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HiDream I1 Fast image generator
Generate an image or batch of images from a text description using the fixed HiDream I1 Fast FP8 model. No uploaded media is required. For a first run, replace the example prompt with your subject, composition, lighting and style. Keep the 1024 × 1024 dimensions and batch size 1, then run to receive the generated image. Width and height are in pixels and must be multiples of 16. Change both to choose an aspect ratio; increase batch size to receive several images together. Larger dimensions and batches require more memory. Seed -1 randomizes each run. Set a specific seed to compare prompt or sampling changes with the same starting noise. Advanced sampling defaults are 16 steps, CFG 1, LCM sampler, normal scheduler, sampling shift 3 and denoising strength 1. The negative prompt has no effect at CFG 1. Raising CFG above 1 applies negative guidance and changes prompt influence. Sampler, scheduler, steps and sampling shift offer alternative sampling configurations; the template defaults provide a starting point. Denoising strength below 1 uses only part of the noise schedule. Because generation starts from an empty latent, it does not preserve a reference image. The diffusion model, supporting text encoders and VAE are fixed. This tool provides text-to-image generation, without image uploads or model selection.
Wan 2.1 video transformation
Generate a silent, prompt-guided video conditioned on an uploaded source video using the fixed Wan 2.1 VACE 14B model. First run: upload a source video, replace the example Video prompt with your desired scene, appearance and action, and leave Reference image and Edge guidance bypassed. Keep the remaining defaults initially: 720 × 720 pixels, 81 frames, 16 FPS, latent batch size 1, and the enabled CausVid LoRA with 4 sampling steps and CFG 1. The default output lasts about 5.1 seconds. The Negative prompt contains supplied Chinese exclusions and can be edited in advanced settings. To add appearance guidance, upload a reference image and enable the Reference image stage. To condition on source edges instead of original source frames, enable Edge guidance; its low and high Canny thresholds apply only in that mode. VACE conditioning strength controls the connected visual conditioning separately from prompt guidance. Frame count and output FPS are independent: duration equals frame count divided by FPS. Width and height are measured in pixels. Source audio and source FPS are not transferred, and the result is newly generated video rather than an exact preservation of the input. The CausVid LoRA can be bypassed. When doing so, the supplied guidance recommends changing Sampling to 20 steps and CFG 6; bypassing does not change those controls automatically. Seed -1 randomizes sampling noise. Advanced settings also retain the sampler, noise scheduler, denoising amount, sampling shift, latent batch size and LoRA strengths. Video playback offers 8- or 10-bit assembly. The supplied source notes possible player and downstream compatibility limits for 10-bit. Video output provides container, codec and CRF choices; automatic container and codec selection and CRF -1 retain transport defaults. Keep latent batch size 1 for the documented single-video use, since separate batch-item output files are not exposed. This Pipe returns one video. The model is fixed; alternate model branches, animated WebP output and edge previews are not selectable outputs.
Remove backgrounds with BiRefNet
Upload an image to remove its background with BiRefNet and return a transparent image. A segmentation mask is generated internally for alpha compositing and is not a separate output.
boring_SDXL: Enhance Images Stylistically AND Topically for Pony Diffusion V6
boringSDXL is one of the Boring embeddings converted to SDXL format using this tool , which is expected to preserve only some of its meaning. It seems to work on Pony Diffusion v6 , but not so well on the base SDXL...
Deep Negative Pony
Since the Pony model and the SDXL model are very different, I trained the Pony version of DeepNegative
Qwen Image 2512 · 360 Panorama
Generate equirectangular 360° panorama images from a scene description with Qwen Image 2512 and a panorama LoRA. Use the negative prompt to describe unwanted attributes, and keep width and height at a 2:1 ratio; the default canvas is 2048×1024. Adjust image count, panorama strength and sampling controls as needed. Optional Lightning mode selects its own LoRA strength, steps and guidance settings, defaulting to 4 steps and CFG 1.
