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        "# Edit Anything\n## Experimental IC LoRA workflow for in-video object editing by Alisson Pereira\n\nAdd, remove, replace, or restyle objects directly inside a video clip using a text prompt. Powered by an in-context (IC) LoRA — results will vary, this one is experimental.\n\n> ⚠️ **Heads up:** this workflow is experimental. Sometimes it just doesn't work — the edit may be ignored, partially applied, or produce artifacts even with a well-formed prompt. If a run fails, try rewording the prompt, simplifying the edit, or re-running. Not every clip + prompt combo will land.\n\n### Controls\n\n**`skip_first_frames`** — trims frames from the start of your clip before processing begins.\n\n**`frame_count`** — sets how many frames to edit after the skip.\n\n**Example:** 600-frame clip → set `skip_first_frames: 60`, `frame_count: 480` = 480 frames starting at frame 60 ✅\n\n---\n\n### Output Resolution\n\nSet `target_resolution_w` and `target_resolution_h` to your desired pixel dimensions.\n\n| Format | W | H |\n|---|---|---|\n| Vertical (TikTok/Reels) | 1080 | 1920 |\n| Square | 1080 | 1080 |\n| Landscape | 1920 | 1080 |\n\nHit **▷ Run** when ready.\n\n> **Prompting matters here** — unlike a style LoRA, this workflow needs a prompt that tells it *what to change*. Match one of the four task patterns below for best results.\n\n---\n\n## Prompt patterns used during training\n\nThe training captions were organized around four core task types. Pick the one that matches your edit:\n\n### Add\n`Add a/an [subject/object] with [clear visual attributes], [precise location in the scene].`\n\n### Remove\n`Remove the [subject/object] [location or identifying description].`\n\n### Replace\n`Replace the [original subject/object] [location] with a/an [new subject/object] with [clear visual attributes].`\n\n### Convert / Style\n`Convert the video into a [style name] style.`\n\n---\n\nThe prompts that worked best were usually:\n\n- action-first\n- visually specific\n- spatially grounded\n- written for video scenes, not still images\n\n### Common subject and object types\n\nSome of the most common object families in the training prompts were:\n\n- men\n- women\n- people\n- robots\n- dogs\n- cats\n- characters\n- laptops\n- buildings\n- plants / trees"
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