Merge branch 'main' into feature_add-cuda-docker-runner

2025-11-04 14:16:57 +00:00 · 2025-08-30 06:56:10 +03:00 · 2025-08-30 06:56:10 +03:00 · 5940aa023f
commit 5940aa023f
parent 3307defa7c 0be33acb57
80 changed files with 5963 additions and 725 deletions
--- a/README.md
+++ b/README.md
@ -22,6 +22,28 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
 **Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep

 ## 🔥 Latest Updates
+### July 15 2025: WanGP v7.0 is an AI Powered Photoshop
+This release turns the Wan models into Image Generators. This goes way more than allowing to generate a video made of single frame :
+- Multiple Images generated at the same time so that you can choose the one you like best.It is Highly VRAM optimized so that you can generate for instance 4 720p Images at the same time with less than 10 GB
+- With the *image2image* the original text2video WanGP becomes an image upsampler / restorer
+- *Vace image2image* comes out of the box with image outpainting, person / object replacement, ...
+- You can use in one click a newly Image generated as Start Image or Reference Image for a Video generation
+
+And to complete the full suite of AI Image Generators, Ladies and Gentlemen please welcome for the first time in WanGP : **Flux Kontext**.\
+As a reminder Flux Kontext is an image editor : give it an image and a prompt and it will do the change for you.\
+This highly optimized version of Flux Kontext will make you feel that you have been cheated all this time as WanGP Flux Kontext requires only 8 GB of VRAM to generate 4 images at the same time with no need for quantization.
+
+WanGP v7 comes with *Image2image* vanilla and *Vace FusinoniX*. However you can build your own finetune where you will combine a text2video or Vace model with any combination of Loras.
+
+Also in the news:
+- You can now enter the *Bbox* for each speaker in *Multitalk* to precisely locate who is speaking. And to save some headaches the *Image Mask generator* will give you the *Bbox* coordinates of an area you have selected.
+- *Film Grain* post processing to add a vintage look at your video
+- *First Last Frame to Video* model should work much better now as I have discovered rencently its implementation was not complete
+- More power for the finetuners, you can now embed Loras directly in the finetune definition. You can also override the default models (titles, visibility, ...) with your own finetunes. Check the doc that has been updated.
+
+
+### July 10 2025: WanGP v6.7, is NAG a game changer ? you tell me
+Maybe you knew that already but most *Loras accelerators* we use today (Causvid, FusioniX) don't use *Guidance* at all (that it is *CFG* is set to 1). This helps to get much faster generations but the downside is that *Negative Prompts* are completely ignored (including the default ones set by the models). **NAG** (https://github.com/ChenDarYen/Normalized-Attention-Guidance) aims to solve that by injecting the *Negative Prompt* during the *attention* processing phase.

