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Z-Image Turbo with PiD 4× upscale
Generate a 4096 × 4096 square image from text with Z-Image Turbo and PiD, and receive the 1024 × 1024 base preview alongside it. The same prompt guides both stages; PiD uses the generated base latent as conditioning for its final pixel-space image. For a first run, replace the example prompt with your subject, composition, lighting and style, then keep the supplied defaults: eight base steps and four PiD steps, guidance of 1 for both stages, res_multistep base sampling and LCM PiD sampling with simple schedules. No media upload is required or supported. The base and PiD seeds control separate stages. Use -1 to randomize each run, or fix both seeds when comparing settings. Changes to base sampling affect both the base preview and the latent that conditions PiD. PiD sampling controls affect the final image. Advanced settings expose each stage's sampler, guidance and schedule, plus base sampling shift, denoising fractions and PiD noise initialization. Keep both denoising fractions at 1 and Add PiD noise enabled for the supplied setup. Disabling PiD noise initialization removes the PiD seed's effect on initial noise. Latent degradation is separate from denoising fraction: zero denotes a clean base latent, while higher values tell PiD to handle corrupted latent outputs. The model pair, square dimensions and single-image batches are fixed for this 1024-to-4096 setup. Both stages run to produce the final result; there is no base-only or uploaded-image upscale mode. There is no editable negative prompt: both stages use zeroed negative conditioning. Alternative sampling settings change the generation behavior without a guaranteed quality improvement.
Qwen Image Edit
Create an edited version of an uploaded image with Qwen Image Edit. Upload your source image, then describe what should change and what should stay the same. Replace the supplied underwater-scene example with instructions for your own image. For a first run, leave Lightning mode off and use the defaults: 20 standard steps, CFG 2.5, Euler sampler, simple scheduler and Denoise 1. Lightning mode selects the fixed four-step Lightning LoRA together with its separate steps and CFG controls, initially 4 and 1. Only the steps and CFG for the selected mode affect the result. Lightning LoRA strength affects only Lightning mode; guidance normalization and sampling shift apply in both modes. Their supplied defaults are 1 for LoRA strength, 1 for normalization strength, normalization before CFG off, and sampling shift 3. Denoise ranges from 0 to 1: lower values retain more source structure, while 1 allows stronger regeneration. Negative prompt describes unwanted content and may remain empty. Seed -1 chooses a new random seed each run; use a fixed seed to compare settings with the same initial noise. Advanced controls expose the sampler, scheduler and separate sampling settings for each mode. Step counts must be positive integers; the downstream sampler supports 1–10000 steps and CFG values from 0 to 100. The source image supplies both visual conditioning and the starting latent. There is no connected output-size control or automatic 1.5-megapixel resize; resize uploads beforehand if you need a different starting size. The uploaded alpha mask is unused, so source transparency is not preserved. There is no mask control to restrict the edit to a selected area. Editing is generative and may change details beyond the requested changes.
CyberRealistic Negative - Anime
Join The Tinkerer on Whop. Membership gets you early releases, private tools and a bunch of extra stuff.
Apoc, Lost, Decay and Ruin - What a perfect day to end the world (Apocalypse Embed Series for 2.1)
As a fan of post apocalyptic series like The Mountain Man or Arisen , and of course great video games like The Last of Us and Project Zomboid, I really wanted an easy way to recreate that "world's gone to hell and we...
ElevenLabs text to speech
Choose a voice and enter the text to speak. Tune speech speed, stability, similarity, and style to shape the delivery. Leave the language code empty for automatic detection. The final output is an MP3; synthesis encoding and final MP3 quality are separate settings. Seed-based reproducibility is not guaranteed.
SimpleNegative | Negative TI
I created 'SimpleNegative' because there are too many repetitive responses influenced by 'EasyNegative,' and it's becoming boring. Please place it in NegativePrompt in the same way.
Photo to geometry maps
Upload one perspective photo to produce two geometry-map images from a shared MoGe-2 estimate. For a first run, upload your image, keep the defaults, and run the Pipe. The first image shows OpenGL normals and the second shows DirectX normals, with inference detail set to 9, automatic camera field of view, and invalid-region filtering enabled. Choose each map independently: OpenGL normals, DirectX normals, depth, colored depth, or the predicted validity mask. OpenGL normals use green = +Y; DirectX normals use green = −Y. Both outputs use the same geometry estimate and inference settings. In Geometry estimation, inference detail ranges from 0 (fastest) to 9 (most detail), as described by the source. Enter the camera's horizontal field of view in degrees when known, or leave 0 to recover it automatically from predicted points. This controls the focal length used to unproject depth into 3D. Invalid-region filtering marks predicted sky and invalid pixels as invalid in points and depth; disable it to retain raw predictions. The predicted mask remains selectable either way. Uploaded transparency is not used as a mask. Automatic resizing checks image width: images wider than 2048 pixels are resized to a 2048-pixel longer edge. Images whose width is at most 2048 remain unchanged, including tall images. Disable automatic resizing to use the original image size. Inference batch size specifies images per inference call, rather than output variations; this Pipe supplies one photo. Force projection is enabled by default, but the supplied source does not explain its effect on the maps. These images are estimates from visible photo content, not guaranteed accurate measurements. This Pipe returns image maps and does not export a mesh or GLB. It requires the fixed moge_2_vitl_normal_fp16.safetensors checkpoint under models/geometry_estimation/.
