed to compare settings. Prompt expansion allows the provider to modify the instruction for more creative generation and makes results nondeterministic even with a fixed seed. This workflow uses a partner API and requires configured access and sufficient credits. Uploaded transparency is not preserved: only the image loaders' RGB image outputs are connected, not their alpha masks.
Seedance 2.0 reference video generator
Generate one Seedance 2.0 video from a prompt, three reference images, a reference video, and reference audio. For a first run, upload all five media inputs and enter your own video prompt. Describe the scene, subject actions, camera movement, and how the references should influence the result. Keep duration at 5 seconds, aspect ratio at portrait 3:4, resolution at 720p, and Generate audio enabled, then run the Pipe. The three images are registered and combined into one reference asset set. They have no assigned start-frame or end-frame roles. Video and audio enter generation as separate references. This connected graph does not establish a mode for omitting references. Image-loader alpha masks are not connected, so uploaded transparency is not preserved as a separate reference mask. Duration is entered in seconds; aspect ratio, resolution, and duration are text fields without an enumerated list of accepted provider values. The supplied defaults are the starting settings. Generate audio changes the generation request and leaves the audio reference connected even when disabled. Seed -1 randomizes each run; a fixed seed does not guarantee identical provider output. Person handling mode and Reference conversion retain source provider inputs whose accepted modes or effects are not documented in the supplied evidence. Keep their defaults for the first run. The result is returned as one video through Sweet Tea. Advanced File type and Codec controls select output encoding; leave both on auto initially. The listed options do not establish support for every container and codec combination.
LTX 2.3 talking and singing portraits
Create talking or singing portrait videos from uploaded audio and portrait images with LTX 2.3. The workflow generates successive segments, spatially upscales and refines them, blends their boundaries, and saves individual segment videos plus an assembled H.264 MP4 at 25 FPS. Both saved outputs use the selected original recording, including accompaniment. For a first run, upload speech or singing and supply an image in all four portrait slots. Reuse the primary portrait in unused slots because every image loader remains connected. Replace shot 1's example prompt, "hello", with a visual description of the subject, setting, expression and motion. Prompts describe visuals; the recording supplies the voice. The source's intended single-shot setup leaves prompts 2–4 empty. Filling them supplies additional image/prompt pairs to the shot looper, but the supplied evidence does not establish its exact empty-slot filtering or rotation order. Audio start time is measured in seconds and defaults to 30; set it to 0 to begin at the start. Duration 0 loads the remaining recording. Select a short duration for an initial trial. Chunk duration is a positive target in whole seconds, not total video duration. One chunker determines loop count from the original recording; another derives segment timings from separated vocals. Both receive the same target duration, but identical boundaries or counts are not established. Their independent variation controls retain the source value of 10; units and exact effects are unspecified. Width and height control shared crop framing for shots 1 and 2, with separate crop positions. Base generation dimensions come from shot 1's crop at half scale, followed by a two-times spatial upscaler; rounding can affect final dimensions. Shots 3 and 4 skip that initial crop, so prepare compatible framing. Only RGB image outputs are connected, so uploaded transparency is not preserved. Generation and refinement have independent image strengths and disable switches. Disabling one stage's image guidance leaves the other stage unchanged and does not remove any upload requirement. Portrait compression is applied before both conditioning stages. The static-camera LoRA affects both sampling stages; strength 0 removes its contribution. Three additional slots accept names of compatible installed LoRAs, each with its own strength. None leaves a slot inactive. Generation steps, schedule shifts, stretching and terminal sigma control the initial sampling schedule. Refinement uses its separate comma-separated sigma sequence. Each stage has an independent seed, CFG value and sampler. Seed -1 randomizes that stage on each run; fix both seeds when comparing other settings. The negative prompt is shared and its influence depends on each stage's CFG. Keep the source sampling defaults for the first run. Decode tiling, overlap and last-frame handling are separate advanced controls; their exact conventions are not fully established by the supplied evidence. Dissolve frames controls boundary blending: 20 frames equals 0.8 seconds at 25 FPS. Overlap trimming is calculated separately, so retain the transition defaults initially. The dissolve curve is a string field; use only values supported by the installed transition node. Final and segment CRF settings independently control H.264 compression. Lower CRF means less compression and larger files. Encoded segments are reloaded for assembly, so final encoding cannot recover detail lost during segment encoding. Final encoding trims the video to the selected audio. Segment outputs are produced within the generation loop and cannot run independently. Longer recordings require more total processing and storage. The workflow relies on its configured models and custom nodes; the supplied dependency catalog does not resolve every required asset.