 ### July 10 2025: WanGP v6.7, is NAG a game changer ? you tell me

--- a/defaults/ReadMe.txt
+++ b/defaults/ReadMe.txt
@ -0,0 +1,13 @@
+Please dot not modify any file in this Folder.
+
+If you want to change a property of a default model, copy the corrresponding model file in the ./finetunes folder and modify the properties you want to change in the new file.
+If a property is not in the new file, it will be inherited automatically from the default file that matches the same name file.
+
+For instance to hide a model:
+
+{
+	"model":
+	{
+		"visible": false
+	}
+}
--- a/finetunes/fantasy.json
+++ b/finetunes/fantasy.json
--- a/finetunes/flf2v_720p.json
+++ b/finetunes/flf2v_720p.json
@ -1,14 +1,14 @@
 {
 	"model":
 	{
-		"name": "First Last Frame to Video 720p (FLF2V)14B",
+		"name": "First Last Frame to Video 720p (FLF2V) 14B",
 		"architecture" : "flf2v_720p",
-		"visible" : false,
+		"visible" : true,
 		"description": "The First Last Frame 2 Video model is the official model Image 2 Video model that supports Start and End frames.",
 		"URLs": [
-			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_FLF2V_720p_14B_bf16.safetensors",
-			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_FLF2V_720p_14B_quanto_int8.safetensors",
-			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_FLF2V_720p_14B_quanto_fp16_int8.safetensors"
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_FLF2V_720p_14B_mbf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_FLF2V_720p_14B_quanto_mbf16_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_FLF2V_720p_14B_quanto_mfp16_int8.safetensors"
 		],
 		"auto_quantize": true
 	},
--- a/defaults/flux_dev_kontext.json
+++ b/defaults/flux_dev_kontext.json
@ -0,0 +1,16 @@
+{
+    "model": {
+        "name": "Flux Dev Kontext 12B",
+        "architecture": "flux_dev_kontext",
+        "description": "FLUX.1 Kontext is a 12 billion parameter rectified flow transformer capable of editing images based on instructions stored in the Prompt. Please be aware that Flux Kontext is picky on the resolution of the input image the output dimensions may not match the dimensions of the input image.",
+        "URLs": [
+            "https://huggingface.co/DeepBeepMeep/Flux/resolve/main/flux1_kontext_dev_bf16.safetensors",
+            "https://huggingface.co/DeepBeepMeep/Flux/resolve/main/flux1_kontext_dev_quanto_bf16_int8.safetensors"
+        ]
+    },
+	"prompt": "add a hat",
+    "resolution": "1280x720",
+    "video_length": 1
+}
+
+	
--- a/defaults/fun_inp.json
+++ b/defaults/fun_inp.json
@ -0,0 +1,13 @@
+{
+	"model":
+	{
+		"name": "Fun InP image2video 14B",
+		"architecture" : "fun_inp",
+		"description": "The Fun model is an alternative image 2 video that supports out the box End Image fixing (contrary to the original Wan image 2 video model).",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Fun_InP_14B_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Fun_InP_14B_quanto_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Fun_InP_14B_quanto_fp16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/fun_inp_1.3B.json
+++ b/defaults/fun_inp_1.3B.json
@ -0,0 +1,11 @@
+{
+	"model":
+	{
+		"name": "Fun InP image2video 1.3B",
+		"architecture" : "fun_inp_1.3B",
+		"description": "The Fun model is an alternative image 2 video that supports out the box End Image fixing (contrary to the original Wan image 2 video model). The 1.3B adds also image 2 to video capability  to the 1.3B model.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Fun_InP_1.3B_bf16.safetensors"
+		]
+	}
+}
--- a/defaults/hunyuan.json
+++ b/defaults/hunyuan.json
@ -0,0 +1,12 @@
+{
+	"model":
+	{
+		"name": "Hunyuan Video text2video 720p 13B",
+		"architecture" : "hunyuan",
+		"description": "Probably the best text 2 video model available.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_720_bf16.safetensors.safetensors",
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_720_quanto_int8.safetensors"
+		]
+	}
+}
--- a/defaults/hunyuan_avatar.json
+++ b/defaults/hunyuan_avatar.json
@ -0,0 +1,12 @@
+{
+	"model":
+	{
+		"name": "Hunyuan Video Avatar 720p 13B",
+		"architecture" : "hunyuan_avatar",
+		"description": "With the Hunyuan Video Avatar model you can animate a person based on the content of an audio input. Please note that the video generator works by processing 128 frames segment at a time (even if you ask less). The good news is that it will concatenate multiple segments for long video generation (max 3 segments recommended as the quality will get worse).",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_avatar_720_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_avatar_720_quanto_bf16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/hunyuan_custom.json
+++ b/defaults/hunyuan_custom.json
@ -0,0 +1,12 @@
+{
+	"model":
+	{
+		"name": "Hunyuan Video Custom 720p 13B",
+		"architecture" : "hunyuan_custom",
+		"description": "The Hunyuan Video Custom model is probably the best model to transfer people (only people for the moment) as it is quite good to keep their identity. However it is slow as to get good results, you need to generate 720p videos with 30 steps.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_custom_720_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_custom_720_quanto_bf16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/hunyuan_custom_audio.json
+++ b/defaults/hunyuan_custom_audio.json
@ -0,0 +1,12 @@
+{
+	"model":
+	{
+		"name": "Hunyuan Video Custom Audio 720p 13B",
+		"architecture" : "hunyuan_custom_audio",
+		"description": "The Hunyuan Video Custom Audio model can be used to generate scenes of a person speaking given a Reference Image and a Recorded Voice or Song. The reference image is not a start image and therefore one can represent the person in a different context.The video length can be anything up to 10s. It is also quite good to generate no sound Video based on a person.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_custom_audio_720_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_custom_audio_720_quanto_bf16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/hunyuan_custom_edit.json
+++ b/defaults/hunyuan_custom_edit.json
@ -0,0 +1,12 @@
+{
+	"model":
+	{
+		"name": "Hunyuan Video Custom Edit 720p 13B",
+		"architecture" : "hunyuan_custom_edit",
+		"description": "The Hunyuan Video Custom Edit model can be used to do Video inpainting on a person (add accessories or completely replace the person). You will need in any case to define a Video Mask which will indicate which area of the Video should be edited.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_custom_edit_720_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_custom_edit_720_quanto_bf16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/hunyuan_i2v.json
+++ b/defaults/hunyuan_i2v.json
@ -0,0 +1,12 @@
+{
+	"model":
+	{
+		"name": "Hunyuan Video image2video 720p 13B",
+		"architecture" : "hunyuan_i2v",
+		"description": "A good looking image 2 video model, but not so good in prompt adherence.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_i2v_720_bf16v2.safetensors",
+			"https://huggingface.co/DeepBeepMeep/HunyuanVideo/resolve/main/hunyuan_video_i2v_720_quanto_int8v2.safetensors"
+		]
+	}
+}
--- a/finetunes/hunyuan_t2v_accvideo.json
+++ b/finetunes/hunyuan_t2v_accvideo.json
--- a/finetunes/hunyuan_t2v_fast.json
+++ b/finetunes/hunyuan_t2v_fast.json
--- a/defaults/i2v.json
+++ b/defaults/i2v.json
@ -0,0 +1,13 @@
+{
+	"model":
+	{
+		"name": "Wan2.1 image2video 480p 14B",
+		"architecture" : "i2v",
+		"description": "The standard Wan Image 2 Video specialized to generate 480p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_image2video_480p_14B_mbf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_image2video_480p_14B_quanto_mbf16_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_image2video_480p_14B_quanto_mfp16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/i2v_720p.json
+++ b/defaults/i2v_720p.json
@ -0,0 +1,14 @@
+{
+	"model":
+	{
+		"name": "Wan2.1 image2video 720p 14B",
+		"architecture" : "i2v",
+		"description": "The standard Wan Image 2 Video specialized to generate 720p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well).",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_image2video_720p_14B_mbf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_image2video_720p_14B_quanto_mbf16_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_image2video_720p_14B_quanto_mfp16_int8.safetensors"
+		]
+	},
+    "resolution": "1280x720"
+}
--- a/defaults/i2v_fusionix.json
+++ b/defaults/i2v_fusionix.json
@ -0,0 +1,10 @@
+{
+	"model":
+	{
+		"name": "Wan2.1 image2video 480p FusioniX 14B",
+		"architecture" : "i2v",
+		"description": "A powerful merged image-to-video model based on the original WAN 2.1 I2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail.",
+		"URLs": "i2v",
+		"loras": ["https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/loras_accelerators/Wan2.1_I2V_14B_FusionX_LoRA.safetensors"]
+	}
+}
--- a/defaults/ltxv_13B.json
+++ b/defaults/ltxv_13B.json
@ -0,0 +1,14 @@
+{
+	"model":
+	{
+		"name": "LTX Video 0.9.7 13B",
+		"architecture" : "ltxv_13B",
+		"description": "LTX Video is a fast model that can be used to generate long videos (up to 260 frames).It is recommended to keep the number of steps to 30 or you will need to update the file 'ltxv_video/configs/ltxv-13b-0.9.7-dev.yaml'.The LTX Video model expects very long prompts, so don't hesitate to use the Prompt Enhancer.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/LTX_Video/resolve/main/ltxv_0.9.7_13B_dev_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/LTX_Video/resolve/main/ltxv_0.9.7_13B_dev_quanto_bf16_int8.safetensors"
+		],
+		"LTXV_config": "ltx_video/configs/ltxv-13b-0.9.7-dev.yaml"
+	},
+	"num_inference_steps": 30	
+}
--- a/defaults/ltxv_distilled.json
+++ b/defaults/ltxv_distilled.json
@ -0,0 +1,14 @@
+{
+	"model":
+	{
+		"name": "LTX Video 0.9.7 Distilled 13B",
+		"architecture" : "ltxv_13B",
+		"description": "LTX Video is a fast model that can be used to generate long videos (up to 260 frames).This distilled version  is a very fast version and retains a high level of quality. The LTX Video model expects very long prompts, so don't hesitate to use the Prompt Enhancer.",
+		"URLs": "ltxv_13B",
+		"loras": ["https://huggingface.co/DeepBeepMeep/LTX_Video/resolve/main/ltxv_0.9.7_13B_distilled_lora128_bf16.safetensors"],
+		"loras_multipliers": [ 1 ],		
+		"lock_inference_steps": true,
+		"LTXV_config": "ltx_video/configs/ltxv-13b-0.9.7-distilled.yaml"
+	},
+	"num_inference_steps": 6
+}
--- a/finetunes/moviigen.json
+++ b/finetunes/moviigen.json
--- a/finetunes/multitalk.json
+++ b/finetunes/multitalk.json
--- a/finetunes/multitalk_720p.json
+++ b/finetunes/multitalk_720p.json
--- a/defaults/phantom_1.3B.json
+++ b/defaults/phantom_1.3B.json
@ -0,0 +1,11 @@
+{
+	"model":
+	{
+		"name": "Phantom 1.3B",
+		"architecture" : "phantom_1.3B",
+		"description": "The Phantom model is specialized in transferring people or objects of your choice into a generated Video. It produces very nice results when used at 720p.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2_1_phantom_1.3B_mbf16.safetensors"
+		]
+	}
+}
--- a/defaults/phantom_14B.json
+++ b/defaults/phantom_14B.json
@ -0,0 +1,13 @@
+{
+	"model":
+	{
+		"name": "Phantom 14B",
+		"architecture" : "phantom_14B",
+		"description": "The Phantom model is specialized in transferring people or objects of your choice into a generated Video. It produces very nice results when used at 720p.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_phantom_14B_mbf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_phantom_14B_quanto_mbf16_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_phantom_14B_quanto_mfp16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/recam_1.3B.json
+++ b/defaults/recam_1.3B.json
@ -0,0 +1,11 @@
+{
+	"model":
+	{
+		"name": "ReCamMaster 1.3B",
+		"architecture" : "recam_1.3B",
+		"description": "The Recam Master in theory should allow you to replay a video by applying a different camera movement. The model supports only video that are at least 81 frames long (any frame beyond will be ignored)",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_recammaster_1.3B_bf16.safetensors"
+		]
+	}
+}
--- a/defaults/sky_df_1.3B.json
+++ b/defaults/sky_df_1.3B.json
@ -0,0 +1,11 @@
+{
+	"model":
+	{
+		"name": "SkyReels2 Diffusion Forcing 1.3B",
+		"architecture" : "sky_df_1.3B",
+		"description": "The SkyReels 2 Diffusion Forcing model has been designed to generate very long videos that exceeds the usual 5s limit. You can also use this model to extend any existing video.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_1.3B_mbf16.safetensors"
+		]
+	}
+}
--- a/defaults/sky_df_14B.json
+++ b/defaults/sky_df_14B.json
@ -0,0 +1,13 @@
+{
+	"model":
+	{
+		"name": "SkyReels2 Diffusion Forcing 540p 14B",
+		"architecture" : "sky_df_14B",
+		"description": "The SkyReels 2 Diffusion Forcing model has been designed to generate very long videos that exceeds the usual 5s limit. You can also use this model to extend any existing video.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_14B_bf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_14B_quanto_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_14B_quanto_fp16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/sky_df_720p_14B.json
+++ b/defaults/sky_df_720p_14B.json
@ -0,0 +1,14 @@
+{
+	"model":
+	{
+		"name": "SkyReels2 Diffusion Forcing 720p 14B",
+		"architecture" : "sky_df_14B",
+		"description": "The SkyReels 2 Diffusion Forcing model has been designed to generate very long videos that exceeds the usual 5s limit. You can also use this model to extend any existing video.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_720p_14B_mbf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_720p_14B_quanto_mbf16_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/sky_reels2_diffusion_forcing_720p_14B_quanto_mfp16_int8.safetensors"
+		]
+	},
+    "resolution": "1280x720"	
+}
--- a/defaults/t2i.json
+++ b/defaults/t2i.json
@ -0,0 +1,13 @@
+{
+    "model": {
+        "name": "Wan2.1 text2image 14B",
+        "architecture": "t2v",
+        "description": "The original Wan Text 2 Video model configured to generate an image instead of a video.",
+		"image_outputs": true,
+        "URLs": "t2v"
+    },
+    "video_length": 1,
+    "resolution": "1280x720"	
+}
+
+	
--- a/defaults/t2v.json
+++ b/defaults/t2v.json
@ -0,0 +1,13 @@
+{
+	"model":
+	{
+		"name": "Wan2.1 text2video 14B",
+		"architecture" : "t2v",
+		"description": "The original Wan Text 2 Video model. Most other models have been built on top of it",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_text2video_14B_mbf16.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_text2video_14B_quanto_mbf16_int8.safetensors",
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_text2video_14B_quanto_mfp16_int8.safetensors"
+		]
+	}
+}
--- a/defaults/t2v_1.3B.json
+++ b/defaults/t2v_1.3B.json
@ -0,0 +1,11 @@
+{
+	"model":
+	{
+		"name": "Wan2.1 text2video 1.3B",
+		"architecture" : "t2v_1.3B",
+		"description": "The light version of the original Wan Text 2 Video model. Most other models have been built on top of it",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_text2video_1.3B_bf16.safetensors"
+		]
+	}
+}
--- a/finetunes/t2v_fusionix.json
+++ b/finetunes/t2v_fusionix.json
--- a/finetunes/t2v_sf.json
+++ b/finetunes/t2v_sf.json
--- a/defaults/vace_1.3B.json
+++ b/defaults/vace_1.3B.json
@ -0,0 +1,11 @@
+{
+	"model":
+	{
+		"name": "Vace ControlNet 1.3B",
+		"architecture" : "vace_1.3B",
+		"description": "The Vace ControlNet model is a powerful model that allows you to control the content of the generated video based of additional custom data : pose or depth video, images or objects you want to see in the video.",
+		"URLs": [
+			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/wan2.1_Vace_1.3B_mbf16.safetensors"
+		]
+	}
+}
--- a/finetunes/vace_14B.json
+++ b/finetunes/vace_14B.json
--- a/finetunes/vace_14B_fusionix.json
+++ b/finetunes/vace_14B_fusionix.json
@ -15,7 +15,7 @@
    "seed": -1,
    "num_inference_steps": 10,
    "guidance_scale": 1,
-    "flow_shift": 5,
+    "flow_shift": 2,
    "embedded_guidance_scale": 6,
    "repeat_generation": 1,
    "multi_images_gen_type": 0,
--- a/defaults/vace_14B_fusionix_t2i.json
+++ b/defaults/vace_14B_fusionix_t2i.json
@ -0,0 +1,16 @@
+{
+    "model": {
+        "name": "Vace FusioniX image2image 14B",
+        "architecture": "vace_14B",
+        "modules": [
+            "vace_14B"
+        ],
+		"image_outputs": true,
+        "description": "Vace control model enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail.",
+        "URLs": "t2v_fusionix"
+    },
+    "resolution": "1280x720",
+	"guidance_scale": 1,
+    "num_inference_steps": 10,	
+    "video_length": 1
+}
--- a/finetunes/vace_14B_sf.json
+++ b/finetunes/vace_14B_sf.json
--- a/finetunes/vace_multitalk_14B.json
+++ b/finetunes/vace_multitalk_14B.json
--- a/docs/FINETUNES.md
+++ b/docs/FINETUNES.md
@ -2,22 +2,30 @@