Wan 2.2 controlled video
Generate a prompted video with Wan 2.2 Fun Control 5B, guided by a source video's frames or extracted edges and an optional reference image. For a first run, upload a source video, replace the example prompt with your desired scene and motion, and either upload a reference image or disable the Reference image stage. That stage is enabled initially, so leaving its upload empty requires disabling it. Keep 704 × 704, 121 frames, 24 FPS, batch size 1, and the default sampling settings. Canny edge guidance is disabled initially: original source frames supply guidance. Enable it to use extracted edges instead. Its normalized low and high thresholds apply only in that mode; keep the low threshold below the high threshold. Reference image guidance can be disabled independently. Generation uses these inputs as conditioning rather than directly editing the source video's pixels. Width and height are pixels in multiples of 16. Frame count uses 4n + 1 values, such as 81 or 121. Playback FPS is independent of generated frame count: 121 frames at 24 FPS yield approximately 5.04 seconds. Changing FPS changes playback duration without requesting more frames. Source audio and source FPS are not carried into the output. Advanced controls include unwanted traits in the negative prompt, seed, sampling steps, prompt guidance, sampler, scheduler, denoise fraction, and sampling shift. Seed -1 randomizes each run; a fixed nonnegative seed reuses sampling noise. Denoise is not a direct source-video preservation strength. The retained negative prompt is the source's Chinese-language list of unwanted traits and can be edited. Video playback offers 8-bit or 10-bit creation; 10-bit support depends on downstream encoding and players. Video output offers container and codec choices; leave both on auto initially. The graph returns one video output with no separate edge preview. Batch size changes the latent batch but does not provide separate output branches for individual clips, so keep it at 1 for an ordinary single-clip run.
Wan 2.5 text to image
Generate still images from English or Chinese prompts with the Wan 2.5 text-to-image preview API. The resulting image appears in the Pipe preview. For a first run, replace the example prompt with a description of your subject, composition, lighting, and style. Keep the default 1024 × 1024 dimensions and prompt enhancement enabled, then run the Pipe. No uploaded image is required. Optionally add a negative prompt describing elements or visual characteristics to avoid. Width and height are independently adjustable from 768 to 1440 pixels in increments of 32; together they determine the aspect ratio. Prompt enhancement allows the service to expand your description. Disable it to submit your wording without automatic expansion. Add watermark requests an AI-generated watermark on the result. Seed -1 randomizes each run; a nonnegative seed requests a particular randomization. The model is fixed to wan2.5-t2i-preview. This tool produces still images even when the example or your prompt describes camera movement. Generation sends your prompt to a remote partner API and requires configured ComfyUI API access, connectivity, and sufficient account credits.
FLUX.2 Klein two-image edit
Generate an edited image from a main image, a second visual reference and written instructions using FLUX.2 Klein 9B Distilled. Use the second image to supply an object, appearance or other visual information for the edit. For a first run, upload the main scene or character as Source image and the additional visual guidance as Reference image. Both uploads are required. Write Edit instructions explaining what to take from each image and how they should relate. The supplied example asks a character to hold a bag with both hands; replace it when your inputs call for another edit. Start with 1 megapixel for each image, one image per run, 4 steps, guidance scale 1 and Euler sampling. Output size resizes the source reference and sets the generated canvas dimensions, approximately retaining the source aspect ratio. Reference resolution independently resizes the second image for conditioning and does not set output dimensions. Each image has its own resize method controlling interpolation before encoding. Larger resolutions and batches require more memory. Advanced controls include second-reference resolution, seed, steps, guidance scale, sampler and images per run. Seed -1 selects fresh random noise each run; use a fixed seed to compare control or instruction changes with the same starting noise. Images per run generates a batch sharing the same instructions and references. Steps controls sampling iterations, sampler chooses the sampling method, and guidance scale changes guidance between instructed and zeroed-text conditioning. Both conditioning branches include both image references. Generation starts from a fresh latent rather than directly denoising the uploaded source. Exact preservation of identity, unchanged regions or object placement is not guaranteed. There is no mask, edit-strength control or separate negative prompt. Uploaded transparency is not preserved because only the loaders' image outputs are used. This tool always uses two image references; editing from one upload is served by the separate single-image edit tool.
Foundation text to image
Generate a batch of images from text, with adjustable dimensions and sampling settings and up to four optional LoRAs. For a first run, enter a positive prompt describing your subject, composition, lighting, and style. Optionally enter unwanted content in the negative prompt. Use the supplied DreamShaper checkpoint option under sd15, leave all LoRA stages disabled, and run with the defaults: four 512 × 512 images, 30 sampling steps, and prompt guidance (CFG) 6. The checkpoint supplies the image model, text encoder, and VAE decoder; the supplied option is not an SDXL checkpoint. Width and height are in pixels; use multiples of 8. Image count sets how many images are generated together with the same prompts and settings. Larger dimensions and batches require more memory and computation. In Sampling, seed -1 requests fresh randomness for each run. Use a fixed seed when comparing otherwise unchanged settings. Steps set the sampling iteration count, CFG controls prompt guidance, and sampler and scheduler select the sampling algorithm and noise schedule. Denoising amount defaults to 1 for full generation. Lower values change the noise-removal range, but cannot preserve a source image: this tool starts from an empty latent and has no image input. For optional LoRAs, select an available checkpoint-compatible LoRA before enabling its stage. Unused stages should remain bypassed. Enabled stages apply in order from LoRA 1 through LoRA 4. Each has independent model and text-encoder strengths; the latter affects both positive and negative prompt encoding. Zero removes that component's contribution, and the source also supports negative strengths. Disabling a stage skips both contributions.
