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Generate a background-removal mask
Generate a segmentation mask from an uploaded image with BiRefNet. The mask is returned as a separate output for compositing or downstream image processing.
Renaissance Style
A Textual Inversion Style Embedding trained on a beautiful late-medieval town archway scene. You can expect to get chaotic red brick/stone and wooden buildings, with floral accents, and a golden hue in your images by...
Wan 2.7 image to video
Animate an uploaded first-frame image into a 2–15 second video with prompted motion and automatically generated background music or sound effects. The uploaded image determines the video's aspect ratio. For a first run, upload your starting image, replace the example motion and scene prompt to match it, and keep 720P resolution and the 10-second duration. Describe the action, camera movement and atmosphere you want. The main prompt supports English and Chinese. The negative prompt describes unwanted visual features; its supplied Chinese default discourages artifacts, distorted anatomy, static scenes and unwanted text. Choose 720P or 1080P and a duration from 2 to 15 seconds in whole-second increments. Prompt enhancement is enabled by default and uses AI assistance to expand your instructions; disable it to submit your wording directly. Leave seed at -1 for randomization on each run, or enter a nonnegative seed to control generation variation. Enable watermark if you want an AI-generated watermark on the result. Under advanced video output settings, leave file type and codec at auto for automatic selection, or choose a container and video codec explicitly. This tool uses a remote API and requires configured provider access. It accepts one starting image. End-frame and audio uploads are not connected, so this tool does not offer an uploaded ending frame or uploaded audio to drive generation.
Wan 2.2 image to video
Generate one silent video from a starting image and a motion prompt using Wan 2.2's paired 14B image-to-video models. For a first run, upload an image, replace the example prompt with the subject's action and camera movement, and keep 640 × 640, 5 seconds, 16 FPS, batch size 1, and Fast mode off. Choose width and height in multiples of 16 to suit the image's composition. An upload is needed while the Source image stage is enabled. Disable that stage to generate without starting-image conditioning, and describe the subject and scene in the prompt as well as their motion. This mode retains the same image-to-video model pair. Uploaded transparency is not preserved by the connected RGB image output. Fast mode selects both Lightx2v acceleration LoRAs and the fast sampling controls. Defaults change from 20 steps, CFG 3.5, and handoff step 10 to 4 steps, CFG 1, and handoff step 2. Only the selected mode's steps, guidance, and handoff apply. Keep total steps between 1 and 10000, guidance between 0 and 100, and each handoff greater than zero and below its total steps. The two LoRA strengths apply only in fast mode. Both stages have independent sampler, scheduler, and noise-schedule shift controls; the supplied recipe uses matching Euler samplers, simple schedulers, and shifts of 5. Duration controls generation length. The graph requests floor(duration × FPS + 1) frames and uses the same FPS for playback. Use a positive duration and 1–120 FPS; choose duration × FPS as a multiple of four for the model's 4n + 1 frame convention. Defaults request 81 frames at 16 FPS, approximately five seconds. Increasing resolution, duration, or FPS increases generation work. Keep batch size 1 for one clip; larger batches still pass through a single video-assembly branch rather than separate clip outputs. Use seed -1 for a new random seed each run or a fixed seed to compare changes with the same initial noise. Advanced controls include a negative prompt for unwanted traits; its influence depends on guidance. The tool returns one video through Sweet Tea, using automatic container and codec selection by default. Available containers are mp4, mkv, and webm; explicit codecs are h264 and av1. Keep automatic selection unless you need a particular compatible combination. Eight-bit output is the default; 10-bit output requires compatible encoding and playback. The graph has no audio input, end-frame conditioning, or upscaling stage.
Z-Image realistic image generator
Write a scene prompt. The realism-focused negative prompt is editable; CFG and seed are also available. Leave seed at -1 to randomize it. Output size is fixed at 1280 × 1920.
All-in-One. Z-Image, Ernie, Klein, t2i, i2i, controlNet, inpaint, outpaint, LLM, WAN, Qwen image, SDXL, Chroma — v6.3
!!Please update Timesaver and RES6LYF nodes!!
Glorious Style
This is a DreamArtist Textual Inversion Style Embedding trained on a single image of glorious, imposing castles, towers, and ivy covered ruins. You can get stunning backdrops in your images by calling Style-Glorious...
Runway image generation
Generate images with Runway from a text prompt and an optional reference image. For a first run, leave Reference image disabled, replace the example portrait prompt with your desired subject, composition, lighting, and style, and keep Output format at 1024:1024 for a square image. Run the Pipe to view the generated images. To use reference guidance, enable Reference image and upload one image. The upload feeds the generator's optional reference-image input; keep the prompt focused on the desired result. Disable Reference image to return to text-only generation. This workflow exposes no reference-strength or exact-editing control. Output format controls the generation format. Use a value supported by the Runway node; 1024:1024 is the supplied square default. Generation uses the Runway partner API and requires configured API access and sufficient account credits. The prompt and enabled reference image are sent to the provider. Generated images are returned for preview through Sweet Tea's output transport; this graph does not permanently save them through SaveImage.
