Update README.md

README.md

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-![GUI Screenshot](images/screenshot.png)
-
-# Recent update
-5/25/2025  
-    Enable full intermediate previews for framepack tab, some change to framepack extension with image input logic.  
-5/24/2025  
-    Batch images from folder now available for framepack. Save only extension option and experimental start image for f1 in framepack extension tab.    
-5/23/2025  
-    Added ability to use the normal framepack model with endframe support in the framepack extension tab. Support additional bucket sizes.    
-5/18/2025  
-    Add video extension tab. Currently only works with f1 model. Full credit to @pfqt and @chaojie for their amazing work!  
-
-# H1111
-
-This is a GUI for tech wizard kohya-ss's musubi tuner's inference script.
-https://github.com/kohya-ss/musubi-tuner
-
-It allows inference with these models:  
-FramePack  
-Hunyuan-t2v  
-Hunyuan-i2v  
-Hunyuan-v2v  
-WanX-t2v  
-WanX-i2v  
-WanX-v2v  
-SkyReels-i2v  
-SkyReels-t2v  
-
-I have mostly been workiing on the framepack tab and the WanX-i2v tab. They are the best to use right now. WanX-i2v is used for skyreels v2 and the fun control models.    
-
-This supports queuing multiple different jobs if you open 2+ browser tabs and use the same model.
-
-If you are running out of vram use more block swapping. Using FP8 scaled is also a decent option to lower memory usage, select fp8 and fp8 scaled to use it. Scaled fp8 tries to duplicate the important parts of the model from FP16. Sage attention is the fastest/lowest vram but difficult to install in windows.
-
-Best quality will be obtained with only enabling block swapping and using the fp16 model with sdpa attention. You can speed things up with cfg skip, fp8 scaled, slg skip is small speedup, sage attention is fastest but all speedups come with quality degradations. I designed this to try to focus on quality over speed.
-
-If you are using a lora that you didn't train with musubi you need to drag it to the convert lora tab and convert it to the default format. It should spit it out into the /lora folder.
-
-If you need additional installation instructions or information create an issue and I will try to help. Also there are alot of settings notes on the musubi github linked above.  
-
-For torch 2.7.0 and windows installation try:  
-pip install typing-extensions  
-pip install torch==2.7.0+cu128 torchvision==0.22.0+cu128 --index-url https://download.pytorch.org/whl/cu128  
-pip install -r requirementsTorch27.txt  
-
-## To Use FramePack
-
-
-download these 5 files from https://huggingface.co/maybleMyers/framepack_h1111 and put them in a subfolder named hunyuan (H1111/hunyuan), or reference where they are in the gui if you have already aquired them.  
-
-FramePackI2V_HY_bf16.safetensors  or  FramePack_F1_I2V_HY_20250503.safetensors for F1  
-
-clip_l.safetensors  
-
-llava_llama3_fp16.safetensors  
-
-model.safetensors  
-
-pytorch_model.pt  
-
-Lora trained with musubi tuner's framepack training confirmed to work great. Normal lora trained for hunyuan kinda suck. Use a lot of block swap this is a different back end than the official repo. If you select fp8 and fp8 scaled it will all fit on a 24gb gpu for fastest speed, about 3s/it or 1:17 per second of video w/ a 4090.  Best quality will be obtained with just block swapping/sdpa attention/full model though.  
-
-Put loras in a /lora subfolder, if not trained with musubi you need to convert them.  
-
-Only unipc is supported for now. Sage attn is experimental. When using the F1 model not all options available for the original framepack model will work, like endframe and sectional images.  
-
-Here is an example prompt for a 5 second video with 4 sections using sectional prompting, also supports longer videos with indexes ie 0-2  ;;;3-5 etc:  
-
-0:A cinematic video showcases a cute blue penguin wearing sunglasses. The penguin runs quickly into mcdonalds.;;;1:The penguin runs quickly into mcdonalds and jumps up on a table and starts eating his food. The penguin's name is Piplup he is a famous Pokemon actor. The video is a fast action sequence animation showing the penguin running into a mcdonalds an jumping up onto a table.;;;2:The penguin is seated at a table and is enjoying his happy meal. The penguin's name is Piplup he is a famous Pokemon actor. The video is a fast action sequence animation showing the penguin running into a mcdonalds and jumping up onto a table.;;;3:The penguin is seated at a table and is happily enjoying his happy meal. The penguin's name is Piplup he is a famous Pokemon actor. The penguin flexes his huge arm muscles at the end of the video.  
-
-I have added support for 4 sectional images during inference. It works best when the images are close together. Refer to the screen shot for an example of a working 5 second video.  
-
-For more details on using framepack with musubi go here https://github.com/kohya-ss/musubi-tuner/blob/main/docs/framepack.md   
-
-Fastest speed will be achieved with fp8 and fp8 scaled, then you can reduce block swapping to your memory constraints. (leave about 1gb free)  
-
-Framepack Extension tab is still a work in progress.  
