Find what you need for your workflow.

Z-Image Turbo1024x10248GB VRAM

MoGe valid geometry mask

Estimate a geometry-validity mask from one perspective image. For a first run, upload an image, leave Automatic resizing enabled and keep Detail level at 2. Each run performs MoGe geometry inference, renders predicted validity and converts the rendered red channel into the output mask. The mask identifies regions with valid predicted geometry. It is not foreground segmentation or an object cutout; sky and other regions with undefined geometry may be excluded. Prediction uses the uploaded RGB image, independently of uploaded transparency. This Pipe exports a mask, not depth, normal maps or a mesh. Automatic resizing checks image width. Images wider than 2048 pixels are resized to a fixed 2048-pixel longer edge before inference. Images at most 2048 pixels wide remain unchanged, even when their height exceeds 2048. Disable Automatic resizing to use the original image. The mask follows the dimensions used for inference, so resizing can change its dimensions relative to the upload. Open Geometry estimation for inference controls. Detail level ranges from 0 to 9; the source identifies 0 as fastest and 9 as most detailed. Horizontal field of view is measured in degrees: leave 0 for automatic recovery from predicted points, or enter a known camera value for geometry unprojection. Exclude invalid geometry sets invalid points and depth to infinity when enabled; disabling it retains raw predicted geometry. The validity mask is returned separately in either mode. Inference batch size controls images per inference call and does not create additional variants from the single upload. Force projection starts enabled; its precise effect is not documented in the supplied source, so retain the default for a first run. These settings do not select other output types. Mask rendering and red-channel conversion are fixed to preserve the validity-mask result. The workflow uses the fixed model moge_2_vitl_normal_fp16.safetensors. For a manually managed ComfyUI installation, the source specifies models/geometry_estimation/ as its location and https://huggingface.co/Comfy-Org/MoGe/resolve/main/geometry_estimation/moge_2_vitl_normal_fp16.safetensors as its download source.

Z Image Turbo pixel-art image generator

Generate one image or a batch from text using the fixed Z Image Turbo model, with optional pixel-art styling. No uploaded image is required. For a first run, leave pixel-art style enabled, edit the pixel-art prompt, and keep the 1024 × 1024 size, batch size 1, LoRA strength 1 and four sampling steps. The source prompt begins with “Pixel art style, retro”; use that as a starting point when describing your own subject. Run the Pipe to receive the generated images. The style toggle switches both the model branch and the prompt. When disabled, the base model uses the separate unstyled prompt. Edit both prompts when comparing modes; changes to one do not update the other. LoRA strength affects only the enabled pixel-art branch. Width and height are output pixels and must be multiples of 16. Batch size controls how many images are returned. The output stage does not permanently save images to the ComfyUI output folder. Advanced sampling controls include seed, steps, guidance scale, sampler, scheduler and denoise fraction. Seed -1 randomizes each run; use a fixed seed to compare changes with otherwise unchanged settings. The source defaults are four steps, guidance 1, res_multistep sampling, the simple scheduler and denoise fraction 1. Negative conditioning is zeroed automatically, so there is no editable negative prompt. The optional sampling-shift stage starts bypassed, matching the source workflow. Enable it before adjusting its shift value; the source value is 3. While bypassed, shift has no effect. Generation starts from an empty latent and does not support image-to-image input. Reducing denoise fraction changes sampling but does not preserve an uploaded image. The model, text encoder, VAE and pixel-art LoRA are fixed by this Pipe.