Flux Kontext image editing
Produce one image with Flux.1 Kontext dev using an uploaded image and written instructions. Use it for subject changes, background edits, style changes, or a new composition inspired by the reference. For a first run, upload a Source image, leave Second reference disabled, and replace the supplied swan portrait prompt with your own Edit instructions. Describe the desired change and anything that should remain unchanged. For example: “Change the background to a garden while keeping the subject's pose and clothing unchanged.” Keep the source defaults of Flux guidance 2.5, Denoise 1 and 20 Steps initially. For two references, enable Second reference and upload another image. The images are joined into one canvas before scaling and encoding. Explain each image's intended role in the instructions. In Reference layout, choose the second image's position, whether to match its adjoining dimension to the source, and an optional gap in pixels. Spacing color matters only when the gap is greater than zero. These layout controls apply only when the second reference is enabled. Output dimensions follow the reference canvas after automatic Kontext scaling; there are no independent width or height controls. Adding a second reference or changing its layout can change the output proportions. The encoded canvas supplies both the starting image and reference conditioning. Denoise ranges from 0 to 1: the default 1 allows full regeneration with reference conditioning, while lower values retain more of the starting canvas and can constrain the requested changes. Seed -1 selects a fresh random seed each run. Use a fixed seed to compare settings with the same starting noise. Steps controls the number of sampling steps; Sampler chooses the sampling algorithm and Scheduler chooses its noise schedule. The source defaults are 20, euler and simple. Flux guidance controls the model's prompt guidance. Sampling also exposes an independent Classifier-free guidance scale, retained at its source default of 1. There is no editable negative prompt; negative conditioning is zeroed internally. Uploaded transparency is not preserved because only the image loaders' RGB outputs are connected. Requests to preserve details are instructions to the model, rather than masked protection of particular pixels.
Magnific precise image upscale
Enlarge and enhance an uploaded image with Magnific Precise V2, adjusting edge definition, texture and fine detail. For a first run, upload your source image and keep the defaults: 2x enlargement, photo style, sharpening 7, smart grain 7, fine detail 30, and automatic input downscaling enabled. Run the Pipe to receive the enhanced image. Choose 2x, 4x, 8x or 16x enlargement. Use sublime for general images, photo for photographs, or photo_denoiser for noisy photographs. Sharpening controls edge definition, smart grain adds grain and texture to counter excessive smoothness, and fine detail controls added textures and micro-detail. Each enhancement control ranges from 0 to 100. Added texture and detail can change the source's appearance. Automatic input downscaling reduces the input when the requested output would exceed the provider's maximum resolution. The result may therefore be smaller than the selected factor implies relative to your original upload. Disabling this option does not remove the resolution limit; the supplied source does not specify that limit or the behavior when it is exceeded with downscaling disabled. This Pipe returns the enhanced image only. Uploaded transparency is not preserved through this graph: the image loader's separate alpha mask output is not connected.
Arthemy Anima comic image
Generate a single comic-style image or a batch from text with Arthemy Comics Anima v2. No uploaded image is required. The supplied prompt describes an elf brawler with gradients, hatched shadows and a dramatic camera angle; edit it to choose your subject, composition, background and visual treatment. For a first run, keep the supplied positive and negative prompts, 1024 × 1344 portrait dimensions and image count of 1, then run the Pipe to receive generated images. The negative prompt discourages unwanted features and styles, including photo, 3D, watercolor and flat-color treatments. Revise both prompts when changing the intended style. Width and height are in pixels, in increments of eight; together they determine the aspect ratio. Image count selects the batch size. Larger dimensions and batches require more memory. Advanced Sampling contains seed, steps, CFG, sampler, scheduler and denoise. Seed -1 randomizes each run; use a fixed seed to compare changes with the same initial noise. The source defaults are 35 steps, CFG 5.5, er_sde sampling and the simple scheduler. CFG controls prompt guidance; excessive guidance can harm the result. Sampler and scheduler choices are supplied by the runtime and change the sampling process. Keep denoise at 1 for full sampling from the empty latent; reducing it changes the sampling range and does not provide image-to-image editing. Runtime requires Arthemy_Comics_Anima_v2.safetensors, Arthemy_Qwen3_6b.safetensors and qwen_image_vae.safetensors. These assets are fixed in this Pipe, and their availability must be established in the runtime. The source's model-merging, block tuning, restoration, chaos and LoRA examples are disconnected from this output. This Pipe generates images and does not expose those operations or export model weights.