 A Finetuned model is model that shares the same architecture of one specific model but has derived weights from this model. Some finetuned models have been created by combining multiple finetuned models.

-As there are potentially an infinite number of finetunes, specific finetuned models are not known by default by WanGP, however you can create a finetuned model definition that will tell WanGP about the existence of this finetuned model and WanGP will do as usual all the work for you: autodownload the model and build the user interface.
+As there are potentially an infinite number of finetunes, specific finetuned models are not known by default by WanGP. However you can create a finetuned model definition that will tell WanGP about the existence of this finetuned model and WanGP will do as usual all the work for you: autodownload the model and build the user interface.
+
+WanGP finetune system can be also used to tweak default models : for instance you can add on top of an existing model some loras that will be always applied transparently.

 Finetune models definitions are light json files that can be easily shared. You can find some of them on the WanGP *discord* server https://discord.gg/g7efUW9jGV

+All the finetunes definitions files should be stored in the *finetunes/* subfolder.
+
 Finetuned models have been tested so far with Wan2.1 text2video, Wan2.1 image2video,  Hunyuan Video text2video. There isn't currently any support for LTX Video finetunes.

-## Create a new Finetune Model Definition
-All the finetune models definitions are json files stored in the **finetunes** sub folder. All the corresponding finetune model weights will be stored in the *ckpts* subfolder and will sit next to the base models.

-WanGP comes with a few prebuilt finetune models that you can use as starting points and to get an idea of the structure of the definition file.
+
+## Create a new Finetune Model Definition
+All the finetune models definitions are json files stored in the **finetunes/** sub folder. All the corresponding finetune model weights when they are downloaded will be stored in the *ckpts/* subfolder and will sit next to the base models.
+
+All the models used by WanGP are also described using the finetunes json format and can be found in the **defaults/** subfolder. Please don’t modify any file in the **defaults/** folder.
+
+However you can use these files as starting points for new definition files and to get an idea of the structure of a definition file. If you want to change how a base model is handled (title, default settings, path to model weights, …) you may override any property of the default finetunes definition file by creating a new file in the finetunes folder with the same name. Everything will happen as if the two models will be merged property by property with a higher priority given to the finetunes model definition.

 A definition is built from a *settings file* that can contains all the default parameters for a video generation. On top of this file a subtree named **model** contains all the information regarding the finetune (URLs to download model, corresponding base model id, ...).

 You can obtain a settings file in several ways:
 - In the subfolder **settings**, get the json file that corresponds to the base model of your finetune (see the next section for the list of ids of base models)
- From the user interface, go to the base model and click **export settings**
+- From the user interface, select the base model for which you want to create a finetune and click **export settings**

 Here are steps:
 1) Create a *settings file*
@ -26,39 +34,54 @@ Here are steps:
 4) Restart WanGP

 ## Architecture Models Ids
-A finetune is derived from a base model and will inherit all the user interface and corresponding model capabilities, here are Architecture Ids:
- *t2v*: Wan 2.1 Video text 2 
- *i2v*: Wan 2.1 Video image 2 480p
- *i2v_720p*: Wan 2.1 Video image 2 720p
+A finetune is derived from a base model and will inherit all the user interface and corresponding model capabilities, here are some Architecture Ids:
+- *t2v*: Wan 2.1 Video text 2 video
+- *i2v*: Wan 2.1 Video image 2 video 480p and 720p
 - *vace_14B*: Wan 2.1 Vace 14B
 - *hunyuan*: Hunyuan Video text 2 video
 - *hunyuan_i2v*: Hunyuan Video image 2 video

+Any file name in the defaults subfolder (without the json extension) corresponds to an architecture id.
+
+Please note that weights of some architectures correspond to a combination of weight of a one architecture which are completed by the weights of one more or modules.
+
+A module is a set a weights that are insufficient to be model by itself but that can be added to an existing model to extend its capabilities.
+
+For instance if one adds a module *vace_14B* on top of a model with architecture *t2v* one gets get a model with the *vace_14B* architecture. Here *vace_14B* stands for both an architecture name and a module name. The module system allows you to reuse shared weights between models.
+
+
 ## The Model Subtree
 - *name* : name of the finetune used to select
 - *architecture* : architecture Id of the base model of the finetune (see previous section)
 - *description*: description of the finetune that will appear at the top
 - *URLs*: URLs of all the finetune versions (quantized / non quantized). WanGP will pick the version that is the closest to the user preferences. You will need to follow a naming convention to help WanGP identify the content of each version (see next section). Right now WanGP supports only 8 bits quantized model that have been quantized using **quanto**. WanGP offers a command switch to build easily such a quantized model (see below). *URLs* can contain also paths to local file to allow testing.
- *modules*: this a list of modules to be combined with the models referenced by the URLs. A module is a model extension that is merged with a model to expand its capabilities. So far the only module supported is Vace 14B  (its id is *vace_14B*). For instance the full Vace model is the fusion of a Wan text 2 video and the Vace module.
+- *modules*: this a list of modules to be combined with the models referenced by the URLs. A module is a model extension that is merged with a model to expand its capabilities. Supported models so far are : *vace_14B* and *multitalk*. For instance the full Vace model is the fusion of a Wan text 2 video and the Vace module.
 - *preload_URLs* : URLs of files to download no matter what (used to load quantization maps for instance)
+-*loras* : URLs of Loras that will applied before any other Lora specified by the user. These loras will be quite often Loras accelerator. For instance if you specified here the FusioniX Lora you will be able to reduce the number of generation steps to -*loras_multipliers* : a list of float numbers that defines the weight of each Lora mentioned above.
 - *auto_quantize*: if set to True and no quantized model URL is provided, WanGP will perform on the fly quantization if the user expects a quantized model
+-*visible* : by default assumed to be true. If set to false the model will no longer be visible. This can be useful if you create a finetune to override a default model and hide it.
+-*image_outputs* : turn any model that generates a video into a model that generates images. In fact it will adapt the user interface for image generation and ask the model to generate a video with a single frame.
+
+In order to favor reusability the properties of *URLs*, *modules*, *loras* and  *preload_URLs* can contain instead of a list of URLs a single text which corresponds to the id of a finetune or default model to reuse.
+
+For example let’s say you have defined a *t2v_fusionix.json* file which contains the URLs to download the finetune. In the *vace_fusionix.json* you can write « URLs » : « fusionix » to reuse automatically the URLS already defined in the correspond file.

