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Create an image of a miniature architectural model in a setting you describe. Upload a building photo or rendering; this Pipe generates a conceptual blueprint, then supplies both that blueprint and the original building image to the scene generator. It returns only the final scene image. Use the separate architectural blueprint tool for a standalone sheet. For a first run, upload your building image, edit the scene prompt to match its appearance, and keep the default 16:9 framing and 1K resolution. The supplied scene prompt requests a purple house on an architect’s desk with a blueprint displayed behind it. Describe the model, materials, surroundings, lighting, and camera view you want. Mention the blueprint in the scene prompt if you want it depicted. Scene aspect ratio and resolution control the final image. Auto framing asks the provider to match an input reference. Under Blueprint reference, edit the drawing views, annotations, and sheet layout for the intermediate image. Blueprint framing and resolution are independent of the final scene settings, but changes to that reference can influence the final composition. Both generation stages run on every request. Each stage has an independent model selector offering gemini-3-pro-image-preview and Nano Banana 2 (Gemini 3.1 Flash Image). Optional generation instructions guide each generator’s foundational behavior separately from its visual prompt; retain their defaults for a first run. Resolution choices are 1K, 2K, and 4K, with the provider’s native upscaler used for 2K and 4K. Each seed defaults to -1, which randomizes that stage on every run. Fixed seeds request best-effort consistency rather than identical results. Fixing only the scene seed does not stabilize an independently randomized blueprint reference. The blueprint and miniature scene are generated concept imagery. Geometry, dimensions, structural details, and annotations may be invented; use them for presentation rather than measured construction documentation. Higher resolution does not establish architectural accuracy.
Generate one silent video from a text description with Wan 2.2. No uploaded image or video is required. For a first run, edit the positive prompt to describe your subject, movement, camera, lighting and setting, then keep the 640 × 640 size, 5-second duration and 16 FPS defaults. The negative prompt independently discourages unwanted content and visual traits; its supplied Chinese text includes artifacts, static frames and clutter. Standard mode uses 20 total steps, CFG 3.5 and a handoff at step 10. Enable Lightning mode to select both Lightx2v LoRAs together with the separate Lightning settings: 4 total steps, CFG 1 and a handoff at step 2. Only the selected mode's steps, CFG and handoff settings apply. LoRA strength controls apply only in Lightning mode. Each handoff must remain strictly between zero and its mode's total step count. The downstream samplers accept 1–10000 total steps and CFG values from 0–100. Width and height are pixels in multiples of 16. Use a positive duration and a frame rate from 1–120 FPS. Requested frame count is floor(duration × FPS) + 1; choose a combination whose floored product is a multiple of 4 for the expected 4n + 1 frame layout. FPS also sets playback speed. The defaults request 81 frames, giving approximately 5.06 seconds of encoded playback. Larger dimensions, longer duration and higher FPS increase generation work and memory use. Seed -1 randomizes each run; use a fixed seed to compare settings. Advanced initial and finishing sampler, scheduler and schedule-shift controls apply in both modes. The initial stage adds noise and runs the high-noise model; the finishing stage continues the same latent without adding new noise. Both stages use the selected total step count, with the handoff dividing their work. The supplied sampler, scheduler and shift values match across stages. Keep file type, codec and encoding quality on automatic selection for a first run. Explicit container and codec choices require compatible encoding and playback support. Video assembly defaults to 8-bit; optional 10-bit assembly can retain smoother gradients but requires downstream support. CRF controls encoding quality separately from generation. Latent batch size remains one; this tool returns one video and does not offer separate batch files. Audio is not generated.
