AQfAIaKratFLWXddB 26AjdQ3xgcsqJA3DO6xvzVn5E
| | Workflow : https://pastebin.com/f32CAsS7 Hardware : RTX 3090 24GB Models : Qwen Q4 GGUF + Wan 2.2 Low GGUF Elapsed Time E2E (2k Upscale) : 300s cold start, 80-130s (0.5MP – 1MP) **Main Takeaway – Qwen Latents are compatible with Wan 2.2 Sampler** Got a bit fed up with the cryptic responses posters gave whenever asked for workflows. This workflow is the effort piecing together information from random responses. There are two stages: 1stage: (42s-77s). Qwen sampling at 0.75/1.0/1.5MP 2stage: (~110s): Wan 2.2 4 step __1st stage can go to VERY low resolutions. Haven’t test 512×512 YET but 0.75MP works__ * Text – text gets lost at 1.5 upscale , appears to be restored with 2.0x upscale. I’ve included a prompt from the Comfy Qwen blog * Landscapes (Not tested) * Cityscapes (Not tested) * Interiors *(untested) * Portraits – Closeups Not great (male older subjects fare better). Okay with full body, mid length. Ironically use 0.75 MP to smooth out features. It’s obsessed with freckles. Avoid. This may be fixed by https://www.reddit.com/r/StableDiffusion/comments/1mjys5b/18_qwenimage_realism_lora_samples_first_attempt/ by the never sleeping u/AI_Characters Next: – Experiment with leftover noise – Obvious question – Does Wan2.2 upscale work well on __any__ compatible vae encoded image ? – What happens at 4K ? – Can we get away with lower steps in Stage 1 submitted by /u/SvenVargHimmel |
It's tempting to treat loop engineering as something invented in a single week in June,…
This post continues our series on best practices with Amazon Bedrock Guardrails. For the previous…
Welcome to The Blueprint, a new feature where we highlight how Google Cloud customers are…
Outside experts—some with a vested interest in peptides—recommended adding a number of the amino acids…
Researchers from the National University of Singapore (NUS) have developed artificial intelligence (AI) methods that…
AI Teammates are agentic AI on Amazon Bedrock, and few engineering organizations run them in…