 Example of **model** subtree
 ```
-	"model":
-	{
-		"name": "Wan text2video FusioniX 14B",
-		"architecture" : "t2v",
-		"description": "A powerful merged text-to-video model based on the original WAN 2.1 T2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail. multiple open-source models and LoRAs to boost temporal quality, expressiveness, and motion realism.",
-		"URLs": [
-			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_fp16.safetensors",
-			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_fp16_int8.safetensors",
-			"https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_bf16_int8.safetensors"
-		],
+        "model":
+        {
+                "name": "Wan text2video FusioniX 14B",
+                "architecture" : "t2v",
+                "description": "A powerful merged text-to-video model based on the original WAN 2.1 T2V model, enhanced using multiple open-source components and LoRAs to boost motion realism, temporal consistency, and expressive detail. multiple open-source models and LoRAs to boost temporal quality, expressiveness, and motion realism.",
+                "URLs": [
+                        "https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_fp16.safetensors",
+                        "https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_fp16_int8.safetensors",
+                        "https://huggingface.co/DeepBeepMeep/Wan2.1/resolve/main/Wan14BT2VFusioniX_quanto_bf16_int8.safetensors"
+                ],
        "preload_URLs": [
        ],
-		"auto_quantize": true
-	},
+                "auto_quantize": true
+        },
 ```

 ## Finetune Model Naming Convention
--- a/docs/LORAS.md
+++ b/docs/LORAS.md
@ -6,18 +6,19 @@ Loras (Low-Rank Adaptations) allow you to customize video generation models by a

 Loras are organized in different folders based on the model they're designed for:

-### Text-to-Video Models
+### Wan Text-to-Video Models
 - `loras/` - General t2v loras
 - `loras/1.3B/` - Loras specifically for 1.3B models
 - `loras/14B/` - Loras specifically for 14B models

-### Image-to-Video Models
+### Wan Image-to-Video Models
 - `loras_i2v/` - Image-to-video loras

 ### Other Models
 - `loras_hunyuan/` - Hunyuan Video t2v loras
 - `loras_hunyuan_i2v/` - Hunyuan Video i2v loras
 - `loras_ltxv/` - LTX Video loras
+- `loras_flux/` - Flux loras

 ## Custom Lora Directory

@ -64,7 +65,7 @@ For dynamic effects over generation steps, use comma-separated values:

 ## Lora Presets

-Presets are combinations of loras with predefined multipliers and prompts.
+Lora Presets are combinations of loras with predefined multipliers and prompts.

 ### Creating Presets
 1. Configure your loras and multipliers
@ -95,16 +96,36 @@ WanGP supports multiple lora formats:
 - **Replicate** format
 - **Standard PyTorch** (.pt, .pth)

-## Safe-Forcing lightx2v Lora (Video Generation Accelerator)

-Safeforcing Lora has been created by Kijai from the Safe-Forcing lightx2v distilled Wan model and can generate videos with only 2 steps and offers also a 2x speed improvement since it doesnt require classifier free guidance. It works on both t2v and i2v models
+## Loras Accelerators
+Most Loras are used to apply a specific style or to alter the content of the output of the generated video.
+However some Loras have been designed to tranform a model into a distilled model which requires fewer steps to generate a video.
+
+You will find most *Loras Accelerators* here:
+https://huggingface.co/DeepBeepMeep/Wan2.1/tree/main/loras_accelerators

 ### Setup Instructions
-1. Download the Lora:
-   ```
-   https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors
-   ```
-2. Place in your `loras/` directory
+1. Download the Lora
+2. Place it in your `loras/` directory if it is a t2v lora or in the `loras_i2v/` directory if it isa i2v lora
+
+## FusioniX (or FusionX) Lora 
+If you need just one Lora accelerator use this one. It is a combination of multiple Loras acelerators (including Causvid below) and style loras. It will not only accelerate the video generation but it will also improve the quality. There are two versions of this lora whether you use it for t2v or i2v
+
+### Usage
+1. Select a Wan t2v model (e.g., Wan 2.1 text2video 13B or Vace 13B)
+2. Enable Advanced Mode
+3. In Advanced Generation Tab:
+   - Set Guidance Scale = 1
+   - Set Shift Scale = 2
+4. In Advanced Lora Tab:
+   - Select CausVid Lora
+   - Set multiplier to 1
+5. Set generation steps from 8-10
+6. Generate!
+
+## Safe-Forcing lightx2v Lora (Video Generation Accelerator)
+Safeforcing Lora has been created by Kijai from the Safe-Forcing lightx2v distilled Wan model and can generate videos with only 2 steps and offers also a 2x speed improvement since it doesnt require classifier free guidance. It works on both t2v and i2v models
+You will find it under the name of *Wan21_T2V_14B_lightx2v_cfg_step_distill_lora_rank32.safetensors*
 
 ### Usage
 1. Select a Wan t2v or i2v model (e.g., Wan 2.1 text2video 13B or Vace 13B)
@ -118,17 +139,10 @@ Safeforcing Lora has been created by Kijai from the Safe-Forcing lightx2v distil
 5. Set generation steps to 2-8
 6. Generate!

+
 ## CausVid Lora (Video Generation Accelerator)
-
 CausVid is a distilled Wan model that generates videos in 4-12 steps with 2x speed improvement.

-### Setup Instructions
-1. Download the CausVid Lora:
-   ```
-   https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors
-   ```
-2. Place in your `loras/` directory
-
 ### Usage
 1. Select a Wan t2v model (e.g., Wan 2.1 text2video 13B or Vace 13B)
 2. Enable Advanced Mode
@ -149,25 +163,10 @@ CausVid is a distilled Wan model that generates videos in 4-12 steps with 2x spe
 *Note: Lower steps = lower quality (especially motion)*


-
 ## AccVid Lora (Video Generation Accelerator)

 AccVid is a distilled Wan model that generates videos with a 2x speed improvement since classifier free guidance is no longer needed (that is cfg = 1).

-### Setup Instructions
-1. Download the AccVid Lora:
-
- for t2v models:
-   ```
-   https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_AccVid_T2V_14B_lora_rank32_fp16.safetensors
-   ```
-
- for i2v models:
-   ```
-   https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_AccVid_I2V_480P_14B_lora_rank32_fp16.safetensors
-   ```
-
-2. Place in your `loras/` directory or `loras_i2v/` directory

