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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,...
Bad Iris Negative (SD 1.5, Embedding)
This negative embedding intends to fix deformed (oval-shaped, weird, etc.) irises in SD 1.5 based models.
Gemini video editing
Generate an edited video from an uploaded reference clip and text instructions using Gemini Omni Flash. For a first run, upload your source video and replace the astronaut-and-sheep example prompt with instructions describing what to change and what should remain recognizable. Include desired style, motion, and audio instructions as needed. Leave temperature, top-p, and output encoding at their defaults. Set aspect ratio to 16:9 for landscape or 9:16 for portrait. Request a duration from 3s to 10s in whole seconds. These controls append instructions to the prompt rather than performing a crop or trim; avoid conflicting framing or duration requests in your editing text. The source model specifies 720p video at 24 FPS with audio. Writing 8K in the prompt does not change that specification. Temperature controls sampling variation, and top-p controls the cumulative probability threshold used for nucleus sampling. Seed -1 randomizes each run; a fixed seed does not make the generated video reproducible. Advanced output settings offer file type and codec choices. This Pipe uses a hosted Gemini API and requires provider access through ComfyUI. Your reference video and instructions are sent to the provider, and generation uses provider billing. The selected result is the generated video; the model's separate text output is not displayed.
Film grain
Create an image with film-style grain from an uploaded source image. For a first run, upload your image and run with the saved settings: grain amount 0.75, size 0.45 and Smooth texture. Adjust amount and size, then run again to compare the texture. Set grain amount to 0 to disable the grain. Grain amount uses a unitless 0 to 1 scale. Grain size controls spatial scale from 0.05 to 3; the source describes 0.3 to 3 as typical. Lower values produce finer texture and higher values produce coarser texture. Choose Smooth for interpolated noise or Grainy for discrete noise, using those exact names if the control appears as a text field. Advanced controls remain useful in both texture modes. Color amount blends monochrome grain at 0 with independent RGB grain at 1. Luminance bias ranges from uniform strength at 0 to strength weighted toward darker pixels at 1, reducing grain in highlights. The noise pattern is deterministic for the same image dimensions and settings; there is no seed control. Added noise is clamped to the available RGB range, so grain can clip at black or white. Output dimensions follow the source image. Uploaded transparency is not preserved because the image loader's separate alpha mask is not used. This tool applies only film grain. The other image adjustments in this family are separate tools and are not combined in this output.
FLUX Canny image generation
The Pipe extracts a Canny edge map from your source image and uses FLUX.1 Canny dev to generate the prompted scene with that structural guidance. Output dimensions follow the source image through conditioning; there are no independent size controls. Edge thresholds tune which outlines guide generation. Guidance controls prompt adherence. Negative conditioning is zeroed internally, so this workflow has no editable negative prompt.
YouTube Thumbnail Generator
Generate a YouTube thumbnail image with Nano Banana 2 from a text prompt, built-in thumbnail art direction and an optional reference image. For a first run, replace the example prompt with your video topic, main subject, emotional reaction and visual hook. Keep 16:9 and 2K, and leave Reference image bypassed for text-only generation. To use visual guidance, enable Reference image and upload an image; an upload is required while that stage is enabled. The reference does not guarantee exact identity or layout preservation. The built-in Thumbnail art direction requests saturated colors, strong contrast, an expressive subject and a simple composition readable at small sizes. If you want lettering, request a short headline; the preset favors 3–5 words. Edit Thumbnail art direction to change this creative brief. Its older Nano Banana Pro wording does not change the actual Nano Banana 2 model. These instructions do not guarantee click-through performance. Aspect ratio selects the frame shape. Auto follows the reference image's ratio when one is supplied; choose 16:9 explicitly for a conventional landscape thumbnail. Resolution offers 1K, 2K or 4K; the provider uses its native upscaler for 2K and 4K. Seed -1 requests a fresh random seed each run. A fixed seed requests best-effort consistency without guaranteeing identical results. Thinking level selects MINIMAL or HIGH provider reasoning. Response mode IMAGE requests an image; IMAGE+TEXT additionally requests response text. This Pipe returns only the generated image, so accompanying text and thought images are not included. Review generated lettering and composition before using the thumbnail.
SCG Emotions Pack - Simple embeds for easily controlling a variety of emotions! (For all 1.5 Models and Merges)
Recently I released my embedding toolkit which has a variety of tools and fixers I've come up with over the past 6 months. I considered adding my emotion embeds to that listing, however I want to keep that specific...
Kling image generation with two references
Generate an image or image series with Kling using a text prompt and two reference images. Upload both reference images and replace the example fashion prompt with a description of your desired result. Explain which subjects, details, or visual qualities to use from each reference. Both references are sent together to the same generation request; this tool provides two required upload slots. Include instructions about unwanted content in the same prompt when needed; there is no separate negative-prompt field. For a first run, keep Kling 3.0 Omni, 1K resolution, and Series amount set to disabled for a single image. Choose the desired aspect ratio. Resolution selects the requested resolution tier, while aspect ratio selects the image proportions. With Kling 3.0 Omni, choose a Series amount from 2 through 9 to request multiple images. Set Series amount to disabled when switching to Kling Image O1, which does not support series generation. Use Seed -1 to randomize on each run. The provider's results remain nondeterministic even with a fixed seed; this control governs rerunning rather than reproducibility. Reference images use the loaders' RGB image outputs. Uploaded transparency is not preserved as an alpha input.
