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Ovis-Image text to image
Generate one image or a batch from text with Ovis-Image. Describe your subject, setting, composition, lighting and style in the positive prompt; include any text you want rendered. Optionally describe unwanted elements in the negative prompt, or leave it empty. For a first run, replace the example positive prompt and keep width and height at 1024 pixels and batch size at 1. Width and height use multiples of 16; together they determine the aspect ratio. Batch size sets the number of returned images. Larger dimensions and batches require more memory. Advanced sampling defaults are 20 steps, prompt guidance (CFG) 5, Euler, the simple scheduler, denoise 1 and sampling shift 3. Steps set the denoising step count; CFG controls prompt guidance. Sampler selects the sampling algorithm, while scheduler distributes noise levels across steps. Sampling shift adjusts the model's noise schedule. Keep these defaults for the first run. Seed -1 selects fresh random noise each run; a fixed seed reuses the initial noise with otherwise unchanged settings. This tool generates from an empty latent and accepts no source image. Denoise ranges from 0 to 1; keep 1 for full text-to-image generation. Lower values shorten the noise trajectory and do not preserve an uploaded image. The output returns every image in the batch. The runtime needs the fixed source files ovis_image_bf16.safetensors in diffusion_models, ovis_2.5.safetensors in text_encoders and ae.safetensors in vae, plus the Sweet Tea output node pack. Model selection is fixed in this Pipe.
Kandinsky 5 image to video
Animate an uploaded still image into a short, silent video with Kandinsky 5 Lite and a scene and motion prompt. For a first run, upload your starting image, replace the example flower prompt with a description matching it, and describe the subject movement and camera movement you want. Keep 768 × 512 pixels, 121 frames, 24 FPS, and the default sampling settings. The default output is an H.264 MP4 lasting approximately five seconds. The source image is resized with Lanczos and center cropping to the selected dimensions. Choose an aspect ratio that keeps important subjects in view. Use positive dimensions of at least 16 pixels, divisible by 16. Uploaded transparency is not preserved: this path uses the image loader's IMAGE output, not its alpha mask. Frame count controls how many frames are generated; use counts of 4n + 1, such as 121. FPS controls how quickly those frames play and does not generate or interpolate additional frames. This Pipe uses the fixed five-second model; increasing frame count does not select the template's ten-second model. Use the optional negative prompt to describe unwanted content. Prompt guidance (CFG) controls conditioning strength. Seed -1 requests a fresh random seed each run; use a fixed seed when comparing settings. Steps, sampler, scheduler, sampling shift, and denoising fraction are advanced sampling controls. Retain their defaults initially. Denoising fraction is not a direct image-preservation slider in this graph. Keep generation batch size at 1 for a single clip. Larger batches feed the same video assembly path rather than separate clip outputs. Video output settings select the container and codec; retain MP4 and H.264 for the first run. No audio input is connected. Larger dimensions or more frames increase processing and memory requirements.
Stable Audio 3 audio generator
Produce MP3 audio from a text description using Stable Audio 3 Medium Base. No uploaded media is required. Describe the instruments or sound source, mood, texture, space, and desired progression; include BPM when it matters for music. For a first run, keep the supplied lo-fi description, Music category, 150-second Duration, prompt expansion enabled, and batch size 1, then generate. For an isolated hit, select One-shot and set a short Duration appropriate to its decay. Enter category values exactly as Music, Instrument, SFX, or One-shot. They select expansion instructions for the same audio generator, rather than separate generation models. Prompt expansion uses Qwen to elaborate your description. Duration sets the audio latent length independently and is converted to an integer for the expansion template. Use whole seconds to align these requests. The downstream duration input accepts 1–1000 seconds; this is an input range, not a guarantee of useful model behavior throughout that range. Category templates contain their own length suggestions, so rewritten length wording may differ from your requested Duration. Disable Expand prompt to send your description directly to audio conditioning. Category and expansion tuning then do not affect the selected prompt. Include desired tempo and length wording yourself. Expansion sampling off has a different purpose: expansion remains selected, but text generation uses no sampling. With sampling on, the expansion seed, temperature, and token selection controls affect the rewrite. Expansion token limit is measured in text tokens, not seconds. Thinking requests model-supported thinking behavior; the graph sends the resulting text directly to audio conditioning without a separate reasoning-text removal stage. Negative prompt supplies exclusion conditioning; leave it empty for the supplied default. Advanced Sampling controls the audio seed, steps, guidance, sampler, schedule, and denoising amount. Keep Denoise at the supplied 1 for full generation from an empty latent. Audio and expansion seeds are independent; -1 randomizes each run. To hold sampled expansion and audio noise choices fixed, set both seeds and retain the other settings. Audio batch controls how many duration-sized latents are sampled together. Longer clips, more steps, and larger batches increase generation work. Instrument and loop descriptions guide generation but do not guarantee isolated stems or seamless loop boundaries. MP3 quality selects V0 variable bitrate, 128k, or 320k compression and does not change the generated audio content. The source specifies stable_audio_3_medium_base.safetensors in checkpoints and t5gemma_b_b_ul2.safetensors in text_encoders from https://huggingface.co/Comfy-Org/stable-audio-3, plus qwen3.5_2b_bf16.safetensors in text_encoders from https://huggingface.co/Comfy-Org/Qwen3.5 for expansion.
