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.
Beginnen Sie stattdessen mit einem Workflow ↗Suchergebnisse
Alle Ressourcen
20 angezeigt of 41.764
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.
SDXL two-reference image generation
Create new images with SDXL Base using a positive prompt and visual conditioning from two uploaded reference images. Both references guide the same generated result. For a first run, upload both references, describe the desired subject and style, and keep the 1024 × 1024 output size, reference strengths of 0.75, and denoise amount of 1. Use the negative prompt to describe unwanted features. Image count sets the number of images generated per run with the same prompts and references. Adjust each reference strength independently to change its contribution. Zero removes that reference's conditioning influence; setting both strengths to zero leaves text conditioning. Both uploads remain required in every case. Reference noise augmentation perturbs the corresponding image embedding rather than adding visible grain to the result. The references guide generation through image embeddings. Sampling starts from an empty latent; this is not pixel editing and does not preserve exact reference layouts or uploaded transparency. Center cropping can omit content near reference-image edges. Under advanced reference framing, choose none to avoid center cropping. Under advanced Sampling, use seed -1 for fresh randomization or a fixed seed to compare settings with the same initial sampling noise. Steps controls the number of denoising steps, CFG controls conditioning guidance, and sampler and scheduler select the sampling algorithm and noise schedule. Denoise controls the sampling denoise amount; lowering it does not preserve reference pixels because neither reference supplies the starting latent. Larger dimensions and image counts require more memory. The workflow uses the fixed sd_xl_base_1.0.safetensors checkpoint and clip_vision_g.safetensors vision encoder.
FLUX.2 Klein two-image editing
Generate edited images from a source image, a second visual reference and written instructions using FLUX.2 Klein 9B. For a first run, upload both required images: the main image as Figure 1 and the visual reference as Figure 2. Replace the example instructions with what to change, what to take from Figure 2 and what to retain from Figure 1. For example, request clothing from Figure 2 on the subject in Figure 1 and describe the desired setting. Keep the supplied defaults of 1 output megapixel, 1 reference megapixel, 1 variant, 4 steps, CFG 1 and Euler sampling, then run to receive an edited image. Output megapixels sets Figure 1's resized area in millions of pixels. Those dimensions determine generation size and scheduling, so changing this control also changes Figure 1's reference resolution. Output proportions follow Figure 1, subject to dimension rounding. Reference megapixels independently sets Figure 2's working resolution without setting output size. Each image has its own resize interpolation control. Advanced controls let you request a batch with Variants, choose sampling steps and algorithm, and adjust CFG between the instructed and zero-text conditioning branches. Both branches carry both image references. Seed -1 selects fresh sampling noise each run; use a fixed seed to compare changes with the same initial noise. Both images condition a newly generated result. Exact preservation of pose, identity or unchanged pixels is not guaranteed. This tool has no mask, denoise control or editable negative prompt, and uploaded transparency is not retained through the RGB generation path. Results are returned to Sweet Tea for preview rather than saved by the original ComfyUI SaveImage node.
Seedance image to video
Animate a starting image into one video with prompted action and camera movement, with optional generated audio on Seedance 1.5 Pro. Upload the required image to use as the first frame, then replace the example prompt with action, camera movement and atmosphere suited to your image. For a first run, keep Seedance 1.5 Pro, 480p resolution, adaptive aspect ratio and the five-second duration. Run the Pipe to receive the generated video in Sweet Tea. Duration is measured in seconds and supports 3–12 seconds. The example requests four shots, dialogue and ambience; simplify it or increase the duration to allow more room for the action. Prompted cuts, dialogue and camera effects are creative directions rather than guaranteed timing. Choose an explicit aspect ratio when you need a particular frame shape. Resolution offers 480p, 720p and 1080p, subject to the selected model's support. Model selects among the supplied Seedance 1.5 Pro, 1.0 Pro, 1.0 Lite and 1.0 Pro Fast choices. Generate audio applies only to Seedance 1.5 Pro; the other choices ignore it. With audio enabled, describe desired dialogue and background sounds in the prompt. Fixed camera adds a stationary-camera instruction but does not guarantee a motionless viewpoint; remove conflicting pans, dollies and other camera movement from your prompt when using it. Seed -1 selects a new random value each run; a fixed seed controls generation randomization. AI watermark requests an AI-generated watermark on the video. The uploaded image's alpha mask is unused, so this Pipe does not preserve uploaded transparency. Generation uses the ByteDance provider and involves transferring the input image and prompt to that provider and a provider-priced generation call.