Wan 2.2 controlled video
Generate a prompted video with Wan 2.2 Fun Control 5B, guided by a source video's frames or extracted edges and an optional reference image. For a first run, upload a source video, replace the example prompt with your desired scene and motion, and either upload a reference image or disable the Reference image stage. That stage is enabled initially, so leaving its upload empty requires disabling it. Keep 704 × 704, 121 frames, 24 FPS, batch size 1, and the default sampling settings. Canny edge guidance is disabled initially: original source frames supply guidance. Enable it to use extracted edges instead. Its normalized low and high thresholds apply only in that mode; keep the low threshold below the high threshold. Reference image guidance can be disabled independently. Generation uses these inputs as conditioning rather than directly editing the source video's pixels. Width and height are pixels in multiples of 16. Frame count uses 4n + 1 values, such as 81 or 121. Playback FPS is independent of generated frame count: 121 frames at 24 FPS yield approximately 5.04 seconds. Changing FPS changes playback duration without requesting more frames. Source audio and source FPS are not carried into the output. Advanced controls include unwanted traits in the negative prompt, seed, sampling steps, prompt guidance, sampler, scheduler, denoise fraction, and sampling shift. Seed -1 randomizes each run; a fixed nonnegative seed reuses sampling noise. Denoise is not a direct source-video preservation strength. The retained negative prompt is the source's Chinese-language list of unwanted traits and can be edited. Video playback offers 8-bit or 10-bit creation; 10-bit support depends on downstream encoding and players. Video output offers container and codec choices; leave both on auto initially. The graph returns one video output with no separate edge preview. Batch size changes the latent batch but does not provide separate output branches for individual clips, so keep it at 1 for an ordinary single-clip run.
Gemini image edit and generation
Edit a reference image or generate images from text using Gemini. For a first run, upload a source image, keep the Source image stage enabled, and describe the changes you want and the features to retain. The supplied example requests anime styling while retaining identity. Keep the defaults: Gemini 3 Pro Image Preview, Auto aspect ratio, 1K resolution and seed -1 for randomization. For text-only generation, disable the Source image stage and replace the editing instruction with a complete scene description. An upload is required while the source stage is enabled. Auto aspect ratio follows the reference when supplied; choose an explicit ratio for text-only generation when output shape matters. Resolution selects a 1K, 2K or 4K target. The source uses Gemini's native upscaler for 2K and 4K. Model selects between Gemini 3 Pro Image Preview and Nano Banana 2 (Gemini 3.1 Flash Image). A fixed nonnegative seed offers best-effort repeatability, but identical results are not guaranteed, including when model or request settings change. System instructions set foundational behavior alongside your image instruction. The supplied instructions direct the model to produce images and interpret requests visually. Model response mode selects IMAGE or IMAGE+TEXT; this Pipe returns images only, so any accompanying text is not surfaced. This tool connects one reference image and has no mask-based editing input or multi-reference batching stage. Uploaded alpha transparency is not preserved because the loader's separate alpha-mask output is not connected.
