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Generate new images with Flux.1 dev conditioned by two Flux Redux visual references. For a first run, upload both images, replace the example prompt with your intended scene, and keep reference strengths at 1, output size at 1024 × 1024, image count at 1, and sampling steps at 20. Clear the prompt if you want to omit descriptive text; both reference uploads remain required, even when a reference strength is zero. Each reference has an independent strength and weighting method. The strengths are not percentages and do not need to add up to 1. Multiply scales reference conditioning; attn_bias applies an attention bias. Keep the default multiply method for the first run. The first reference is applied to the text conditioning, then the second reference is added to that combined conditioning. This is not a pixel blend of the uploaded images. Advanced framing controls select center cropping or no center crop independently for each reference before visual encoding. They affect reference preparation, not output dimensions. Set output width and height in pixels, in multiples of 16; image count selects the number generated in one batch. Advanced controls include Flux guidance, seed, steps, sampler, and noise scheduler. Seed -1 selects fresh random noise for each run; use a fixed seed when comparing control changes. The sampling schedule fraction defaults to 1 for the full schedule. Reducing it does not preserve a reference image because generation starts from an empty latent. Sampling calibration exposes maximum shift, base shift, and reference dimensions used to calculate the Flux sampling shift. Those reference dimensions default to 1024 × 1024 and do not automatically follow output size. The result is a newly generated image or image batch, rather than a direct edit of either upload. Exact composition, identity, and pixels are not guaranteed. Uploaded alpha masks are not connected to generation, so transparency is not preserved. There is no negative-prompt branch.
Generate a prompted, silent video transition between a starting image and an ending image using Wan 2.1. For a first run, upload both endpoint images, replace the flower example in Motion prompt with the action or transformation connecting them, and run with the existing defaults: 720 × 1280 pixels, 33 frames and 16 FPS. This produces approximately 2.06 seconds of video. Both uploads are required because each feeds its own visual encoder as well as endpoint conditioning. Images enter through RGB outputs; uploaded transparency is not preserved. The endpoint images guide generative animation rather than exact frame interpolation. Describe subject motion and camera movement explicitly. The supplied negative prompt lists unwanted visual artifacts and static results; edit it in Advanced to suit your scene. Width and height are measured in pixels and must be divisible by 16. Use frame counts of 4n + 1, such as 33, 49 or 81. Duration is approximately frame count divided by FPS. FPS changes playback speed without generating additional frames. Advanced sampling controls include seed, steps, prompt guidance, sampler, scheduler, denoising strength and noise schedule shift. Seed -1 selects a new random seed each run; a fixed seed keeps the initial noise consistent when comparing changes. Start with the supplied sampling defaults. Denoising strength acts on the video latent and is not a direct endpoint-image preservation setting. Start and end guidance crops independently choose none or center for each image's visual encoding. They do not change the separate endpoint-image connections. Sequence batch size defaults to 1; larger batches feed the same video creator and do not yield separate video outputs. Playback also offers 8-bit or 10-bit video; 10-bit compatibility depends on downstream encoding and playback software. Video encoding supports file types auto, mp4, mkv and webm, and codecs auto, h264 and av1. Keep both on auto initially, or choose a compatible container and codec for your delivery needs. This Pipe provides one video output, has no one-image mode or audio input, and uses a fixed Wan model with matching encoders.
