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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.
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.
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.
Vidu Q1 text to video
Generate one five-second 1080p video from a text prompt with Vidu Q1. For a first run, use the supplied snowboarding prompt or replace it with a description of your subject, setting, action, and camera framing. Keep aspect ratio at 16:9 and movement amplitude at auto, then run. No uploaded image is required. Choose 16:9 for landscape, 9:16 for portrait, or 1:1 for square framing. Movement amplitude requests small, medium, or large object movement; auto lets the service choose. Duration is fixed at five seconds and resolution at 1080p in this configuration. Camera framing is described through the prompt. Use seed -1 to randomize each run, or enter a nonnegative seed to request a particular variation. Identical results across provider runs are not guaranteed. The advanced Video output stage offers file type and codec choices. Leave both at auto for automatic encoding selection. The generated video is returned to Sweet Tea through its preview transport. Generation sends your prompt to the Vidu partner API and requires an authenticated runtime with sufficient credits and service access.
Sketch and collage to image
Transform an uploaded sketch, collage, or image into one generated image with Seedream 5.0 Pro and written editing instructions. For a first run, leave Source image enabled, upload your image, and replace the example living-room instructions with a description suited to your source. Specify which elements to preserve, replace, or reinterpret, then choose Output size and run. The result appears in Sweet Tea's image preview; this workflow does not permanently save a PNG. Custom width and Custom height set the generated dimensions in pixels only when Output size is Custom. These are separate from the reference canvas dimensions. To work without an upload, disable Source image and open Reference canvas. Canvas width, Canvas height, and Background color define a blank reference image when drawing data is empty. Describe the desired result in Editing instructions. This mode still sends a reference image to Seedream; it is not a reference-free text-to-image mode. Uploaded sketches and collages are supported, but this interface does not expose an interactive drawing editor or Painter's undocumented drawing-data encoding. Seed -1 requests a new random seed on each run; a nonnegative seed controls generation randomization. Enable AI watermark to add an AI-generated watermark. The model remains Seedream 5.0 Pro, and prompt reasoning remains enabled because both modes supply a reference image. Reasoning can increase generation time.
Wan 2.1 transparent animated WebP
Generate a transparent animated WebP from a text description using Wan 2.1 and its RGBA adaptation. Color and alpha are decoded from the same generated video latents and combined before encoding. No uploaded image is required. For a first run, replace the example prompt with your subject, movement, framing, and visual style, and request a transparent background. Keep 832 × 480 pixels, 33 frames, batch size 1, and 16 playback FPS for approximately 2.06 seconds. Width and height use multiples of 16; frame counts follow 1, 5, 9, and so on. Larger dimensions and more frames increase generation work. FPS changes playback speed and duration without generating more frames. Advanced Sampling controls include Seed, Steps, CFG, sampler, scheduler, and denoising amount. Seed -1 randomizes each run; use a fixed seed to compare settings. The default configuration uses 20 steps, CFG 6, uni_pc, simple scheduling, and full denoising. The negative prompt lists unwanted content and artifacts; its supplied default is in Chinese. Lightx2v is optional and bypassed by default. To use the documented accelerated configuration, enable that stage and manually set Steps to 4 and CFG to 1. Its strength has no effect while bypassed. Keep RGBA adaptation strength 1 and sampling shift 5 for a first run. RGBA adaptation strength changes the model, not output opacity. WebP output exposes playback FPS and encoding controls. Lossless encoding is enabled by default. Disable it for lossy encoding, where Quality controls detail retention; in lossless mode Quality controls compression effort. Keep Compression method at default initially. Keep batch size 1 for one animation: this selected output encodes the resulting image batch into animated WebP and does not provide separate video outputs. The result is animated WebP rather than MP4. Use a viewer or compositing tool that supports animated WebP and alpha. Generated subject boundaries and fine details may need review before compositing.