Wan 2.2 controlled video · Four-step
Generate a silent video from a scene prompt and control footage using Wan 2.2 Fun Control with a four-step Lightx2v preset. For a first run, upload a control video and replace the example scene prompt. Leave Canny edge extraction disabled for already prepared control frames. For ordinary footage, enable Canny edge extraction; its normalized thresholds apply only when enabled, and the low threshold should stay below the high threshold. Reference-image conditioning is disabled initially. To use it, upload an image and enable Reference image. Leave it disabled when no image is supplied. The reference upload supplies RGB image conditioning; the loader's alpha mask is not connected. Start with 640 × 640 pixels, 81 frames and 16 playback FPS, producing approximately 5.1 seconds. Width and height use increments of 16 pixels; frame count follows 1 + 4n. Playback FPS is independent of source FPS. Source audio is not included, and there is no source-FPS resampling control. The result is newly generated footage guided by control frames. Leave batch size at 1 for a single clip; this Pipe exposes one video output rather than separate outputs for batch items. Both generation stages run for every result. Initial sampling uses the high-noise model for steps 0–2; refinement uses the low-noise model for steps 2–4 without adding fresh noise. Both use a four-step schedule, Euler sampling, the simple scheduler, CFG 1, sampling shift approximately 8 and their corresponding Lightx2v LoRA at strength 1. Refinement is part of generation rather than a standalone postprocessing mode. The standard sibling uses a twenty-step path without this LoRA pair. Use seed -1 for randomization or a fixed nonnegative seed to reuse initial noise with unchanged inputs and settings. Advanced controls expose independent guidance, samplers, schedulers, schedule boundaries, model shifts and LoRA strengths. Preserve the supplied preset initially. When changing schedules, keep both schedule lengths equal, coordinate schedulers, match Initial end step to Refinement start step within the schedule, and set Refinement end step to complete it. LoRA strength changes do not automatically change the schedule. Negative conditioning is available but may have no effect at CFG 1. Leave output container, codec and CRF on automatic selection initially. CRF -1 uses automatic selection; explicit values range from 0 to 63 and their effects depend on the codec. Explicit container and codec choices must be compatible. Video construction offers 8-bit and 10-bit representation; the source documents smoother gradients with 10-bit and possible player or downstream compatibility limitations.
Wan 2.5 text to image
Generate still images from English or Chinese prompts with the Wan 2.5 text-to-image preview API. The resulting image appears in the Pipe preview. For a first run, replace the example prompt with a description of your subject, composition, lighting, and style. Keep the default 1024 × 1024 dimensions and prompt enhancement enabled, then run the Pipe. No uploaded image is required. Optionally add a negative prompt describing elements or visual characteristics to avoid. Width and height are independently adjustable from 768 to 1440 pixels in increments of 32; together they determine the aspect ratio. Prompt enhancement allows the service to expand your description. Disable it to submit your wording without automatic expansion. Add watermark requests an AI-generated watermark on the result. Seed -1 randomizes each run; a nonnegative seed requests a particular randomization. The model is fixed to wan2.5-t2i-preview. This tool produces still images even when the example or your prompt describes camera movement. Generation sends your prompt to a remote partner API and requires configured ComfyUI API access, connectivity, and sufficient account credits.
FLUX.2 Klein two-image edit
Generate an edited image from a main image, a second visual reference and written instructions using FLUX.2 Klein 9B Distilled. Use the second image to supply an object, appearance or other visual information for the edit. For a first run, upload the main scene or character as Source image and the additional visual guidance as Reference image. Both uploads are required. Write Edit instructions explaining what to take from each image and how they should relate. The supplied example asks a character to hold a bag with both hands; replace it when your inputs call for another edit. Start with 1 megapixel for each image, one image per run, 4 steps, guidance scale 1 and Euler sampling. Output size resizes the source reference and sets the generated canvas dimensions, approximately retaining the source aspect ratio. Reference resolution independently resizes the second image for conditioning and does not set output dimensions. Each image has its own resize method controlling interpolation before encoding. Larger resolutions and batches require more memory. Advanced controls include second-reference resolution, seed, steps, guidance scale, sampler and images per run. Seed -1 selects fresh random noise each run; use a fixed seed to compare control or instruction changes with the same starting noise. Images per run generates a batch sharing the same instructions and references. Steps controls sampling iterations, sampler chooses the sampling method, and guidance scale changes guidance between instructed and zeroed-text conditioning. Both conditioning branches include both image references. Generation starts from a fresh latent rather than directly denoising the uploaded source. Exact preservation of identity, unchanged regions or object placement is not guaranteed. There is no mask, edit-strength control or separate negative prompt. Uploaded transparency is not preserved because only the loaders' image outputs are used. This tool always uses two image references; editing from one upload is served by the separate single-image edit tool.
