Ressourcenverzeichnis
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MoGe depth and normal visualization
Render estimated depth, surface normals or geometry validity from one perspective image. For a first run, upload an image, leave Rendering mode at depth_colored, and run with the existing defaults. The fixed MoGe 2 model estimates geometry, and the Pipe returns one rendered image. Rendering mode selects depth for the alternate depth display, depth_colored for colorized depth, normal_opengl for normals with the +Y-up green-channel convention, normal_directx for normals with the -Y-down convention, or mask for predicted geometry validity displayed as an image. The output is not numeric depth, a mesh or a typed MASK. Validity is predicted from RGB pixels; uploaded transparency is not used, and the upload's alpha channel is not preserved. In Geometry estimation, Detail level ranges from 0 to 9 and defaults to 2. The source identifies 0 as fastest and 9 as most detailed. Horizontal field of view is in degrees, from 0 to 170: leave 0 for automatic recovery from predicted points, or enter a known camera value to set the focal length used for geometry unprojection. Exclude invalid geometry defaults to enabled and sets predicted invalid points and depth to infinity. Disable it to retain raw predicted geometry; the predicted validity mask remains available in mask rendering mode. Automatic resizing defaults to enabled. It checks image width: only images wider than 2048 pixels are resized to a 2048-pixel longer edge before inference. Images at most 2048 pixels wide remain unchanged, including tall images. Disable this option to use the original image dimensions. Resizing can change the rendered output dimensions relative to the upload. Advanced inference options retain the source defaults. Inference batch size sets images per inference call and starts at 4; it does not create additional outputs from this single-image input. Force projection starts enabled. Its precise effect is not documented in the supplied source, so retain the default for a first run. The fixed model file is moge_2_vitl_normal_fp16.safetensors, installed under models/geometry_estimation/. Its retained download source is https://huggingface.co/Comfy-Org/MoGe/resolve/main/geometry_estimation/moge_2_vitl_normal_fp16.safetensors. Image output transport uses the Sweet Tea nodes package at https://github.com/tea-time-labs/sweet-tea-nodes.
OmniGen2 Image Editor
Create edited images from a source image and natural-language instructions, optionally using a second image as visual reference. For a first run, upload a Source image, replace the crystal-transformation example in Edit instructions with your desired changes, and adjust Negative prompt to describe unwanted features. Leave Additional reference encoding bypassed and keep the supplied guidance and sampling defaults. Image count selects the number of results. Seed -1 randomizes each run; a fixed seed reuses sampling randomness with unchanged settings. Output megapixels sets the approximate pixel budget in millions of pixels. The resized primary image supplies the output dimensions and aspect ratio; independent width and height controls are unavailable. Primary resize method selects interpolation for that preprocessing. Generation starts from an empty latent and uses the source as reference conditioning, so unchanged details are not guaranteed. Uploaded transparency is not preserved. For a second reference, upload an Additional reference image and turn off bypass on Additional reference encoding. Explain in Edit instructions how to use it. The upload is needed only while this encoder is enabled. Reference megapixels and Reference resize method control its preprocessing independently and do not determine output dimensions. Bypassing the encoder restores primary-reference-only editing without requiring the second upload. Text guidance scales the difference between instruction and negative-prompt conditioning with the same enabled references. Reference guidance scales the difference between reference-conditioned negative text and negative text alone; it affects both references together when the second image is enabled. Guidance composition selects regular or nested combination of these terms; the supplied setup uses regular. Advanced sampling controls select the algorithm, noise schedule and step count. Sampling denoise selects the fraction of the schedule used, rather than directly controlling how much of the uploaded image is retained; the supplied setup uses 1. Larger image budgets and batches require more memory. The workflow uses the configured OmniGen2 diffusion model, Qwen 2.5 VL text encoder and ae VAE.
Magnific creative upscale
Enlarge an uploaded image with Magnific's prompt-guided creative upscaler and return the enhanced image. For a first run, upload your source image and replace the example detail prompt with a description of its subject, style and desired details. Keep the 4x scale, standard content preset and supplied tuning defaults, then run the Pipe. Choose 2x, 4x, 8x or 16x enlargement and a content preset suited to your image. Creativity controls reinterpretation; resemblance controls fidelity to the original. Detail definition adjusts definition and detail, while detail intricacy adjusts prompt strength and intricacy per square pixel. These four numeric controls range from -10 to 10. Engine selection offers automatic, magnific_illusio, magnific_sharpy and magnific_sparkle processing choices. Creative upscaling can change image content. Automatic downscaling reduces the input when the requested output would exceed the provider's maximum pixel limit, so final dimensions may be smaller than the selected scale implies. Leave it enabled unless you know the requested output fits that limit. The workflow connects only the loader's IMAGE output and does not preserve uploaded transparency.
