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Flux 2 two-image design transfer
Generate a new image with Flux 2 using a design reference, a scene reference, and a prompt describing the transfer. For a first run, upload the design as Reference image 1 and the objects or scene as Reference image 2. Both uploads are required. Keep the supplied transfer prompt or describe precisely how each reference should contribute. Start with 1 output megapixel, 1 image, and Turbo mode off, then run to receive the generated image. Both references provide conditioning; generation starts from an empty latent. The result is a newly generated interpretation, so original pixels and object details are not guaranteed to remain unchanged. Output dimensions are derived from the resized first reference, following its aspect ratio subject to latent dimension rounding, even when the second reference supplies the scene. Output megapixels also controls the first reference's processing resolution. Second reference megapixels affects its conditioning resolution independently and does not determine output dimensions. Each reference has its own resize interpolation control. Turbo mode switches model adaptation and sampling budget together. Off uses the base model and Standard steps, initially 20. On uses the supplied Flux2TurboComfyv2 LoRA and Turbo steps, initially 8. Turbo strength affects only Turbo mode and defaults to 1. Enter step counts from 1 to 4096; only the active mode's step count is used. Guidance and sampler apply in both modes. Use seed -1 for fresh randomness each run, or a fixed seed to compare settings with the same initial noise. Image count sets the generated batch size. Larger resolutions and batches increase memory and processing requirements. Uploaded transparency is not retained through either reference path. The tool has one main prompt and no separate negative-prompt input.
HappyHorse image to video
Animate an uploaded first-frame image into one 3–15 second video using a motion prompt. The image determines the output aspect ratio. For a first run, upload your image, replace the example prompt with movement and camera directions appropriate to it, and keep HappyHorse 1.1, 720P resolution, and the 5-second duration. Prompts support English and Chinese. The uploaded image is required; the example prompt refers to a woman and horse and should be adapted to your subject. Choose 720P or 1080P output and a duration from 3 to 15 seconds in whole-second increments. HappyHorse 1.0 is also available. Seed -1 requests a fresh random seed each run; a fixed seed controls generation randomization. Enable Watermark to add the provider's AI-generated watermark. Generation sends the image and prompt to the HappyHorse partner API, requires configured API access, and uses provider-priced generation. The result is returned as one video for preview. 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 the first run.
PonyScores
Embedding created by merging score tokens " score9, score8up, score7up, score6up, score5up, score4up " for Pony based models.
Style Asian Less
This tool doesn't specifically race-swap asian to white-caucasian, it just removes asian so your prompts can be more effective with whatever other race or culture you're trying to portray. This is due mostly to heavy...
PixelDiT image generator
Generate one image from text using the fixed PixelDiT 1300M model, Gemma text encoder, and pixel-space decoding. No source image is required. For a first run, replace the positive prompt with your subject, setting, composition, lighting, and desired appearance. Keep or edit the prefilled negative prompt to describe features to discourage. Start with the square, 1-megapixel default and the supplied sampling settings: 30 steps, CFG 4, er_sde sampler, simple scheduler, and denoise 1. Run the Pipe to obtain the generated image. Aspect ratio sets the image shape; megapixels sets the approximate total pixel count in millions. Width and height are calculated together. A square image at 1 MP is approximately 1024 × 1024. Prompt text such as “8k” does not set actual output resolution. Larger images require more resources. Advanced sampling controls include seed, steps, CFG, sampler, scheduler, and denoise. Seed -1 requests fresh randomization on each run; a fixed nonnegative seed reuses the noise initialization. Steps set the sampling step count. CFG controls prompt guidance; excessive values can harm the result. Sampler selects the sampling algorithm, while scheduler sets the progression of noise levels. Changing these controls can change the generated appearance. Keep denoise at 1 for full generation from the empty latent. Lower values sample a shorter noise range. This is text-to-image generation: there is no uploaded image to edit or preserve. Image count is fixed at one, and model selection is fixed. The execution environment needs pixeldit_1300m_1024px_bf16.safetensors, gemma_2_2b_it_elm_bf16.safetensors, and the Sweet Tea output nodes.
Replace video background
Replace a video's background with a supplied image using Bria and receive one processed video. For a first run, upload the foreground video under Source video and a replacement image under Background image. Keep Background image enabled and leave File type and Codec on Auto, then run the Pipe. Background image is optional and bypassable. Bypassing it omits the replacement image; the supplied provider definition does not specify the resulting appearance. This Pipe has no replacement background video input. Uploaded image transparency is not carried through the connected RGB image output. Under Background replacement, Seed defaults to -1 to randomize on each run. Seed controls provider re-execution; results remain non-deterministic even with a fixed seed. Under Video output, File type offers Auto, MP4, MKV and WebM; Codec offers Auto, H.264 and AV1. Keep Auto for a first run, or choose a compatible container and codec together. The preserved hidden CRF control lacks documented semantics in the supplied evidence.
