This just released at few moments ago.

TLDR: More detail in a prompt is not necessarily better. Avoid unnecessary or overly abstract verbiage. Favor details that are concrete or can at least be visualized. Conceptual or mood-like terms should be limited to those which would be widely recognized and typically used to caption an image. [Much more explanation in the first comment]

NB The github page for the release : https://github.com/lllyasviel/FramePack Please read it for what it can do.

The original post here detailing the release : https://www.reddit.com/r/StableDiffusion/comments/1k1668p/finally_a_video_diffusion_on_consumer_gpus/

I'll start with - it's honestly quite awesome, the coherence over time is quite something to see, not perfect but definitely more than a few steps forward - it adds on time to the front as you extend .

Yes, I know, a dancing woman, used as a test run for coherence over time (24s) , only the fingers go a bit weird here and there but I do have Teacache turned on)

24s test for coherence over time

Credits: u/lllyasviel for this release and u/woct0rdho for the massively destressing and time saving sage wheel

On lllyasviel's Github page, it says that the Windows installer will be released tomorrow (18th April) but for those impatient souls, here's the method to install this on Windows manually (I could write a script to detect installed versions of cuda/python for Sage and auto install this but it would take until tomorrow lol) , so you'll need to input the correct urls for your cuda and python.

Install Instructions

Note the NB statements - if these mean nothing to you, sorry but I don't have the time to explain further - wait for tomorrows installer.

1. Make your folder where you wish to install this
2. Open a CMD window here
3. Input the following commands to install Framepack & Pytorch

NB: change the Pytorch URL to the CUDA you have installed in the torch install cmd line (get the command here: https://pytorch.org/get-started/locally/ )

git clone https://github.com/lllyasviel/FramePack cd framepack python -m venv venv venv\Scripts\activate.bat python.exe -m pip install --upgrade pip pip install -r requirements.txt pip uninstall torch torchvision torchaudio pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 python.exe -s -m pip install triton-windows 

NB2: change the version of Sage Attention 2 to the correct url for the cuda and python you have (I'm using Cuda 12.6 and Python 3.12). Change the Sage url from the available wheels here https://github.com/woct0rdho/SageAttention/releases

4.Input the following commands to install the Sage2 and Flash attention models - you could leave out the Flash install if you wish (ie everything after the REM statements) and install it later if you wish).

pip install https://github.com/woct0rdho/SageAttention/releases/download/v2.1.1-windows/sageattention-2.1.1+cu126torch2.6.0-cp312-cp312-win_amd64.whl @REM the above is one single line.Packaging below should not be needed as it should install @REM ....with the Requirements . Packaging and Ninja are for installing Flash-Attention pip install packaging pip install ninja set MAX_JOBS=4 pip install flash-attn --no-build-isolation 

To run it -

NB I use Brave as my default browser, but it wouldn't start in that (or Edge), so I used good ol' Firefox

1. Open a CMD window in the Framepack directory

   venv\Scripts\activate.bat python.exe demo_gradio.py

You'll then see it downloading the various models and 'bits and bobs' it needs (it's not small - my folder is 45gb) ,I'm doing this while Flash Attention installs as it takes forever (but I do have Sage installed as it notes of course)

NB3 The right hand side video player in the gradio interface does not work (for me anyway) but the videos generate perfectly well), they're all in my Framepacks outputs folder

https://preview.redd.it/0e9m3fqn7dve1.png?width=1853&format=png&auto=webp&s=6e1522836b6d4be19679c99a1c2fcf64065e7a16

And voila, see below for the extended videos that it makes -

NB4 I'm currently making a 30s video, it makes an initial video and then makes another, one second longer (one second added to the front) and carries on until it has made your required duration. ie you'll need to be on top of file deletions in the outputs folder or it'll fill quickly). I'm still at the 18s mark and I have 550mb of videos .

https://reddit.com/link/1k18xq9/video/16wvvc6m9dve1/player

https://reddit.com/link/1k18xq9/video/hjl69sgaadve1/player

What's the best online image AI tool to take an input image and an image of a person, and combine it to get a very similar image, with the style and pose?
-I did this in Chat GPT and have had little luck with other images.
-Some suggestions on platforms to use, or even links to tutorials would help. I'm not sure how to search for this.

