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Two-speaker dialogue
Enter each speaker's dialogue and choose a voice for each entry. ElevenLabs generates the entries together, and the result is saved as MP3. Stability controls emotional variation versus consistency. Leave the language code empty for automatic detection. Voice cloning is not connected to this output.
Winter Style
This is a DreamArtist Textual Inversion Style Embedding trained on images of a snowy winter forest/cabin scene. You can get amazing winter colors and snow effects in your images by calling Style-Winter in the...
Mage-Flow Turbo image edit
Edit one uploaded image with written instructions using the fixed Mage-Flow Edit Turbo model. Describe what to keep and what to change, such as content, background, lighting or style; no painted mask is used. For a first run, upload your source image, replace the example instruction with an edit suited to it, and keep Image count at 1. Leave the source sampling configuration at four steps, guidance scale (CFG) 1, Euler, the simple scheduler and denoise fraction 1. Seed -1 chooses new sampling noise each run; use a fixed seed when comparing instructions or settings. Resize source image is enabled by default at approximately one megapixel. Target megapixels controls the processed reference area, retaining aspect ratio and aligning dimensions to multiples of 16. Reference resampling selects the interpolation used for this resizing. Turn off Resize source image to condition on the original reference dimensions. Match source size makes the output follow the processed reference dimensions, including any reference resizing. Turn it off to use Width and Height instead. Enter manual dimensions in pixels divisible by 16; the supplied guidance recommends 512–2048 pixels per side. Very wide or tall references can exceed that range under automatic sizing, so choose manual dimensions when needed. Image count generates a batch of edited images; larger batches require more memory. Negative prompt describes attributes to discourage and has little or no influence at CFG 1. Increasing guidance makes negative conditioning relevant. Advanced sampling controls remain available for comparisons, but more steps or stronger guidance do not guarantee a better Turbo edit. Denoise fraction changes the sampling schedule; it is not established here as a reliable source-image preservation strength. This graph accepts one reference image and returns the decoded edited image batch. It does not expose multiple references or a selection mask. Uploaded transparency is not preserved because only the loader's RGB image output is connected.
Reference-guided relighting
Generate one relit image from a subject image and a lighting reference, or from the subject and text instructions alone. For a first run, upload the subject as image 1 and a lighting reference as image 2. Keep the lighting-reference stage enabled, retain the supplied prompt, and leave Lightning mode off. The supplied Chinese prompt asks to remove image 1's original lighting and relight it using image 2's lighting and color tone. Standard sampling uses 20 steps and CFG 4. Enable Lightning mode to select the additional 4-step Lightning LoRA and its separate settings, initially 4 steps and CFG 1. Switching modes selects the current values for that mode; it does not reset edited values. Each mode's steps and CFG remain independently adjustable. Lightning mode uses the 4-step model artifact, even if an upstream source note mentions an 8-step artifact. The light-migration LoRA remains active in both modes. To relight without a reference, disable the lighting-reference stage and rewrite the instructions to describe the desired lighting directly, without referring to image 2. No reference upload is needed in this mode. A subject upload is always required; when the reference stage is enabled, upload a reference too. Advanced controls include an optional negative prompt, both LoRA strengths, denoise, seed, sampler, scheduler, guidance normalization and sampling shift. Lower denoise retains more of the initial subject's structure and can limit the lighting change. Use seed -1 for a fresh random seed, or a fixed seed to compare edits with the same initial noise. LoRA strengths control their model contributions; Lightning strength only affects Lightning mode. Changing an inactive mode's steps or CFG affects subsequent runs in that mode. The subject is automatically scaled before conditioning and sampling, with no independent output-size control. The Pipe returns one generated image. Relighting may change subject details, and uploaded transparency is not preserved because only the image loaders' RGB outputs are connected.