Magazine packaging presentation
Create a magazine packaging presentation in two generative stages: first design a flat cover, then reinterpret that cover as a physical magazine in packaging. Choose this tool when you want a presentation image showing materials, closures and a setting around the magazine. For a first run, upload a photo, replace the masthead and cover lines in Cover prompt, and leave the packaging instructions at their defaults. The default result requests a magazine inside a lightly glossy, transparent plastic envelope with a string-and-button closure on a black background. To generate without a photo, bypass Source image and describe the cover imagery as well as its typography in Cover prompt. Cover model changes only the cover-generation stage; Packaging model changes only the presentation-generation stage. Each independently supports gemini-3-pro-image-preview or Nano Banana 2 (Gemini 3.1 Flash Image), with the source Pro selection retained by default. Cover aspect ratio and resolution shape the intermediate cover. Packaging aspect ratio and resolution shape the returned presentation; auto follows the generated cover's aspect ratio. The 2K and 4K settings use the provider's native upscaler. Every run includes upstream cover generation before packaging generation; uploading an existing cover does not skip that first operation. Packaging prompt controls the material, closure, reflections, background and composition. Both stages generate images rather than applying a precise layout or compositing operation, so review spelling, cover fidelity and physical details. Transparent plastic describes the depicted material, not an output alpha channel. Seeds are independent: -1 randomizes each stage on each run, and fixed values request best-effort repeatability without guaranteeing identical results.
FLUX.1 Krea image generation
Create images from text using the fixed FLUX.1 Krea dev model, CLIP-L and T5 text encoders, and VAE. No uploaded image is required. For a first run, describe your subject, composition, lighting, and style in the positive prompt. Keep the default 1024 × 1024 size, one image, 20 steps, Euler sampler, and Simple scheduler. The supplied portrait prompt is an example; replace it for another subject. Width and height are measured in pixels and must be multiples of 16. Image count generates a batch with the same prompt and dimensions. Larger dimensions and image counts require more memory and computation. Open Sampling for advanced controls. Seed defaults to -1, which randomizes each run. Use a fixed seed when comparing prompt or sampling changes with other settings unchanged. Steps controls the number of denoising steps; sampler selects the sampling algorithm, and scheduler distributes noise levels across those steps. CFG defaults to 1; higher values amplify the difference between positive conditioning and zeroed negative conditioning. This graph has no editable negative prompt or separate FLUX guidance control. Denoise controls the fraction of the sampling schedule used, from 0 to 1. Keep its default of 1 for full generation. The workflow always starts from an empty latent, so reducing denoise does not enable image editing or preserve an existing image. Model and encoder selection are fixed in this tool.
Add music to a video
Generate a soundtrack from your video's content and receive a video combining that music with the original audio. For a first run, upload a video containing an audio track and lasting no more than six minutes. Leave Music prompt empty to let Sonilo rely entirely on the video, or describe the soundtrack you want. Run with the existing gain and export defaults, then listen to the result and adjust the two audio levels as needed. Original audio volume and Music volume independently adjust the tracks in decibels before they are added together. Both default to +1 dB; 0 dB leaves a track unchanged, positive values raise its level and negative values lower it. The input must still contain audio even when its level is reduced. Adding tracks can increase the combined level, so lower their gains if the mix sounds overloaded. The result uses the source video's frames and frame rate. Advanced Video output controls select the container, codec and encoder CRF; leave these at their defaults for a first run. The graph also saves generated music separately before the Music volume adjustment, without the original audio. Advanced Music export controls select FLAC, MP3 or Opus; the supplied MP3 configuration offers V0, 128k and 320k quality settings. Sonilo currently ignores seed, so this tool does not offer reproducible generation through a seed setting.
