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Silicone Valley - Suzi
Suzi is a saltwater aquarium hobbyist and clownfish breeder. She enjoys Diana Ross albums and courtroom drama TV shows.
LtxApp360 - CUSTOM AUDIO DRIVEN 60 SECONDS VIDEO WITH 3 PROMPTS - i2v/t2v — v1.1
This version uses the newest ltx-2.3-22b-distilled-1.1.safetensors instead of the ltx-2.3-22b-dev-fp8.safetensors . It's heavier on VRAM but will get better results in almost the same generation time as version 1.0....
ZImageTurboPiD Upscale Decode Workflow — ZturbotoPiD
ZImageTurboPiDv2 +Color transfer from lora image as PiD tends to raise the gamma +Optional SageAttention +ModelSampling shift to PiD model Update your ComfyUI for PixelDiT support For models/text encoder INT8 Models
wan2.2_t2v_low_noise_14B_fp8_scaled
Wan 2.2 14B low noise Model
Anima Prompt Structure and Wildcard Workflow — v1.0
Moving to Anima from Illustrious has been daunting due to its reliance upon natural language descriptions to get quality results, whereas with Illustrious you relied upon tags with highly defined concepts as...
Wan 2.2 Daily Driver Workflow — v1.0
This workflow is the cleaned-up version of what I generally use when I'm rendering videos with Wan 2.2 or 2.1. It includes most of the options and settings that I most commonly use, so it's obviously tailored to work...
Basic Krea 2 Workflow
Important Update: The Fantastic Lora Loader is broken when using the latest version from the repository. Clone my fork of the repo here to fix it.
3D Asset to Futuristic Industrial Design Render — v1.0
3D Asset to Futuristic Industrial Design Render Transforms your 3D asset images into futuristic industrial design renders. By tweaking the lighting, materials, and composition, it gives your models a sleek, high-tech...
PixelWave
PixelWave FLUX.1-schnell 04 - Apache 2.0!
SDXL QR Code Art Generator — v2.0
Reduced the number of custom nodes
HS-Anima 4K Final Output Workflow — LoRA+Face Fix+Upscale
I will be offering 1,000 Patreon membership subscriptions to Civitai users, completely free of charge.
SDXL PONY ILLUSTRIOUS DMD2 Workflow By Stable Yogi — v2.0
DMD2 based Multi pass Workflow with VR2 Upscale extremely Fast and Detailed.
ComfyUI - Lossless Edit — FLUX Kontext Dev
Remove/edit any object with lossless quality - Flux Kontext Dev & Flow Assistor (ComfyUI)
Wan2.1 14B i2v (Native|GGUF) Self-Forcing(lightx2v) Single/Dual Sampling — 🟩v3 (SingleSamp)
Go here for Wan2.2 WF ≈➤ 🍥 Wan 2.2 (GGUF) [i2v / FFLF] + [t2v] Workflow
Vidu Q3 image to video
Animate a starting image into one short video, with optional generated dialogue and sound effects. Upload the required starting image and replace the example chase prompt with action and camera instructions suited to your image. For a first run, keep Vidu Q3 Pro, 720p and the supplied six-second duration. This Pipe uses the Vidu provider integration: configured provider authentication is required, the image and generation request are sent to the provider, and generation uses provider billing. The resulting video is returned to Sweet Tea's video preview output. The prompt may be empty and supports up to 2,000 characters. If you describe timed shots, keep their timing within the selected duration; changing Duration does not rewrite the prompt. Duration supports 1–16 seconds. Pro supports 720p, 1080p and 2K; Turbo supports 720p and 1080p. When switching from Pro at 2K to Turbo, also select a supported resolution. Generate audio is enabled by default. Describe desired dialogue and sound effects in the prompt, or disable it for silent video. Seed -1 randomizes each run; use a fixed seed when desired, without a guarantee of identical provider results.
Wan 2.2 first–last frame video
Generate a silent video guided by start and end images and a motion prompt using Wan 2.2 14B. For a first run, upload a Start image and an End image, replace the saved ice-kitten-to-beast prompt with a description of your scene and transition, and keep the generation and encoding defaults. The default result is 640 × 640 pixels with 81 frames at 16 FPS, approximately 5.1 seconds. The saved negative prompt is Chinese text discouraging visual artifacts, blur, overexposure, static imagery, anatomical errors and clutter; edit it as needed. Each endpoint can be disabled independently. Upload an image for every enabled endpoint; disable its node when omitting it. With only one endpoint enabled, only that boundary is image-conditioned. With both disabled, generation uses the text conditioning without endpoint images. The image loaders supply RGB images only; uploaded alpha masks are unused and transparency preservation is not supported. Width and height use increments of 16 pixels. Frame count follows 1, 5, 9 and so on. Duration equals frame count divided by FPS; changing FPS changes playback timing without adding generated frames. Keep batch size 1 for a single clip: this Pipe exposes one video output, not separate batch-member outputs. Advanced controls include negative text, seed, independent guidance for the initial and refinement stages, sampling algorithms, schedules and model sampling shifts. Seed -1 randomizes the initial noise choice; use a fixed nonnegative seed to hold that choice constant. The default high-noise stage executes steps 0–10 and passes its noisy latent to the low-noise stage, which continues from step 10 through the end of the same 20-step schedule. Refinement runs as part of the complete generation process. If editing schedules, keep both total step counts equal, match their schedulers, and keep the initial handoff step equal to the refinement start step within that schedule. The saved samplers are Euler, guidance is 4 for each stage, and sampling shifts are approximately 8. This variant uses no acceleration LoRAs; the family's four-step Lightx2v variant is a separate configured tool. Output controls select container, codec and CRF. Leave these automatic for the first run; CRF -1 selects automatic quality. Video creation supports 8-bit and 10-bit; the source notes that 10-bit can reduce gradient banding but has player and downstream compatibility limitations. No audio is connected. The graph uses the Wan 2.2 high- and low-noise 14B model files, UMT5 text encoder and Wan 2.1 VAE named in the package source.