https://preview.redd.it/xq7rtycaaeve1.png?width=743&format=png&auto=webp&s=26981d15d4eacfa621a1dff89b1b3bf02fc76529

https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P#run-first-last-frame-to-video-generation

https://preview.redd.it/s19xfu8paeve1.png?width=1884&format=png&auto=webp&s=569398af249853c7482219e8fcaba5a44d504973

https://github.com/kijai/ComfyUI-WanVideoWrapper/tree/main/example_workflows
Kijai Hugging Face model : https://huggingface.co/Kijai/WanVideo_comfy/tree/main

HiDream ComfyUI native workflow used: https://comfyanonymous.github.io/ComfyUI_examples/hidream/

• Model: hidream_i1_full_fp16.safetensors
• shift: 3.0
• steps: 50
• sampler: uni_pc
• scheduler: simple
• cfg: 5.0

In the comparison Flux.Dev image goes first then same generation with HiDream (selected best of 3)

Prompt 1: "A 3D rose gold and encrusted diamonds luxurious hand holding a golfball"

Prompt 2: "It is a photograph of a subway or train window. You can see people inside and they all have their backs to the window. It is taken with an analog camera with grain."

Prompt 3: "Female model wearing a sleek, black, high-necked leotard made of material similar to satin or techno-fiber that gives off cool, metallic sheen. Her hair is worn in a neat low ponytail, fitting the overall minimalist, futuristic style of her look. Most strikingly, she wears a translucent mask in the shape of a cow's head. The mask is made of a silicone or plastic-like material with a smooth silhouette, presenting a highly sculptural cow's head shape."

Prompt 4: "red ink and cyan background 3 panel manga page, panel 1: black teens on top of an nyc rooftop, panel 2: side view of nyc subway train, panel 3: a womans full lips close up, innovative panel layout, screentone shading"

Prompt 5: "Hypo-realistic drawing of the Mona Lisa as a glossy porcelain android"

Prompt 6: "town square, rainy day, hyperrealistic, there is a huge burger in the middle of the square, photo taken on phone, people are surrounding it curiously, it is two times larger than them. the camera is a bit smudged, as if their fingerprint is on it. handheld point of view. realistic, raw. as if someone took their phone out and took a photo on the spot. doesn't need to be compositionally pleasing. moody, gloomy lighting. big burger isn't perfect either."

Prompt 7 "A macro photo captures a surreal underwater scene: several small butterflies dressed in delicate shell and coral styles float carefully in front of the girl's eyes, gently swaying in the gentle current, bubbles rising around them, and soft, mottled light filtering through the water's surface"

1-Workflow link (free)

https://www.patreon.com/posts/video-face-swap-126488680?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

2-Video tutorial link

https://youtu.be/n8q39TF-3zI

Set up FramePack and wanted to show some first results. WSL2 conda environment. 4090

definitely worth using teacache with flash/sage/xformers as the 30s still took 40 minutes with all of them, also keeping in mind without them it would well over double in time rendered. teacache adds so blur but this is early experimentation.

quite simply, amazing. there's still some of hunyuans stiffness but was still just to see what happens. I'm going to bed and I'll put a 120s one to run while I sleep. Its interesting the inference runs backwards, making the end of the video and working towards the front., which could explain some of the reason it gets stiff.

Project page: https://fantasy-amap.github.io/fantasy-talking/
Github: https://github.com/Fantasy-AMAP/fantasy-talking
Paper: https://arxiv.org/abs/2504.04842

After seeing that HiDream had GGUF's available, and clip files (Note: It needs a Quad loader; Clip_g, Clip_l, t5xx1_fp8_e4m3fn, and llama_3.1_8b_instruct_fp8_scaled) from this card on HuggingFace: The Huggingface Card I wanted to see if I could run them and what the fuss is all about. I tried to match settings between Flux1D and HiDream, so you'll see on the image captions they all use the same seed, without Loras and using the most barebones workflows I could get working for each of them.