Z-Image upscale and refine
Upload an image to produce one resized, RealESRGAN-upscaled and Z-Image Turbo diffusion-refined image. The selected result includes the entire processing chain; there is no separate upscale-only output or refinement bypass. For a first run, upload your source image and describe its subject, composition and desired details in the positive prompt. Keep the source defaults: 1 megapixel working size, 0.5 final scaling, 0.33 refinement strength, five sampling steps and prompt guidance (CFG) 1. The default prompt, “masterpiece, 8k,” is a minimal starting point; its text does not determine resolution. Working size sets the pixel budget before the fixed 4× RealESRGAN upscaler. Final scaling then multiplies the upscaled width and height before diffusion refinement. Approximate output megapixels equal working megapixels × (4 × final scaling)². The defaults therefore produce approximately 4 megapixels. Aspect ratio follows the upload, subject to image and latent rounding. This is not a fixed enlargement relative to the original upload; the working-size stage can also shrink a large source. Refinement strength controls how much diffusion can reinterpret the resized image. Lower values maintain more source structure; higher values allow stronger changes. Use a prompt that describes the image and the changes you want. Refinement can alter details rather than merely recover them. The negative prompt has no effect at CFG 1. It becomes useful for describing unwanted attributes when experimenting with guidance above 1. Steps, sampler and scheduler change refinement behavior. Advanced model sampling shift is a separate noise-schedule control, retained at the source default of 3 for the first run. The model setup is fixed to Z-Image Turbo with the Qwen text encoder, ae VAE and RealESRGAN_x4plus upscaler. Seed -1 randomizes each run. Use a fixed seed to compare settings with the same noise initialization. The Pipe uses the loader's RGB image output and does not preserve uploaded transparency.
Create a HeyGen talking-head video
Generate one talking-head video with a HeyGen avatar speaking your written script. For a first run, replace the demo script, keep Annie Lounge Standing Side and (avatar's default voice), and leave resolution at 720p, aspect ratio at auto, and speech speed at 1. Avatar and voice are text fields for supported HeyGen selections; this Pipe does not supply selection lists. Custom avatar and voice ID fields accept existing HeyGen identifiers. Leave them empty when using the regular selections. These fields do not create or train an avatar or voice. Choose 720p or 1080p resolution and an automatic, landscape, portrait, square, 4:5, or 5:4 aspect ratio. Background color accepts a HeyGen-supported color value; leave it empty for the provider's default behavior. Speech speed is a multiplier from 0.5 to 1.5: 1 is normal speed, lower values slow speech, and higher values accelerate it. The advanced Video output section offers automatic, MP4, MKV, or WebM file type and automatic, H.264, or AV1 codec selection. Leave both on auto for a first run. Seed -1 randomizes each run; a fixed seed does not guarantee identical provider-generated video. This retained workflow uses the automatic avatar engine and script speech only. It has no audio upload, reference-image input, separate language selector, or selectable alternative engine or speech branch.
Kandinsky 5 text to video
Generate a short silent video from a scene and motion description using Kandinsky 5 Lite's fixed 5-second text-to-video model. For a first run, replace the example positive prompt with your subject, action, setting, camera movement, lighting and visual style. Optionally enter unwanted content or visual traits in the negative prompt, or leave it blank. No image or audio upload is required or connected. Keep 768 × 512 pixels, 121 frames, 24 FPS, batch size 1 and the supplied sampling settings: 50 steps, CFG 5, Euler ancestral sampler, beta scheduler, sampling shift 5 and denoise 1. These defaults produce approximately five seconds of video, returned as an H.264 MP4. Seed -1 randomizes each run; use a fixed seed when comparing changes. Width and height use multiples of 16. Frame count uses the 4n + 1 pattern, such as 121. FPS sets playback timing: approximate duration in seconds is frame count divided by FPS. Changing FPS plays the same generated frames faster or slower; it does not generate additional frames. Larger dimensions and frame counts increase computation and memory requirements. Changing frame count does not select a different model duration variant. Advanced sampling controls change denoising behavior and prompt guidance. Negative-prompt influence depends on CFG. Keep denoise at 1 initially: this workflow starts from an empty latent, so lowering denoise does not preserve an uploaded image. Batch size controls how many latent sequences are generated together, but the output path assembles one video and provides no separate clip outputs for batch members. Video encoding offers auto, MP4, MKV and WebM containers, with auto, H.264 or AV1 codecs. Keep MP4 with H.264 initially; other combinations depend on output transport support. Quality (CRF) defaults to -1, which uses the transport's default encoder setting. Encoding controls affect the returned file rather than the generated scene. This Pipe has no connected image animation or audio mode.