Grok text to image
Generate images from a text prompt with Grok and preview the results in Sweet Tea. No source image is required. For a first run, use the supplied neon arcade portrait prompt with grok-imagine-image, square aspect ratio, 1K resolution, and one image. Replace the prompt to describe your own subject, composition, lighting, and style. Choose an aspect ratio for the image's width-to-height shape, select the provider's 1K or 2K resolution tier, and request 1–10 images per run. The model selector offers grok-imagine-image, grok-imagine-image-quality, and grok-imagine-image-pro. Seed controls whether the provider node reruns; it does not make generated images reproducible. Leave it at -1 to randomize that value on each run. This workflow has one main prompt and no separate negative prompt. Results are returned for preview; the workflow does not permanently save image files.
Danger Zoo ~ Johnsons Negative Embeddings ~ Helper Collection!
I put this together to make it easy for people to get them all in one page. Full details and examples can be found on the original model pages.
Gemini image refinement
Refine or edit an uploaded image with Gemini, or generate an image from a prompt without a reference. For a first editing run, enable Source image, upload an image and keep the supplied refinement prompt, Auto aspect ratio and 4K resolution. Adjust the prompt to describe the changes and details to retain. The supplied prompt asks for sharper detail while retaining composition and text; these are model instructions, not guarantees of exact preservation. For text-only generation, disable Source image and replace the refinement prompt with a description of the complete image you want. An upload is required while Source image is enabled. Auto aspect ratio follows the reference image when supplied; choose an explicit ratio when you need a particular canvas shape, especially without a reference. Resolution selects 1K, 2K or 4K; the source describes native Gemini upscaling for 2K and 4K. Model selects the Gemini image model for editing or generation. Seed -1 randomizes each run; a fixed seed requests best-effort consistency, but identical results are not guaranteed. Provider response format requests IMAGE or IMAGE+TEXT, but this Pipe returns only the generated image and discards accompanying text. System instructions set foundational model behavior; the supplied instructions prioritize producing an image and interpreting abstract input visually. Generation sends the prompt and any enabled reference image to Gemini through a paid provider call. Uploaded transparency is not carried through the connected RGB image path. The selected output is an image preview; this graph does not save a local image file.
FireRed image edit
Edit an uploaded image with text instructions using FireRed Image Edit 1.1 and return one edited image. For a first run, upload your source image, replace the example positive prompt with the changes or finished appearance you want, and leave output size at 1 megapixel with Lightning mode off. The resized source supplies both visual conditioning and the initial latent. Output size preserves its aspect ratio. Denoise defaults to 1 for a full transformation; lower values retain more source structure while the image remains available as visual conditioning. Lightning mode selects the supplied FireRed Image Edit 1.0 Lightning 8-step LoRA together with separate sampling settings. Its defaults are 8 steps and CFG 1; standard mode uses 40 steps and CFG 4. Only the active mode's step and CFG values affect the result. Lightning LoRA strength affects the selected model only when Lightning mode is on. Negative prompting is optional and has little or no influence at CFG 1. Use seed -1 for a fresh random seed, or a fixed seed to compare settings with the same initial noise. Sampler and scheduler affect either mode; Euler and Simple are the source defaults. Resampling method controls how the source is resized before conditioning and encoding. Advanced guidance normalization and sampling shift also apply in both modes; retain their source defaults for the first run. Step values must be integers from 1 to 10,000, and CFG values must stay between 0 and 100, even though the upstream value controls expose broader entry ranges. This tool requires one source image and does not provide mask-based editing or preserve uploaded transparency. The base model, text encoder, VAE and Lightning LoRA file are fixed to the source configuration. The edited image is returned through Sweet Tea's preview output; this graph does not permanently save an image file.
HiDream E1.1 image editor
Edit an uploaded image using natural-language instructions with HiDream E1.1. Upload a source image and describe the change you want, including details you want retained. The Pipe resizes the source to a target pixel count, uses it as image conditioning, and returns an edited image. For a first run, upload your image and replace the example hair-edit instruction with a request suited to it. Keep the default 1 megapixel resolution, 20 steps, Euler sampler, simple scheduler and guidance settings. Resolution approximately follows the source aspect ratio. Resize method controls interpolation of the source before editing. There is no mask input, so requests to leave other regions unchanged are instructions rather than guarantees. Uploaded transparency is not preserved. In the default regular guidance mode, instruction guidance compares the edit-prompt prediction against the negative-prompt prediction, with image conditioning in both branches. Secondary guidance compares the negative-prompt image-conditioned prediction against the edit-prompt text-only baseline. These controls are coupled; change one at a time. Guidance combination also offers nested mode, which changes how the two comparisons are combined. Guidance normalization provides a strength control. Normalize before CFG is off by default, retaining post-CFG attenuation. Turning it on rescales before CFG without clamping and can amplify predictions; it interacts with normalization strength and both guidance values. Advanced sampling controls expose steps, sampler, scheduler and denoise. Denoise changes the noise schedule and is not a direct source-pixel preservation control. Seed -1 produces a new random variation each run; use a fixed seed when comparing settings. This Pipe produces a single edited image and has no mask-based editing or alpha-preservation mode.