Ideogram 4 image generator
Generate images from scene prompts using the fixed Ideogram 4 conditional and unconditional model pair. Enter your completed scene description in Positive prompt; structured JSON is recommended for compositions containing text and positioned objects. No uploaded media is required. For a first run, keep the supplied monochrome knight-and-horse magazine-cover prompt, square aspect ratio, 1 megapixel output, Default sampling preset and Image count 1, then generate. The example mentions image_0f5a20.png, but there is no reference-image input and that file is not supplied to the model. Replace that mention with a written typography description when adapting the example. Technical configuration text inside the prompt is requested lettering in the image; it does not change the actual sampler, steps or seed. Output size controls both the latent canvas and scheduler dimensions. Dimensions round upward to multiples of 16, with a minimum of 256 pixels per side. Changing the canvas aspect ratio does not rewrite the prompt's JSON aspect_ratio or bounding boxes; adjust those manually to suit your composition. Optional boxes use [y1,x1,y2,x2] coordinates normalized to 0–1000. Sampling preset selects coordinated scheduler settings: Default uses 20 steps, mu 0 and std 1.75; Quality uses 48 steps, mu 0 and std 1.5; Turbo uses 12 steps, mu 0.5 and std 1.75. These names identify sampling configurations rather than guaranteed output quality. Advanced controls expose guidance, a guidance override interval, seed, sampler and image count. Main CFG defaults to 7; Override CFG defaults to 3 between progress 0.7 and 1.0. Keep Override start below Override end. Set the override equal to main CFG to remove their guidance difference. Seed -1 selects fresh noise each run; a fixed seed allows comparisons with the same noise initialization. Euler is the source sampler default. Larger image counts and resolutions require more memory and computation. The workflow uses ideogram4_int8_convrot.safetensors and ideogram4_unconditional_int8_convrot.safetensors in diffusion_models, qwen3vl_8b_fp8_scaled.safetensors in text_encoders, and flux2-vae.safetensors in vae. These fixed models must be available in the execution environment. The supplied text-encoder installation source is https://huggingface.co/Comfy-Org/Qwen3-VL/resolve/main/text_encoders/qwen3vl_8b_fp8_scaled.safetensors. There is no independent negative prompt: negative conditioning is zeroed from positive conditioning and passed to the unconditional model. The source's separate external-LLM instruction template is outside this image output. This Pipe does not call an LLM or automatically turn an idea into JSON; paste your finished prompt directly into Positive prompt.
SeedVR2 image upscale
Create an enlarged, restored image with SeedVR2 and optional source color matching. Upload the image to restore, leave Scale factor at 4 and Color matching at none, then run. The default multiplies both dimensions by 4, producing approximately 16 times the source pixel count before restoration. No text prompt is required: the uploaded image supplies conditioning. The model is fixed to SeedVR2 3B int8 with its matching VAE. Scale factor controls the initial resize: values above 1 enlarge, 1 retains the source dimensions and values below 1 reduce them. SeedVR2 restoration still runs in each case. Resampling method controls interpolation before restoration; lanczos is the source default for enlargement, area supports reduction and nearest-exact retains hard pixel boundaries during resizing. Restoration may subsequently change those details. Color matching uses the resized source as its reference. Choose none to skip color transfer, lab for CIELAB color transfer, wavelet to transfer broad color while retaining restored high-frequency detail, or adain to match per-channel mean and standard deviation. Advanced restoration settings include Seed, Denoise, Steps, Conditioning guidance, Sampler and Scheduler. Seed -1 randomizes each run; use a fixed seed to keep the sampling noise consistent when comparing settings. For a first run, retain denoise 1, one step, guidance 1, Euler sampling and the simple schedule. Other sampling configurations are experimental for this source setup. Advanced input and output tiling independently control spatial encoding and decoding. Tile size and overlap are in pixels; keep each overlap below its corresponding tile size. Larger output dimensions increase the amount of image data processed. Restoration can reinterpret fine details rather than recover exact missing information. Uploaded image and alpha-mask outputs are joined before resizing and preprocessing, but final output transparency is not established; do not rely on this tool to preserve transparency.
DALL·E 2 image generation
Generate one or more square images from a text prompt with DALL·E 2. For a first run, replace the example prompt with your subject, composition, lighting and visual style, keep Output size at 1024×1024 and Image count at 1, then generate. Choose 256×256 or 512×512 for smaller images, or request up to eight images using the same prompt and size. Results are returned as an image batch for preview; this workflow does not permanently save them. Generation sends your prompt to OpenAI and uses a paid provider call. Only text-to-image generation is connected: reference-image editing and masked inpainting are unavailable in this Pipe. The source backend does not implement seed, so a fixed seed does not provide reproducible results.