Grok image edit
Create one or more Grok-edited images from an uploaded source image and text instructions. For a first run, upload the image you want to edit, replace the robot example prompt with your own instructions, and use the default grok-imagine-image model, 1K resolution, and one result. Describe both the changes you want and any details or framing you want retained, then run the tool. Choose among grok-imagine-image, grok-imagine-image-quality, and grok-imagine-image-pro. Resolution offers the provider's 1K or 2K setting rather than an exact width and height. Request 1–10 edited results from the same source image and instructions; this does not add source images. Aspect ratio stays automatic because the provider's explicit aspect-ratio setting requires multiple source images, while this tool supplies one. Seed controls whether the provider node re-runs; results remain nondeterministic even when a seed is repeated. Leave it at -1 to randomize the re-run seed each run. Uploaded transparency is not preserved: only the loader's RGB image output reaches the editor.
Magnific portrait skin enhancement
Produce a skin-enhanced portrait using Magnific's creative mode. Upload a portrait, leave sharpening at 0 and smart grain at 15 for your first run, then run the Pipe to receive the enhanced image. Sharpening controls sharpening intensity; smart grain controls grain intensity. Both use a 0–100 scale and can be adjusted independently. Creative mode allows artistic changes to the portrait's appearance. This Pipe supports creative mode only; faithful and flexible modes are unavailable. Uploaded transparency is not preserved because processing uses the loader's RGB image output. Processing sends the portrait to the Magnific partner API, requires provider authentication configured in the runtime, and uses provider-priced processing.
SD3.5 Large blur-guided image generation
Generate new images from a prompt and an already preprocessed blur reference using SD3.5 Large and its blur ControlNet. Upload the required reference and describe the desired subject, appearance, materials, and details in the positive prompt. This tool does not blur or otherwise preprocess the upload. For a first run, replace the example prompt with your description, upload a reference with the intended composition, and choose output dimensions matching its aspect ratio. Width and height are in pixels and use multiples of 16. Start with one image and the defaults: 1024 × 1024, control strength 1, 30 steps, CFG 4, Euler sampler, Simple scheduler, and denoise fraction 1. Control strength determines the influence of the reference. Lower it for more freedom; set it to 0 for generation without reference guidance, while still supplying the required upload. Control start and end select the portion of sampling during which guidance applies: 0 is the beginning and 1 is the end. Keep start below end. Open Sampling for variation and tuning. Seed -1 selects a random seed each run; use a fixed seed when comparing settings with the same initial noise. CFG controls prompt guidance. Steps, sampler, scheduler, and denoise fraction change the sampling process. Keep denoise at 1 for the initial run. Image count generates a batch using the same prompt, reference, and dimensions; larger batches and dimensions require more memory. The checkpoint and blur ControlNet are fixed in this tool. Generation starts from an empty latent rather than an encoded copy of the upload, so this is not pixel-preserving restoration. Lowering denoise does not turn it into image-to-image restoration. There is no editable negative prompt: its text conditioning is zeroed internally before ControlNet is applied. Uploaded alpha is not used.
Seedance 2.0 text to video
Generate one video from a scene description, with optional generated audio. No uploaded media is required. For a first run, use the supplied five-second fantasy prompt with Seedance 2.0, 720p, 16:9 and Generate audio enabled. Alternatively, replace the prompt with your subject, setting, action, visual style and camera movement. Include desired sound when audio is enabled. This workflow calls the ByteDance provider API, requires runtime authentication and uses provider-priced generation. Choose Seedance 2.0, Seedance 2.0 Fast or Seedance 2.0 Mini. Resolution is a text field: enter a value supported by the selected model rather than assuming every resolution works with every model. Aspect ratio sets the frame shape; adaptive leaves that choice to the provider. Duration accepts 4–15 seconds. When changing it, update any duration or shot timestamps in the prompt as well. Prompt wording such as '8K' does not replace the Resolution control. Disable Generate audio for a silent result. Watermark requests a provider watermark. Seed defaults to -1 for randomization on each run. A fixed seed does not reproduce the same video; the source describes generation as non-deterministic regardless of seed. The generated video is returned as one preview result. Under advanced Video encoding, leave File type and Codec at auto for automatic selection, or choose a supported container and compatible codec. This output uses Sweet Tea's preview transport rather than the original SaveVideo stage.