-Thanks to @pftq https://github.com/pftq and @chaojie https://github.com/chaojie for their work on the extension logics.  
-
-## To Use the new Skyreels-V2 models
-
-I have provided these 2 at https://huggingface.co/maybleMyers/wan_files_for_h1111  
-
- SkyReels-V2-I2V-14B-720P-FP16.safetensors  
- SkyReels-V2-I2V-14B-540P-FP16.safetensors  
-
-You can just drop them into the wan folder and use them in the WanX-i2v tab. Skyreels-V2 is a fine tune from Wan2.1.  
-If you have download the kijai variants the will not work because he added extra keys to the model.  
-
-## To Use WanX
-
-To use wanX download these and toss them in the wan subfolder:
-Download the T5 `models_t5_umt5-xxl-enc-bf16.pth`, vae `Wan2.1_VAE.pth` and CLIP `models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth` from the following page: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P/tree/main    
-
-Download the DiT weights from the following page: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/diffusion_models  
-ie : wan2.1_i2v_720p_14B_fp16.safetensors  
-
-For the fun control option in WanX-i2v I recommend the fp16 weights here: https://huggingface.co/maybleMyers/wan_files_for_h1111/tree/main  
-Wan2.1-Fun-14B-Control_fp16.safetensors  
-
-git pull to update the installation
-pip install -r requirements.txt
-
-I have tested the 14B i2v and t2v models so far to be working
-
-## Requirements
-
-- Python 3.10
-- CUDA 12.4
-
-## Basic Installation (Linux)
-
-Tested on ubuntu 24
-
-to update navigate to H1111 and git pull
-
-```powershell
-git clone https://github.com/maybleMyers/H1111
-cd H1111
-python -m venv env
-#(if you have another version of python do python3.10 -m venv env after you install it with sudo apt install python3.10 python3.10-venv python3.10-distutils)
-source env/bin/activate 
-pip install torch==2.5.1 torchvision --index-url https://download.pytorch.org/whl/cu124 
-pip install -r requirements.txt
-pip install flash-attn --no-build-isolation
-pip install sageattention==1.0.6
-might need python3.10-dev as well for sage attention to work
-
-```
-
-run with  
-source env/bin/activate  
-python h1111.py
-
-for GPU1
-CUDA_VISIBLE_DEVICES=1 python h1111.py
-
-## Basic Installation (Windows)
-
-
-
-First, open PowerShell and navigate to your desired installation directory. Then run these commands:
-
-```powershell
-git clone https://github.com/maybleMyers/H1111
-cd H1111
-python -m venv env
-./env/scripts/activate
-pip install torch==2.5.1 torchvision --index-url https://download.pytorch.org/whl/cu124 
-pip install -r requirements.txt
-
-```
-
-## To run
-
-```
-env/scripts/activate
-python h1111.py
-```
-
-open 127.0.0.1:7860 in a browser
-
-You can set cuda device to 1,2,3,4,5,6,7 etc in the env once activated in a separate terminal to run unlimited copies at once if you have another gpu.  
-ie for linux on the second gpu: CUDA_VISIBLE_DEVICES=1 python h1111.py
-
-## full changlog
-5/25/2025  
-    Enable full intermediate previews for framepack tab, some change to framepack extension with image input logic.  
-5/24/2025  
-    Batch images from folder now available for framepack. Save only extension option and experimental start image for f1 in framepack extension tab.    
-5/23/2025  
-    Added ability to use the normal framepack model with endframe support in the framepack extension tab. Support additional bucket sizes.    
-5/18/2025  
-    Add video extension tab. Currently only works with f1 model. Full credit to @pfqt and @chaojie for their amazing work!  
-5/12/2025  
-    Add skip button to framepack.  
-5/9/2025  
-    Add testing branch for framepack F1 end image, kinda glitchygo https://github.com/maybleMyers/H1111/tree/f1_end  
-5/5/2025  
-    Update an experimental hunyuan to framepack convert lora option in the convert lora tab.  
-    Add tea cache to frame pack.  
-5/3/2025  
-    Add support for framepack F1! download from https://huggingface.co/maybleMyers/wan_files_for_h1111/blob/main/FramePack_F1_I2V_HY_20250503.safetensors  put it in your hunyuan folder. You might need to reinstall reqs "pip install -r requirements.txt"  
-    Add support for Wan2.1 i2v-14B-FC-1.1. It is a fun control model and is very good. Use it in the WanX-i2v tab and make sure to select the task i2v-14B-FC-1.1 at the bottom of the page.  Download the weights from https://huggingface.co/maybleMyers/wan_files_for_h1111  
-4/30/2025  
-    Previews for framepack.    
-4/29/2025  
-    Add initial preview support to the wanX-i2v tab based. If you want to use them use the preview branch. Thanks to Sarania.   
-    Wan2.1-Fun-V1.1-14B-InP-FP16.safetensors is available at https://huggingface.co/maybleMyers/wan_files_for_h1111  
-    Fix bug in hunyuan-t2v not loading lora.  
