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Hentai Manga Style Concept
This is a embedding i trained on just hentai images. Therefore you will get a lot of open mouths, ahegao, sweating, saliva etc.. from just using the embedding.
Character Expression Sheet
Create a labeled sheet of 12 expressions from a character image, or render one selected preset or custom expression. Start with a character image and set its longest-side size. Batch mode is on by default and creates angry, anxious, confident, confused, contemptuous, disgusted, fearful, happy, sad, shy, sleepy, and surprised expressions in one sheet. Turn batch mode off to render one selected preset or enter a custom expression instruction. Expression strength and seed are available as advanced controls.
Topaz video upscale
Enhance an uploaded video with Topaz Starlight Precise 2.5 and return one video at FullHD (1080p) or 4K (2160p). For a first run, upload your source video, keep FullHD (1080p), leave Compression level at Low, and leave File type and Codec on auto. Run the Pipe to receive the enhanced video. A source video is required; no sample video is supplied as the upload default. Output resolution selects the Topaz enhancement resolution. Compression level selects Topaz's CQP setting from Low, Mid and High. In Video encoding, you can choose MP4, MKV or WebM and H.264 or AV1 instead of automatic selection. Manual file type and codec combinations must be compatible and supported by the runtime. These output encoding choices are separate from Topaz's compression setting. This Pipe uses Starlight Precise 2.5 with frame interpolation disabled. Other enhancement models, creative-model tuning, target frame rate and slow-motion settings are not offered because their required mode-specific bindings are absent from this configuration. Processing sends the uploaded video to the Topaz partner API, requires runtime authentication, and uses provider-priced processing.
Qwen illustration
Generate illustrations from text using Qwen-Image with the fixed Alvdansen Illustration 1.0 LoRA. Describe the subject, composition, colors and desired style in Image prompt, or keep the included example for a first run. Optionally list unwanted elements in Negative prompt; it can remain empty. No uploaded image is required or supported. For a first run, keep the 1024 × 1024 dimensions, image count 1 and illustration strength 1. The advanced sampling defaults are 45 steps, prompt guidance (CFG) 3.5, Euler sampler, Simple scheduler, denoising amount 1 and sampling shift 7. Width and height are pixels and must be multiples of 16. Image count sets the batch size; larger dimensions and batches increase resource use. The output returns every decoded image in the batch through Sweet Tea's image preview transport. Illustration style is enabled by default. Reduce its strength for less LoRA influence, or disable that stage to generate with the base Qwen-Image model. Illustration strength has no effect while the stage is disabled; prompts, output dimensions and sampling controls still apply. Seed -1 selects fresh starting noise each run. Use a fixed seed when comparing changes with the same starting noise. Advanced controls adjust denoising steps, prompt guidance, the sampling algorithm and noise schedule. Keep denoising amount at 1 initially: lowering it does not preserve an input image because generation starts from an empty latent. The model, text encoder, VAE and illustration LoRA are fixed in this tool.
myself-badhand
This embedding focuses on correcting issues related to the number and shape of fingers, which may result in a change in the model's style.
HTRP Mesugaki/Gyaru
This isn't a character model; it's a concept. When combined with LORA, the character becomes a fusion of gyaru and mesugaki.
Corneo's Power (Chainsaw Man) TI Embedding
This is a textual inversion embedding for Power from Chainsaw Man. Chainsaw Man is an incredible series that you should try out if you haven't already.
Vidu Q2 start and end frame video
Generate a short video guided by a start image, an end image, and a description of the motion between them. For a first run, upload both endpoint images and replace the example prompt with the action, camera movement, and scene transition you want. Both images are required; there is no start-frame-only mode. Keep the defaults of Vidu Q2 Pro Fast, 5 seconds, 720p, and small movement initially. Prompts support up to 2000 characters. Duration supports 2–8 seconds in whole-second increments. Resolution offers 720p or 1080p. Movement amplitude sets object movement to small, medium, large, or automatic; describe camera movement in the prompt. Match the requested action to the selected duration. The model selector also offers Vidu Q2 Pro and Turbo. Seed -1 randomizes each run; a fixed seed controls generation randomization. The Pipe returns one generated video. Advanced video output settings offer automatic, MP4, MKV, or WebM file type and automatic, H.264, or AV1 codec selection. Keep both on auto for a first run. Generation sends the endpoint images and prompt to Vidu through a paid provider call. Endpoint images guide a generative transition rather than a deterministic blend.