GPT Image edit and generation
Edit a reference image or generate images from text using GPT Image. For a first run, upload one source image, keep the Source image stage enabled, and describe the changes you want and the features to retain. The supplied example requests anime styling while retaining identity. Keep the defaults: GPT Image 1.5, Auto output size, Auto background, High quality and one image. For text-only generation, disable the Source image stage and replace the editing instruction with a complete scene description. An upload is required while that stage is enabled. Model selects GPT Image 1, GPT Image 1.5 or GPT Image 2. Output size requests pixel dimensions or Auto. GPT Image 2 enables additional sizes and custom dimensions. To use custom dimensions, select GPT Image 2 and Custom output size, then enter width and height from 1024 to 3840 pixels in multiples of 16. These fields have no effect with other output-size selections. Background requests automatic, opaque or transparent background handling. Quality selects Low, Medium or High and affects generation time and cost. Image count requests 1 to 8 images using the same instruction and optional reference; the selected output returns those images. This tool connects one RGB reference image. Uploaded alpha transparency is not preserved, and the loader's separate alpha-mask output is unused. Although the generation node supports an optional inpainting mask, this tool does not connect or expose one, so it has no mask-based editing mode. The source seed setting is hidden because the backend does not implement it; it cannot make results reproducible.
fal.ai LTX Video
Describe a scene and its motion to generate a video through fal.ai's LTX Video 13B Distilled endpoint. This workflow accepts text only and returns the generated video. For a first run, enter a prompt describing the subject, setting, action, and camera movement. Keep the source defaults of 480p, 16:9, and 121 frames. The negative prompt provides a starting list of visual and motion defects to discourage. Resolution and aspect ratio set the video shape. Frame count sets its length in frames; this workflow does not expose FPS, so frame count is not a duration in seconds. Frame count must be entered as an integer, without units. Alternative shape values and frame counts must be supported by the provider. Seed defaults to randomization on each run. A fixed seed is available for exploring variations under otherwise similar settings, without a guarantee of identical provider results. Generation requires configured fal.ai access.
Qwen Image masked inpainting
Replace a masked region of an existing image with prompted content using Qwen Image and inpainting ControlNet. The result is composited over the working image; this tool edits within the existing canvas. For a first run, prepare an image whose longest edge is 1536 pixels and create its edit mask at that same size. Upload an alpha-bearing image with transparent pixels where replacement is wanted, or paint an edit mask if a loader mask editor is available. There is no separate mask-upload field. An opaque image without an edit mask provides no useful replacement region. Output transparency is not preserved. Replace the example replacement prompt with your desired content. Keep the longest edge at 1536, denoise at 1, steps at 20 and CFG at 2.5 for the initial run. Seed -1 randomizes each run; use a fixed seed to compare settings. A negative prompt can discourage unwanted content, but has little or no effect at CFG 1. Working image size resizes only the image, not its mask. Prepare the image at the chosen size before masking, or disable that stage to retain the uploaded dimensions. Changing the resize target after masking can misalign the edit region. Image resampling affects only this resize operation and is inactive when the stage is disabled. Mask expansion grows or contracts the editable region in uploaded-mask pixels; tapered corners affect nonzero expansion. Blur radius sets the Gaussian filter's support, and sigma independently controls its spread. The processed mask controls latent noise, inpainting guidance and the final blend. Pixels outside that mask come from the working image, which may be resized; softened edges blend original and generated pixels. Lower denoise retains more existing content within the edit region. Inpainting guidance strength controls the influence of the image and mask, while Guidance start and Guidance end set its active sampling interval as fractions from 0 to 1. Keep start below end. Sampler and scheduler select the denoising algorithm and noise-level distribution; retain Euler and Simple for the first run. Sampling shift is a separate model-specific schedule adjustment with source default 3.1. The optional Lightning LoRA starts disabled. When enabling it at strength 1, try 4 steps and CFG 1 together, as described in the source guidance. Its toggle does not change those independent controls. When disabling Lightning, manually restore 20 steps and CFG 2.5 to return to the baseline configuration. For canvas expansion with directional padding and an automatically created padding mask, use the separate outpainting tool.