Boring_e621 Negative Embedding: Enhance Images Stylistically AND Topically
This is a download site for the negative embedding boringe621. Its original huggingface repository, which goes into more detail about how it was trained and what it does, can be found here .
Grok video
Generate one short video from a starting image and prompt, or from text alone with grok-imagine-video. For a first run, upload a starting image, keep Source image enabled, and replace the example prompt with a description of your scene's action, camera movement, and atmosphere. Keep grok-imagine-video-1.5, 720p, and the five-second duration, then run the Pipe to receive the video result. Grok-imagine-video-1.5 requires a starting image and supports 480p, 720p, and 1080p. For text-only generation, select grok-imagine-video and bypass Source image; choose 480p or 720p. That model also accepts a starting image when Source image remains enabled. Duration is measured in seconds, from 1 to 15. Aspect ratio sets the video's proportions; auto leaves the choice to the generator. Resolution sets the actual output resolution, regardless of resolution words such as 4K in the prompt. Leave Seed at -1 for a fresh randomized value each run. The seed controls whether the provider node re-runs; repeating a value does not reproduce the same video. Advanced Video output controls let you select the file type and codec; keep auto for the first run. Generation uses the Grok provider, sends the prompt and any starting image externally, and involves a provider-priced call.
Flux 2 Klein Base text to image
Create images from a text description using Flux 2 Klein Base 4B. This tool uses the base model with separate positive and negative prompts and adjustable CFG guidance. For a first run, replace the example positive prompt with your subject, setting, composition, and desired visual style. Leave the negative prompt empty and keep the source defaults: 1024 × 1024 pixels, one image, 20 steps, CFG 5, and Euler sampling. Run the Pipe to receive the generated image in Sweet Tea. No uploaded image is required. Width and height set both the latent image dimensions and the resolution used by the sampling schedule. Enter positive multiples of 16, at least 16 pixels. Together they determine the aspect ratio. Images per run creates a batch with the same prompt and dimensions; larger dimensions and batches require more memory. Use the negative prompt to describe elements or visual traits to discourage, or leave it empty. CFG adjusts the influence of positive and negative conditioning; at CFG 1, the negative prompt has no effect. In the advanced controls, steps set the sampling iteration count and sampler selects the sampling algorithm. Keep 20 steps and Euler for the source setup; changing either can change the result. More steps do not guarantee a better image. Seed -1 selects fresh random starting noise each run. Use a fixed seed to compare prompt or sampling changes with the same starting noise. This Pipe returns only the base-model branch's images. The separate distilled four-step Klein model belongs to the sibling tool and is not a selectable mode here. The diffusion model flux-2-klein-base-4b.safetensors, text encoder qwen_3_4b.safetensors, and VAE flux2-vae.safetensors are fixed dependencies. The output transport returns images to Sweet Tea without permanently saving them through ComfyUI.
Atompunk Style
Follow me to make sure you see new poses and Nobodys when I post them. Things move fast on this site, it's easy to miss.
Recompose a graphic for a new format
Recompose a flattened graphic for a new aspect ratio through generative background extension and layout rearrangement. For a first run, upload a graphic, keep Nano Banana 2 selected, and use its default 16:9 aspect ratio and 2K resolution. Read the layout instructions and adjust them to identify the text, logos, imagery and placement constraints you want preserved. Run the Pipe and inspect the returned graphic. Nano Banana 2 and Nano Banana Pro receive the same source image and layout instructions, but have independent aspect ratio, resolution and seed controls. Configure the settings for the model you select; the model choice determines which image reaches the output. Choose auto aspect ratio to match the source format. Nano Banana Pro offers 1K, 2K and 4K resolution settings. Nano Banana 2 also exposes thinking level, temperature and top p; its supplied resolution default is 2K, and the supplied schema does not enumerate other accepted values. A seed of -1 randomizes each run; fixed seeds provide best-effort repeatability without guaranteeing identical results. The provider response format can request IMAGE or IMAGE+TEXT. Only the selected model's final image is returned; text responses and thinking images are not included. This edits a flattened image rather than editable design layers. Text, logos, proportions and placement can change despite preservation instructions, so inspect the result before using it. Uploaded transparency is not preserved end to end: only the loader's image output is connected, not its alpha mask. Google Gemini API access is required; provider generation sends the source image and instructions externally and can incur provider charges.