 ### Usage
 1. Select a Wan t2v model (e.g., Wan 2.1 text2video 13B or Vace 13B) or Wan i2v model
@ -268,6 +267,7 @@ In the video, a man is presented. The man is in a city and looks at his watch.
 --lora-dir-hunyuan path           # Path to Hunyuan t2v loras
 --lora-dir-hunyuan-i2v path       # Path to Hunyuan i2v loras
 --lora-dir-ltxv path              # Path to LTX Video loras
+--lora-dir-flux path              # Path to Flux loras
 --lora-preset preset              # Load preset on startup
 --check-loras                     # Filter incompatible loras
 ``` 
--- a/docs/MODELS.md
+++ b/docs/MODELS.md
@ -2,6 +2,8 @@

 WanGP supports multiple video generation models, each optimized for different use cases and hardware configurations. 

+Most models can combined with Loras Accelerators (check the Lora guide) to accelerate the generation of a video x2 or x3 with little quality loss
+

 ## Wan 2.1 Text2Video Models
 Please note that that the term *Text2Video* refers to the underlying Wan architecture but as it has been greatly improved overtime many derived Text2Video models can now  generate videos using images.
@ -65,6 +67,12 @@ Please note that that the term *Text2Video* refers to the underlying Wan archite

 ## Wan 2.1 Specialized Models

+#### Multitalk
+- **Type**: Multi Talking head animation
+- **Input**: Voice track + image
+- **Works on**: People
+- **Use case**: Lip-sync and voice-driven animation for up to two people
+
 #### FantasySpeaking
 - **Type**: Talking head animation
 - **Input**: Voice track + image
@ -82,7 +90,7 @@ Please note that that the term *Text2Video* refers to the underlying Wan archite
 - **Requirements**: 81+ frame input videos, 15+ denoising steps
 - **Use case**: View same scene from different angles

-#### Sky Reels v2
+#### Sky Reels v2 Diffusion
 - **Type**: Diffusion Forcing model
 - **Specialty**: "Infinite length" videos
 - **Features**: High quality continuous generation
@ -107,22 +115,6 @@ Please note that that the term *Text2Video* refers to the underlying Wan archite

 <BR>

-## Wan Special Loras
-### Safe-Forcing lightx2v Lora
- **Type**: Distilled model (Lora implementation)
- **Speed**: 4-8 steps generation, 2x faster (no classifier free guidance)
- **Compatible**: Works with t2v and i2v Wan 14B models
- **Setup**: Requires Safe-Forcing lightx2v Lora (see [LORAS.md](LORAS.md))
-
-
-### Causvid Lora
- **Type**: Distilled model (Lora implementation)
- **Speed**: 4-12 steps generation, 2x faster (no classifier free guidance)
- **Compatible**: Works with Wan 14B models
- **Setup**: Requires CausVid Lora (see [LORAS.md](LORAS.md))
-
-
-<BR>

 ## Hunyuan Video Models

--- a/finetunes/put
+++ b/finetunes/put
--- a/flux/init.py
+++ b/flux/init.py
@ -0,0 +1,13 @@
+try:
+    from ._version import (
+        version as __version__,  # type: ignore
+        version_tuple,
+    )
+except ImportError:
+    __version__ = "unknown (no version information available)"
+    version_tuple = (0, 0, "unknown", "noinfo")
+
+from pathlib import Path
+
+PACKAGE = __package__.replace("_", "-")
+PACKAGE_ROOT = Path(__file__).parent
--- a/flux/main.py
+++ b/flux/main.py
@ -0,0 +1,18 @@
+from fire import Fire
+
+from .cli import main as cli_main
+from .cli_control import main as control_main
+from .cli_fill import main as fill_main
+from .cli_kontext import main as kontext_main
+from .cli_redux import main as redux_main
+
+if __name__ == "__main__":
+    Fire(
+        {
+            "t2i": cli_main,
+            "control": control_main,
+            "fill": fill_main,
+            "kontext": kontext_main,
+            "redux": redux_main,
+        }
+    )
--- a/flux/_version.py
+++ b/flux/_version.py
@ -0,0 +1,21 @@
+# file generated by setuptools-scm
+# don't change, don't track in version control
+
+__all__ = ["__version__", "__version_tuple__", "version", "version_tuple"]
+
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple
+    from typing import Union
+
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '0.0.post58+g1371b2b'
+__version_tuple__ = version_tuple = (0, 0, 'post58', 'g1371b2b')
--- a/flux/flux_main.py
+++ b/flux/flux_main.py
@ -0,0 +1,109 @@
+import os
+import re
+import time
+from dataclasses import dataclass
+from glob import iglob
+from mmgp import offload as offload
+import torch
+from wan.utils.utils import calculate_new_dimensions
+from flux.sampling import denoise, get_schedule, prepare_kontext, unpack
+from flux.modules.layers import get_linear_split_map
+from flux.util import (
+    aspect_ratio_to_height_width,
+    load_ae,
+    load_clip,
+    load_flow_model,
+    load_t5,
+    save_image,
+)
+
+class model_factory:
+    def __init__(
+        self,
+        checkpoint_dir,
+        model_filename = None,
+        model_type = None, 
+        base_model_type = None,
+        text_encoder_filename = None,
+        quantizeTransformer = False,
+        save_quantized = False,
+        dtype = torch.bfloat16,
+        VAE_dtype = torch.float32,
+        mixed_precision_transformer = False
+    ):
+        self.device = torch.device(f"cuda")
+        self.VAE_dtype = VAE_dtype
+        self.dtype = dtype
+        torch_device = "cpu"
+
+        self.t5 = load_t5(torch_device, text_encoder_filename, max_length=512)
+        self.clip = load_clip(torch_device)
+        self.name= "flux-dev-kontext"
+        self.model = load_flow_model(self.name, model_filename[0], torch_device)
+
+        self.vae = load_ae(self.name, device=torch_device)
+
+        # offload.change_dtype(self.model, dtype, True)
+        if save_quantized:
+            from wgp import save_quantized_model
+            save_quantized_model(self.model, model_type, model_filename[0], dtype, None)
+
+        split_linear_modules_map = get_linear_split_map()
+        self.model.split_linear_modules_map = split_linear_modules_map
+        offload.split_linear_modules(self.model, split_linear_modules_map )
+
+    
+    def generate(
+            self,
+            seed: int | None = None,
+            input_prompt: str = "replace the logo with the text 'Black Forest Labs'",
+            sampling_steps: int = 20,
+            input_ref_images = None,
+            width= 832,
+            height=480,
+            guide_scale: float = 2.5,
+            fit_into_canvas = None,
+            callback = None,
+            loras_slists = None,
+            batch_size = 1,
+            **bbargs
+    ):
+            
+            if self._interrupt:
+                return None
+
+            device="cuda"
+            if input_ref_images != None and len(input_ref_images) > 0: 
+                image_ref = input_ref_images[0]
+                w, h = image_ref.size
+                height, width = calculate_new_dimensions(height, width, h, w, fit_into_canvas)
+
+            inp, height, width = prepare_kontext(
+                t5=self.t5,
+                clip=self.clip,
+                prompt=input_prompt,
+                ae=self.vae,
+                img_cond=image_ref,
+                target_width=width,
+                target_height=height,
+                bs=batch_size,
+                seed=seed,
+                device=device,
+            )
+
+            inp.pop("img_cond_orig")
+            timesteps = get_schedule(sampling_steps, inp["img"].shape[1], shift=(self.name != "flux-schnell"))
+            def unpack_latent(x):
+                return unpack(x.float(), height, width) 
+            # denoise initial noise
+            x = denoise(self.model, **inp, timesteps=timesteps, guidance=guide_scale, callback=callback, pipeline=self, loras_slists= loras_slists, unpack_latent = unpack_latent)
+            if x==None: return None
+            # decode latents to pixel space
+            x = unpack_latent(x)
+            with torch.autocast(device_type=device, dtype=torch.bfloat16):
+                x = self.vae.decode(x)
+
+            x = x.clamp(-1, 1)
+            x = x.transpose(0, 1)
+            return x
+
--- a/flux/math.py
+++ b/flux/math.py
@ -0,0 +1,54 @@
+import torch
+from einops import rearrange
+from torch import Tensor
+from wan.modules.attention import pay_attention
+
+
+def attention(qkv_list, pe: Tensor) -> Tensor:
+    q, k, v = qkv_list
+    qkv_list.clear()
+    q_list = [q] 
+    q = None
+    q = apply_rope_(q_list, pe)
+    k_list = [k] 
+    k = None
+    k = apply_rope_(k_list, pe)
+    qkv_list = [q.transpose(1,2), k.transpose(1,2) ,v.transpose(1,2)]
+    del q,k, v
+    x = pay_attention(qkv_list).transpose(1,2)
+    # x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = rearrange(x, "B H L D -> B L (H D)")
+
+    return x
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+    assert dim % 2 == 0
+    scale = torch.arange(0, dim, 2, dtype=pos.dtype, device=pos.device) / dim
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos, omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.float()
+
+
+def apply_rope_(q_list, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+    xq= q_list[0]
+    xqshape = xq.shape
+    xqdtype= xq.dtype
+    q_list.clear()
+    xq = xq.float().reshape(*xqshape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq[..., 0]
+    xq = freqs_cis[..., 1] * xq[..., 1]
+
+    xq_out.add_(xq)
+    # xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+
+    return xq_out.reshape(*xqshape).to(xqdtype)
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
--- a/flux/model.py
+++ b/flux/model.py
@ -0,0 +1,168 @@