Character turnaround
Generate one image containing multiple views of a character. For a first run, upload a character reference in Source image, keep the default prompt, 9:16 aspect ratio and 2K resolution, then run. The default requests four close-up face views on a white background. To generate without a reference, bypass Source image and add the character's appearance to the prompt; leaving the upload empty alone does not select text-only mode. For full-body framing, change the prompt to: “A 360 turnaround full body view of the subject, straight in 4 different angles. Front view, half turn, side profile, back view. Full-body shots, all white background.” Set Aspect ratio to 16:9, matching the source full-body branch. Headshot and full-body framing are prompt choices within the same generator; there is no separate preset switch. Aspect ratio controls the complete image, not individual view panels. Auto follows the reference image's ratio when supplied; choose an explicit ratio when generating without a reference if the output shape matters. Resolution offers 1K, 2K and 4K; 2K and 4K use Gemini's native upscaler. Model, Response format and System instructions remain independently editable. System instructions set the provider's foundational behavior. IMAGE+TEXT requests an accompanying provider text response, but this Pipe returns only the image. Seed -1 requests a new random seed each run. Fixed seeds offer best-effort repeatability rather than deterministic results. This tool performs one generation, rather than separately generating and stitching headshot and full-body panels. Requested view counts, pose accuracy and identity consistency are generation instructions rather than guarantees.
Text-to-Image · Flux.1 Schnell Full: Text to Image
Generate images quickly with Flux.1 Schnell full version. Uses Apache2.0 license, requires only 4 steps to generate images while maintaining good image quality.
Wan 2.2 speech-driven video
Generate a speech-driven video from a reference image, a speech recording and a scene prompt. Separated vocals condition generation; the trimmed original recording supplies the soundtrack. Additional outputs preview the prepared reference, separated vocals and instruments. First run: upload both required files, replace the example positive prompt to match your reference and desired motion, and retain the source defaults: 960 × 528 fill/crop framing, 49 frames per chunk, 5 steps, CFG 1, uni_pc sampling, simple scheduling and 16 FPS. The negative prompt describes unwanted content and also feeds negative-attention guidance. Seeds of -1 randomize each run; use fixed seeds when you want to retain the noise choices. Width, height and framing prepare the reference and determine generation dimensions. Use positive dimensions divisible by 16. Stretch, crop and pad offer different ways to fit the reference; keep-proportion mode can change the resulting dimensions. Frames per chunk follows the 1 + 4n convention, such as 49. It does not choose the number of chunks: the graph generates an initial segment and one continuation, while five further source examples remain bypassed and are not selectable here. Advanced settings retain independent initial and continuation denoising, generation batch size, three stacked LoRA strengths, sampling shift and negative-attention guidance strength, mixing and clipping. Start with their authored defaults. Batch size 1 is the single-video recipe, not a chunk-count setting. The Lightning HIGH and LOW LoRAs are applied in series, followed by Lightx2v; they are not alternative model modes. FPS changes playback timing without generating more frames or retiming audio. The starting frame offset defaults to 2 and changes the final frame selection without trimming the soundtrack. Both controls can affect synchronization. Audio trimming remains fixed at source positions 0–6; the supplied evidence does not establish those positions' units or boundary behavior. Output duration is not automatically matched to the recording. The video output defaults to MP4 with NVENC H.264 and requires a compatible encoding runtime. Model and encoder selections retain the authored recipe. Uploaded transparency is not preserved through the RGB reference-image path.
Hamunaptra Style
This is a Style embedding which turns everything in your image towards an Ancient Egyptian style! Think Pyramids, the river Nile, and Pharaohs! Simply throw Style-Hamunaptra on to the end of your prompt (or the...
FLUX.2 Klein Base 9B text to image
Create an image or a batch of images from text using the fixed FLUX.2 Klein Base 9B model. Describe the subject, composition, lighting and style in the positive prompt; optionally list unwanted elements in the negative prompt. No uploaded image is required. For a first run, keep the 1024 × 1024 size, one image, 20 steps, CFG 5 and Euler sampler. The supplied motorcycle prompt is a ready-to-run example. Width and height jointly set the latent image size and the resolution-aware sampling schedule; use multiples of 16. Larger dimensions and batches increase memory requirements. Advanced controls let you tune guidance, steps, sampler and batch size. Higher CFG increases the influence of prompt conditioning and can change the visual balance; more steps increase sampling work without guaranteeing a better result. Seed -1 randomizes each run; use a fixed seed to reuse the initial noise while comparing other settings. The runtime requires flux-2-klein-base-9b-fp8.safetensors, qwen_3_8b_fp8mixed.safetensors and full_encoder_small_decoder.safetensors. The source graph uses the Base model with its supplied sampling settings; it does not provide an image-editing mode or a selectable distilled-model preset.