Video to pose animation

Turn a source video into a rendered whole-body pose animation using SDPose. The output is one pose video, rather than an overlay on the original footage or a downloadable keypoint dataset. It uses the source frame rate and includes source audio when available. For a first run, upload a clip with one clearly visible person, keep the default 1024-pixel longer edge and drawing settings, and run the Pipe. The required checkpoint is sdpose_wholebody_fp16.safetensors. Frames are resized while preserving aspect ratio before pose extraction; Longer edge sets both detection resolution and rendered frame dimensions. Use Draw body, Draw hands, Draw face, Draw feet and Draw head to choose the rendered features. These switches change visualization, not what the model extracts. Confidence threshold controls which keypoints are drawn: higher values suppress uncertain points, while lower values include more points. Line width and Face point size are measured in output pixels; Face point size applies when Draw face is enabled. Resampling method controls frame resizing before detection. Area is suited to downscaling and Lanczos to upscaling. Processing batch size controls how many frames the extractor processes together; lower it if memory is limited. It does not change video duration. Frame rate is inherited from the source and cannot be independently adjusted here. This workflow supplies no bounding boxes, which the extractor requires for multi-person detection. Use single-person footage rather than expecting reliable multi-person capture. Advanced output controls select the container, codec, encoding quality and video bit depth. Keep File type and Codec on automatic, Quality (CRF) at -1 and Bit depth at 8 for a first run. Explicit container and codec choices must be compatible. The 10-bit option requires compatible encoding and playback and does not improve pose detection.

CLIP1024x10248GB VRAM

HiDream-E11024x10248GB VRAM

HiDream E1 image editor

Generate an edited image from an uploaded source and a written instruction using HiDream E1. The source supplies visual conditioning, and the instruction describes the requested transformation. The generated image is returned as a Pipe preview using the fixed HiDream E1 model, associated text encoders and VAE. For a first run, upload your image, replace the example instruction with the change you want, and keep 768 × 768 pixels, 28 steps, text guidance 5, image guidance 2 and regular guidance combination. Use positive width and height values divisible by 8. Center fitting crops the source to the chosen aspect ratio before resizing; disabled fitting retains the full image but can stretch it when aspect ratios differ. Resize method selects the interpolation used for this preprocessing. Text guidance and image guidance control separate conditioning contributions. In regular mode, text guidance scales the difference between instruction-conditioned and image-conditioned predictions, while image guidance scales the difference between image-conditioned and negative predictions. Nested mode changes how the two guidance values combine. The negative prompt describes unwanted content or qualities and supplies text to both the image-conditioned branch and the separate negative branch. Advanced controls include seed, steps, sampler, scheduler and denoise. Seed -1 selects a new random seed each run; use a fixed seed to compare settings with consistent initial sampling noise. Steps sets sampling iterations, sampler selects the algorithm, and scheduler sets the distribution of noise levels. Denoise selects the fraction of the noise schedule used. Sampling starts from an empty latent rather than the encoded source, so reducing denoise is not a conventional source-preservation strength control. This is a whole-image generative edit with no supplied editing mask and no guarantee that untouched details remain identical. Uploaded transparency is not preserved through the connected RGB image path.

Nano Banana1024x10248GB VRAM

Image restyle round-trip video

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.

Upscaling1024x10248GB VRAM

Topaz image enhancement

Enhance an uploaded image with the workflow’s saved Topaz configuration. For your first run, upload a source image and run the Pipe. The enhanced image appears in the result preview. The saved configuration includes “Wonder 3.5.” Enhancement settings are fixed in this interface; there are no selectable enhancement modes, prompt controls or output-size controls. The resulting dimensions and degree of alteration depend on that configuration, and a specific upscale factor is not established. Uploaded transparency is not preserved through the source image connection. The selected output is an image preview; the original save node’s PNG format, bit depth and color-space settings do not apply to this output.

PMRF image enhancer

Upload a source image to produce a PMRF-enhanced image. For a first run, keep the source defaults of 2× scale, 25 steps, and Lanczos interpolation. Scale controls enlargement; use 1× to process at the original size. Larger scales increase output dimensions and resource requirements. Steps controls the processing iteration count and trades additional computation for a different restoration result. Interpolation selects the resizing method. Seed lets you vary the result; the default randomizes on each run. This workflow has no prompt or restoration-strength control. Uploaded transparency is not preserved because only the loader's RGB image output is connected.