Generate one image or a batch from text using SDXL 1.0 Base followed by SDXL 1.0 Refiner. No source image is required. The fixed checkpoints sd_xl_base_1.0.safetensors and sd_xl_refiner_1.0.safetensors must be available to the runtime. For a first run, describe your scene in Positive prompt and copy that description into Refiner positive prompt. Enter unwanted features in both negative prompts, then run with the default 1024 × 1024 dimensions and Image count of 1. All four prompt fields are independent: editing a base prompt does not update its refiner counterpart. Keep their intent aligned, or deliberately change the refiner wording to emphasize finishing details. Width and Height are in pixels and use multiples of 8. Image count generates a batch using the same prompts. Larger dimensions and batches require more memory. In advanced settings, Seed controls the initial base noise: -1 selects a new seed each run, while a fixed seed lets you compare changes using the same initial noise. Guidance and sampler choices are independent for Base and Refiner. The default schedule has 25 total steps: Base performs the first 20, passes its partially denoised latent onward, and Refiner completes the remaining five without adding fresh noise. Refinement is integral to this output path and cannot be bypassed through this interface. Advanced schedule controls must be adjusted together. Keep Base schedule steps equal to Refiner schedule steps, use matching schedulers, and set Base handoff step equal to Refiner handoff step below the total schedule length. The schedule-step fields describe the full schedule for each stage, not how many steps that stage executes. Begin with the coordinated defaults before changing the schedule.
Generate an image or batch from text using the source's fixed Moody Base/Turbo checkpoint pair. A Base pass establishes composition, the latent is resized, Turbo refines it, and tiled diffusion produces the enlarged result. The Pipe returns only the finished image collection, without permanently saving images through the source SaveImage node. For a first run, replace the positive prompt with your subject, composition, lighting and style, and review the negative prompt. Keep the 640 × 960 starting size, refinement scale of 1.7, enlargement factor of 2 and supplied sampling defaults. No uploaded image is required. Final dimensions depend on both scale factors and latent-size rounding; final enlargement applies to the decoded Turbo result. Scale factors below 1 reduce dimensions. Larger dimensions, scales and image counts increase memory and processing requirements. The negative prompt affects the Base pass only. Turbo refinement, tiled enlargement and face refinement use zeroed negative conditioning. Enlargement normally reuses the main positive prompt. Enable Use separate enlargement prompt and edit Enlargement prompt to replace it for tiled redraw. Higher enlargement denoise permits more changes to existing details and content. Advanced sampling controls belong to separate stages. Base defaults use a 17-step schedule starting at 0 and stopping at 12, retaining noise for refinement. Turbo defaults use an 11-step schedule beginning at 5 and completing the schedule. These schedule lengths are not the number of iterations each pass executes. When changing them, review the corresponding start and end boundaries; keep the Turbo starting boundary below its schedule length. Each stage has its own seed. -1 randomizes that stage on each run; use explicit seeds for all active stages when retaining their random choices. Enlargement uses automatically calculated tile dimensions that depend on the decoded image size and enlargement factor. Tile batch size controls how many redraw tiles are processed together, separate from Image count, and can change results. Tile padding controls redraw context, while the automatic dimension formulas retain a fixed 96-pixel allowance. Seam repair starts at None. Select a repair mode before adjusting its denoise, blur or padding; Seam band width applies to Band Pass repair. Face refinement is optional and initially bypassed. Enable it to detect and redraw faces, then replace the portrait-specific Face prompt for your subject. Face denoise controls redraw strength. Detection threshold and minimum size determine which detections are accepted; crop context, region expansion and feathering control the processed regions and their blending. Detection may miss small or obscured faces. Face sampling and cycle controls apply only when this stage is enabled. Skin contrast finishing is optional and initially bypassed. Enable it to apply the fixed skin contrast model, then adjust Skin contrast strength. With Normal blending, 0 keeps the image before finishing and 1 uses the full finishing result; strength has no effect while finishing is bypassed. Other blend modes can alter the image even with finishing bypassed because they combine two identical inputs differently. The source's manual mask detailer and native editor bridge remain hidden and bypassed. This Pipe does not provide a live paint-mask session or portable manual mask refinement. Optional finishing stages process the generated image and still run the upstream generation and enlargement stages. Model selections are fixed to the source's Moody checkpoints; stock Z-Image catalog entries do not establish that those specific files are installed.
