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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.
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.
Z-Image-Turbo: Text to image
Generate images from a text prompt with the fixed Z-Image-Turbo model. No uploaded image is required. For a first run, replace the example prompt with a description of your subject, composition, setting, lighting and visual style. Keep 1024 × 1024 pixels, one image and eight sampling steps, then run the tool to receive the generated image. The workflow uses z_image_turbo_bf16.safetensors, qwen_3_4b.safetensors and ae.safetensors; these model files must be available to the runtime. Set width and height together to choose the aspect ratio. Both dimensions must be multiples of 16. Image count generates a batch from the same prompt. Larger dimensions and batches increase memory use and runtime. In advanced sampling settings, seed -1 selects a new random seed on each run. Use a fixed seed to compare changes under the same configuration. The source defaults are eight steps, guidance scale 1, res_multistep sampler and simple scheduler. Sampler, scheduler, steps and sampling shift affect the sampling process and interact; sampling shift defaults to 3. More steps do not guarantee a better image. Denoise amount defaults to 1. This tool starts from an empty latent, so reducing denoise changes the sampling range rather than preserving an existing image. Negative conditioning is zeroed internally, with no editable negative prompt. The model, text encoder and VAE are fixed; this tool provides text-to-image generation and batch generation, with no image-to-image mode.
Flux Fill outpaint
Generate an expanded image with prompt-guided surroundings using Flux Fill. For a first run, upload the image to extend and replace the example scene prompt with a description of the complete scene, lighting, style, and surroundings you want to add. Keep the other defaults and run the Pipe. It returns one image. The default canvas adds 400 pixels on the left, right, and bottom, with no extension above. Choose each extension in pixels, in increments of 8. Set a side to zero to leave that edge unextended. The padded canvas width is the source width plus the left and right extensions; its height is the source height plus the top and bottom extensions. Boundary feathering sets the width in pixels of the soft mask transition into the original image near extended edges. Advanced controls include Flux guidance for prompt influence and the separate sampler CFG, which defaults to 1. Sampling defaults are 20 steps, Euler, the normal scheduler, and denoise 1. Denoise controls sampling strength; lower values retain more of the initial latent and may leave added regions insufficiently filled. Seed -1 chooses a new random variation each run; use a fixed seed when comparing settings. Use sampling mask is disabled by default. Enable it to restrict sampling using the padding mask and its feathered boundary. This mode can substantially change results and may not work well with the model. Differential mask strength controls differential diffusion's mask handling when masked sampling is enabled, from 0 for no effect to 1 for the full effect. There is no final compositing step that restores the original pixels, so the original area can change; masked sampling does not promise exact pixel preservation. The mask comes from canvas padding, not uploaded transparency. This Pipe does not accept a painted inpainting mask or preserve uploaded alpha. Negative conditioning is automatically zeroed, so there is no negative-prompt field.
7 Dirty Words negative prompt
This is a negative embedding trained on the output you get from entering George Carlin's "7 dirty words" in the positive prompt. It was trained mostly on output from various anime models, so it's likely to work...
Kling two-shot storyboard
Generate one Kling video containing two consecutive storyboard shots, with optional first-frame guidance and native audio. For a first run, replace both shot prompts with your subjects, action, setting and camera movement. Set each shot's duration in seconds; the defaults request two seconds followed by three seconds. Each prompt supports up to 512 characters, and each shot duration supports 1–15 seconds. These controls request the segments and their combined duration; the example prompts describe a boxing sequence. Upload a start image and leave Start image enabled for image-to-video generation. An upload is required while that stage is enabled. For text-to-video generation, bypass Start image; no upload is required. Aspect ratio offers landscape 16:9, portrait 9:16 or square 1:1 in text mode and is ignored when a start image is supplied. Kling V3 is selected by default and supports 720p, 1080p and 4k, with optional native audio. You can also choose Kling 3.0 Turbo, but select 720p or 1080p before running it. Turbo always generates native audio and ignores the audio toggle. This tool supplies exactly two storyboard segments; other segment counts and ordinary single-prompt generation are not configured here. Leave Seed at -1 for a fresh run. A fixed seed does not make the provider's output reproducible. Generation uses authenticated Kling API access, sends prompts and any supplied start image to the provider, and incurs provider charges. The result is returned as one video. Advanced Video output controls offer file type and codec selection; leave both at auto for automatic selection, or choose a compatible explicit combination.