First and last frame to video · LTX-2.5
Generate one video with generated audio, guided by a starting image, an ending image and a motion prompt. Both images are required endpoint guides; they do not guarantee exact copies of the uploaded frames. For a first run, upload both images and describe the action connecting them, camera movement and desired sound. Keep 1280 × 720, 5 seconds, 24 FPS, endpoint strengths of 0.7 and prompt enhancement enabled. Both uploads are center-cropped to the shared dimensions. Separate advanced image-fit controls can stretch either image instead. Uploaded alpha masks are not connected to generation. Prompt enhancement uses your text and the resized, compressed first image; it does not inspect the last image. Turn enhancement off to use your written prompt directly. Within enhancement tuning, sampling on/off selects sampled or unsampled text expansion independently of the enhancement toggle. Temperature, seed and token sampling filters apply to sampled expansion. The length limit and built-in template are also available. Thinking mode is conditional on model support, which the supplied evidence does not establish for the configured model. Use positive whole-second durations and integer frame rates from 1 to 120. The graph requests duration × FPS + 1 frames: keep duration × FPS divisible by 8 and the resulting count at or below 1000. Playback can be about one frame longer than requested. Duration is entered manually. Use positive image dimensions of at least 64 pixels and keep the supplied dimensions for the first run. Larger dimensions or longer clips increase generation and decoding work. Adjust the starting and ending guide strengths independently to change each image's influence. Video and audio text-guidance scales are separate and default to 1; negative conditioning may have little or no effect at those defaults. The negative prompt starts empty so you can add exclusions suited to your own scene. Audio is generated jointly with the video and included in the returned clip; this interface has no audio upload or silent-output mode. Generation noise and sampled prompt enhancement have separate seeds. Use -1 for fresh randomization. For comparisons with sampled enhancement enabled, fix both seeds. Advanced controls also expose image compression, ancestral noise and the comma-separated distilled noise schedule. Keep their source defaults for the first run. Video and audio batch counts must match; keep both at 1 for the supported single-video output. Separate clips from larger batches are not established by this graph. This is a fixed LTX-2.5 distilled model recipe. Install ltx-2.5-22b-distilled-transformer-comfy-int8-convrot.safetensors in diffusion_models; gemma4-12b-with-proj-ltx-2.5-comfy-int8-convrot.safetensors in text_encoders; and ltx-2.5-video-vae-bf16.safetensors plus ltx-2.5-audio-vae-bf16.safetensors in vae. Their source is https://huggingface.co/Lightricks/LTX-2.5. Prompt enhancement additionally uses gemma4_e2b_it_bf16.safetensors in text_encoders from https://huggingface.co/Comfy-Org/gemma-4. Host authorization may be required to obtain model files.
HunyuanVideo text to video
Generate a silent video from a scene and motion prompt using HunyuanVideo. Describe the subject, setting, visual style, action and camera movement. No uploaded image or video is required. For a first run, replace or edit the example prompt and keep 848 × 480 pixels, 73 frames, 24 FPS and 20 sampling steps. This produces approximately three seconds of video. Use dimensions divisible by 16 and frame counts of the form 4n + 1, such as 49, 73 or 97. Larger dimensions and more frames increase computation and memory requirements. Frames determines how many frames are generated. FPS changes playback timing without generating additional frames: duration is approximately Frames divided by FPS. Keep 8-bit initially; 10-bit offers smoother gradients but requires compatible encoding and playback support. Advanced controls include prompt guidance, seed, sampling steps, sampler, scheduler and sampling shift. Seed -1 chooses fresh starting noise each run; use a fixed seed when comparing settings. Start with the supplied generation settings. The sampling shift modifies the model used by the guider, while the scheduler receives the original model, following the source graph. The sampling schedule fraction defaults to 1 for generation from an empty latent; lowering it shortens the schedule and does not preserve an uploaded image. Keep latent batch size at 1 for a first run. Increasing it samples multiple latent videos, but the workflow assembles decoded frames into one video output rather than returning separate clips. Tiled decoding exposes spatial tile size and overlap, plus temporal frame chunk size and overlap. Start with the supplied settings and keep each overlap smaller than its corresponding tile or chunk size. Video encoding offers automatic, MP4, MKV or WebM container selection and automatic, H.264 or AV1 codec selection. Keep format and codec on auto initially. Explicit selections must be compatible with each other, the bit depth and the intended player. CRF controls encoding compression separately from generation; -1 uses the output node's default, and explicit values depend on the selected codec. The workflow uses a fixed HunyuanVideo model, matching text encoders and a video VAE. It has one positive text prompt, no negative-prompt branch, no audio input and no image conditioning. The selected result is one silent video.
Gemini reference images to video
Generate one 720p, 24 FPS video with audio from two reference images and a scene or story prompt using Gemini Omni Flash. For a first run, upload both required images: a primary character, scene or style reference as Reference image 1, and a storyboard or complementary reference as Reference image 2. Replace the example prompt with your own action, visual style, camera behavior and desired sound. Explain how each image should guide the result. For a storyboard, describe panel reading order and the action to animate. Use 9:16 for portrait or 16:9 for landscape, and a requested duration from 3s through 10s. The defaults request a 3-second portrait clip. These values are appended to the prompt as provider instructions; avoid conflicting duration or aspect-ratio instructions in your main prompt. Both images are general references, without guaranteed start-frame or end-frame roles. Only their image outputs are supplied; uploaded alpha masks are not passed to generation. Generation tuning contains Temperature, Top P and Seed. Temperature controls sampling randomness; Top P controls the cumulative probability threshold for nucleus sampling. Keep their defaults for a first run. Seed -1 randomizes the re-run seed each run. Seed affects re-running and caching; a fixed seed does not reproduce the same video. Video output offers optional file type, codec and encoding-quality settings. Leave file type and codec on auto and CRF at -1 for the first run. These settings affect output encoding, rather than the provider's creative generation. Generation sends your prompt and reference images to a hosted Gemini service, requires configured provider access and uses provider billing.
