Ressourcenverzeichnis
Finden Sie, was Sie für Ihren Workflow benötigen.
Guides: techniques, setup, and troubleshootingDurchsuchen Sie Sweet Tea, Pipes, externe Workflows, Modelle, Knotenpakete und Anleitungen in einem faktenbasierten Katalog.
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Create a miniature architectural scene
Create an image of a miniature architectural model in a setting you describe. Upload a building photo or rendering; this Pipe generates a conceptual blueprint, then supplies both that blueprint and the original building image to the scene generator. It returns only the final scene image. Use the separate architectural blueprint tool for a standalone sheet. For a first run, upload your building image, edit the scene prompt to match its appearance, and keep the default 16:9 framing and 1K resolution. The supplied scene prompt requests a purple house on an architect’s desk with a blueprint displayed behind it. Describe the model, materials, surroundings, lighting, and camera view you want. Mention the blueprint in the scene prompt if you want it depicted. Scene aspect ratio and resolution control the final image. Auto framing asks the provider to match an input reference. Under Blueprint reference, edit the drawing views, annotations, and sheet layout for the intermediate image. Blueprint framing and resolution are independent of the final scene settings, but changes to that reference can influence the final composition. Both generation stages run on every request. Each stage has an independent model selector offering gemini-3-pro-image-preview and Nano Banana 2 (Gemini 3.1 Flash Image). Optional generation instructions guide each generator’s foundational behavior separately from its visual prompt; retain their defaults for a first run. Resolution choices are 1K, 2K, and 4K, with the provider’s native upscaler used for 2K and 4K. Each seed defaults to -1, which randomizes that stage on every run. Fixed seeds request best-effort consistency rather than identical results. Fixing only the scene seed does not stabilize an independently randomized blueprint reference. The blueprint and miniature scene are generated concept imagery. Geometry, dimensions, structural details, and annotations may be invented; use them for presentation rather than measured construction documentation. Higher resolution does not establish architectural accuracy.
Detail Tweaker LoRA (细节调整LoRA)
This is a LoRA for enhancing/diminishing detail while keeping the overall style/character; it works well with all kinds of base models (incl anime & realistic models)/style LoRA/character LoRA, etc.
Animate an image with Vidu Q1
Turn a starting image and motion prompt into a five-second, 1080p video with Vidu Q1. For a first run, upload your starting image, replace the example leopard prompt with action and camera instructions suited to that image, and leave Motion amount on auto. Generation uses the Vidu partner API and requires configured API access and sufficient credits. Motion amount requests the amplitude of object movement: auto lets the service choose, while small, medium, and large express an explicit preference. Describe camera movement and atmosphere in the prompt. Duration is fixed at five seconds and resolution at 1080p in this workflow; frame-rate wording in the prompt does not configure actual FPS. Seed -1 randomizes each run. A positive integer supplies a fixed generation seed without guaranteeing identical results; the provider treats 0 as random as well. The Video output advanced section offers automatic or explicit container and codec selection, plus CRF encoding quality. Leave File type and Codec on auto and Quality (CRF) on -1 for automatic settings. These controls affect output encoding rather than the generated action. The result is returned as a video through Sweet Tea.
Grok image generation
Describe the image you want and generate one or more images with Grok. For a first run, use the supplied portrait prompt with the default model, square aspect ratio, 1K resolution, and one image. Change aspect ratio to set the composition and resolution to choose the output size tier; request 1–10 images for variations. The model selector offers the three Grok models present in the source. This workflow accepts text only and has no source-image editing input. Seed controls whether the provider node reruns, but the provider's results remain nondeterministic even with a fixed seed. The default seed randomizes on each run.
Hades style textual inversion
Textual Inversion trained on art from the Hades game, tested on Anything V3 model. Put this TI in the embeddings folder, type "Hades-8500" in prompt to activate. Also I recommend to use words "realistic", "cartoon",...
SD3.5 Large Canny-guided images
Generate new images from a prompt while following edges extracted from a reference image. Outputs include the generated image batch and one reference edge-map preview. For a first run, upload a reference image and replace the example prompt with your desired subject, colors, setting, and style. Keep output and reference dimensions at 1024 × 1024, Canny thresholds at 0.3 and 0.6, control strength at 0.66, and denoise at 1. The fixed SD3.5 Large checkpoint and matching Canny ControlNet are used together. Control strength sets how strongly reference edges influence generation. Set it to 0 for generation without edge influence; the reference upload is still required and its edge map is still returned. Guidance start and end are fractions of the denoising schedule from 0 to 1. Keep start at or below end. Reference resizing and generated output dimensions are independent. Match their aspect ratios for consistent framing. Center crop can remove content near the reference borders; disabled cropping resizes to the requested shape and can stretch the image. Reference interpolation changes resizing before edge detection, not generated output dimensions. Inspect the edge preview when changing crop, interpolation, or thresholds. Keep the low threshold below the high threshold; lower thresholds retain more faint detail, while higher thresholds select stronger contours. Variations sets the number of generated images using the same prompt and edge map. Sampling offers seed, steps, prompt guidance scale (CFG), sampler, scheduler, and denoise. Seed -1 selects a fresh random seed each run; use a fixed seed to compare settings with the same initial noise. Begin with the supplied sampling defaults. Denoise 1 uses the full generation schedule; lower values shorten denoising from the empty latent. The reference supplies edges rather than an initial image latent, so this tool does not preserve uploaded pixels. Uploaded transparency is not carried through the connected image path. Negative conditioning is zeroed internally; there is no editable negative prompt.
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.
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.
