Krea-2 GGUF/fp8 | LOW VRAM Workflow

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: Krea-2 GGUF/fp8 | LOW VRAM Workflow
- URL: https://www.youtube.com/watch?v=fehuludVgxs
- Topic: Interfaces + Open Design
- My current learning frame: Use the transcript anchors for Krea-2 GGUF/fp8: it opens with this base model is the raw checkpoint used for post-training fine-tune.
- Why this matters: New playlist item from REBEL AI; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:23 / Evidence 1: "this base model is the raw checkpoint used for post-training fine-tune. This is a diffusion transformer architecture running on the Quinn family architecture of text encoders. There really is not much other information on this model. If you..."
- 3:18 / Evidence 2: "the FP8, you can use the load diffusion model and just hold shift and drag this over. Now, the turbo workflow is identical. It just needs eight steps with a CFG of one. But with that being said,..."
- 5:02 / Evidence 3: "snow. And here we have a woman at a dinner with a glass of wine. Here we have a hiker on a mountain overlooking a beautiful scenery. This was a pretty random prompt that I kind of wanted..."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A UI critique sheet for judging whether an AI interface improves control.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Canvas -> Artifact -> Preview -> Feedback -> Iteration
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "Krea-2 GGUF/fp8 | LOW VRAM Workflow", not a generic Interfaces + Open Design essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.