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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.
HappyHorse image to video
Animate an uploaded first-frame image into one video using HappyHorse and a motion prompt. For a first run, upload your image and replace the example horse prompt with instructions matching it. Describe subject movement, camera motion, and atmosphere in English or Chinese. Keep HappyHorse 1.0, 720P, and 5 seconds initially. The uploaded image determines the video's aspect ratio; resolution is controlled separately, regardless of resolution words in the prompt. Choose HappyHorse 1.0 or 1.1, request 720P or 1080P, and set duration from 3 to 15 whole seconds. Both model choices expose these controls. Seed -1 randomizes each run; an explicit seed does not guarantee identical provider results. Enable Watermark to request an AI-generated watermark. Generation sends the image and generation instructions to the HappyHorse partner API and requires configured provider authentication. Calls are provider-priced. Each run returns one video for preview with automatic format and codec selection.
ByteDance Seedance Video
Generate a video from a text prompt using Seedance 2.0, Fast, or Mini, with optional generated audio. No source image is required. For a first run, describe the scene, subject action, camera movement, and visual style in Prompt. Keep the defaults: Seedance 2.0 Fast, 480p, 16:9, four seconds, and Generate audio disabled. Run the tool to produce the video preview. Enable Generate audio when you want audio generated with the video. Choose a model and a compatible resolution together. Fast and Mini support 480p and 720p; Seedance 2.0 also supports 1080p and 4k. Enter one of those resolution values. Set Duration to a whole number from 4 to 15 seconds. For Aspect ratio, enter 16:9, 4:3, 1:1, 3:4, 9:16, or 21:9, or use adaptive to let the model choose the frame shape. Seed controls cache and re-run behavior, not reproducibility: provider results remain non-deterministic even with a fixed seed. Keep -1 to request a randomized seed on each run. Enable Watermark to add a watermark to the generated video.
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.
Seedance 2.0 text to video
Generate one video from a text prompt with a selectable Seedance 2.0 model and optional generated audio. No uploaded media is required. Configured ByteDance provider access is required; generation sends the prompt to the provider and uses a paid API. For a first run, keep Mini, 720p, 16:9, five seconds, and audio enabled. Use the supplied space-battle prompt or replace it with your subject, setting, action, camera movement, and visual style. Describe desired sound when audio is enabled. The example's '8K' wording is a style instruction; the Resolution field controls output resolution. Choose Seedance 2.0, Fast, or Mini, then check the resolution. Resolution is entered as text and must be supported by the selected provider model; this interface does not enumerate model-specific choices. Duration accepts 4–15 seconds in whole-second increments. Update prompt timing when changing duration. Aspect ratio controls frame shape, with an adaptive provider option also available. Disable Generate audio when audio is unwanted. Watermark requests a provider watermark and is disabled by default; remove the example's 'no watermark' instruction if enabling it. Seed -1 randomizes each run. A fixed seed does not guarantee reproducible results; the source uses it to control whether generation re-runs. The generated video is returned through Sweet Tea's video output. Advanced Video encoding controls provide file type, codec, and CRF settings. Keep file type and codec on auto and CRF at -1 for a first run. These settings configure output encoding, while the generation controls determine the video's content and dimensions.
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.
Veo 2 video generation
Create a 5–8 second video with Google Veo 2 from a prompt, optionally guided by an uploaded reference image. For a first run, upload a reference image with the Reference image stage enabled. Replace the example prompt with a description of your subject, setting, action and camera movement; describe how the uploaded scene should move. Choose landscape (16:9) or portrait (9:16), then select a duration from 5 to 8 seconds in whole-second increments and run the tool. It returns one video. For text-only generation, disable the Reference image stage; no upload is needed in that mode. Both modes use the same generation controls. The reference image guides generation rather than guaranteeing an exact reproduction. Use the negative prompt to describe unwanted content or visual characteristics, or leave it blank. Enhance prompt enables AI-assisted expansion of your description and defaults to off. People generation selects whether generating people is allowed or blocked. Seed -1 selects a new random seed each run; a specific seed controls generation randomization. The advanced Video output stage exposes container format (auto, mp4, mkv or webm) and codec (auto, h264 or av1). Leave both on auto for automatic selection. Encoding quality remains at the source's automatic setting. This tool uses a partner API and requires configured API access and sufficient account credits. Your prompt and, in image-guided mode, reference image are sent to the provider. The model is fixed to Veo 2.
LTX-Video text to video
Generate a silent video from a scene description using the fixed LTX-Video 2B v0.9 model and T5 text encoder. No source image or video is required. Describe subjects, actions, camera framing or movement, lighting and visual style in the positive prompt. Use the optional negative prompt to describe unwanted qualities. For a first run, replace the example positive prompt, then keep 768 × 512 pixels, 97 frames, batch size 1, 30 steps, prompt guidance (CFG) 3 and the Euler sampler. Keep Add noise enabled and output container, codec and quality on their automatic defaults. Run the Pipe to receive one video without audio. At the default playback rate of 24 FPS, 97 frames produce approximately 4.04 seconds of video. Use dimensions in multiples of 32 and frame counts of 8n + 1, such as 49, 97 or 145. Width and height determine the aspect ratio. Increasing dimensions, frame count or batch size increases resource use. Batch size above 1 generates multiple samples whose decoded frames enter the same video creation node; it does not provide separate video outputs. Playback FPS controls how quickly the returned frames play and therefore the clip duration. Conditioning FPS supplies the intended motion rate to the model. These independent fields default to 24 and 25 respectively; set both to the same value for matching generation and playback timing. Changing playback FPS does not generate or interpolate additional frames. Seed -1 randomizes each run. A fixed seed reuses the starting noise with unchanged settings. Steps, prompt guidance and sampler control sampling; maximum shift and base shift adjust the latent-dependent noise schedule rather than playback timing. Retain their supplied defaults for an initial run. Disabling Add noise skips starting-noise initialization, but this workflow still uses an empty latent rather than a source video. Video playback offers 8-bit and 10-bit output. The 10-bit option can retain smoother gradients, but some players and downstream nodes may not support it. Under Video output, choose automatic encoding or select a container and codec compatible with your intended player. Leave quality (CRF) at -1 for automatic quality selection. This Pipe has no image upload, audio input, interpolation or separate batch-output branches.