Image grid
Combine two to four uploaded images into one collage. With all four inputs enabled and the default directions, the first pair forms the top row and the second pair forms the bottom row. For a first run, upload all four images and keep the defaults for a 2048 × 2048 output without gaps. Top-left and bottom-left images are required. Top-right and bottom-right are independently optional: disable their input stages when omitting them. With default directions, disabling both right inputs produces two images stacked vertically; disabling only one produces rows with different image counts. Open the advanced layout stages to change each pair's direction and the direction used to join the completed pairs. Each direction places the second image or pair relative to the first: right, down, left or up. Input position labels describe the default grid; changing directions changes their final positions. Size matching is enabled independently for each pair and for the final assembly. Gap widths and colors are also independent at those three joins. Gap widths are measured in pixels before the final resize, so resizing changes their final displayed size. A pair's direction, size matching and gap controls have no effect when its optional second image is disabled. Set the final width and height in pixels. Center cropping preserves proportions but can cut off collage content when its aspect ratio differs from the requested dimensions. Select disabled cropping to stretch instead, or set one dimension to zero to calculate it from the other while preserving aspect ratio. Keep at least one dimension greater than zero. Resampling controls pixel interpolation during this final resize. The result is one image returned through the Pipe output. Uploaded alpha masks are not connected, so uploaded transparency is not preserved.
Seedream 4 reference image generation
Create or edit images with Seedream 4 from one reference image and a text prompt, with an optional related-image mode. For a first run, upload your reference image and replace the example prompt with the result or edits you want. Describe which reference details should remain. Keep Output size at 2048x2048 (1:1) and Related images disabled to request one square image, then run the Pipe. This workflow requires one upload and does not provide additional reference inputs. Uploaded transparency is not preserved as an alpha mask. Choose an output size preset, or select Custom to use Width and Height in pixels. Custom width accepts even values from 1024 to 6240; custom height accepts even values from 1024 to 4992. These fields affect generation only when Custom is selected. For related scenes or variations, describe them in the prompt, set Related images to auto, and increase Maximum images. The model decides how many images to generate; Maximum images is a ceiling rather than an exact count. Input and generated images together cannot exceed 15, so with this one reference image keep Maximum images at 14 or below. Seed -1 randomizes each run; a fixed seed controls generation randomization. Enable Watermark to add an “AI generated” mark. Fail on partial results is enabled by default and aborts when requested images are missing or return an error; disable it to accept available partial results. Generation sends your reference image and prompt to ByteDance and uses a paid provider call.
Capybara: Image to Video
Create one silent video from an uploaded image using the fixed Capybara v0.1 model. The image supplies both the resized start frame and a separate visual reference. For a first run, upload an image and replace the example positive prompt with scene details, subject motion and camera movement appropriate to your image. Keep 640 × 640 pixels, 81 frames and 24 FPS for approximately 3.4 seconds of playback. Use positive dimensions divisible by 16 and frame counts of 4n + 1, such as 49 or 81. Larger dimensions and more frames increase generation work. The start image is resized to fit the output dimensions and padded when its aspect ratio differs. Padding color selects white or black borders; resize interpolation controls resampling. Reference crop selects center or none for the separate visual-reference encoding and does not change start-frame padding. Only the uploaded RGB image is connected; uploaded transparency is not preserved. Advanced controls include the negative prompt, seed, CFG, sampling settings and image preparation. Seed -1 randomizes each run; a fixed nonnegative seed reuses the initial noise. CFG controls the contrast between positive and negative conditioning. Begin with the existing sampling defaults: 20 steps, simple scheduler, Euler sampler, denoise 1 and model shift 7. Denoise changes the sampling schedule rather than directly controlling source-image preservation. Model shift changes the sampling model's noise-time behavior. FPS changes playback duration without changing the generated frame count. Choose 8-bit video for compatibility or 10-bit for smoother gradients where encoding and playback support it. File type and codec control the returned video's container and encoding; begin with auto for both, and use compatible combinations when selecting them manually. One video is returned through Sweet Tea's preview output, without a server-side save stage. No audio is connected. Batch size remains one in the normal interface because decoded frames are assembled into a single video rather than exported as separate variation clips.