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)
Loving this model? Get the Pro version of my models here for exclusive perks and early access to unique resources.
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.
Topaz creative image enhancement
Enhance and resize an uploaded image with Topaz Reimagine, returning one processed image. For a first run, upload your source image and optionally enter an Enhancement prompt describing details or an aesthetic to emphasize or retain. Leave the prompt empty for enhancement without text guidance. The defaults request 1080 × 774 pixels with Creativity 3, Enhance faces and Preserve facial identity enabled, and Preserve colors disabled. Enter output dimensions in pixels, using values from 0 to 32000 even though the dimension input widgets accept a wider integer range. Set one dimension to 0 for automatic sizing from the other, or both to 0 to use the original size. When the requested aspect ratio differs from the source, the default fit letterboxes the image. Enable Crop to fill to fill the requested frame by cropping. Creativity ranges from 1 to 9. Subject detection offers All, Foreground, or Background. Enable Enhance faces to apply face treatment; Face creativity and Face sharpness each range from 0 to 1 and apply to that treatment. Face sharpness controls how sharp enhanced faces are relative to the background. Preserve facial identity and Preserve colors separately request retention of likeness and the original palette. The model is fixed to Reimagine. Creative enhancement can change image details and should not be treated as faithful historical reconstruction. There is no seed control. Uploaded transparency is not preserved through an alpha-mask path: only the loader's image output reaches enhancement. Processing sends the image to the remote Topaz partner API and requires configured API access and authentication.
Anima edge-guided illustration
Generate prompted Anima illustrations guided by an uploaded image’s outlines, and receive an edge-map preview alongside the generated images. For a first run, upload a reference with the composition you want and replace the example positive prompt with your desired subject, colors and style. Review the negative prompt too: its example includes sepia, which conflicts with the positive example’s sepia palette. Keep output size at 1 megapixel, control strength at 1, batch size at 1 and Turbo mode off. Seed -1 chooses fresh random noise each run; use a fixed seed when comparing settings. The reference is processed into dark edges on a light background. Its proportions determine the output proportions, subject to latent sizing. It supplies outline guidance rather than an encoded starting image, so uploaded colors, texture and transparency are not preserved. The edge-map preview is shown before resizing; the guide used for generation is resized to the requested megapixel target. Control strength adjusts outline influence; 0 removes that influence while retaining reference-derived sizing. Edge detection thresholds range from 0.01 to 0.99: keep the low threshold below the high threshold. Lower thresholds can retain more contours; higher thresholds can reduce clutter. Control start and end are fractions of sampling from 0 to 1; keep start below end to define the interval during which guidance applies. Guide interpolation controls how the edge map is resized. Turbo mode jointly selects the fixed Turbo LoRA and the separate Turbo steps and CFG settings. Standard defaults are 30 steps and CFG 4; Turbo defaults are 8 steps and CFG 1. Only the active mode’s steps and CFG affect sampling, and Turbo LoRA strength affects the selected model only when Turbo is enabled. Use positive step counts. Sampler and scheduler apply to both modes; the source presets are Euler and simple. Keep denoise at 1 initially: lowering it changes sampling from the empty latent and does not turn this into image-to-image preservation. Batch size controls the number of generated variants sharing the prompt and guide. Larger batches and output sizes increase memory requirements. The preview remains a guide preview rather than another generated variant. The fixed model setup uses anima-base-v1.0.safetensors in diffusion_models, anima-lllite-any-test-like-v2.safetensors in model_patches, qwen_3_06b_base.safetensors in text_encoders, qwen_image_vae.safetensors in vae, and anima-turbo-lora-v0.2.safetensors in loras. These files are referenced by the workflow; their availability is not established by this review. The base model, text encoder and VAE come from circlestone-labs/Anima on Hugging Face, the patch from Comfy-Org/Anima-LLLite, and the LoRA from circlestone-labs/Anima-Official-LoRAs. Generated-image transport also requires the declared tea-time-labs/sweet-tea-nodes package.