Video pose maps and overlays
Upload a video to produce a rendered skeleton map or pose overlay with person detection boxes. The output uses the source frame rate and available audio. Analysis frames and background frames share the same longer-edge resize, preserving aspect ratio. For a first run, upload your source video and keep the 1024-pixel longer edge, maximum of two people, normal blend mode, and pose blend of 0.5. Run the tool to obtain the visualization video. In normal mode, pose blend 0 shows resized source frames with detection boxes, 0.5 mixes those frames with the pose map, and 1 shows the standalone pose map. Detection and pose extraction still run at every blend setting. Multiply, screen, overlay, soft light, and difference are optional layer-combination modes; the normal-mode endpoint examples do not apply to them. Maximum people limits detections in each frame by descending confidence. It does not select or track a particular identity. Detection confidence filters person boxes used for both the overlay and pose extraction. Keypoint confidence filters the points drawn in the pose map. Lower thresholds can admit uncertain detections or points. Under Pose drawing, choose body, hands, face, head, and feet independently, and adjust skeleton line width and face point size in pixels. These controls change the pose layer; their visible contribution depends on the blend settings. The output is a rendered video, not a keypoint data export. Set the longer edge to a positive integer number of pixels. Larger dimensions increase processing demand. Processing batch size sets how many frames pose extraction processes together; reduce it if extraction exhausts memory. For a first run, retain automatic file type and codec with 8-bit output. Optional MP4, MKV, or WebM file types and H.264 or AV1 codecs must be mutually compatible and supported by your player. Ten-bit output can represent smoother gradients but requires compatible encoding and playback.
Temperature, tint, vibrance and saturation
Create a color-adjusted image with temperature, tint, vibrance and saturation controls. For a first run, upload a source image and run with all four saved controls at 0 to preview the image without color adjustments. Change a control and run again to compare the result. All controls use unitless -100 to 100 scales; temperature is not measured in Kelvin. Positive temperature adds red and reduces blue, warming the image; negative temperature cools it. Positive tint adds green while reducing red and blue; negative tint shifts toward magenta. These adjustments combine and are clamped before vibrance is applied. Positive vibrance boosts less saturated colors more, with some reduction for warm colors. This is a color heuristic rather than skin detection or guaranteed skin-tone preservation. Negative vibrance reduces color intensity, reaching grayscale at -100. Saturation then adjusts overall color intensity: positive increases it, negative reduces it, and -100 produces grayscale. Increasing saturation cannot restore colors already removed by vibrance at -100. The four controls combine in one output, in the order temperature and tint, vibrance, then saturation. Strong adjustments can clip colors and lose detail. There are no additional selectable modes. Output dimensions follow the source image. Uploaded transparency is not preserved because the image loader's separate alpha mask is unused. This output applies only these four adjustments; other effects in this family are separate tools.
bad-picture negative embedding for ChilloutMix
March 17, 2023 edit: quick note on how to use a negative embeddings. You download the file and put it into your embeddings folder. Since I use A1111 Webui, that is stable-diffusion-webui/embeddings. If you use a...
SD3.5 depth-guided image
Generate a prompted SD3.5 Large image guided by depth estimated from a source image. Provide a source image and describe the image to generate. Output size retains the source aspect ratio at the selected megapixel budget. Depth guidance controls its strength and the portion of generation during which it applies. Advanced controls tune generation, randomization and Lotus depth estimation. The inverted depth map supplies both ControlNet guidance and the initial generation latent; lowering Denoise retains more of that depth-map initialization. Negative conditioning is zeroed automatically, so there is no separate negative prompt. The output is the final generated image.
Ideogram 4 image generator
Generate images from scene captions using the fixed Ideogram 4 conditional and unconditional model pair. No uploaded media is required. For a first run, keep the supplied skateboard poster caption, 9:16 portrait aspect ratio, 1 megapixel resolution, Default sampling preset, and image count of 1, then run the Pipe. Replace Positive prompt with your own scene afterward. Prefer a structured JSON caption like the example, describing the scene summary, style, background, and individual objects. Plain natural language is also supported. Optional caption bounding boxes use normalized [y1, x1, y2, x2] coordinates from 0 to 1000. These are prompt content, not separate placement controls. There is no separate negative prompt: the unconditional branch receives zeroed conditioning. Prompt refinement is not automatic; paste any externally prepared caption into Positive prompt. Aspect ratio and megapixels jointly determine the output dimensions. Each dimension is rounded up to a multiple of 16 with a minimum of 256 pixels, so the resulting ratio and pixel count can differ from the request. Sampling presets coordinate the step count and scheduler settings: Quality uses 48 steps, mu 0, and std 1.5; Default uses 20 steps, mu 0, and std 1.75; Turbo uses 12 steps, mu 0.5, and std 1.75. Advanced controls expose image count, seed, sampler, and guidance. Seed -1 chooses fresh random noise for each run; use a fixed seed to reuse the initial noise while comparing settings. Euler is the source sampler default. Main prompt guidance defaults to 7, with override guidance of 3 during the final 30% of sampling. Override start and end are fractions from 0 to 1; keep start below end. Increasing resolution, image count, or sampling steps increases resource use. The workflow requires ideogram4_fp8_scaled.safetensors and ideogram4_unconditional_fp8_scaled.safetensors in diffusion_models, qwen3vl_8b_fp8_scaled.safetensors in text_encoders, and flux2-vae.safetensors in vae. Dependency evidence currently records these files as missing. Their supplied installation sources are the Comfy-Org Ideogram-4, Qwen3-VL, and flux2-dev repositories on Hugging Face. These model choices are fixed in this Pipe. The disconnected caption-construction template does not invoke an LLM or feed image generation. Describing transparency in a caption does not establish an alpha-channel output capability.