Recreate an image with realistic detail
The reference image supplies a generated scene description, rather than direct image conditioning. A style prefix and detail suffix surround that description, and the resulting positive prompt drives both Grok generation and Z-Image Turbo refinement. The default style is black and white photography. Refinement denoise controls how much the generated image changes. Before refinement, the image is resized to the chosen base megapixel count, enlarged fourfold with RealESRGAN, and reduced by half, producing approximately four times the base pixel count. Grok resolution affects the initial generation; base megapixels determines the final refinement size. The negative prompt affects only refinement and becomes useful when refinement CFG is increased above its default of 1.
HiDream O1 image studio
Create an image batch with HiDream O1 Image Dev by editing an uploaded reference or generating from a text description. For a first run, upload a source image, keep Image edit enabled, replace the example Positive prompt with your requested change, leave Refine prompt off, and run with the existing defaults. Results are returned through Sweet Tea's image output; this graph does not save files through ComfyUI SaveImage. An upload is required by this interface in both modes. With Image edit enabled, the original upload supplies reference conditioning. Output width and height follow the upload, rounded down to multiples of 32. Use dimensions of at least 64 pixels and keep the calculated canvas within 4096 pixels per dimension. To calculate a different edit canvas, enable the currently bypassed Resize edit output stage and set Edit megapixels, measured in millions of pixels. Aspect ratio follows the upload. This stage changes canvas sizing only; reference conditioning and prompt refinement still receive the original upload. Resize method affects the intermediate sizing copy, rather than directly filtering the generated result. Disable Image edit for text generation and describe the complete desired image in Positive prompt. Text width and Text height specify pixels, in multiples of 32 from 64 to 4096. Text batch count and Edit batch count independently select the number of results for their respective modes. Text mode does not use the upload as image-generator reference conditioning, but the interface still requires it. Refine prompt optionally uses Gemma 4 and the upload to expand your request into an English visual description, including in text mode. It may add interpretation or inferred details; leave it off when exact wording matters. Refinement tuning controls text sampling, its separate seed and the response token limit. Temperature and token filters apply when Sample refinement text is on. An insufficient token limit or malformed JSON response can prevent prompt extraction. Refinement thinking requests model-supported thinking behavior; false is the source configuration for JSON extraction. The starting image settings are 28 steps, normal scheduler, CFG 1 and Add noise enabled. CFG changes conditioning influence; at 1, conventional classifier-free negative guidance is inactive. Denoise changes the sampling schedule from an empty latent in both modes, rather than directly controlling preservation of uploaded pixels. Advanced Sampling noise tuning exposes the initial and final per-step noise multipliers and clipping threshold. Image Seed and Refinement seed control separate random processes: -1 randomizes each run, while fixed seeds help compare settings. This tool accepts one reference image. Uploaded alpha is not carried through the RGB reference path. Edit canvas dimensions are rounded rather than clamped, so check the resulting size when using a very small image or an extreme aspect ratio.
Grok Imagine image generator
Generate one or more images from a text prompt using Grok Imagine. Describe the subject, composition, lighting, style, and any desired text. No uploaded image is required. For a first run, use the supplied example or replace it with your own prompt. Keep grok-imagine-image-quality, the square 1:1 aspect ratio, 1K resolution, and one image, then run the Pipe. Returned images appear in Sweet Tea results; the output stage does not permanently save files. The model field selects the Grok Imagine model. Aspect ratio sets the width-to-height shape, while resolution selects a size tier supported by that model. Keep the supplied values unless you know another combination is supported; the supplied evidence does not enumerate alternative model, aspect-ratio, or resolution values. Number of images requests 1–10 results from the same prompt. Seed controls whether the provider node re-runs, rather than making images reproducible. Leave it at -1 to randomize on each run. A fixed seed does not guarantee the same image. Generation sends the prompt to the external Grok provider and uses a paid provider call.
