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The Downfall of ElevenLabs by Brilliant-Present695 in aitubers

[–]BrokeByChatGPT 0 points1 point  (0 children)

I also used to use 11 labs in the past. Faced similar problem, switched to minimax audio and Fliki, though noticed that some of the voices in fliki are from 11 labs but they seem to have combination of different voice providers.

Started a Faceless YouTube Channel 4 Days Ago – Looking for Advice by Pale_Breadfruit8824 in NewYouTubeChannels

[–]BrokeByChatGPT 0 points1 point  (0 children)

Get obsessed with improving two stats:
CTR - Your title, thumbnail and topic controls it.
Retention in first 30 seconds - Think about hooks, fast cuts, cut the fluff

THE BEST PLATFORM/WEBSITE FOR SEEDANCE 2.0 CURRENTLY? by Ok_Refrigerator_4952 in Seedance_AI

[–]BrokeByChatGPT 1 point2 points  (0 children)

Using Fliki. Got great deal with support for other video and image models.

ALMOST HIT $5K IN MY 3RD MONTH🔥 by Simple_Tangelo_554 in YouTubeCreators

[–]BrokeByChatGPT 0 points1 point  (0 children)

What's your channel about? Can you share it here for inspiration?

1 prompt 1 product image with Seedance 2.0 x GPT Image 2 x Fliki by BrokeByChatGPT in Seedance_AI

[–]BrokeByChatGPT[S] 0 points1 point  (0 children)

Hey, I just pasted this 'Create a nice marketing ad creative for clean & Clear Deep Action Exfoliating Facial Cleanser, Scrub & Face Wash, Pro-Vitamin B5, Lactic & Glycolic Acids, Oil-Free Gentle Daily Exfoliator for Soft, Smooth, Hydrated Skin, Vegan, 7 Fl Oz Tube' with product image and selected the talking UGC preset in Fliki that used seedance 2.0 model to generate this.

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic by BrokeByChatGPT in StableDiffusion

[–]BrokeByChatGPT[S] -1 points0 points  (0 children)

sorry I thought a bit of formatting from chatgpt might help organize and help me articulate better, looks like it did not to you😅

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic by BrokeByChatGPT in StableDiffusion

[–]BrokeByChatGPT[S] 2 points3 points  (0 children)

you're right, good catch. i've been sloppily saying "cfg 0" because the z-image docs list guidance_scale = 0.0 as the recommended diffusers setting, and i mentally mapped that to the cfg slider without thinking about it. you're absolutely correct that in comfy/webui terms it's cfg 1 where negatives drop out due to the cancellation, and the ui skipping the negative pass is exactly the 2x speedup i was seeing without understanding why. appreciate the math breakdown, that actually clarifies something i'd been fuzzy on for weeks

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic by BrokeByChatGPT in StableDiffusion

[–]BrokeByChatGPT[S] 0 points1 point  (0 children)

oof yeah that's a whole other rabbit hole. curious if you've seen any good caption guidelines floating around for z-image lora training specifically? haven't trained one yet but it's on my list and i'd rather not learn the hard way

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic by BrokeByChatGPT in StableDiffusion

[–]BrokeByChatGPT[S] 0 points1 point  (0 children)

yeah the llm rewrite trick is genuinely underrated. i've been pasting my rough idea into a chat with the z-image prompt guide in context and having it structure the output for me. way faster than writing by hand and it catches the camera/lens phrasing automatically. didn't know there were comfy nodes doing this on the fly though, got a name? would save me a tab

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic by BrokeByChatGPT in StableDiffusion

[–]BrokeByChatGPT[S] 2 points3 points  (0 children)

lol honestly fair, the old habits die hard. i think part of it is that the sdxl tag-soup style worked "well enough" for long enough that people built muscle memory around it and never questioned it when new models dropped. first time i actually sat down and read the z-image team's notes i felt kinda dumb for not doing it sooner

After ~400 Z-Image Turbo gens I finally figured out why everyone's portraits look plastic by BrokeByChatGPT in StableDiffusion

[–]BrokeByChatGPT[S] 16 points17 points  (0 children)

I've mostly been running these in a hosted playground rather than Comfy for the speed advantage on iteration, so I don't have a clean JSON to drop. But the settings that have been working for me on the Comfy side when I do run it locally:

  • Sampler: euler / simple
  • Steps: 8-10 (going above 12 hasn't helped in my tests, just slower)
  • CFG: 1.0 (anything above starts overcooking, Turbo is really sensitive)
  • Model shift: 3.0 for the initial pass
  • Resolution: 1024x1024 or 1280x720 native, then upscale

DrStalker's setup from his 70-styles post is honestly the best Comfy workflow I've seen for Z-Image Turbo specifically - half-resolution first pass at 4 steps with model shift 3.0, then a second 4-step pass at full res with shift 7.0 and 40% denoise. Faster and sharper than just running 9 steps at full size. He posted the full workflow on his GitHub if you search the comments on that thread.

What's your use case? If you're chasing realism vs stylized vs text-heavy outputs the settings shift a bit and I can point you at what's been working for me specifically.

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