SDXL Turbo
Generate images from text with the fixed SDXL Turbo checkpoint. Describe your subject, setting, composition, and visual style in Positive prompt; no uploaded image is required. The workflow requires sd_xl_turbo_1.0_fp16.safetensors in ComfyUI's checkpoints directory. For a first run, replace the example positive prompt and keep 512 × 512 pixels, one image, one sampling step, CFG 1, Euler ancestral, Denoise 1, and Add noise enabled. Width and height use pixels in multiples of 8 and determine the aspect ratio. Image count generates a batch of variations. Larger dimensions and batches increase memory use. In advanced Sampling, seed -1 selects new random starting noise each run; use a fixed seed to compare changes from the same starting noise. Advanced controls expose steps, sampler, CFG, denoise, and Add noise. Steps range from 1 to 10; additional steps increase sampling work without guaranteeing a better result. Changing the sampler changes generation behavior. The negative prompt has no effect at CFG 1; raising CFG introduces its influence and changes the source Turbo preset's behavior. Denoise changes the noise schedule used with the empty latent, not the strength of editing an input image. Keep Add noise enabled for normal generation; disabling it removes starting random noise without supplying a source image. This tool produces generated images and does not offer image editing or checkpoint selection.
Qwen-Image depth-guided creation
Generate a prompted Qwen-Image result using the depth structure of an uploaded reference, and receive a preview of the depth map used for guidance. For a first run, upload a reference image whose composition you want to use and replace the supplied example positive prompt with your desired subject, setting, lighting, and style. Leave Lightning acceleration enabled with 4 generation steps, CFG 1, Euler sampling, Simple scheduling, and Denoise 1. Seed -1 requests a new random seed each run; use a fixed seed to compare changes under the same settings. The negative prompt may remain empty and has little effect at CFG 1. The Pipe automatically scales the reference, estimates depth with Lotus, inverts the depth map, and uses that map for InstantX Union ControlNet guidance. Control strength adjusts its influence. Control start and end are fractions of sampling from 0 to 1; keep start below end. Strength 0 removes ControlNet influence but does not change the depth-derived initial latent. The initial generation latent is encoded from the depth map, not the original photograph. Lowering generation Denoise retains more of the depth-map appearance rather than preserving the photograph's colors or details. Output dimensions follow the automatically scaled reference; there are no independent width or height controls. Uploaded alpha is not used by this graph. For base Qwen-Image mode, bypass Lightning acceleration and set generation steps to 20 and CFG to 2.5, following the supplied reference settings. Lightning strength applies only while that stage is enabled. The depth preview remains available in either mode. Advanced depth controls affect Lotus estimation rather than final-image sampling. Depth intensity sets the first sigma; Depth sampler selects the algorithm, and Depth steps, Depth scheduler, and Depth denoise construct the schedule before its first sigma is replaced. Inspect the depth preview when adjusting these settings. Sampling shift affects the Qwen-Image generation schedule independently.
Flux Redux reference blend
Generate new images with Flux.1 dev conditioned by two Flux Redux visual references. For a first run, upload both images, replace the example prompt with your intended scene, and keep reference strengths at 1, output size at 1024 × 1024, image count at 1, and sampling steps at 20. Clear the prompt if you want to omit descriptive text; both reference uploads remain required, even when a reference strength is zero. Each reference has an independent strength and weighting method. The strengths are not percentages and do not need to add up to 1. Multiply scales reference conditioning; attn_bias applies an attention bias. Keep the default multiply method for the first run. The first reference is applied to the text conditioning, then the second reference is added to that combined conditioning. This is not a pixel blend of the uploaded images. Advanced framing controls select center cropping or no center crop independently for each reference before visual encoding. They affect reference preparation, not output dimensions. Set output width and height in pixels, in multiples of 16; image count selects the number generated in one batch. Advanced controls include Flux guidance, seed, steps, sampler, and noise scheduler. Seed -1 selects fresh random noise for each run; use a fixed seed when comparing control changes. The sampling schedule fraction defaults to 1 for the full schedule. Reducing it does not preserve a reference image because generation starts from an empty latent. Sampling calibration exposes maximum shift, base shift, and reference dimensions used to calculate the Flux sampling shift. Those reference dimensions default to 1024 × 1024 and do not automatically follow output size. The result is a newly generated image or image batch, rather than a direct edit of either upload. Exact composition, identity, and pixels are not guaranteed. Uploaded alpha masks are not connected to generation, so transparency is not preserved. There is no negative-prompt branch.
ComfyUI-DareMerge
ComfyUI powertools for SD1.5 and SDXL model merging