Seedance text to video
Generate one short video from a scene description with Seedance, with optional generated audio on Seedance 1.5 Pro. No uploaded media is required. For a first run, keep Seedance 1.5 Pro selected and use the example prompt or describe your own subject, setting, action, and camera movement. The defaults request a five-second, 720p, widescreen video with generated audio. Include dialogue or sound intent in the prompt when audio is enabled, and keep the action and dialogue appropriate for the chosen duration. Choose a duration from 3–12 seconds, a resolution of 480p, 720p, or 1080p, and the aspect ratio for your intended frame shape. Model lets you select the listed Seedance 1.0 alternatives; those models ignore Generate audio. With Seedance 1.5 Pro, turn Generate audio off for a silent result. Fixed camera adds a stationary-camera instruction to the prompt but does not guarantee camera lock. Remove conflicting camera movement instructions when enabling it. AI watermark requests an “AI generated” watermark. Seed -1 randomizes each run; a nonnegative seed controls the generation's random input. The generated video is returned through Sweet Tea's video output transport with automatic format and codec selection by default. Generation sends your prompt to the ByteDance provider and uses provider-priced billing.
Amateur Snapshot Photo - [STYLE] [LORA] [FLUX]
Latest page update: Updated recommended workflow again to a version 2, which is slightly even better.
Grok reference-to-video
Generate a short Grok video guided by two reference images and a description of the scene and motion. Upload both reference images, then describe the action, visual style, camera movement, and intended role of each image. For a first run with the supplied prompt, use a smartphone image as reference 1 and a character image as reference 2. For other subjects, replace the example prompt to match your uploads. Both images guide content or style; they are not designated start and end frames. The default requests a six-second, 720p landscape video at 16:9. Choose a duration from 2 to 10 seconds in whole-second increments, one of the available landscape, square, or portrait aspect ratios, and 480p or 720p resolution. Describe how the two references should relate within the chosen duration. Leave Seed at -1 for a fresh value each run. A fixed seed controls provider re-running and caching, but generated results remain nondeterministic. Under Video encoding, leave file type and codec on auto for automatic selection, or choose the available container and codec settings. Generation uses the hosted Grok service through ComfyUI's partner API and requires configured access. Reference images guide the result rather than guarantee exact reproduction.
bad X (bad sdxl negative embedding)
This is a textual inversion based on SDXL 1.0 images with the refiner, tested and trained to enhance the effectiveness of the output of human figures. It is meant to correct mutation like symptoms of the face and...
Wan 2.1 VACE text to video
Generate a silent video from text using Wan 2.1 VACE 14B. For a first run, replace the supplied car-scene prompt with your subject, setting, visual style and intended motion. Keep the remaining defaults: 1280 × 720 pixels, 81 frames, 16 FPS, batch size 1, CausVid enabled, 4 sampling steps and CFG 1. The output lasts about 5.1 seconds. No media upload is required. Width and height use multiples of 16. Frame count advances by four from 1. Frame count determines how many frames are generated; FPS changes playback speed and duration without adding frames. Keep batch size 1 for a single sequence: this Pipe assembles one video output and does not offer separate files per batch item. The advanced negative prompt discourages unwanted traits; its supplied Chinese text includes static frames, blur, overexposure, text overlays and visual artifacts. CausVid uses a fixed LoRA with separate model and text-encoder strengths. The supplied guidance recommends testing model strength between 0.3 and 0.7 if motion is shaky or blurry. To use the base-model mode, bypass CausVid and manually set Sampling steps to 20 and CFG to 6. Bypassing does not update these settings automatically. Use seed -1 for randomization or a fixed nonnegative seed to reuse starting noise. Sampler, scheduler, denoising amount and schedule shift remain available for advanced adjustment; keep their supplied defaults for a first run. Video playback includes an 8- or 10-bit choice; 10-bit may reduce gradient banding but is not supported by every player or downstream node. Video export provides container, codec and CRF controls. Keep automatic container and codec selection and CRF -1 for the first run; these settings affect encoding rather than generated content. This is a text-only configuration: no reference image, control video, control mask or audio is connected. The VACE strength field does not add those inputs. The model and CausVid LoRA are fixed; smaller-model alternatives and animated WebP export are not selectable modes in this Pipe.