Wan Animate 2: Motion transfer
Animate a reference character using motion from a driving video, with text control over appearance and background. For a first run, upload a character image and a driving video with similar subject framing. Replace the appearance example with a description of your character and desired background, and replace the motion example with the action shown in your video. Leave the sampling defaults and context windows off initially, then generate the animation. The default requests one 81-frame segment at 482 × 854 pixels, using the driving video's frame rate and audio when present. Width and height resize the driving frames; the reference image is resized to the resulting dimensions. Both use center cropping for aspect-ratio mismatches. Set one dimension to 0 to derive it from the other using the driving video's aspect ratio; do not set both to 0. The reference upload's alpha mask is not connected, so uploaded transparency is not preserved. Frame count sets the requested segment length. Motion strength and reference strength independently control their respective conditioning. Motion guidance start and end specify the sampling interval for pose guidance, with 0 to 1 covering the full process. Starting frame offsets motion conditioning, but the driving video's first frame still supplies its vision conditioning and the attached audio is not offset. Audio synchronization is therefore not guaranteed when changing the starting frame or removing the first generated frame. Optional context windows switch to temporal-window sampling. Window length, overlap, schedule, blending and FreeNoise apply only when enabled. Stride applies only to uniform schedules, and closed loop only to looped schedules; this toggle does not guarantee a seamless video loop. The schedule and blending fields retain source names without an enumerated list of alternatives, so use the defaults unless you know another value supported by the installed node. Advanced sampling retains the source's Lightx2v distillation LoRA at strength 1, six steps, lcm sampler, simple scheduler, CFG 1 and sampling shift 5. Seed -1 randomizes noise; a fixed nonnegative seed reuses its initialization. Denoise, added noise, latent batch size and LoRA strength remain available for advanced changes. Batch size defaults to 1; this Pipe returns one video rather than separate outputs for batch members. Output container and codec default to automatic selection, and CRF -1 uses the transport default. The delivered result is the generated animation. The source's side-by-side comparison is auxiliary, and its disconnected continuation stage does not extend this output.
Exploded view round trip
Create a combined video that animates from a recreated source image to a destination frame and back. Upload a source image and describe separated components in the destination prompt for an exploded view. Tune each direction's motion prompt, duration, camera and seed independently; keep both video stages' resolution and aspect ratio matched. Each clip defaults to four seconds, giving approximately eight seconds total. The combined video uses the outward clip's frame rate and audio only. Starting and destination frame settings remain independent.
Z-Image Turbo text to image
Create images from a text description using the fixed Z-Image Turbo INT8 model, Qwen text encoder and paired VAE. No uploaded image is required. For a first run, replace the example prompt with your subject, composition, lighting and style. Keep the supplied 1024 × 1024 dimensions, one image, eight steps and CFG 1, then run the tool. Width and height are separate pixel dimensions in multiples of 16 and determine the actual aspect ratio; composition wording in the prompt does not change them. Increase image count to return a batch, with greater memory and processing requirements. Seed -1 selects new random starting noise each run. Set a fixed seed when comparing prompt or sampling changes. In advanced Sampling, the sampler selects the denoising algorithm and the scheduler sets the noise levels across steps. Start with res_multistep and simple. CFG 1 uses positive conditioning without classifier-free amplification; higher values amplify guidance against zeroed negative conditioning. There is no editable negative prompt. Denoising fraction controls how much of the sampling schedule is used. Keep its supplied value of 1 for the initial run. Noise schedule exposes the model sampling shift, initially 3, which changes the schedule before denoising. These controls interact with the sampler, scheduler and steps; a fixed seed helps compare their effects. This tool generates from an empty latent and cannot transform an uploaded image. The model, text encoder and VAE are fixed. Every decoded image in the batch is returned to Sweet Tea; the selected output stage does not permanently save images through ComfyUI SaveImage.
Lens Turbo text to image
Generate images from a written description with the fixed Lens Turbo model, GPT-OSS text encoder and FLUX.2 VAE. No uploaded image is required. For a first run, replace the positive prompt with your subject, setting, composition and style. Choose an aspect ratio and keep the default 2 megapixels and one image. Run the Pipe to receive the generated image batch. Aspect ratio and megapixels jointly determine dimensions, which are rounded down to multiples of eight. The megapixel setting is an approximate size target. Larger dimensions and batches require more memory. The saved sampling setup uses four steps, CFG 1, Euler and the simple scheduler. At CFG 1, the negative prompt has no effect; use it to describe unwanted content when experimenting with guidance above 1. Seed -1 selects a new random seed each run. Set a fixed seed to compare prompt or sampling changes. Advanced controls expose the sampler, scheduler, steps, CFG, noise initialization, denoise schedule, guidance normalization and resolution-dependent sampling shifts. Keep Add noise enabled and Denoise at 1 for the default generation setup. Lowering denoise changes the noise schedule; it does not provide image-to-image preservation because generation starts from an empty latent. Normalize before CFG switches from post-CFG attenuation to unclamped normalization before CFG combination, which can amplify the combined noise. Base and maximum sampling shifts work with the chosen output dimensions. The model, text encoder and VAE are fixed in this Pipe. All requested batch images are returned through the same image output; the output stage does not permanently save them.