LTX-2.5 image to video with audio
Animate a starting image into one video with generated audio using LTX-2.5. Upload a first frame and describe the scene, action, camera movement, and desired sounds or dialogue. There is no audio-upload input. For a first run, replace the example prompt to match your image and keep 5 seconds, 24 FPS, 16:9, and 0.9 output megapixels. Use an image with a similar aspect ratio when composition matters. Aspect ratio and megapixels determine output dimensions rounded to a 32-pixel multiple. Initial generation uses half those dimensions, followed by 2× spatial latent upsampling and joint video/audio refinement. Increasing resolution, duration, or FPS increases the work required. Enhance prompt selects a rewrite informed by your text and uploaded image. Turn it off to select your original wording. Enhancement length controls the rewrite's text budget, not video duration. Sample enhanced wording uses on/off choices; temperature, probability filters, penalties, and enhancement seed apply when sampling is on. Thinking mode depends on support from the fixed enhancement model, and Use enhancement template selects its built-in template when available. Text-to-video mode bypasses starting-frame injection in both stages. An image upload remains required because preprocessing stays connected. Also turn off Enhance prompt to make generation conditioning come only from your text. Initial and refinement image strengths are independent and inactive in text-to-video mode. The uploaded alpha mask is not connected; transparency is not preserved as an output feature. Enter positive whole-second duration and a whole frame rate from 1 to 120 FPS. Choose duration × FPS divisible by 8, with duration × FPS + 1 at most 1000. At 24 FPS, whole-second durations from 1 through 41 meet these conditions. The default requests 121 frames, so playback can be about one frame longer than five seconds. Generation, refinement, and sampled prompt enhancement have separate seeds. Use -1 to randomize each run. For comparisons with fixed random inputs, set all applicable seeds explicitly and keep enhancement settings unchanged. Both stages share the negative prompt and expose separate video and audio guidance inputs. Keep guidance at 1 for a first run; supported tuning ranges are not established by the supplied evidence. Advanced settings preserve independent samplers and editable comma-separated noise schedules for both stages, plus source-image compression from 0 to 100. Keep their authored defaults initially. There is no separate Steps control: the manual schedules define sampling transitions. Image compression affects the image used for enhancement and conditioning. Export settings offer automatic or explicit container and codec selection, with 8- or 10-bit video assembly. Keep auto and 8-bit for a first run. The source notes that 10-bit supports smoother gradients but may have more limited compatibility. Export settings affect encoding rather than generation. The retained CRF field stays hidden at -1 because its effective quality behavior is not established by the supplied evidence.
Kling text to video
Generate one video from a text description through the Kling partner API. Provider authentication and access are required; generation sends your prompt to the provider and incurs provider charges. No image upload is required. For a first run, use the supplied example prompt or describe your subjects, action, setting, camera movement, and visual style. Keep Kling 3.0 Omni, 16:9 framing, five seconds, and 720p resolution, then run to receive the video result. Audio generation is enabled by default. Include things to avoid in the same prompt when needed. Choose 16:9 for landscape, 9:16 for portrait, or 1:1 for square framing. Duration is in seconds and accepts 3–15; resolution offers 720p, 1080p, and 4k. The model selector also offers Kling video o1. Audio support with o1 is not established by the supplied evidence; turn Generate audio off when trying that model. Model, duration, resolution, and audio settings can affect provider cost. This tool supports one main prompt. Storyboard mode stays disabled because editable segment prompts and durations are unavailable in this interface. Leave Seed at -1 for a randomized rerun seed. A fixed seed can affect rerunning and cache reuse, but provider results remain nondeterministic. The advanced Video output section controls file type, codec, and encoding quality. Leave auto for file type and codec, and -1 for CRF on a first run. These settings control the returned video's encoding; codec-specific CRF behavior is not documented in the supplied evidence.
Luma image transformation
Transform one uploaded source image with a text prompt using Luma Photon-1 and preview the generated result. For a first run, upload your source image, replace the example prompt with your desired subject, appearance, and setting, then run the Pipe. Image weight ranges from 0 to 0.98 and defaults to 0, allowing substantial reinterpretation. Higher values modify the original less; increase the weight when you want the result to stay closer to the source. Leave Seed at -1 to randomize each run. Changing Seed requests a fresh provider run, but reusing a seed does not reproduce an image: Luma results are nondeterministic. The model is fixed to Photon-1 in this Pipe. The image and prompt are sent to Luma through a paid provider call, which requires API access and sufficient credits. Only one source image is supported. Uploaded transparency is not preserved because only the loader's RGB image output is connected.