Product scene compositing
Generate a new product-scene composite from a product photo and a background scene using Seedream 4.5. Both uploads are required: the scene becomes reference 1 and the resized, padded product becomes reference 2. For your first run, upload both images, replace the example Product description with your product's shape, colors, branding, materials, texture, and reflectivity, then edit Compositing instructions. Replace its bracketed material examples and specify the placement, scale, and lighting you want. Keep {product_description} in the instructions to insert your separate description. Keep the default 2048 × 2048 output and Related-image mode disabled to generate one composite. Output size controls generated dimensions. Width and Height apply only when Output size is Custom; both are measured in pixels. Product reference preparation resizes and pads the product before generation. Reference padding changes that input preparation, not the generated scene background; Resize interpolation changes how the product reference is resampled. Set Related-image mode to auto and describe the desired group in Compositing instructions to request related images. Maximum images is an upper bound, not a guaranteed count. Use at most 13 generated images because the two references count toward the provider's 15-image total. Require complete results aborts when requested images are missing or return an error; disable it to return available images from a partially successful request. Seed -1 randomizes each run; a fixed seed reuses the generation's randomization setting. AI watermark requests an AI generated watermark on the output. This is generative compositing: preservation of logos, colors, texture, and shape is requested through instructions rather than guaranteed. Review product and brand details in the result. Uploaded alpha masks are not connected, so transparency is not used as a compositing mask. Running this Pipe requires access to the Seedream provider through ComfyUI.
Wan 2.7 text to video
Generate one 2–15 second video from a text prompt using the Wan 2.7 partner API. Configured partner API access is required; generation sends your prompt to the provider and uses provider billing. For a first run, keep the supplied florist prompt, 720P resolution, 16:9 aspect ratio and five-second duration, then run the tool. Replace the prompt to describe your scene, subject movement, lighting, visual style and camera movement in English or Chinese. Optionally enter a negative prompt describing unwanted elements; it can remain empty. Choose 720P or 1080P resolution. Aspect ratios 16:9 and 4:3 are landscape, 9:16 and 3:4 are portrait, and 1:1 is square. Duration accepts whole seconds from 2 to 15. AI prompt enhancement is enabled by default and allows AI assistance to expand your wording. Disable it to submit your original wording without enhancement. Seed -1 randomizes each run; a fixed seed supplies a generation seed without guaranteeing identical provider results. Enable Watermark to request an AI-generated watermark. This tool has no image or audio upload. With no audio supplied, the model automatically generates matching background music or sound effects. The generated video is returned to Sweet Tea with automatic container and codec selection by default.
Mage-Flow text to image
Generate square images from a text description using the configured Mage-Flow INT8 model. No uploaded image is required or supported. For a first run, replace the example positive prompt with your subject, composition, lighting and style. Leave the negative prompt empty, choose one image, and keep the supplied sampling defaults: 30 steps, prompt guidance (CFG) 5, Euler sampler, Simple scheduler and denoising strength 1. The source resolution stage is configured for square output at approximately 1024 × 1024 pixels; this Pipe has no output-size control. Use the negative prompt to describe unwanted content or visual traits. Images per run controls the batch size; larger batches require more memory. The seed defaults to -1 for a new random seed each run. Use a fixed seed to revisit a variation with otherwise unchanged settings. Advanced sampling exposes steps, prompt guidance, sampler, scheduler and denoising strength. Steps set the number of denoising iterations. CFG controls adherence to the prompt; excessive guidance can degrade the image. The sampler chooses the denoising algorithm, and the scheduler controls the progression of noise levels. Denoising strength ranges from 0 to 1 and defaults to full denoising; reducing it does not enable image editing or preserve an uploaded image. The model is fixed. Other model variants, image-editing modes and reference-image inputs are not selectable in this Pipe. Generated images are returned as a batch.
[LamaPanama] Waven Chibi Style
Hypernetwork for generating images stylized as waven chibi characters. Actually produces good results even in different proportions of strength. Trained with danbooru tags.