Wan 2.1 first-to-last frame video
Generate a prompted, silent video transition between a starting image and an ending image using Wan 2.1. For a first run, upload both endpoint images, replace the flower example in Motion prompt with the action or transformation connecting them, and run with the existing defaults: 720 × 1280 pixels, 33 frames and 16 FPS. This produces approximately 2.06 seconds of video. Both uploads are required because each feeds its own visual encoder as well as endpoint conditioning. Images enter through RGB outputs; uploaded transparency is not preserved. The endpoint images guide generative animation rather than exact frame interpolation. Describe subject motion and camera movement explicitly. The supplied negative prompt lists unwanted visual artifacts and static results; edit it in Advanced to suit your scene. Width and height are measured in pixels and must be divisible by 16. Use frame counts of 4n + 1, such as 33, 49 or 81. Duration is approximately frame count divided by FPS. FPS changes playback speed without generating additional frames. Advanced sampling controls include seed, steps, prompt guidance, sampler, scheduler, denoising strength and noise schedule shift. Seed -1 selects a new random seed each run; a fixed seed keeps the initial noise consistent when comparing changes. Start with the supplied sampling defaults. Denoising strength acts on the video latent and is not a direct endpoint-image preservation setting. Start and end guidance crops independently choose none or center for each image's visual encoding. They do not change the separate endpoint-image connections. Sequence batch size defaults to 1; larger batches feed the same video creator and do not yield separate video outputs. Playback also offers 8-bit or 10-bit video; 10-bit compatibility depends on downstream encoding and playback software. Video encoding supports file types auto, mp4, mkv and webm, and codecs auto, h264 and av1. Keep both on auto initially, or choose a compatible container and codec for your delivery needs. This Pipe provides one video output, has no one-image mode or audio input, and uses a fixed Wan model with matching encoders.
Z-Image text to image
Create images from text using the fixed INT8 Z-Image model. No uploaded image is required; decoded images are returned through Sweet Tea's image output. For a first run, use the supplied botanical fashion portrait prompt or replace it with your own subject, setting, composition, lighting, and style. Leave the negative prompt empty unless you want to discourage specific features. Start with one image and the saved sampling settings: 25 steps, prompt guidance (CFG) 4, res_multistep sampler, simple scheduler, denoise fraction 1, and sampling shift 3. Output dimensions are supplied by the upstream resolution selector, whose saved widgets indicate square, approximately one-megapixel settings. This Pipe exposes no resolution controls. Portrait or 8K wording in a prompt does not set image dimensions. Seed -1 randomizes each run. Use a fixed seed when comparing prompt or sampling changes with consistent initial noise. Image count generates a batch with the same prompts and dimensions; larger batches require more memory. More sampling steps increase processing time. Advanced controls include the sampler, scheduler, denoise fraction, and model sampling shift. Denoise starts at 1 for generation from an empty latent; lowering it changes sampling and does not provide image editing. Sampling shift adjusts the model's sampling schedule independently of the sampler and scheduler. The model, text encoder, and VAE are fixed. This workflow has no uploaded-image mode or upscale stage.
Recraft digital illustration
Generate raster digital illustrations from a text prompt with Recraft, an illustration substyle and optional RGB color guidance. For a first run, replace the skateboard example in Positive prompt with your subject, action, composition and desired appearance. Keep the default 2d_art_poster style, 1536×1024-pixel landscape size and one image, then run. No uploaded image is required. Use Negative prompt for unwanted elements, or leave it empty. Illustration style guides the appearance alongside your prompt. Output size specifies width and height in pixels; Image count requests 1–6 images per run. Results appear as raster image previews; this Pipe does not generate SVG output. Color guidance is enabled by default. Its advanced RGB controls start with blue, magenta and yellow-green. Background and palette color supplies both the background guidance and the first palette entry, so changing it affects both. The second and third palette colors are independent. Each RGB channel accepts 0–255. Bypass Color guidance to omit both background and palette guidance; RGB edits have no effect in that mode. Guidance does not guarantee exact color placement or coverage. Generation sends the prompt and generation settings to the Recraft partner API and requires configured access and sufficient credits. Seed -1 randomizes the re-run value for each run. A fixed seed controls re-running or caching but does not make Recraft results reproducible.
Disco Elysium style
Trained on vanilla SD1.5 but works better with art-focused checkpoints like Elldreth's and others from this website. Training dataset contained game portraits, key art, and some cherrypicked fanart.
Dark Fantasy Style
A Dark fantasy style embedding i made using 24 images to train. Turned out pretty good, and you get results just by using the embedding. No trigger words. Also adding things like: scenery, forest, nature, snow,...