Foundation text to image
Generate a batch of images from text, with adjustable dimensions and sampling settings and up to four optional LoRAs. For a first run, enter a positive prompt describing your subject, composition, lighting, and style. Optionally enter unwanted content in the negative prompt. Use the supplied DreamShaper checkpoint option under sd15, leave all LoRA stages disabled, and run with the defaults: four 512 × 512 images, 30 sampling steps, and prompt guidance (CFG) 6. The checkpoint supplies the image model, text encoder, and VAE decoder; the supplied option is not an SDXL checkpoint. Width and height are in pixels; use multiples of 8. Image count sets how many images are generated together with the same prompts and settings. Larger dimensions and batches require more memory and computation. In Sampling, seed -1 requests fresh randomness for each run. Use a fixed seed when comparing otherwise unchanged settings. Steps set the sampling iteration count, CFG controls prompt guidance, and sampler and scheduler select the sampling algorithm and noise schedule. Denoising amount defaults to 1 for full generation. Lower values change the noise-removal range, but cannot preserve a source image: this tool starts from an empty latent and has no image input. For optional LoRAs, select an available checkpoint-compatible LoRA before enabling its stage. Unused stages should remain bypassed. Enabled stages apply in order from LoRA 1 through LoRA 4. Each has independent model and text-encoder strengths; the latter affects both positive and negative prompt encoding. Zero removes that component's contribution, and the source also supports negative strengths. Disabling a stage skips both contributions.
Caption folder images with Florence 2
Generate caption text for an original image selected from a folder using Florence 2. This tool does not run diffusion upscaling. First run 1. Enter a Source folder accessible to the execution host. This is a server-side directory path, not a browser upload. 2. Keep Image selection at incremental_image and Task at more_detailed_caption. Leave Task text blank for ordinary captioning. 3. Review Caption cleanup: it replaces “The image shows” with “sharp photo of” by default. Edit the phrases for your material, or bypass cleanup to retain the original response. 4. Run and review the returned text. Image selection offers single_image, incremental_image and random modes; the graph has no explicit whole-folder loop. Single-image selection uses the zero-based Image index. File pattern filters candidate files. Selection seed controls random image selection independently of Caption seed; -1 randomizes each run. Other Florence tasks include OCR, document questions and region-related tasks. Supply Task text when the selected task needs a question or task-specific text. Only the text response is returned—not region images, masks or structured JSON. Bypass phrase replacement when you need the task's unmodified response. Sample text controls sampling during text generation. Maximum new tokens limits generated tokens, not words; Beams controls decoding beams. The model is fixed to microsoft/Florence-2-base. This tool returns text, not caption files or matched image/text training pairs. It captions the original selected image, not the upscaling sibling's result. The archived upload branch is inactive and is not an input-selection mode. Review generated descriptions for accuracy before using them as labels.
Edit video with Grok
Edit a short uploaded video with written instructions using Grok Imagine. For a first run, upload a clip no longer than 8.7 seconds and no larger than 50 MB. Replace the example motorcycle prompt with the changes you want for your clip, such as subjects, action or visual style. Leave Seed at -1, then run to receive one edited video in Sweet Tea. This workflow uses grok-imagine-video and requires a source video. The clip and prompt are sent to the Grok provider through a paid provider call. Changing Seed triggers a fresh provider run; reusing a fixed seed does not guarantee identical results. The edited video output defaults to automatic file type and codec selection. The output section also supports MP4, MKV or WebM file types and H.264 or AV1 codecs. These settings control output encoding, while the editing prompt controls the requested creative changes.
Aissist XL - Negative Embedding
AissistXL is a negative embedding fine-tuned to be working on Animagine XL v3 and its derivatives. It trained on the standard negative prompt for Animagine XL v3 plus some extra parameters to make sure you always...