Krea 2 Turbo text to image
Generate an image or batch of images from text with Krea 2 Turbo. For a first run, replace the example positive prompt with your subject, composition, lighting and medium. Keep square 1 MP output, one image, prompt refinement enabled, style LoRA disabled and the supplied 8-step Euler sampling settings, then run the Pipe to receive the image batch. The runtime needs krea2_turbo_fp8_scaled.safetensors, qwen3vl_4b_fp8_scaled.safetensors and qwen_image_vae.safetensors, plus the Sweet Tea image output node. Using the default style additionally requires krea2_darkbrush.safetensors. Model selection for the base generator, text encoder and VAE is fixed in this Pipe. Prompt refinement uses the local text model and fixed instructions to expand or polish your description before image conditioning. Disable refinement to use your original wording. Refinement length, thinking mode and built-in template settings apply only when refinement is enabled. With refinement sampling on, temperature, token filters, repetition and presence penalties, and the refinement seed control sampled wording. Turning sampling off disables those sampling controls. Image noise and sampled refinement have separate seeds. Use -1 for a new random seed each run. To compare runs with stable wording and image noise, fix both seeds and retain the other settings, or disable refinement and fix the image seed. Enable style LoRA to apply the selected adapter and append the style trigger to the original or refined prompt. The default adapter is Darkbrush and the default phrase is 'monochrome ink wash style'. Adapter selection and trigger text are independent: update both when choosing another installed compatible LoRA. LoRA strength controls adapter influence only when style LoRA is enabled. Disabling style removes both the adapter and its appended phrase. Aspect ratio and megapixels jointly determine dimensions, rounded to multiples of eight. Image count sets the output batch size; larger dimensions and batches require more memory. Advanced sampling controls change image generation, with Euler, simple scheduling, 8 steps and CFG 1 supplied as defaults. Keep denoise at 1 for ordinary generation from the empty latent. This Pipe has no uploaded image input or editable negative prompt; its negative conditioning is zeroed.
Image upscale and refinement
Refine an uploaded image with prompt-guided diffusion, enlarge it with tiled refinement, and sharpen the final image. The preset Illustrious anime checkpoint, micro-detail LoRA and AnimeSharp upscaler target illustrated imagery. Generative refinement can change details, textures and anatomy. For a first run, upload an illustration, keep the default prompts, 2× upscale factor and refinement strengths, then run. Both diffusion stages share the positive and negative prompts. Add a description of your subject to the positive prompt for specific guidance. The default positive prompt includes addmicrodetails. Negative terms discourage traits; they do not guarantee removal. Initial denoise controls changes before enlargement. Tile denoise controls changes during the enlarged redraw. Lower values retain more incoming structure; higher values allow stronger reinterpretation. Each stage has independent seed, steps, CFG, sampler and scheduler controls. Seeds default to -1 for fresh randomization. Set both seeds to fixed nonnegative values when comparing settings with repeatable noise. Upscale factor multiplies image width and height; tile width and height specify processing regions in pixels rather than output dimensions. Tile pattern changes redraw order. Tile mask blur softens redraw boundaries, and padding adds surrounding context. Larger tiles, padding and tile batches require more memory. Tile batch size affects the main redraw, not seam correction. Uniform tiles keeps boundary tiles at the specified dimensions; disabling it allows smaller boundary tiles. Tiled decoding applies to the enlarged refinement stage, not the initial decoder. Seam correction is enabled by default and performs additional generative processing. Its default denoise is 1, so seam regions can change substantially. Lower seam denoise to retain more existing detail, or disable seam correction to skip that processing. Seam band width applies only to Band Pass mode; seam mask blur and padding control blending and surrounding context. The advanced detail LoRA controls independently adjust diffusion-model influence in both passes and positive-prompt encoder influence. Negative conditioning uses the encoder without this LoRA. Set both LoRA strengths to zero to disable its influence. The checkpoint, LoRA file and upscaler model are preset rather than selectable. Final sharpening has amount, radius and sigma controls. Reduce amount if edges develop halos, or bypass sharpening for the unsharpened upscale. The result is one final image; intermediate images are not saved. Uploaded transparency is not preserved.
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.