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from flux.modules.layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+from flux.modules.lora import LinearLora, replace_linear_with_lora
+
+
+@dataclass
+class FluxParams:
+    in_channels: int
+    out_channels: int
+    vec_in_dim: int
+    context_in_dim: int
+    hidden_size: int
+    mlp_ratio: float
+    num_heads: int
+    depth: int
+    depth_single_blocks: int
+    axes_dim: list[int]
+    theta: int
+    qkv_bias: bool
+    guidance_embed: bool
+
+
+class Flux(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, params: FluxParams):
+        super().__init__()
+
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = params.out_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
+        )
+        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
+
+    def preprocess_loras(self, model_type, sd):
+        new_sd = {}
+        if len(sd) == 0: return sd
+
+        first_key= next(iter(sd))
+        if first_key.startswith("transformer."):
+            src_list = [".attn.to_q.", ".attn.to_k.", ".attn.to_v."]
+            tgt_list = [".linear1_attn_q.", ".linear1_attn_k.", ".linear1_attn_v."]
+            for k,v in sd.items():
+                k = k.replace("transformer.single_transformer_blocks", "diffusion_model.single_blocks")
+                k = k.replace("transformer.double_transformer_blocks", "diffusion_model.double_blocks")
+                for src, tgt in zip(src_list, tgt_list):
+                    k = k.replace(src, tgt)
+
+                new_sd[k] = v
+            
+        return new_sd    
+
+    def forward(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor | None = None,
+        callback= None,
+        pipeline =None,
+
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        vec = self.time_in(timestep_embedding(timesteps, 256))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec +=  self.guidance_in(timestep_embedding(guidance, 256))
+        vec +=  self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        for block in self.double_blocks:
+            if callback != None:
+                callback(-1, None, False, True)
+            if pipeline._interrupt:
+                return None
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
+        img = torch.cat((txt, img), 1)
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+        img = img[:, txt.shape[1] :, ...]
+
+        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+        return img
+
+
+class FluxLoraWrapper(Flux):
+    def __init__(
+        self,
+        lora_rank: int = 128,
+        lora_scale: float = 1.0,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+
+        self.lora_rank = lora_rank
+
+        replace_linear_with_lora(
+            self,
+            max_rank=lora_rank,
+            scale=lora_scale,
+        )
+
+    def set_lora_scale(self, scale: float) -> None:
+        for module in self.modules():
+            if isinstance(module, LinearLora):
+                module.set_scale(scale=scale)
--- a/flux/modules/autoencoder.py
+++ b/flux/modules/autoencoder.py
@ -0,0 +1,320 @@
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+
+@dataclass
+class AutoEncoderParams:
+    resolution: int
+    in_channels: int
+    ch: int
+    out_ch: int
+    ch_mult: list[int]
+    num_res_blocks: int
+    z_channels: int
+    scale_factor: float
+    shift_factor: float
+
+
+def swish(x: Tensor) -> Tensor:
+    return x * torch.sigmoid(x)
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+        self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1)
+
+    def attention(self, h_: Tensor) -> Tensor:
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b 1 (h w) c").contiguous()
+        k = rearrange(k, "b c h w -> b 1 (h w) c").contiguous()
+        v = rearrange(v, "b c h w -> b 1 (h w) c").contiguous()
+        h_ = nn.functional.scaled_dot_product_attention(q, k, v)
+
+        return rearrange(h_, "b 1 (h w) c -> b c h w", h=h, w=w, c=c, b=b)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return x + self.proj_out(self.attention(x))
+
+
+class ResnetBlock(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.norm1 = nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        self.norm2 = nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True)
+        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+        if self.in_channels != self.out_channels:
+            self.nin_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = swish(h)
+        h = self.conv1(h)
+
+        h = self.norm2(h)
+        h = swish(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            x = self.nin_shortcut(x)
+
+        return x + h
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        # no asymmetric padding in torch conv, must do it ourselves
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
+
+    def forward(self, x: Tensor):
+        pad = (0, 1, 0, 1)
+        x = nn.functional.pad(x, pad, mode="constant", value=0)
+        x = self.conv(x)
+        return x
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Tensor):
+        x = nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+        x = self.conv(x)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        resolution: int,
+        in_channels: int,
+        ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        # downsampling
+        self.conv_in = nn.Conv2d(in_channels, self.ch, kernel_size=3, stride=1, padding=1)
+
+        curr_res = resolution
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        self.down = nn.ModuleList()
+        block_in = self.ch
+        for i_level in range(self.num_resolutions):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level != self.num_resolutions - 1:
+                down.downsample = Downsample(block_in)
+                curr_res = curr_res // 2
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # end
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_in, 2 * z_channels, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, x: Tensor) -> Tensor:
+        # downsampling
+        hs = [self.conv_in(x)]
+        for i_level in range(self.num_resolutions):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](hs[-1])
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                hs.append(h)
+            if i_level != self.num_resolutions - 1:
+                hs.append(self.down[i_level].downsample(hs[-1]))
+
+        # middle
+        h = hs[-1]
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        # end
+        h = self.norm_out(h)
+        h = swish(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        ch: int,
+        out_ch: int,
+        ch_mult: list[int],
+        num_res_blocks: int,
+        in_channels: int,
+        resolution: int,
+        z_channels: int,
+    ):
+        super().__init__()
+        self.ch = ch
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.ffactor = 2 ** (self.num_resolutions - 1)
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        curr_res = resolution // 2 ** (self.num_resolutions - 1)
+        self.z_shape = (1, z_channels, curr_res, curr_res)
+
+        # z to block_in
+        self.conv_in = nn.Conv2d(z_channels, block_in, kernel_size=3, stride=1, padding=1)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+        self.mid.attn_1 = AttnBlock(block_in)
+        self.mid.block_2 = ResnetBlock(in_channels=block_in, out_channels=block_in)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level != 0:
+                up.upsample = Upsample(block_in)
+                curr_res = curr_res * 2
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.norm_out = nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True)
+        self.conv_out = nn.Conv2d(block_in, out_ch, kernel_size=3, stride=1, padding=1)
+
+    def forward(self, z: Tensor) -> Tensor:
+        # get dtype for proper tracing
+        upscale_dtype = next(self.up.parameters()).dtype
+
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+
+        # cast to proper dtype
+        h = h.to(upscale_dtype)
+        # upsampling
+        for i_level in reversed(range(self.num_resolutions)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+            if i_level != 0:
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = swish(h)
+        h = self.conv_out(h)
+        return h
+
+
+class DiagonalGaussian(nn.Module):
+    def __init__(self, sample: bool = True, chunk_dim: int = 1):
+        super().__init__()
+        self.sample = sample
+        self.chunk_dim = chunk_dim
+
+    def forward(self, z: Tensor) -> Tensor:
+        mean, logvar = torch.chunk(z, 2, dim=self.chunk_dim)
+        if self.sample:
+            std = torch.exp(0.5 * logvar)
+            return mean + std * torch.randn_like(mean)
+        else:
+            return mean
+
+
+class AutoEncoder(nn.Module):
+    def __init__(self, params: AutoEncoderParams, sample_z: bool = False):
+        super().__init__()
+        self.params = params
+        self.encoder = Encoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.decoder = Decoder(
+            resolution=params.resolution,
+            in_channels=params.in_channels,
+            ch=params.ch,
+            out_ch=params.out_ch,
+            ch_mult=params.ch_mult,
+            num_res_blocks=params.num_res_blocks,
+            z_channels=params.z_channels,
+        )
+        self.reg = DiagonalGaussian(sample=sample_z)
