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Anima image generation
Generate square images from text with Anima, using standard sampling or Turbo mode. No source image is required; the result is an image batch returned through Sweet Tea's preview transport, without permanent ComfyUI image saving. For a first run, replace the example positive prompt with your subject, composition, lighting and style. Review the negative prompt for attributes you want to exclude. Leave image count at 1 and Turbo mode disabled, then run with the supplied sampling defaults. Seed -1 produces fresh randomization on each run; use a fixed seed to compare changes with the same initial noise. Output size is fixed to the source's square, approximately 1-megapixel setting, about 1024 × 1024 pixels with dimensions rounded to multiples of 16. This interface has no resolution control. Image count determines the number of returned images; larger batches require more memory. Turbo mode selects the supplied Anima Turbo LoRA together with separate Turbo steps and guidance. Standard defaults are 30 steps and CFG 4; Turbo defaults are 8 steps and CFG 1. Only the active mode's steps and guidance affect sampling. Turbo LoRA strength affects the selected model only in Turbo mode. At CFG 1, negative conditioning does not influence ordinary classifier-free guidance. Advanced controls include both modes' steps and guidance, Turbo LoRA strength, seed, sampler, scheduler and denoise strength. Keep Euler, the simple scheduler and denoise strength 1 for the first run. Steps must be within 1–10000 and CFG within 0–100, even though their primitive input fields accept broader values. Lower denoise strength shortens the noise schedule; this text-to-image workflow has no source image to preserve. The runtime needs these source-specified model files in the corresponding ComfyUI model directories: [anima-base-v1.0.safetensors](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/diffusion_models/anima-base-v1.0.safetensors) in models/diffusion_models; [anima-turbo-lora-v0.2.safetensors](https://huggingface.co/circlestone-labs/Anima-Official-LoRAs/resolve/main/anima-turbo-lora-v0.2.safetensors) in models/loras; [qwen_3_06b_base.safetensors](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/text_encoders/qwen_3_06b_base.safetensors) in models/text_encoders; and [qwen_image_vae.safetensors](https://huggingface.co/circlestone-labs/Anima/resolve/main/split_files/vae/qwen_image_vae.safetensors) in models/vae. The selected output also requires [Sweet Tea nodes](https://github.com/tea-time-labs/sweet-tea-nodes). Standard and Turbo share the same image output.
Boogu Image Turbo
Generate an image or batch of images from a text prompt with the fixed Boogu Image Turbo model. No source image is required. For a first run, replace the example prompt with your subject, composition, lighting and style. Keep the square aspect ratio, 1-megapixel resolution and one image per run, then generate. The source sampling preset is 4 steps, CFG 1, lcm sampler, sgm_uniform scheduler and denoise 1. Aspect ratio and megapixels jointly determine the output dimensions. Megapixels measures total image resolution; the supplied size guidance describes a square 1 MP image as approximately 1024 × 1024 pixels. Images per run controls batch size. Larger resolutions and batches require more memory. Advanced Sampling exposes the seed, steps, prompt guidance, sampler, scheduler and denoise amount. Seed -1 selects a new random seed each run; use a fixed seed when comparing settings. Lower denoise values shorten the denoising schedule, but there is no source image whose structure can be preserved. Negative conditioning is zeroed internally, so there is no separate negative prompt. This tool does not provide image editing or model switching. Runtime requires boogu_image_turbo_fp8_scaled.safetensors in diffusion_models, qwen3vl_8b_fp8_scaled.safetensors in text_encoders, and ae.safetensors in vae, plus the Sweet Tea output node pack. The supplied dependency inventory marks these assets as missing; they must be available in the execution environment.
Photo to whole-body pose map
Upload a photo to produce a standalone pose map and an inspection preview blending that map with the resized photo and detected person boxes. Both outputs belong to the same pose extraction run; the preview is not a separate detection tool. For a first run, keep the 1024-pixel longer edge and the default drawing settings. The source default processes at most one person, choosing detections by descending confidence. Increase Maximum people for a group photo. Detection threshold controls which person boxes reach pose extraction; Keypoint threshold separately controls which estimated points are drawn. Lower thresholds can reveal uncertain detections or points, while higher thresholds can omit people or parts of a pose. The longer-edge setting drives both image resize branches and preserves aspect ratio. Choose which body regions to draw, then tune line width and face point size if needed. Preview blend and blend mode affect only the inspection preview, not the standalone pose map. This workflow produces rendered images, not a saved keypoint data file, and does not preserve uploaded transparency through its RGB image path. Required models are sdpose_wholebody_fp16.safetensors in checkpoints and rt_detr_v4-x-hgnet_fp16.safetensors in diffusion_models. Their supplied installation sources are https://huggingface.co/Comfy-Org/SDPose/resolve/main/checkpoints/sdpose_wholebody_fp16.safetensors and https://huggingface.co/Comfy-Org/SDPose/resolve/main/diffusion_models/rt_detr_v4-x-hgnet_fp16.safetensors.