Image to creative prompt
Create and save a descriptive text-to-image prompt from a reference image. For a first run, upload a Source image, leave the default Request and Response instructions in place, and run. The saved text describes visual details such as subject, style, environment, lighting, composition, colors, and mood. An image is needed while Source image is enabled; the empty upload default requires your own reference. Edit Request to specify what to describe or emphasize. In advanced Response settings, Response instructions independently set writing style, content priorities, and output format. Maximum response tokens limits generated output to a budget of 16–16384 tokens; it is not an exact word count, and a small limit may cut the response short. Context truncation controls oversized input: Auto permits input truncation, while Disabled makes an oversized request fail. For a text-only task, bypass Source image and replace both the image-focused Request and Response instructions with instructions suited to your task. Model is selectable; when using an image, choose a model that accepts image context. This workflow sends the request and enabled image context to OpenAI and uses a provider-priced API call. Its output is text, not a generated image, and it does not guarantee that another image generator will recreate the reference.
Capybara image edit
Edit an uploaded image with written instructions using Capybara v0.1 and receive still-image results. For a first run, upload a source image, replace the example edit instruction with the changes you want, and state which subjects or details should remain. Keep the default sampling settings and generate one image. The prepared source supplies both image conditioning and visual reference features; these do not guarantee that requested details remain unchanged. Width and Height set both the prepared source size and the generated image dimensions in pixels. The default is 1024 × 1024 with a center crop. Center preserves proportions but can remove content near the edges; Disabled stretches the source to fit. Use dimensions divisible by 16, such as 1024 × 1024 or 1360 × 768. Setting one dimension to zero derives it from the other using the source aspect ratio; do not set both to zero, and ensure the derived dimension is also divisible by 16. Resampling method controls source interpolation: Area is suited to reduction, Lanczos to enlargement, and Nearest-exact to pixel-art edges. Images per run requests multiple edited variants. Seed -1 selects fresh randomization each run; a fixed seed reuses the sampling noise. Negative prompt lists unwanted features, and Prompt guidance (CFG) scales positive guidance relative to negative conditioning. Steps sets the sampling step count. Start with the default Euler sampler, Simple scheduler, 20 steps, CFG 6, and sampling shift 7; the advanced controls change sampling behavior. Visual reference crop optionally adds a center crop when encoding reference features, after the source has already been resized. It does not change output dimensions or the separate source-image conditioning branch. Noise schedule fraction controls how much of the sampling noise schedule is used; it is not a direct source-preservation strength control. The output is still images despite the HunyuanVideo conditioning node names. Frame count comes from the prepared image batch rather than an editable duration. There is no mask-based regional edit input, and uploaded transparency is not preserved. Results are returned through the image preview output.
Seedream image generation
Generate an image from a text description with Seedream and preview the result in Sweet Tea. This Pipe uses the ByteDance provider API and requires configured provider access; generation uses provider billing. It has no reference-image input or separate negative prompt. For a first run, keep the supplied cyberpunk poster prompt or replace it with your subject, composition, visual style and desired text, then run the Pipe. The default model is Seedream 5.0 Pro, and the default output size is 1024 × 1024 pixels. Choose Model and Output size together: recommended size presets depend on the selected model. Select Custom to use Custom width and Custom height; otherwise those fields have no effect. Their stored defaults are 2048 × 2048 pixels. The exposed custom limits are 1024–3136 pixels wide and 1024–2496 pixels high, in steps of two pixels, as supplied for the default Pro model. Prompt reasoning enables the model's prompt-optimization step and is on by default. The source notes that it can substantially increase generation time, especially with 5.0 Pro; this text-only Pipe allows it to be disabled. Seed -1 selects a fresh random seed per run; use a nonnegative seed to control generation randomization. Enable Watermark to add an “AI generated” mark. The selected output returns an image preview through Sweet Tea rather than using the source workflow's permanent image-saving stage.