+
+        self.scale_factor = params.scale_factor
+        self.shift_factor = params.shift_factor
+
+    def get_VAE_tile_size(*args, **kwargs):
+        return []
+    def encode(self, x: Tensor) -> Tensor:
+        z = self.reg(self.encoder(x))
+        z = self.scale_factor * (z - self.shift_factor)
+        return z
+
+    def decode(self, z: Tensor) -> Tensor:
+        z = z / self.scale_factor + self.shift_factor
+        return self.decoder(z)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.decode(self.encode(x))
--- a/flux/modules/conditioner.py
+++ b/flux/modules/conditioner.py
@ -0,0 +1,38 @@
+from torch import Tensor, nn
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+import os
+
+class HFEmbedder(nn.Module):
+    def __init__(self, version: str, text_encoder_filename, max_length: int, is_clip = False, **hf_kwargs):
+        super().__init__()
+        self.is_clip = is_clip
+        self.max_length = max_length
+        self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
+
+        if is_clip:
+            self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
+            self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
+        else:
+            from mmgp import offload as offloadobj
+            self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(os.path.dirname(text_encoder_filename),  max_length=max_length)
+            self.hf_module: T5EncoderModel  = offloadobj.fast_load_transformers_model(text_encoder_filename) 
+
+        self.hf_module = self.hf_module.eval().requires_grad_(False)
+
+    def forward(self, text: list[str]) -> Tensor:
+        batch_encoding = self.tokenizer(
+            text,
+            truncation=True,
+            max_length=self.max_length,
+            return_length=False,
+            return_overflowing_tokens=False,
+            padding="max_length",
+            return_tensors="pt",
+        )
+
+        outputs = self.hf_module(
+            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
+            attention_mask=None,
+            output_hidden_states=False,
+        )
+        return outputs[self.output_key].bfloat16()
--- a/flux/modules/image_embedders.py
+++ b/flux/modules/image_embedders.py
@ -0,0 +1,99 @@
+import cv2
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from PIL import Image
+from safetensors.torch import load_file as load_sft
+from torch import nn
+from transformers import AutoModelForDepthEstimation, AutoProcessor, SiglipImageProcessor, SiglipVisionModel
+
+from flux.util import print_load_warning
+
+
+class DepthImageEncoder:
+    depth_model_name = "LiheYoung/depth-anything-large-hf"
+
+    def __init__(self, device):
+        self.device = device
+        self.depth_model = AutoModelForDepthEstimation.from_pretrained(self.depth_model_name).to(device)
+        self.processor = AutoProcessor.from_pretrained(self.depth_model_name)
+
+    def __call__(self, img: torch.Tensor) -> torch.Tensor:
+        hw = img.shape[-2:]
+
+        img = torch.clamp(img, -1.0, 1.0)
+        img_byte = ((img + 1.0) * 127.5).byte()
+
+        img = self.processor(img_byte, return_tensors="pt")["pixel_values"]
+        depth = self.depth_model(img.to(self.device)).predicted_depth
+        depth = repeat(depth, "b h w -> b 3 h w")
+        depth = torch.nn.functional.interpolate(depth, hw, mode="bicubic", antialias=True)
+
+        depth = depth / 127.5 - 1.0
+        return depth
+
+
+class CannyImageEncoder:
+    def __init__(
+        self,
+        device,
+        min_t: int = 50,
+        max_t: int = 200,
+    ):
+        self.device = device
+        self.min_t = min_t
+        self.max_t = max_t
+
+    def __call__(self, img: torch.Tensor) -> torch.Tensor:
+        assert img.shape[0] == 1, "Only batch size 1 is supported"
+
+        img = rearrange(img[0], "c h w -> h w c")
+        img = torch.clamp(img, -1.0, 1.0)
+        img_np = ((img + 1.0) * 127.5).numpy().astype(np.uint8)
+
+        # Apply Canny edge detection
+        canny = cv2.Canny(img_np, self.min_t, self.max_t)
+
+        # Convert back to torch tensor and reshape
+        canny = torch.from_numpy(canny).float() / 127.5 - 1.0
+        canny = rearrange(canny, "h w -> 1 1 h w")
+        canny = repeat(canny, "b 1 ... -> b 3 ...")
+        return canny.to(self.device)
+
+
+class ReduxImageEncoder(nn.Module):
+    siglip_model_name = "google/siglip-so400m-patch14-384"
+
+    def __init__(
+        self,
+        device,
+        redux_path: str,
+        redux_dim: int = 1152,
+        txt_in_features: int = 4096,
+        dtype=torch.bfloat16,
+    ) -> None:
+        super().__init__()
+
+        self.redux_dim = redux_dim
+        self.device = device if isinstance(device, torch.device) else torch.device(device)
+        self.dtype = dtype
+
+        with self.device:
+            self.redux_up = nn.Linear(redux_dim, txt_in_features * 3, dtype=dtype)
+            self.redux_down = nn.Linear(txt_in_features * 3, txt_in_features, dtype=dtype)
+
+            sd = load_sft(redux_path, device=str(device))
+            missing, unexpected = self.load_state_dict(sd, strict=False, assign=True)
+            print_load_warning(missing, unexpected)
+
+            self.siglip = SiglipVisionModel.from_pretrained(self.siglip_model_name).to(dtype=dtype)
+        self.normalize = SiglipImageProcessor.from_pretrained(self.siglip_model_name)
+
+    def __call__(self, x: Image.Image) -> torch.Tensor:
+        imgs = self.normalize.preprocess(images=[x], do_resize=True, return_tensors="pt", do_convert_rgb=True)
+
+        _encoded_x = self.siglip(**imgs.to(device=self.device, dtype=self.dtype)).last_hidden_state
+
+        projected_x = self.redux_down(nn.functional.silu(self.redux_up(_encoded_x)))
+
+        return projected_x
--- a/flux/modules/layers
+++ b/flux/modules/layers
@ -0,0 +1,327 @@
+import math
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+from flux.math import attention, rope
+
+def get_linear_split_map():
+    hidden_size = 3072
+        _modules_map =  {
+                                "qkv" : {"mapped_modules" : ["q", "k", "v"] , "split_sizes": [hidden_size, hidden_size, hidden_size]},
+                                "linear1" : {"mapped_modules" : ["linear1_attn_q", "linear1_attn_k", "linear1_attn_v", "linear1_mlp"] , "split_sizes":  [hidden_size, hidden_size, hidden_size, 7*hidden_size- 3*hidden_size]}
+                                }
+    return split_linear_modules_map
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+
+        return emb.unsqueeze(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+        t.device
+    )
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int):
+        super().__init__()
+        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+        self.silu = nn.SiLU()
+        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.scale = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * self.scale
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.query_norm = RMSNorm(dim)
+        self.key_norm = RMSNorm(dim)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.norm = QKNorm(head_dim)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+        qkv = self.qkv(x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+        x = attention(q, k, v, pe=pe)
+        x = self.proj(x)
+        return x
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True)
+        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True)
+        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated.mul_(1 + img_mod1.scale)
+        img_modulated.add_(img_mod1.shift)
+
+        shape = (*img_modulated.shape[:2], self.num_heads, int(img_modulated.shape[-1] / self.num_heads) )
+        img_q = self.img_attn.q(img_modulated).view(*shape).transpose(1,2)
+        img_k = self.img_attn.k(img_modulated).view(*shape).transpose(1,2) 
+        img_v = self.img_attn.v(img_modulated).view(*shape).transpose(1,2)
+        del img_modulated
+
+        # img_qkv = self.img_attn.qkv(img_modulated)
+        # img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated.mul_(1 + txt_mod1.scale)
+        txt_modulated.add_(txt_mod1.shift)
+        # txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+
+        shape = (*txt_modulated.shape[:2], self.num_heads, int(txt_modulated.shape[-1] / self.num_heads) )
+        txt_q = self.txt_attn.q(txt_modulated).view(*shape).transpose(1,2)
+        txt_k = self.txt_attn.k(txt_modulated).view(*shape).transpose(1,2) 
+        txt_v = self.txt_attn.v(txt_modulated).view(*shape).transpose(1,2)
+        del txt_modulated
+
+        # txt_qkv = self.txt_attn.qkv(txt_modulated)
+        # txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        q = torch.cat((txt_q, img_q), dim=2)
+        k = torch.cat((txt_k, img_k), dim=2)
+        v = torch.cat((txt_v, img_v), dim=2)
+
+        qkv_list = [q, k, v]
+        del q, k, v
+        attn = attention(qkv_list, pe=pe)
+
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img blocks
+        img.addcmul_(self.img_attn.proj(img_attn), img_mod1.gate)
+        img.addcmul_(self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift), img_mod2.gate)
+
+        # img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        # img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+        # calculate the txt blocks
+        txt.addcmul_(self.txt_attn.proj(txt_attn), txt_mod1.gate)
