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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.
nartfixer for Illuminati Diffusion v1.1
This negative embedding is intended for use with Illuminati Diffusion v1.1, feel free to use it elsewhere but I can't guarantee your success.
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.
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.
comfyui-liveportrait
LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control
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.
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.
