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Wan text to video
Generate a short video from an English or Chinese text prompt using Wan 2.5 or Wan 2.6, with optional generated audio. For a first run, keep the supplied detective scene, Wan 2.5 preview, 1080p landscape, and a 5-second duration, then run the Pipe. Replace the prompt to describe your subjects, setting, action, visual style, and camera movement. Audio generation and AI prompt enhancement are enabled by default. Choose Output size for resolution and aspect ratio. Use 5 or 10 seconds with Wan 2.5; select Wan 2.6 for 15 seconds. The negative prompt describes unwanted elements. Disable Enhance prompt to submit your wording without AI expansion, or disable Generate audio when you do not want automatic audio generation. Seed -1 randomizes each run; a fixed nonnegative seed controls generation randomness. Watermark requests an AI-generated watermark on the result. This Pipe accepts text only and has no connected soundtrack or voice-recording input. Generation uses a partner API and requires configured access and sufficient credits. Sweet Tea delivers the generated video. Under Video encoding, leave File type and Codec on Auto for the supplied encoding behavior, or select an available format and codec.
LexChen — v0.3(Anime)
slightly modified for Anime
Sketch and collage to image: mechanisms and practical controls
Transform a sketch or collage into one Seedream image, keeping reference-canvas settings separate from generated dimensions.
Ultra photo-realistic workflow | refiner, upscaling, FaceDetailer (+index elements), inpainting, controlnet, Civitai Metadata (NOOBAI-XL/ILLUSTRIOUS/SDXL) ComfyUi — v2.0
STEPS BASE (after this value the refiner kicks in) should never exceed the value of TOTAL STEPS unless you want to use only the first KSampler.
Ultra photo-realistic workflow | refiner, upscaling, FaceDetailer (+index elements), inpainting, controlnet, Civitai Metadata (NOOBAI-XL/ILLUSTRIOUS/SDXL) ComfyUi — v1.0
STEPS BASE (after this value the refiner kicks in) should never exceed the value of TOTAL STEPS unless you want to use only the first KSampler.
Gemini image editing with document context
Create an image from a required reference image and written instructions using Gemini, with optional text or PDF context. The generated image is delivered as the Pipe result. For a first run, upload a reference image, replace the example prompt with the changes you want, and describe which details to retain. Leave Aspect ratio on auto to follow the reference image's proportions, leave both document stages disabled, and run the Pipe. Choose an explicit aspect ratio to request different output proportions. To add document context, place a .txt or .pdf file in ComfyUI/input, enter its filename in the relevant document field, and enable that stage. These fields select existing files rather than uploading documents, and their empty defaults must be filled before enabling them. Either stage can be used independently; enable both with distinct files to supply two documents. Explain in your prompt how Gemini should use their contents. Seed -1 randomizes each run. A fixed seed requests best-effort consistency; identical results are not guaranteed. Generation instructions govern Gemini's interpretation of the prompt. Retain the directive to produce an image when editing them. Response format supports IMAGE and IMAGE+TEXT, but accompanying text is not delivered by this Pipe. This workflow requires a reference image even though Gemini supports other generation modes. Uploaded transparency is not preserved through the connected RGB image path. Image creation uses the Gemini provider and sends the reference image, instructions and enabled document context to that provider; provider billing applies.
Z-Image upscale and refine: mechanisms and practical controls
Choose output size and diffusion refinement strength for the retained RealESRGAN and Z-Image Turbo chain, with exact prerequisites and preservation limits.
Qwen-Image 2512 edge-guided generation
Generate new images from a prompt while following an uploaded reference image's Canny edges. The Pipe returns generated images and an edge-map preview. For a first run, upload a reference and replace the example positive prompt with your desired subject, appearance, lighting and style. Review the negative prompt for unwanted attributes. Keep output size at 1.6 megapixels, control strength at 1 and Lightning mode off, then run. Output dimensions follow the resized reference's aspect ratio. The reference supplies edges and dimensions; generation starts from an empty latent and does not directly preserve source colors or texture. Uploaded transparency is not retained. Lower control strength for more freedom, or set it to zero to remove edge conditioning while retaining the reference-derived dimensions and preview. The Canny thresholds are normalized values: keep the low threshold at or below the high threshold. Lower thresholds admit more edges; higher thresholds focus on stronger boundaries. Inspect the returned edge map when changing them. Control start and end are fractions of denoising progress from 0 to 1; keep start below end. Lightning mode selects the fixed four-step LoRA and separate sampling controls. Its supplied defaults are 4 steps and CFG 1; standard mode uses 50 steps and CFG 4. Only the selected mode's steps and CFG apply. Use positive step counts; the downstream sampler supports 1–10000 steps and CFG 0–100. Euler and the simple scheduler are the supplied choices for both modes. Increase Image count for a batch. Seed -1 randomizes each run; use a fixed seed to compare changes with otherwise matching settings. Advanced controls also expose reference interpolation, denoising amount, sampling shift and Lightning LoRA strength. Denoise defaults to 1; lowering it does not turn this into pixel-preserving image editing. Sampling shift affects both modes. LoRA strength affects Lightning mode only and does not automatically change its steps or CFG. Larger output sizes and batches increase memory use. This interface supports Canny guidance only. Pose or depth guidance would require graph changes.
