Same GPU: "It won't fit in, Daddy!"

@ZweiBelle Which one are you running? I have the 4070 RTX and it runs the Q8.

If you are running the full model then you need to set the steps to 50. Otherwise move up to a higher quantization like q4, q8, F16 etc.

Because Q2 is a very harsh Quant.

Sampler, Euler isn't that great for this so far.

In theory you should be able to run the F16 Model. Full vs Dev is only a speed vs quality difference. They both have the same Vram requirements. I switch between both. If you want scenes with lots of detail, then Full works best but if you have a singular focus in mind like single object or 2d sprites then I think Dev is better.

I am seeing some issues. I have some concerns about this upload. I am running the 4 bit from here:
https://github.com/hykilpikonna/HiDream-I1-nf4.git

and it's quality is top tier for a 4 bit model. The one from github is running directly from a python script, and not from ComfyUI. I mean it could be my settings, but I don't think that is the case. I will do some more testing and comparison, but I am starting to question this upload.

Use this:
https://github.com/Jonseed/ComfyUI-Detail-Daemon

@Ada321 it really changes the game. i use amount (detail 0.15 --> start from 0.10 to end 0.80)

GB

That would be nice.

Yes, I believe you should be able to get the q8 GGUF working on a 3090.

@rusty2930 If you have the VRAM I got the f16 version working on mine (24G VRAM).

@RalFinger idk but it works. Tho, the thing is I use the fp8_e5m2 option for the Load Diffusion node. Otherwise, it takes 30% longer to generate. Still works either way.

All I'm waiting for now is a Wavespeed and/or Teacache update for support. For 50 steps, it's nearly 3 minutes per gen. At 30, it's about 1:30, and that would work for illustrations, drawings and the sort but not for realism so much.

for me it runs on a RTX4060 with 8GB VRAM, 64GB RAM. It tooks time but it works without OOM

even the Full model-FP16.gguf works on my Asus RTX 3090 (using 23.6 GB vram) with fully loaded clip text encoders + the model itself

@simartem07 oh nice! how long for each generation and do you see good quality difference

@cutetodeath78409597 i am not sure if its the best setup, i am using python 3.13.2 with pytorch version: 2.6.0+cu124. Using Flash Attention. Set vram state to: LOW_VRAM. DeviceNVIDIA GeForce RTX 3090. Using hidream_full_f16_gguf --> 50 steps, each step takes about between 4.50 s/it ~5.60 s/it. Mostly ~ 250 sec. total for single image (1440 x 1440 px image resolution)

@simartem07 My setup is pretty much the same. The only thing im not utilizing is low vram, and it takes several seconds longer ~260. What scheduler and sampler do you use? I swear when i make full shots of subjects i get this low res look when you zoom in. That's using uni_pc/simple.

@simartem07 Hmmm, so when i bumped up the aspect ratio/img res to 1440x1440 my inference shot up to 8.65s/i. :O I wonder if my settings are off now besides the low vram. It took aprox 7 minutes (~441 seconds). xD

@MysticMindAi you are right, i double checked inference values and my previous post had a typo, values 4.50 to 5.60 s/it is for 1024x1024, and it takes up to 8-14 s/it for 1440x1440. Sorry for that :-)
to be honest i am not yet stuck on a sampler/scheduler pair yet because trying to figure out which works best for what kind of generation style (realistic, illustration, etc..) but mostly using dpmpp_2m/beta for most generations. It's been a pain in my comfui env. to test xyz plot, but if i figure out an easy way, would it be helpful to iterate same seed generations with multiple sampler/scheduler values. Because not everytime a particular one works the best, i agree with you on some generations showing blurry and noisy outputs

thanks y'all for the first hand insight (3090 user, 5090 pre-order)
i'll fiddle around HI Dream once i get that MFBFGPU hopefuly.

@MysticMindAi full workflow?

@JustPara would you like my workflow?

@MysticMindAi yes please.

@MysticMindAi Sorry for answering late. But yes, still possible?

@JustPara no worries. Give me a bit.

@JustPara sent link in chat

@MysticMindAi Thanks^^

Appreciate the knowledge share but unless someone has done this quantization wrong Q8 should provide quality similar to FP16 and superior to FP8. I don't know about video models but that's how the Flux models performed which is what HiDream is based on so we should expect the same to hold true here.

@KingLord Thanks for the headsup! I've just looked into it further and you're right. FP8 is at the same level of Q6. I'll update the above order

@RalFinger 30 days have pasted since i clicked que, lmao... but really how long for say 20 steps at 1080

@RalFinger cool cool, had to check

It works on a 3060 but you can expect a full 40 mints for 4-5 seconds of video. Video quality is excellent

@RalFinger Can you switch the type of this to checkpoint? I found that images posted here aren’t searchable as hidream when the type is workflows. Thanks.

If anyone is looking for the 28 Step Dev model I have uploaded it here: https://civitai.com/models/1515789/hi-dream-dev

The fp16 version of Dev (32GB) is still uploading right now.

good comparison, thank you!

@simartem07 Thanks. Your welcome.