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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.
FLUX.2 image editing
Generate edited images from an uploaded reference and written instructions using FLUX.2, with standard and Turbo modes. For a first run, upload your source image and replace the example instructions with the changes you want and details to retain. Keep Output megapixels at 1, Image count at 1, Turbo mode off, Standard steps at 20, Guidance at 4, and the Euler sampler. Run the Pipe to receive the generated image batch. Output megapixels resizes the reference while retaining its aspect ratio. The resized reference supplies both reference conditioning and the dimensions used for generation; latent sizing may round dimensions to the model's supported grid. Resize method controls interpolation before reference encoding. Larger images and batches increase resource use. Turbo mode selects the fixed Turbo LoRA and Turbo steps together. Standard steps apply only with Turbo off; Turbo steps and Turbo LoRA strength apply only with Turbo on. The supplied Turbo defaults are 8 steps and LoRA strength 1. Both step controls feed a scheduler supporting 1–4096 iterations. Guidance, sampler, seed, output size, and image count apply in both modes. Seed -1 requests a new random seed each run; use a fixed seed with other settings held constant to compare changes. This generates a new image conditioned on the reference. Unchanged pixels and exact identity are not guaranteed. There is no negative prompt, editing mask, or denoise-strength control. Uploaded transparency is not preserved: only the core image loader's IMAGE output is connected, while its separate MASK output is unused. The supplied dependency evidence marks all four model files and the Sweet Tea output node pack as missing. Install the Sweet Tea node pack from https://github.com/tea-time-labs/sweet-tea-nodes and obtain the fixed model files from their supplied sources: - diffusion_models/flux2_dev_fp8mixed.safetensors: https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/diffusion_models/flux2_dev_fp8mixed.safetensors - text_encoders/mistral_3_small_flux2_bf16.safetensors: https://huggingface.co/Comfy-Org/flux2-dev/resolve/main/split_files/text_encoders/mistral_3_small_flux2_bf16.safetensors - loras/Flux_2-Turbo-LoRA_comfyui.safetensors: https://huggingface.co/ByteZSzn/Flux.2-Turbo-ComfyUI/resolve/main/Flux_2-Turbo-LoRA_comfyui.safetensors - vae/full_encoder_small_decoder.safetensors: https://huggingface.co/black-forest-labs/FLUX.2-small-decoder/resolve/main/full_encoder_small_decoder.safetensors Place these files in the corresponding ComfyUI model directories before running.
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.
Brenda Vicious
BRENDA VICIOUS Behold Brenda Vicious, the blonde bombshell who defies conventional career paths as a professional golf ball diver. Armed with a swimsuit, determination, and a complete disregard for conventional...