Image 1 is using the full HiDream BF16 GGUF which clocks in about 33gb on disk, which means my 4080s isn't able to load the whole thing. It takes considerably longer to render the 18 steps than the Q5_K_M used on image 2, and even then the Q5_K_M which clocks in at 12.7gb also loads alongside the four clips which is another 14.7gb in file size so there is loading and offloading, but it still gets the job done a touch faster than Flux1D, clocking in at 23.2gb

HiDream has a bit of an edge in generalized composition. I used the same prompt "A photo of a group of women chatting in the checkout lane at the supermarket." for all three images. HiDream added a wealth of interesting detail, including people of different ethnicities and ages without request, where as Flux1D used the same stand in for all of the characters in the scene.

Further testing lead to some of the same general issues that Flux1D has with female anatomy without layers of clothing on top. After some extensive testing consisting of numerous attempts to get it to render images of just certain body parts it came to light that its issues with female anatomy are that it does not know what the things you are asking for are called. Anything above the waist, HiDream CAN do, but it will default 7/10 to clothed even when asking for things bare. Below the waist, even with careful prompting it will provide you either with still layer covered anatomy or mutations and hallucinations. 3/10 times you MIGHT get the lower body to look okay-ish from a distance, but it definitely has a 'preference' that it will not shake. I've narrowed it down to just really NOT having the language there to name things what they are.

Something else interesting with the models that are out now, is that if you leave out the llama 3.1 8b, it can't read the clip text encode at all. This made me want to try out some other text encoding readers, but I don't have any other text readers in safetensor format, just gguf for LLM testing.

Another limitation I noticed in the log about this particular set up is that it will ONLY accept 77 tokens. As soon as you hit 78 tokens and you start getting the error in your log, it starts randomly dropping/ignoring one of the tokens. So while you can and should prompt HiDream like you are prompting Flux1D, you need to keep the character count limited to 77 tokens and below.

Also, as you go above 2.5 CFG into 3 and then 4, HiDream starts coating the whole image in flower like paisley patterns on every surface. It really wants CFG of 1.0-2.0 MAX for best output of images.

I haven't found too much else that breaks it just yet, but I'm still prying at the edges. Hopefully this helps some folks with these new models. Have fun!

Link to the post on CivitAI - https://civitai.com/posts/15514296

I keep using the "no workflow" flair when I post because I'm not sure if sharing the link counts as sharing the workflow. The post in the Link will provide details on prompt, Lora's and model though if you are interested.

https://preview.redd.it/cfebynh3xbve1.jpg?width=2828&format=pjpg&auto=webp&s=0485c4ef5d2e851eabb5255d33257b6278decf33

Hi everyone!

Thank you for your continued interest and support for Nunchaku and SVDQuant!

Two weeks ago, we brought you v0.2.0 with Multi-LoRA support, faster inference, and compatibility with 20-series GPUs. We understand that some users might run into issues during installation or usage, so we’ve prepared tutorial videos in both English and Chinese to guide you through the process. You can find them, along with a step-by-step written guide. These resources are a great place to start if you encounter any problems.

We’ve also shared our April roadmap—the next version will bring even better compatibility and a smoother user experience.

If you find our repo and plugin helpful, please consider starring us on GitHub—it really means a lot.
Thank you again! 💖

https://civitai.com/models/1476477/3d-oneclick

• Please respect the effort we put in to meet your needs.

Demystifying HiDream: Your Guide to Full, Dev, Fast

Confused by the different HiDream AI model versions?

🤔 Full, Dev, Fast !?

I've written a comprehensive guide breaking down EVERYTHING you need to know about the HiDream .

Inside this deep dive on Civitai, you'll find:

Clear explanations of HiDream Full, Dev, and Fast versions & their ideal uses.

A breakdown of .safetensors vs .gguf formats and when to use each.

Details on required Text Encoders (CLIP, T5XXL) & VAE.

Crucial GPU VRAM guidance – which model fits your 8GB, 12GB (like the RTX 3060!), 16GB, or 24GB+ card?

Direct download links for all necessary files.

Make informed decisions, optimize your setup, and start creating amazing images faster! 🚀

Read the full guide here: 👉 https://civitai.com/articles/13704

I've chosen (Q4km-dev)> (12G vram) 👉 https://civitai.com/models/1479706/hidream-dev

#HiDream #AI #ArtificialIntelligence #StableDiffusion #ComfyUI #AIart #ImageGeneration #GPU #VRAM #TechGuide #AINews #Civitai #MachineLearning

generated with Flux Dev, locally. happy to share the prompt if anyone would like.