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Qwen-Image-2.1-viggle-turbo

Viggle

**Built with Qwen.** A few-step distilled version of Qwen/Qwen-Image-2.1 by Viggle. Text-to-image and instruction-driven editing with 1–3 reference images in **6 steps instead of 40**, with **no classifier-free guidance**. About **5× faster** than the 40-step base model end to end, and very competitive with it in quality: on the official Qwen examples the two are hard to tell apart on most prompts. The clearest gaps are small, dense text and complicated edits (Known limitations). Compare them yourself in the **Comparison** tab of the demo Space. * **6 steps, a different balance.** Against v0.2.1: less grain and cleaner surfaces, fine texture a little softer; diversity (still close to the base model's) and small-text accuracy about the same. It is not a strict upgrade: if you prefer the crisper look, v0.2.1 is still in the repository. * **New 9-step mode:** 7 turbo steps, then the LoRA is switched off and the base model finishes the last two. Finer detail, and small text comes out right more often (not always). It takes about 1.4–1.5× as long as 6 steps (still about 3.5× faster than the base model). It works in diffusers and the demo Space only (9 steps). * **We think 6 steps is close to its capacity.** Since v0.2.1, every gain we found at 6 steps cost something elsewhere: sharper came with more grain, less grain came with a softer look. Beyond this point, quality most likely has to be paid for with steps, which is what the 9-step mode does.

Image generation

Input Text→ Output Image

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Viggle-Animate

Viggle

**Try the demo**  ·  **Research write-up**  ·  **ComfyUI nodes**  ·  **viggle.ai/h3**  ·  Built on **MiniMaxAI/MiniMax-H3** Made with Viggle-Animate by @cocktailpeanut , not by us. Click through to watch it on X. White House Funk: a preview of Viggle-Animate v2, coming soon. Not made with the weights in this repository. The cast of a two-minute music video replaced through its fast cuts, from at most one repainted frame per shot. AI-generated parody: the people shown did not take part in, approve or endorse it.

Category pending

Input Not verified→ Output Not verified

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COST EXPLORER

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Cost analysis

Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

MODEL HANDBOOK

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Selection guide & FAQ

How should API prices be compared?+

Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

Do open weights mean free commercial use?+

Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.

Is the top-ranked model the best for me?+

Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.