Text to sound effects
Generate an MP3 sound effect from a text description using ElevenLabs eleven_sfx_v2. For a first run, replace the traffic example in Sound description with the effect you want, or keep it to try the supplied example. Keep the 5-second duration, looping disabled and prompt influence at 0.3, then run the tool. No uploaded audio is required. Duration accepts 0.5–30 seconds. Enable Seamless loop to request a smoothly looping effect. Prompt influence ranges from 0 to 1; higher values make generation follow your description more closely. Generation audio format selects the generator's MP3 or Opus response. The connected output stage saves either response as MP3, so this choice does not change the final file type. In the advanced MP3 output section, choose V0, 128k or 320k for the final encoding setting. The tool provides one connected MP3 output.
ACE-Step 1.5 music generator
Generate music with ACE-Step 1.5 XL from a musical description and lyrics, and export MP3. No media upload is required. For a first run, keep the supplied description, lyrics, and defaults, then generate: the example requests a two-minute ambient song with English vocals at 72 BPM in E minor. Musical instructions guide generation rather than guaranteeing exact performance. To create your own track, describe genre, instruments, mood, vocal delivery, production, and arrangement in Music description. Enter sung words and optional bracketed section cues in Lyrics, then select the matching vocal language. Keep tempo, key, and meter references consistent across the text and musical controls. The time-signature selector offers source values 2, 3, 4, and 6, with no separate denominator control. Output duration sets the audio length in seconds. Conditioning duration is independent: update both to the same value when changing length. Track count sets the batch size using the same musical conditioning. Seed is shared by audio-code generation and diffusion sampling; -1 randomizes it, while a fixed nonnegative integer lets you reuse a seed with the same settings. Generate audio codes enables the additional language-model stage. Its guidance, temperature, top-p, top-k, and min-p controls apply only when enabled. Disabling it leaves the text-conditioned diffusion path available. Advanced Audio sampling controls set diffusion steps, CFG, sampler, scheduler, and denoising strength. Sampling configuration exposes the source sampling shift, defaulting to 3. Full denoising defaults to 1 because generation starts from an empty audio latent. Negative conditioning is automatically zeroed; there is no editable negative prompt. MP3 quality selects V0 variable-bitrate encoding or fixed 128k or 320k encoding and affects export rather than music generation. The workflow uses the ACE-Step XL base diffusion model, its two Qwen text encoders, and the ACE 1.5 audio VAE. It has no uploaded audio reference, continuation, or audio-editing input.
Poster remake and image edit
Create a new poster from a reference image, apply direct image edits, or generate an image from text using Gemini. For a first run, upload a reference poster, leave Direct prompt mode off, and replace the example Prompt with your new title, supporting text, subject, and desired content. Leave the Reference image stage enabled when using an upload. The default Poster instructions ask Gemini to borrow the reference's style, colors, and typography while changing people's faces, clothing, and poses. They request a large, mainly white artistic title that avoids the face and omit reference watermarks and tiny text. These are creative directions, not guarantees of exact layout, lettering, or identity preservation. Turn Direct prompt mode on to send your brief without Poster instructions. With a reference enabled, describe the edits to apply. For text-only generation, disable the Reference image stage and describe the image you want; Direct prompt mode avoids the default poster instructions that refer to an uploaded picture. Generation instructions still apply in both modes. Auto aspect ratio follows the reference when present. Choose an explicit ratio when you need a particular format, especially without a reference. Resolution defaults to 2K; the source uses Gemini's native upscaler for 2K and 4K. Model selects the Gemini image model for all modes. Use seed -1 for a new random seed each run; a fixed seed requests best-effort consistency, but identical results are not guaranteed. Adjust Poster instructions under advanced controls to change the reusable poster direction. They are prepended to your brief only when Direct prompt mode is off. Generation instructions control the shared foundational direction sent to Gemini. This Pipe returns generated images through its preview output; it does not include the model's separate text response or a workflow file-saving stage. Prompts and enabled reference images are sent to Gemini through a provider-priced API call.