Product placement photo
Generate a still product-placement image from three product photos and a composition reference. Upload all four required images: products occupy images 1–3, and the scene reference is image 4. In Placement prompt, describe how those products should appear and which aspects of the reference to retain or change. For a first run, keep the default model, auto aspect ratio and 1K resolution. The default prompt assumes products held in a hand in a fanned arrangement; revise those instructions if your reference depicts another composition. Auto follows an input image's aspect ratio, but the source does not specify which of the four takes precedence. Choose an explicit aspect ratio when framing matters. Model selects Nano Banana 2 (Gemini 3.1 Flash Image) or Nano Banana 2 Lite. Both use all four references and the same aspect-ratio and thinking-level controls. For Image resolution, enter 1K, 2K or 4K with the full model; Lite supports only 1K, so set 1K before switching to Lite. Thinking level offers MINIMAL or HIGH; HIGH requests additional model reasoning. Only the final image is returned, not text or thinking images. Seed -1 randomizes each run. A fixed seed requests best-effort repeatability, without guaranteeing identical output. Temperature controls generation randomness from 0 to 2; nucleus sampling controls the sampling threshold from 0 to 1. Lower values favor more focused choices. Keep their supplied defaults initially. Image generation instructions optionally change the generator's foundational instructions; use Placement prompt for ordinary scene changes. Inspect product identity, packaging and label text before using the result: the prompt requests fidelity, but exact reproduction is not guaranteed. Uploaded alpha masks are not connected, so uploaded transparency is not preserved through a separate mask input. Generation uses a provider API and requires its runtime access. This tool returns a still image. The separate product-placement video tool also generates the composition, writes motion direction and animates it.
Z-Image Turbo with PiD 4× upscale
Generate a 4096 × 4096 square image from text with Z-Image Turbo and PiD, and receive the 1024 × 1024 base preview alongside it. The same prompt guides both stages; PiD uses the generated base latent as conditioning for its final pixel-space image. For a first run, replace the example prompt with your subject, composition, lighting and style, then keep the supplied defaults: eight base steps and four PiD steps, guidance of 1 for both stages, res_multistep base sampling and LCM PiD sampling with simple schedules. No media upload is required or supported. The base and PiD seeds control separate stages. Use -1 to randomize each run, or fix both seeds when comparing settings. Changes to base sampling affect both the base preview and the latent that conditions PiD. PiD sampling controls affect the final image. Advanced settings expose each stage's sampler, guidance and schedule, plus base sampling shift, denoising fractions and PiD noise initialization. Keep both denoising fractions at 1 and Add PiD noise enabled for the supplied setup. Disabling PiD noise initialization removes the PiD seed's effect on initial noise. Latent degradation is separate from denoising fraction: zero denotes a clean base latent, while higher values tell PiD to handle corrupted latent outputs. The model pair, square dimensions and single-image batches are fixed for this 1024-to-4096 setup. Both stages run to produce the final result; there is no base-only or uploaded-image upscale mode. There is no editable negative prompt: both stages use zeroed negative conditioning. Alternative sampling settings change the generation behavior without a guaranteed quality improvement.
Magazine cover
Create a magazine-cover image from a reference photo or a written description. For a first run, upload a photo, replace the masthead and cover lines in Cover prompt, and keep the portrait 3:4 aspect ratio and 2K resolution. To generate from text alone, disable the Source image stage and describe the subject, background and typography in Cover prompt. Choose an explicit aspect ratio for text-only generation. The default recipe requests flat, minimalist editorial typography: a large pale-yellow masthead, small white cover lines, asymmetric placement and clean negative space. Replace the example wording with your own text. For a street-poster border collage, request cropped lettering, rotated microtext and symbols around the edges while keeping the center and subjects clear. For a foreground-subject variation, explicitly ask for the subjects to appear in front of the typography. Apply these recipes by editing Cover prompt; they are generative instructions rather than separate stages or editable layers. Model selects between gemini-3-pro-image-preview and Nano Banana 2 (Gemini 3.1 Flash Image). Aspect ratio sets the cover's width-to-height proportions; auto follows a supplied reference image. Resolution offers 1K, 2K and 4K, with the provider's native upscaler used for 2K and 4K. Seed -1 randomizes each run; a fixed seed requests best-effort consistency without guaranteeing identical results. The result is a generated image rather than editable text or layout layers. Review spelling, text placement, subject preservation and changes to the reference photo before use. Only the image is returned; requesting IMAGE+TEXT does not add a text output. This tool returns the cover directly without running the separate packaging-presentation stage.