Luma video with camera motion
Generate one Luma video from a prompt, optional start and end images, and camera-motion concepts. Configured partner API access and sufficient credits are required; the prompt and enabled reference images are sent to the provider. For a first run, upload a Start image, replace the example prompt with a description of your scene and movement, and keep ray-2, 720p, and 5s. Start image is enabled initially, so upload an image or bypass that stage before running. To generate from text alone, bypass Start image and leave End frame disabled. Camera motion initially combines zoom_in and orbit_left. Each of its four slots is independent; set a slot to None to omit it. Additional camera motion is disabled initially and supplies four more slots when enabled. Both camera stages must be enabled for those additional slots to affect generation. Bypass Camera motion to omit the entire concept chain. To guide the ending, enable End frame and upload an end image. Loop requests a looping video. Model, resolution, duration, looping, and frame-reference combinations must be supported by the provider; this draft does not enumerate all valid combinations. Seed -1 randomizes the rerun seed. A fixed seed controls whether the API node reruns, but does not reproduce the same generated video. The result is one video. Under Video output, keep File type and Codec on auto for automatic selection, or choose the exposed container and codec settings. These settings affect output encoding rather than the scene, motion, or generation model.
Animate an image with Vidu Q2
Generate one short video from an uploaded starting image using Vidu Q2, with optional text instructions and adjustable movement. For a first run, upload your image, describe the subject's action and motion, and keep the defaults: Vidu Q2 Pro Fast, 5 seconds, 720p, and automatic movement amplitude. The motion prompt may be empty and supports up to 2,000 characters. Generation sends your image and prompt to the paid Vidu provider. Duration supports 1–10 seconds, and resolution offers 720p or 1080p. Movement amplitude offers auto, small, medium, or large to influence how much objects move. The model selector offers Pro Fast, Pro, and Turbo; these are selectable generation variants. Seed -1 randomizes each run. You can supply a fixed seed, but identical provider results are not guaranteed. Advanced video output settings offer automatic selection or explicit file type and codec choices; leave both on auto initially. The Pipe returns one video through Sweet Tea.
Floral Style
This is a subtle Textual Inversion Style Embedding trained on colorful wildflowers. You can get amazing bright flowers and nature-greens in your images by calling Style-Petal in the positive prompt, and if you want...
Text to audio with Stable Audio Open
Generate music or sound from a text description with Stable Audio Open and export it as MP3. For a first run, keep the example audio prompt or replace it with a description of your desired sound, instruments, mood, or musical style. Keep the source defaults: 47.6 seconds, one audio sample, and V0 MP3 encoding. Run the Pipe to generate and export the audio. No media upload is required. The workflow requires stable-audio-open-1.0.safetensors in the checkpoints model directory and t5-base.safetensors in the text_encoders model directory. Both are identified by the source; the supplied dependency records mark them as missing, so they must be available before execution. Duration sets the requested length in seconds. Audio count generates a batch of samples with the same prompts and settings. MP3 quality selects V0, 128k, or 320k export encoding. In Advanced, leave the negative prompt empty or describe unwanted qualities. Seed -1 randomizes generation; a fixed seed reuses the starting noise for comparisons with otherwise unchanged settings. Advanced sampling controls expose the source's denoising steps, prompt guidance, sampler, noise schedule, and denoising strength. Start with the supplied settings. The workflow starts from an empty audio latent and does not accept existing audio for editing. The duration widget's broad range and the sampler menu do not establish model support for every length or alternative algorithm. Changing MP3 encoding affects export, rather than the generated content.
