MiniMax H3: Two-Pass Latent Upscaling
Follow the Turbo LoRA workflow from a coarse first pass through learned latent upscaling and a second detail pass, with text-to-video and first/last-frame modes.
Tutorial library
Published walkthroughs for LTX 2.5, MiniMax H3, and Krea 2. Find the task you need, read the hardware context, and jump to the relevant chapter in the original video.
Showing 8 of 8 entries
Follow the Turbo LoRA workflow from a coarse first pass through learned latent upscaling and a second detail pass, with text-to-video and first/last-frame modes.
Track an object with SAM 3.1, clean its mask, and regenerate a cropped region before blending it back into the source video.
Arrange first and last frames, additional image guides, timed prompts, and video references in Director 2.0 to give a shot a clearer structure.
Set up the matching LTX 2.5 model stack in ComfyUI, choose dimensions and duration deliberately, and inspect the limits of the early results.
Assign clear jobs to reference images and video clips, then build a sequence using short continuation references instead of loading unnecessary frames.
Understand the FP8/INT8 model options, matching encoders and VAEs, native audio, and the resolution-duration tradeoff on the tutorial's 8 GB GPU.
Explore the identity-edit LoRA through restaging, object edits, and clothing references—with both the successful edits and the failure cases kept in view.
Explore image generation across environments, materials, portraits, and stylized scenes, then walk through the local FP8 setup and sizing controls.
These separately labeled outlines do not represent published videos or completed hardware tests.
A planned tutorial on finding the settings that drive memory use before a large node graph becomes difficult to debug.
A planned walkthrough of tracing models, conditioning, sampling, decoding, and output paths in an unfamiliar graph.
A planned troubleshooting guide that narrows memory failures without changing several variables at once.
A planned guide to reasoning about weights, context, cache, offloading, and system RAM before choosing a local model.