DALL·E 3 image generator
Generate an image from a text prompt using DALL·E 3 and view the result in the Pipe output. Enter a prompt describing the subject, composition, lighting, and visual treatment, or use the supplied example for a first run with the default square size, vivid style, and HD quality. Choose dimensions in pixels: 1024x1024 for square images, 1024x1792 for portrait, or 1792x1024 for landscape. Vivid favors dramatic, hyper-real imagery; natural favors a less exaggerated appearance. Use the prompt to specify the artistic medium. Quality selects the provider's standard or HD mode independently of output size. This workflow accepts text only; it has no image input or separate negative prompt. It requires authenticated access to the ComfyUI partner API service and sufficient credits, and generation sends the prompt to the external provider. The source marks backend seed support as unimplemented, so a fixed seed does not guarantee repeatable images. The selected output stage returns the generated image for preview rather than saving it through the original ComfyUI SaveImage node.
Brightness and contrast
Create a brighter, higher-contrast image from an uploaded source image. For a first run, upload your image and run with the saved settings: Brightness 13 and Contrast 51. These settings apply an adjustment rather than a neutral preview. To start with no tonal adjustment, set both controls to 0, then change either control and run again. Both controls use a unitless 0 to 100 scale. Brightness adds a linear-light offset from 0 to 1. Contrast uses a multiplier from 1 to 2 around 18% linear middle gray, making values below that pivot darker and values above it brighter. The brightness offset is added after contrast, so the two controls combine in the result. This interface supports brightening and increasing contrast; it does not offer negative adjustments for darkening or reducing contrast. Processing approximates sRGB conversion with gamma 2.2 and clamps adjusted linear RGB values to the available range. Strong settings can clip shadow or highlight detail. Output dimensions follow the source image. Uploaded transparency is not preserved because the image loader's separate alpha mask is not used. This output applies only brightness and contrast; the other adjustments in this family are separate tools.
Vidu Q1 reference to video
Generate one five-second 1080p video from three reference images and a scene prompt using Vidu Q1. References guide the subjects and setting; they are not prescribed start or end frames. For a first run, upload an image into each of the three required reference slots. Replace the example prompt with a description matching your images, explaining how their subjects or elements appear together, what happens, and how the camera moves. Choose landscape 16:9, portrait 9:16, or square 1:1 framing, and leave movement amplitude on Auto initially. Small, Medium, and Large request different amounts of object movement; describe specific actions in the prompt. Leave seed at -1 to randomize each run, or enter a positive integer to request a particular generation seed. The provider also treats 0 as random. Duration is fixed at five seconds, resolution at 1080p, and the model at Vidu Q1. This Pipe uses exactly three connected reference slots even though the provider supports up to seven references. Generation sends your reference images and prompt to the Vidu partner service, requires access to that service, and incurs provider charges. The generated video is returned to Sweet Tea for preview with automatic format and codec selection.
Lip sync video to audio
Create one video with lip movements adjusted to follow an uploaded speech or vocal track. For a first run, upload the source video containing the person to synchronize and upload the audio track their lip movements should follow. Both files are required. Leave the advanced Video output settings at their defaults, then run the Pipe. Video output lets you choose a file type (Auto, MP4, MKV or WebM), codec (Auto, H.264 or AV1) and output encoding CRF. These settings affect the returned video's encoding, not the lip-sync processing. This Pipe uses uploaded audio and offers no recording mode. Lip-sync processing uses the exported node configuration; there are no exposed seed, processing-mode, trimming or duration controls. Behavior when the video and audio lengths differ is not established by the supplied evidence.
Nano Banana image editor
Edit a reference image with written instructions using Nano Banana 2 or Nano Banana 2 Lite, and view the generated image in Sweet Tea. For a first run, upload an image and describe the changes you want, including features that should remain unchanged. The existing example prompt makes an F1 racing poster with a yellow car, replacement lettering and motion effects. Use it with a car image, or rewrite it for your own subject. There is no separate negative prompt; include all constraints in Edit instructions. The default uses Nano Banana 2 Lite at 1K. Select the model before setting resolution: use 1K with Lite, or 1K, 2K or 4K with Nano Banana 2. Auto aspect ratio follows the reference image; another ratio requests different output proportions and composition. Thinking level selects MINIMAL or HIGH reasoning effort. Temperature controls generation randomness from 0 to 2, and nucleus sampling controls the sampling threshold from 0 to 1. Seed -1 requests a new random seed each run. A fixed seed requests best-effort consistency, but identical results are not guaranteed, especially when model or sampling settings change. System instructions provide optional foundational behavior; the supplied default asks the provider to produce an image. This workflow sends the reference image and instructions to the Gemini provider and uses a provider-priced generation call. Generative editing does not guarantee preservation of geometry, lettering or fine details. The uploaded alpha mask is not connected, so transparency preservation is not supported by this graph. Only the generated image is returned; provider text and thinking images are not separate results. The selected output is a Sweet Tea preview, replacing the original file-saving stage.