Audio-guided human video
Generate one human-focused video from source audio and positive and negative prompts, optionally guided by a reference image. The uploaded audio supplies HuMo conditioning and is also included as the soundtrack. For a first run, upload the desired audio excerpt and describe the person, clothing, setting, framing, and action in Positive prompt. Retain or edit Negative prompt to specify unwanted visual features; its supplied default is in Chinese. Upload a reference image to guide appearance and composition, or bypass Reference image to use audio and prompts alone. The reference stage starts enabled, so explicitly bypass it when no image is supplied. Start with 640 × 640 pixels, 97 frames, 25 FPS, and latent batch size 1. The generated frames represent approximately 3.88 seconds of playback. Prepare an audio excerpt appropriate for that span before uploading: this interface provides no verified audio trimming or time-stretching controls. Width and height use multiples of 16. Frame count follows 1 plus a multiple of 4, such as 97, 101, or 105. Frame count controls the number of generated frames; frame rate controls their playback duration. Changing frame rate does not change audio encoding or stretch the soundtrack. Optional 10-bit video can retain smoother gradients, but downstream codec and player support varies; the default is 8-bit. Advanced sampling exposes seed, steps, CFG, sampler, scheduler, denoise fraction, and sampling shift. Seed -1 randomizes each run; a fixed seed reuses sampling noise. The supplied preset uses six steps, CFG 1, uni_pc, simple scheduling, denoise 1, and shift 8. The fixed Lightx2v step-distillation adapter starts at strength 1; its strength is not a dedicated motion control. Keep these settings initially. The model, text encoder, audio encoder, and VAE remain fixed. The result is one video with the uploaded audio, with automatic file type and codec by default. Advanced Video output settings expose container and codec choices. Larger latent batches still enter the same video assembly rather than producing separate clip outputs. There is no separate image or audio output.
Portrait lighting transfer
Generate a relit portrait or subject image with Qwen Image Edit and the Light Migration LoRA. For a first run, upload the required subject image and a lighting reference, keep the default relighting instruction, and leave Lightning mode off. Start with standard steps 20, standard CFG 4, Light Migration strength 1 and denoise 1. Run the Pipe to receive one edited image. Add subject details you want retained to the positive prompt; the negative prompt can remain blank. For text-directed lighting, bypass the Lighting reference stage and replace the default instruction with a direct description of the desired illumination and colors. Leaving the upload empty alone does not select this mode. The subject image remains required. Lightning mode adds the supplied Qwen Image Edit 2509 four-step Lightning LoRA and switches steps and CFG together. Its defaults are four steps and CFG 1. Standard steps and CFG have no effect while Lightning mode is on; Lightning steps, CFG and LoRA strength have no effect while it is off. Light Migration strength applies in both modes. The required model files must be available, including the four-step Lightning LoRA when using that mode. Denoise ranges from 0 to 1 and controls regeneration of the initial subject latent. Lower values retain more source structure but may weaken the lighting change. Light Migration strength controls the lighting LoRA's model influence, rather than an exact percentage of transferred lighting. Use seed -1 for a fresh random seed or a fixed seed to compare edits with otherwise unchanged settings. Advanced controls include separate steps and CFG for each mode, Lightning LoRA strength, sampler, scheduler, guidance normalization and sampling shift. The source sampler and scheduler defaults are Euler and Simple. Steps must be 1–10000 and CFG 0–100, matching the downstream sampler. Guidance normalization and sampling shift apply in both modes; normalization before CFG can amplify guidance. The subject is automatically scaled before generation, so output dimensions may differ from the upload. There is no independent output-size control. This is generative editing: identity, edges and fine details may change. Uploaded transparency is not preserved.