Create one silent round-trip video from an uploaded image and a description of its transformed appearance. Gemini generates the restyled image and separate motion prompts; Wan 2.2 generates the outward and return segments, which are concatenated in that order. First run: upload a source image, replace the Restyle prompt with the appearance you want, and leave the remaining defaults. The supplied prompt requests a back-facing anime illustration. With batch sizes of 1, the default 81 outward frames plus 81 return frames produce 162 frames, or 6.75 seconds at 24 FPS. The video size starts at a 1-megapixel source-image budget and follows that image's aspect ratio. Adjust the two frame counts independently using their supported increments of four from 1. Playback FPS changes how quickly the generated frames play; it does not generate more frames. Restyle aspect ratio affects the Gemini image, while Restyled image megapixels controls its separate resize budget. Neither sets the final video dimensions. For motion or camera changes, edit the advanced motion instructions. These tell Gemini how to write each segment's positive prompt. The comparison image always has the original on the left and the restyled image on the right: outward instructions should describe left to right, and return instructions right to left. Retain the request for prompt-only text. Negative prompts apply separately to each video segment. Image generation, each motion-prompt response, and each video segment have independent seeds. -1 randomizes. Fixed Gemini seeds request repeatability but do not guarantee identical responses. Holding a video seed steady alone does not hold the generated image or motion text steady. Keep batch sizes at 1 for one round trip. Larger batches feed all outward frames followed by all return frames into the same video; they do not create separate paired exports. The return segment is newly generated, not a reversed copy. Endpoint frames are not explicitly removed at the segment join or loop boundary, so a seamless loop is not guaranteed. Uploaded alpha transparency is not preserved, and no audio is connected. The source uses fixed high- and low-noise Wan 2.2 models with Lightx2v four-step LoRAs. Advanced sampling controls remain available independently. For the first run, retain the coordinated defaults: both stages use a four-step schedule, CFG 1, Euler/Simple, shift 5, and LoRA strength 1; initial sampling stops at step 2 and refinement resumes at step 2 without new noise. These controls form a coordinated setup rather than independent quality presets. Leave output container, codec, and compression quality on Auto initially. Final 10-bit output is optional, but both intermediate segment videos are fixed at 8-bit, so it cannot restore precision lost upstream and may have player compatibility limitations.
Generate a silent video from a scene and motion prompt using HunyuanVideo. Describe the subject, setting, visual style, action and camera movement. No uploaded image or video is required. For a first run, replace or edit the example prompt and keep 848 × 480 pixels, 73 frames, 24 FPS and 20 sampling steps. This produces approximately three seconds of video. Use dimensions divisible by 16 and frame counts of the form 4n + 1, such as 49, 73 or 97. Larger dimensions and more frames increase computation and memory requirements. Frames determines how many frames are generated. FPS changes playback timing without generating additional frames: duration is approximately Frames divided by FPS. Keep 8-bit initially; 10-bit offers smoother gradients but requires compatible encoding and playback support. Advanced controls include prompt guidance, seed, sampling steps, sampler, scheduler and sampling shift. Seed -1 chooses fresh starting noise each run; use a fixed seed when comparing settings. Start with the supplied generation settings. The sampling shift modifies the model used by the guider, while the scheduler receives the original model, following the source graph. The sampling schedule fraction defaults to 1 for generation from an empty latent; lowering it shortens the schedule and does not preserve an uploaded image. Keep latent batch size at 1 for a first run. Increasing it samples multiple latent videos, but the workflow assembles decoded frames into one video output rather than returning separate clips. Tiled decoding exposes spatial tile size and overlap, plus temporal frame chunk size and overlap. Start with the supplied settings and keep each overlap smaller than its corresponding tile or chunk size. Video encoding offers automatic, MP4, MKV or WebM container selection and automatic, H.264 or AV1 codec selection. Keep format and codec on auto initially. Explicit selections must be compatible with each other, the bit depth and the intended player. CRF controls encoding compression separately from generation; -1 uses the output node's default, and explicit values depend on the selected codec. The workflow uses a fixed HunyuanVideo model, matching text encoders and a video VAE. It has one positive text prompt, no negative-prompt branch, no audio input and no image conditioning. The selected result is one silent video.