Realisian Negative Embedding
This Negative Embedding is meant to be used with Realisian models but it can work on other realistic models (it has NOT been tested on anime models).
Corneo's Makima (Chainsaw Man) TI Embedding
This is a textual inversion embedding for Makima from Chainsaw Man. Chainsaw Man is an incredible series that you should try out if you haven't already.
SeedVR2 image upscale
Restore and resize an uploaded image with SeedVR2, producing one output image. For a first run, upload your source image and keep the supplied scale factor of 4, Lanczos resampling, denoise of 1 and color matching set to none. Scale factor multiplies both width and height while preserving aspect ratio. A value of 4 produces roughly sixteen times as many pixels and increases processing demand. Values above 1 enlarge, 1 retains the source dimensions, and values below 1 reduce. Resampling controls interpolation before restoration: Lanczos is the supplied enlargement default, area supports reduction, and nearest-exact retains hard pixel boundaries during resizing. Color matching compares the restored result with the resized source. lab transfers color in CIELAB space; wavelet transfers low-frequency color while retaining generated fine detail; adain matches global per-channel mean and standard deviation. none skips color transfer while retaining geometry alignment. Advanced restoration settings include seed, denoise, steps, guidance scale, sampler and scheduler. Use seed -1 for a fresh random seed each run, or a fixed value to reuse the noise seed. Lower denoise values change the restoration schedule and may retain more source structure. Start with the source baseline of denoise 1, one step, guidance scale 1, Euler sampling and simple scheduling; other sampling configurations are experimental for this tool. Advanced input and output tiling controls separately set spatial encoding and decoding tile sizes and overlaps in pixels. Keep each overlap below its corresponding tile size. The supplied settings are 512-pixel tiles with 128-pixel overlap for both stages. The workflow uses the fixed SeedVR2 7B INT8 model and matching VAE, with conditioning derived from the image rather than an editable text prompt. Generated details may differ from the original; inspect the result when exact fidelity matters.
Bad_NegAnatomy Textual Inversion
his is a Textual Inversion I have created, this is mainly useful to improve your overall aesthetics and character anatomy.
Cherry Nobody
Follow me to make sure you see new poses and Nobodys when I post them. Things move fast on this site, it's easy to miss.
Recraft image generation from style references
Generate new images from a text prompt using a custom Recraft style created from one to five reference images. The references guide the visual style; the positive prompt describes the subject, scene, and composition. For a first run, upload the primary style reference, replace the example positive prompt with your own description, choose realistic_image for photographic imagery or digital_illustration for illustrated imagery, and keep the default 1024x1024 output size and image count of one. Run the tool to receive the generated image. Additional style references 2–5 are available in the advanced sections and are bypassed by default. Enable each stage you want to use and upload its reference image. Every enabled reference stage needs an image; leave unused stages bypassed. Use references with a consistent visual style. The negative prompt optionally describes unwanted elements. Output size specifies dimensions in pixels; use a size supported by Recraft. Image count requests one to six images using the same prompt and custom style. This workflow uses the Recraft service and creates a custom style before generating images. It does not expose an existing-style-ID mode or a standalone style-ID output. The references guide style rather than serving as images to edit. Seed -1 randomizes the rerun seed; a fixed seed controls rerun behavior but does not make Recraft results reproducible.
Photography Tools By Stable Yogi (Sd1.5)
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HiDream I1 Dev text to image
Create images from a written description using the fixed HiDream I1 Dev FP8 model and its four text encoders. No uploaded image is required. For a first run, replace the positive prompt and keep the supplied 1024 × 1024 size, one image, 28 steps, LCM sampler, normal scheduler, CFG 1 and sampling shift 6. Seed -1 selects fresh randomness on each run. Increase Images per run to generate a batch; larger dimensions and batches require more memory. Width and height are measured in pixels and use multiples of 16. The negative prompt is connected to negative conditioning, but has no effect at the default CFG of 1. It becomes relevant when CFG is increased above 1. The supplied settings are the source's Dev configuration; changing CFG, sampler, scheduler or sampling shift is experimental tuning rather than a model variant selector. This tool generates from an empty latent. It does not accept a source image or provide image editing.