Qwen Image masked inpainting
Replace a masked region of an existing image with prompted content using Qwen Image and inpainting ControlNet. The result is composited over the working image; this tool edits within the existing canvas. For a first run, prepare an image whose longest edge is 1536 pixels and create its edit mask at that same size. Upload an alpha-bearing image with transparent pixels where replacement is wanted, or paint an edit mask if a loader mask editor is available. There is no separate mask-upload field. An opaque image without an edit mask provides no useful replacement region. Output transparency is not preserved. Replace the example replacement prompt with your desired content. Keep the longest edge at 1536, denoise at 1, steps at 20 and CFG at 2.5 for the initial run. Seed -1 randomizes each run; use a fixed seed to compare settings. A negative prompt can discourage unwanted content, but has little or no effect at CFG 1. Working image size resizes only the image, not its mask. Prepare the image at the chosen size before masking, or disable that stage to retain the uploaded dimensions. Changing the resize target after masking can misalign the edit region. Image resampling affects only this resize operation and is inactive when the stage is disabled. Mask expansion grows or contracts the editable region in uploaded-mask pixels; tapered corners affect nonzero expansion. Blur radius sets the Gaussian filter's support, and sigma independently controls its spread. The processed mask controls latent noise, inpainting guidance and the final blend. Pixels outside that mask come from the working image, which may be resized; softened edges blend original and generated pixels. Lower denoise retains more existing content within the edit region. Inpainting guidance strength controls the influence of the image and mask, while Guidance start and Guidance end set its active sampling interval as fractions from 0 to 1. Keep start below end. Sampler and scheduler select the denoising algorithm and noise-level distribution; retain Euler and Simple for the first run. Sampling shift is a separate model-specific schedule adjustment with source default 3.1. The optional Lightning LoRA starts disabled. When enabling it at strength 1, try 4 steps and CFG 1 together, as described in the source guidance. Its toggle does not change those independent controls. When disabling Lightning, manually restore 20 steps and CFG 2.5 to return to the baseline configuration. For canvas expansion with directional padding and an automatically created padding mask, use the separate outpainting tool.
Bernese Mountain Dog
My LORA/Ti work with any CK, if you want you can support me with my CK. My CK, cost 10 euros forever:
Neo-Noir
In order for this model to work correctly, it is necessary to insert a request at the beginning of the prompt.
Flux Union Controlnet Pro workflow — v1.0
ComfyUI workflow for the Union Controlnet Pro from InstantX / Shakker Labs. Available modes: Depth / Pose / Canny / Tile / Blur / Grayscale / Low quality
LTX-2.3 Image-to-Video Workflow — QwenVL Auto-Prompt, No Drift — v1.0
Initial release. Pure LTX 2.3 22B i2v, QwenVL auto-prompt, locked camera, dual-stage upscale to 1920x1088.
【FLUX】LoRA TESTER — v3.3
Automatic download of models in auto version
【FLUX】LoRA TESTER — v3.2
Some correction
Ace Step 1.5 XL Turbo and SFT - TEXT to AUDIO model with Ollama — v1.0
Ace Step 1.5. Turbo and SFT model with Ollama for song-tags and lyrics
WanApp (wan2.2) EASY APP MODE for Wan2.2 — v1.0
WanApp is my APP Mode version
Krea2 → LTX-2.5 Motion Suite — Text Prompt to Video with Native Synced Audio (SFW · 16GB)
Initial release — SUBJECT + POSE prompt in, animated video with native audio out. Krea2 Turbo T2I feeds LTX-2.5 I2V (int8-convrot, two-stage distilled) via a double VRAM-safe cleanGpuUsed bridge, with QwenVL...
【SDXL】OUTPAINT — v3.0
🔄 Full Nodes 2.0 rewrite
DyPE for Anima workflow — v1.0
DyPE for Anima — Dynamic Position Extrapolation