Krea 2 Turbo style reference
Generate new images from a scene prompt and one visual style reference using Krea 2 Turbo and its style reference adapter. For a first run, upload a reference image, replace the example Scene prompt with your desired subjects, actions, composition, and medium, and run with the supplied settings: square 1-megapixel output, one image, adapter strength 1, and 8-step Euler/simple sampling. Leave Refine prompt off to use your original text. Put requested visible words in quotes; exact text rendering is not guaranteed. Aspect ratio and Megapixels determine the generated dimensions independently of the reference image. Images per run returns a batch of variants. Larger dimensions and batches require more memory. Image Seed uses -1 for a new random seed each run; a fixed seed controls the initial noise. Enable Refine prompt to expand your text using fixed expansion instructions. Refinement receives text only and does not inspect the reference image. Its advanced token limit and text-sampling controls affect the expanded prompt separately from image generation. Refinement sampling off disables sampled text generation, rather than disabling refinement; use Refine prompt to choose the original text. Thinking mode applies only if the loaded text model supports it. The selected output is generated images, not a separate refined-text result. Adapter strength scales the fixed style reference LoRA. At 0, its contribution is removed while the reference image remains connected to conditioning. It is not an image-editing denoise amount or a guaranteed measure of visual similarity. Generation starts from an empty latent, so the upload is a conditioning reference rather than the starting canvas. Advanced image controls expose steps, sampler, scheduler, prompt guidance, noise schedule fraction, resolution-dependent sampling shifts, and the reference-latent convention. The source uses guidance 1, noise schedule fraction 1, base shift 0.5, maximum shift 1.15, and index_timestep_zero reference conditioning. These controls change sampling or conditioning; the reference-latent options are not visual style presets. Negative conditioning is zeroed internally, with no editable negative prompt. Only the reference loader's RGB image output is connected, so uploaded transparency is not preserved.
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.
Flux.2 Klein base 4B image edit
Generate edited images from a reference image and a written instruction using Flux.2 Klein base 4B. For a first run, upload a source image, describe your desired changes and any features to retain, and keep the supplied defaults: 1 megapixel, one image, CFG guidance 5, 20 sampling steps and Euler. The example instruction requests anime styling while retaining identity. Leave the negative prompt empty unless you want to describe attributes to discourage. Seed -1 selects random sampling noise. Output megapixels resizes the reference while following its aspect ratio. The resized image's dimensions also set the generation size. Resize method controls interpolation of that reference before encoding; it does not add a separate upscaling stage. Advanced controls provide image count, negative prompting, CFG guidance, seed, sampling steps and sampler choice. Use a fixed nonnegative seed to reuse the noise setting; changing the reference, instruction or sampling settings can still change the result. CFG combines positive and negative conditioning, with the image reference attached to both branches. The source image supplies reference conditioning, while generation starts from an empty latent. There is no image-to-image denoise-strength control, text-only mode or mask-based editing mode in this tool. Uploaded alpha transparency is not preserved by the connected RGB image path. The output contains generated images. The workflow uses flux-2-klein-base-4b-fp8.safetensors, qwen_3_4b.safetensors as its Flux.2 text encoder, and flux2-vae.safetensors. It uses the base 4B model despite the original subgraph's historical distilled title.
Three-scene story video
Gemini develops the brief into three scene prompts. The first scene also guides Nano Banana 2 to create a reference image, which Kling uses to generate the storyboard video. Disable Source image to work from text alone. The default storyboard has three four-second scenes and generates audio. Video and reference-image aspect ratios are separate source settings; keep them aligned for consistent framing. Scene prompts are generated automatically, and the saved script-format instructions maintain the three-scene structure.
Qwen-Image 2512 Turbo Image Generator
Generate an image from a text prompt with Qwen-Image 2512 and its fixed two-step Turbo LoRA. Set the output dimensions and batch size; advanced controls include the Turbo LoRA strength, seed, step count, guidance, sampler, scheduler, and denoise strength. There is no independent negative prompt; the workflow zeroes negative conditioning internally.
GPT Image generation and editing
Generate one to eight images from a text prompt, or edit an uploaded reference with GPT Image. Returned images appear together in the Pipe result. For a first run, leave Source image disabled, replace the example prompt with a description of your desired image, and keep the default square size and one image. For editing, enable Source image, upload a reference, and describe the desired changes. The upload supplies two inputs: an RGB reference image and a separate mask derived from its alpha channel. Transparent areas become white mask areas designated for replacement. Uploaded transparency is not retained in the RGB reference. There is no separate mask upload or independent mask control; the same upload supplies both inputs. Choose a model and an Output size preset or automatic sizing. Custom width and height apply only when Output size is Custom and Model is GPT Image 2. Each dimension must be a multiple of 16 from 1024 to 3840 pixels. Image count requests one to eight results. Quality affects provider cost and generation time. Background requests automatic, opaque, or transparent provider output; preservation of generated alpha in the returned Pipe images is not established. The provider backend does not implement seed support, so seeds cannot reproduce earlier results. Runs use the partner API and require its access and credits.
ComfyUI-Tensor-Operations
ComfyUI nodes implementing some helpful tensor operations.
LTX 2.3 basic GGUF 720p workflow — v1.0
This is same as default WF in ComfyUI, but it uses GGUF custom node. Basically, you can insert images, audio, and video into any frame, so anything is possible.
z-tipo-extension
A sd-webui extension for utilizing DanTagGen to "upsample prompts".
ComfyUI-Show_Any_Text_in_CMD_Console-SG
Simple node to show ANY output text in the CMD console.
ComfyUI_ProfilerX
Node and workflow profiling. Find bottlenecks in your workflows. See trends over time.
Atomico
Atomico V2 “Chlorine” focuses on ultra-clean linework, pale aesthetics, and glossy sci-fi rendering. Expect sharp outlines , glassy chlorine-white eyes , smooth reflective suits , and stable, elegant faces even at...
Antonia LastName
Antonia is the first in a new set of custom LastName characters for anyone who wants a consistent character for their SD 1.5 renders.
ComfyUI-JMESPath
A ComfyUI node that runs a JMESPath query against input JSON and outputs the result
TungstenDispo Embedding
The embedding were trained using A1111 TI for the 768px Stable Diffusion v2.0 model. The embedding should work on any model that uses SD v2.0 as a base.
ComfyUI-MieNodes
Offering a series of utility nodes designed to simplify workflows and enhance efficiency.