Qwen-Image edge-guided generation
Upload a source image and describe the image you want to create. The Pipe resizes the upload to approximately the chosen megapixel count while retaining its aspect ratio, extracts Canny edges, and uses the encoded edge map as both reference conditioning and the sampler's starting latent. It returns the generated image and the edge-map preview. For a first run, keep the 1 MP size, default edge thresholds, full denoise, and standard mode. Replace the example positive prompt with your desired subject, appearance, and style. Inspect the edge preview: lower thresholds admit more edges, while higher thresholds suppress weaker details. Keep the low threshold below the high threshold. Enable Lightning mode to select the supplied four-step Lightning LoRA together with its separate steps and CFG settings. Standard mode defaults to 20 steps and CFG 2.5; Lightning defaults to 4 steps and CFG 1. Each mode retains its own tuning fields, and only the selected mode's fields affect a run. Denoise operates on the edge-map latent, not the original photograph. Lower values can leave more of that edge-map appearance in the result; this is not a control for preserving the source image's colors. Union LoRA strength adjusts the model adaptation used for reference guidance rather than guaranteeing an exact edge match. Keep Lightning strength at 1 initially. The resize stage can be disabled to work at the upload's native dimensions; megapixel and resampling settings then have no effect, and large uploads can substantially increase resource use. This graph implements Canny guidance only, despite the Union model supporting other control types. It does not use the upload's alpha mask.
Flux.1 Inpaint
Fill missing parts of images using Flux.1 Fill Inpainting.
Magnific reference style transfer
Create a restyled image from a source image, a visual style reference and a descriptive prompt. For a first run, upload both images and replace the fantasy example prompt with a description of your subject and intended appearance. Keep style strength 70, structure strength 41, faithful flavor, balanced engine and fixed generation enabled, then run the Pipe to view the resulting image. Style strength controls the reference style's influence from 0 to 100 percent. Structure strength controls retention of the original image's structure on a separate 0–100 scale. Adjust these independently to balance restyling with composition retention. Flavor selects the service's style-transfer treatment, while engine selects its processing engine. Both offer alternative treatments of the same inputs. Disable fixed generation to introduce more randomness across runs; enabling it does not guarantee identical results. Processing sends the images and prompt to the Magnific partner service through its API node, requires service authentication and uses provider-priced generation. Portrait enhancements are unavailable in this Pipe and remain disabled. Uploaded alpha masks are not connected, so source transparency is not preserved.
Gemini image refinement
Refine and upscale a reference image with Gemini, or generate a new image from a prompt. For a first run, upload a Source image, leave that stage enabled, and use the default prompt to request refined details, textures and materials while retaining composition and text. The default resolution is 4K. This is generative editing: exact text, details and composition may change. Uploaded transparency is not preserved because only the loader's IMAGE output is connected. Change Prompt to describe other edits and what should remain. Aspect ratio set to auto follows the reference image's proportions; an explicit ratio requests a different output shape. Resolution selects a target size tier of 1K, 2K or 4K rather than exact pixel dimensions. The 2K and 4K settings use Gemini's native upscaler, and dimensions also depend on aspect ratio. For text-to-image generation, bypass Source image and replace the refinement prompt with a complete description of the image you want. Choose an explicit aspect ratio to specify the output proportions. This Pipe accepts one reference image. Model selects the Gemini image model. Seed -1 requests a new random seed each run; a fixed seed requests best-effort repeatability, but identical results are not guaranteed. System instructions set foundational provider behavior alongside Prompt; the default directs Gemini to produce an image. Provider response format requests either IMAGE or IMAGE+TEXT, but only the image is connected to this Pipe's result. Generation uses the Gemini provider and sends the prompt and any reference image to that service through a paid provider call. The generated image is returned through Sweet Tea's preview transport; this workflow does not permanently save an image file.