-4/26/2025  
-    Add SkyReels-V2-I2V-14B-720P-FP16.safetensors to supported models.  
-    Added alot better options for Framepack including working sectional images, Thanks to kohya!  
-4/25/2025  
-    Framepack backend updates for better LoRa support for LoRa's trained with musubi tuner. Also better weighting options.  
-4/24/2025  
-    Update FramePack backend to musubi backend instead of original. Offers much improved speed and some quality improvements.  
-    Add support for torch 2.7.0 + cuda 12.8  
-4/18/2025  
-    Add initial support for FramePack. https://github.com/lllyasviel/FramePack  
-4/15/2025  
-    Add much improved functionality for the wan fun control model. Added strength imrpovements and dropoff code to choose when to apply the control video. Thanks wordbrew.  
-4/3/2025  
-    Add support for hunyuan i2v model. Download the clip vision from https://huggingface.co/maybleMyers/H1111_Hunyuan_i2v And download the official model from hunyuan's website and rename it to mp_rank_00_model_states_i2v.pt https://huggingface.co/tencent/HunyuanVideo-I2V/tree/main/hunyuan-video-i2v-720p/transformers  add both to your hunyuan folder.  
-3/29/2025  
-    Added support for fun models! download dit from https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control and specify correct task type and dit location. I renamed it from diffusion_pytorch_model to Wan2.1-Fun-14B-control. Works in the normal WanX-i2v tab when you select the control option at the bottom of the page.  
-3/23/2025  
-    Added Wanx cfg skip functionality to skip cfg guidance during inference for faster generations but less following of the prompt  
-3/22/2025  
-    Added WanX-i2v end frame functionality  
-3/20/2025  
-    Added WanX-v2v functionality.  
-3/18/2025  
-    Added Skip Layer Guidance for WanX-i2v.  
-3/13/2025  
-    Added extend video functionality to WanX-i2v. It kind of works .  
-3/12/2025  
-    Added ability to send the last frame of a video to the input in WanX-i2v. Also you can now use this to extend the video. You can do multiple batches at each step and pick the best extended video then generate an even longer one.  
-3/9/2025  
-    Added batching ability for a folder full of images in WanX-i2v tab. Added flash attn for windows prebuilt wheel.  
-3/8/2025  
-    Added support for wan lora's. Remember to convert them first in the convert lora tab.  
-3/5/2025  
-    Added ability to batch a folder of images with skyreels i2v, so you can make a video with every image in a folder.
-3/2/2025  
-    Added initial support for wanX-2.1 Image to Video and Text to Video inference.  
-3/1/2025  
-    Added support for Skyreels Video to Video and Text to Video.   
-2/23/2025  
-    Added initial support for skyreels-V1 using musubi's skyreel implementation. (thanks  sdbds)
-download models from https://huggingface.co/Kijai/SkyReels-V1-Hunyuan_comfy and add them to your hunyuan folder
-skyreels_hunyuan_i2v_bf16.safetensors
-skyreels_hunyuan_t2v_bf16.safetensors
-
-
-## to use stock hunyuan models
-
-https://huggingface.co/tencent/HunyuanVideo/resolve/main/hunyuan-video-t2v-720p/transformers/mp_rank_00_model_states.pt  
-
-https://huggingface.co/tencent/HunyuanVideo/resolve/main/hunyuan-video-t2v-720p/vae/pytorch_model.pt  
-
-https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged/resolve/main/split_files/text_encoders/llava_llama3_fp16.safetensors  
-
-https://huggingface.co/Comfy-Org/HunyuanVideo_repackaged/resolve/main/split_files/text_encoders/clip_l.safetensors  
-
-#fp8 dit model
-
-https://huggingface.co/kohya-ss/HunyuanVideo-fp8_e4m3fn-unofficial/resolve/main/mp_rank_00_model_states_fp8.safetensors  
-
-place models in H1111/hunyuan folder
-
-### Optional: Install Xformers
-```powershell
-pip install --no-deps xformers --index-url https://download.pytorch.org/whl/cu124
-```
-
-### Optional: Install Flash Attention
-Note: This can take 1-5 hour to install even on a good CPU, but provides faster generation.  
-I have uploaded a wheel for windows users to match cuda 12.4 and python 3.10.(thanks lldacing)
-https://huggingface.co/maybleMyers/wan_files_for_h1111/resolve/main/flash_attn-2.7.4%2Bcu124torch2.5.1cxx11abiFALSE-cp310-cp310-win_amd64.whl?download=true  
-
-```powershell
-pip install flash-attn --no-build-isolation
-
-If you have downloaded the wheel you can install it with:
-
-pip install "flash_attn-2.7.4+cu124torch2.5.1cxx11abiFALSE-cp310-cp310-win_amd64.whl"
-```
-```
+---
+sdk: gradio
+title: Framepack STUDIO H111
+app_file: studio3.py
+pinned: false
+license: apache-2.0
+short_description: fast video generation from images & text
+sdk_version: 5.31.0
+---