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.
Flux.2 Dev image edit
Generate edited images from a reference image and a written instruction using Flux.2 Dev. For a first run, upload a source image, describe the changes you want and the features to retain, and keep the supplied defaults: 1 megapixel, Lanczos resizing, one image, guidance 4, 20 sampling steps and Euler. The example instruction requests anime styling while retaining identity. Seed -1 selects random sampling noise. Output megapixels sets the target total pixel count of the reference while following its aspect ratio. The resized reference supplies editing conditioning and determines generation dimensions. Resize method changes reference interpolation before encoding; it does not add an output upscaling stage. Advanced controls provide image count, seed, Flux guidance, sampling steps and sampler choice. A fixed nonnegative seed reuses the noise setting; changing the reference, instruction or sampling settings can still change the result. Guidance adjusts instruction conditioning rather than the amount of source-image denoising. Generation starts from an empty latent, with the encoded source attached as reference conditioning. This tool has no text-only mode, negative-prompt control, image-to-image denoise-strength control or mask-based editing mode. Uploaded alpha transparency is not preserved by the connected RGB image path. The selected output contains generated images. The workflow uses flux2_dev_fp8mixed.safetensors, mistral_3_small_flux2_bf16.safetensors as its Flux.2 text encoder, and flux2-vae.safetensors.
Topaz creative upscale
Upload an image to produce one enhanced image at a scale-based size with Topaz Reimagine. For a first run, keep the 4× scale and creativity of 3; leave the enhancement prompt empty or describe details and appearance to guide the result. Scale multiplies the source width and height, approximately preserving its aspect ratio. Values below 1 request a smaller image. Keep each calculated dimension within 32,000 pixels. The sizing branch supplies dimensions; Topaz receives the original image directly. Processing sends the image to Topaz's external service. Creativity controls reinterpretation on a 1–9 scale; inspect details that must remain faithful to the source. Subject detection offers All, Foreground, and Background modes. Enhance faces enables additional face treatment; Face creativity and Face sharpness apply when it is enabled. Preserve face identity and Preserve colors request retention of facial identity and original colors. Crop to fill crops instead of letterboxing when output and source aspect ratios differ. Scale-based sizing normally preserves the source aspect ratio, so this setting usually has little effect. The model is fixed to Reimagine, and independent width and height overrides are unavailable. Uploaded transparency is not retained through the connected RGB image path.
Topaz video upscale
Enhance an uploaded video with Topaz Starlight (Astra) Fast and return one video at 1080p or 4K. For a first run, upload your source video, keep FullHD (1080p) output and Low compression, and leave the advanced export settings on auto. Run the Pipe to receive the enhanced video. Choose 4K (2160p) when that is your desired output resolution. Compression level selects the Topaz service's CQP compression setting: Low, Mid or High. Advanced export settings control the returned video's codec and container separately from the service's compression setting. Leave both on auto unless you need a particular format. Explicit codec values are h264 and av1; explicit container values are mp4, mkv and webm. Choose a compatible combination. Processing sends the source video to the authenticated Topaz provider API and uses provider-priced enhancement. Execution requires provider access. This Pipe uses the fixed Starlight (Astra) Fast model with frame interpolation disabled. It does not offer alternative enhancement models, creative prompts, frame-rate changes or slow motion.