Qwen 2511 image edit
Edit a source image with a written instruction using Qwen Image Edit 2511. For a first run, upload an image, describe the changes you want and the features to retain, and keep the supplied defaults: denoise strength 1, 40 sampling steps, CFG guidance 4, Euler sampler and Simple scheduler. The example instruction requests anime styling while retaining identity. Leave the negative prompt empty unless you want to describe attributes to discourage. Seed -1 randomizes the sampling noise. The image is automatically resized before editing. The resized RGB image supplies both positive and negative reference conditioning and is encoded as the initial sampling latent. Lower denoise values retain more of that initial image's structure. CFG guidance adjusts prompt conditioning separately from denoise strength. A fixed nonnegative seed reuses the noise setting; changing the image, instruction or sampling controls can still change the result. Output dimensions come from the automatically scaled source image. This tool has no manual output-size control, text-only mode, mask-based editing input or separate output-upscaling stage. Uploaded alpha transparency is not preserved because the loader's alpha-mask output is not connected. The selected output returns the decoded edited image. Advanced settings include negative prompting, guidance normalization strength, normalization before CFG and sampling shift. Normalization defaults to strength 1 with pre-CFG normalization off, using post-CFG attenuation. Enabling pre-CFG normalization rescales noise before CFG combination without clamping and can amplify it. Sampling shift defaults to 3.1 and changes the model's sampling configuration independently of denoise and CFG. The workflow uses qwen_image_edit_2511_bf16.safetensors, qwen_2.5_vl_7b_fp8_scaled.safetensors as its Qwen image text encoder, and qwen_image_vae.safetensors.
WaveSpeed image upscale
Upload a source image, choose an upscale model and target resolution, and run to produce the upscaled image through WaveSpeed. For a first run, keep SeedVR2 and the 2K preset; try Ultimate to compare the alternative model, or select 4K or 8K for a larger target. Resolution choices are provider presets, not independently specified width and height. This workflow provides no prompt or detail-strength controls. Uploaded transparency is not preserved through the connected RGB image path.
nfixer for Illuminati Diffusion v1.1
This negative embedding is intended for use with Illuminati Diffusion v1.1, feel free to use it elsewhere but I can't guarantee your success.
Wan 2.7 Reference to Video
Generate a short video featuring subjects from two uploaded reference images using Wan 2.7 Reference to Video. For a first run, upload both required images, then rewrite the example prompt to describe your subjects, their actions, the scene, and camera movement. The source recommends identifiers such as character1 and character2 for reference characters. Keep the default 5-second duration, 720P resolution, and 16:9 aspect ratio initially. The images supply subject references; they are not designated start and end frames. Choose a duration from 2 to 10 whole seconds, 720P or 1080P resolution, and one of the supported landscape, portrait, or square aspect ratios. Use the negative prompt to describe unwanted visual elements. Its supplied default is in Chinese and discourages blur, static scenes, anatomical defects, and text; you can edit it to match your needs. Seed -1 requests a fresh random seed for each run; a fixed seed requests a particular generation variation. Enable Watermark to request the provider's AI-generated watermark. Advanced video output settings offer automatic or explicit container and codec selection; leave both on auto for your first run. This Pipe uses a hosted API node and requires configured provider access. It returns one generated video. The selected graph has two reference-image inputs, no reference-video input, and no separate motion-strength control; describe desired movement in the prompt.
Vidu Q2 text to video
Generate a short video from a written scene description using Vidu Q2, with optional background music. No source image is required. Generation sends your prompt and settings to Vidu through a paid provider call. For a first run, use the supplied scene prompt with the default 5-second duration, 16:9 aspect ratio, and 720p resolution. Run the Pipe to return one generated video to Sweet Tea. To create your own scene, describe the subject, action, setting, camera movement, and visual style in a prompt of up to 2,000 characters. Duration ranges from 1 to 10 seconds in whole-second increments. Choose landscape framing with 16:9 or 4:3, portrait framing with 9:16 or 3:4, or square framing with 1:1. Resolution can be 720p or 1080p. Enable Background music to request music with the generated video. Seed -1 randomizes each run. A fixed seed supplies a specific value to the provider without guaranteeing identical results. Output format and codec default to automatic selection; the output stage returns the video to Sweet Tea rather than saving it through the original ComfyUI SaveVideo node.