+        txt.addcmul_(self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift), txt_mod2.gate)
+        # txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        # txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float | None = None,
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        # proj and mlp_out
+        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.norm = QKNorm(head_dim)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+        mod, _ = self.modulation(vec)
+        x_mod = self.pre_norm(x)
+        x_mod.mul_(1 + mod.scale)
+        x_mod.add_(mod.shift)
+
+        ##### More spagheti VRAM optimizations done by DeepBeepMeep !
+        # I am sure you are a nice person and as you copy this code, you will give me proper credits:
+        # Please link to https://github.com/deepbeepmeep/Wan2GP and @deepbeepmeep on twitter  
+
+        # x_mod = (1 + mod.scale) * x + mod.shift
+
+        shape = (*x_mod.shape[:2], self.num_heads, int(x_mod.shape[-1] / self.num_heads) )
+        q = self.linear1_attn_q(x_mod).view(*shape).transpose(1,2)
+        k = self.linear1_attn_k(x_mod).view(*shape).transpose(1,2)
+        v = self.linear1_attn_v(x_mod).view(*shape).transpose(1,2)
+
+        # shape = (*txt_mod.shape[:2], self.heads_num, int(txt_mod.shape[-1] / self.heads_num) )
+        # txt_q = self.linear1_attn_q(txt_mod).view(*shape)
+        # txt_k = self.linear1_attn_k(txt_mod).view(*shape)
+        # txt_v = self.linear1_attn_v(txt_mod).view(*shape)
+
+        # qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        qkv_list = [q, k, v]
+        del q, k, v
+        attn = attention(qkv_list, pe=pe)
+        # compute activation in mlp stream, cat again and run second linear layer
+
+        x_mod_shape = x_mod.shape
+        x_mod = x_mod.view(-1, x_mod.shape[-1])
+        chunk_size = int(x_mod_shape[1]/6)
+        x_chunks = torch.split(x_mod, chunk_size)
+        attn = attn.view(-1, attn.shape[-1])
+        attn_chunks =torch.split(attn, chunk_size)
+        for x_chunk, attn_chunk in zip(x_chunks, attn_chunks):
+            mlp_chunk = self.linear1_mlp(x_chunk)
+            mlp_chunk = self.mlp_act(mlp_chunk)
+            attn_mlp_chunk = torch.cat((attn_chunk, mlp_chunk), -1)
+            del attn_chunk, mlp_chunk 
+            x_chunk[...] = self.linear2(attn_mlp_chunk)
+            del attn_mlp_chunk
+        x_mod = x_mod.view(x_mod_shape)
+        x.addcmul_(x_mod, mod.gate)
+        return x
+
+        # output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        # return x + mod.gate * output
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
--- a/flux/modules/layers.py
+++ b/flux/modules/layers.py
@ -0,0 +1,328 @@
+import math
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+from flux.math import attention, rope
+
+def get_linear_split_map():
+    hidden_size = 3072
+    split_linear_modules_map =  {
+                                "qkv" : {"mapped_modules" : ["q", "k", "v"] , "split_sizes": [hidden_size, hidden_size, hidden_size]},
+                                "linear1" : {"mapped_modules" : ["linear1_attn_q", "linear1_attn_k", "linear1_attn_v", "linear1_mlp"] , "split_sizes":  [hidden_size, hidden_size, hidden_size, 7*hidden_size- 3*hidden_size]}
+                                }
+    return split_linear_modules_map
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+
+        return emb.unsqueeze(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+        t.device
+    )
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int):
+        super().__init__()
+        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+        self.silu = nn.SiLU()
+        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.scale = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * self.scale
+
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.query_norm = RMSNorm(dim)
+        self.key_norm = RMSNorm(dim)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+        if k != None:
+            return self.key_norm(k).to(v)
+        else: 
+            return self.query_norm(q).to(v)
+        # q = self.query_norm(q)
+        # k = self.key_norm(k)
+        # return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.norm = QKNorm(head_dim)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+        raise Exception("not implemented")
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+def split_mlp(mlp, x, divide = 4):
+    x_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+    chunk_size = int(x_shape[1]/divide)
+    x_chunks = torch.split(x, chunk_size)
+    for i, x_chunk  in enumerate(x_chunks):
+        mlp_chunk = mlp[0](x_chunk)
+        mlp_chunk = mlp[1](mlp_chunk)
+        x_chunk[...] = mlp[2](mlp_chunk)
+    return x.reshape(x_shape)      
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True)
+        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True)
+        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated.mul_(1 + img_mod1.scale)
+        img_modulated.add_(img_mod1.shift)
+
+        shape = (*img_modulated.shape[:2], self.num_heads, int(img_modulated.shape[-1] / self.num_heads) )
+        img_q = self.img_attn.q(img_modulated).view(*shape).transpose(1,2)
+        img_k = self.img_attn.k(img_modulated).view(*shape).transpose(1,2) 
+        img_v = self.img_attn.v(img_modulated).view(*shape).transpose(1,2)
+        del img_modulated
+
+
+        img_q= self.img_attn.norm(img_q, None, img_v)
+        img_k = self.img_attn.norm(None, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated.mul_(1 + txt_mod1.scale)
+        txt_modulated.add_(txt_mod1.shift)
+
+        shape = (*txt_modulated.shape[:2], self.num_heads, int(txt_modulated.shape[-1] / self.num_heads) )
+        txt_q = self.txt_attn.q(txt_modulated).view(*shape).transpose(1,2)
+        txt_k = self.txt_attn.k(txt_modulated).view(*shape).transpose(1,2) 
+        txt_v = self.txt_attn.v(txt_modulated).view(*shape).transpose(1,2)
+        del txt_modulated
+
+
+        txt_q = self.txt_attn.norm(txt_q, None, txt_v)
+        txt_k = self.txt_attn.norm(None, txt_k, txt_v)
+
+        # run actual attention
+        q = torch.cat((txt_q, img_q), dim=2)
+        del txt_q, img_q
+        k = torch.cat((txt_k, img_k), dim=2)
+        del txt_k, img_k
+        v = torch.cat((txt_v, img_v), dim=2)
+        del txt_v, img_v
+
+        qkv_list = [q, k, v]
+        del q, k, v
+        attn = attention(qkv_list, pe=pe)
+
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img blocks
+        img.addcmul_(self.img_attn.proj(img_attn), img_mod1.gate)
+        mod_img = self.img_norm2(img)
+        mod_img.mul_(1 + img_mod2.scale)
+        mod_img.add_(img_mod2.shift)
+        mod_img = split_mlp(self.img_mlp, mod_img)
+        # mod_img = self.img_mlp(mod_img)
+        img.addcmul_( mod_img, img_mod2.gate)
+        mod_img = None
+
+        # calculate the txt blocks
+        txt.addcmul_(self.txt_attn.proj(txt_attn), txt_mod1.gate)
+        txt.addcmul_(self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift), txt_mod2.gate)
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float | None = None,
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        # proj and mlp_out
+        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.norm = QKNorm(head_dim)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+        mod, _ = self.modulation(vec)
+        x_mod = self.pre_norm(x)
+        x_mod.mul_(1 + mod.scale)
+        x_mod.add_(mod.shift)
+
+        ##### More spagheti VRAM optimizations done by DeepBeepMeep !
+        # I am sure you are a nice person and as you copy this code, you will give me proper credits:
+        # Please link to https://github.com/deepbeepmeep/Wan2GP and @deepbeepmeep on twitter  
+
+        # x_mod = (1 + mod.scale) * x + mod.shift
+
+        shape = (*x_mod.shape[:2], self.num_heads, int(x_mod.shape[-1] / self.num_heads) )
+        q = self.linear1_attn_q(x_mod).view(*shape).transpose(1,2)
+        k = self.linear1_attn_k(x_mod).view(*shape).transpose(1,2)
+        v = self.linear1_attn_v(x_mod).view(*shape).transpose(1,2)
+
+        q = self.norm(q, None, v)
+        k = self.norm(None, k, v)
+
+        # compute attention
+        qkv_list = [q, k, v]
+        del q, k, v
+        attn = attention(qkv_list, pe=pe)
+        # compute activation in mlp stream, cat again and run second linear layer
+
+        x_mod_shape = x_mod.shape
+        x_mod = x_mod.view(-1, x_mod.shape[-1])
+        chunk_size = int(x_mod_shape[1]/6)
+        x_chunks = torch.split(x_mod, chunk_size)
+        attn = attn.view(-1, attn.shape[-1])
+        attn_chunks =torch.split(attn, chunk_size)
+        for x_chunk, attn_chunk in zip(x_chunks, attn_chunks):
+            mlp_chunk = self.linear1_mlp(x_chunk)
+            mlp_chunk = self.mlp_act(mlp_chunk)
+            attn_mlp_chunk = torch.cat((attn_chunk, mlp_chunk), -1)
+            del attn_chunk, mlp_chunk 
+            x_chunk[...] = self.linear2(attn_mlp_chunk)
+            del attn_mlp_chunk
+        x_mod = x_mod.view(x_mod_shape)
+        x.addcmul_(x_mod, mod.gate)
+        return x
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
--- a/flux/modules/lora.py
+++ b/flux/modules/lora.py
@ -0,0 +1,94 @@
+import torch
+from torch import nn
+
+
+def replace_linear_with_lora(
+    module: nn.Module,
+    max_rank: int,
+    scale: float = 1.0,
+) -> None:
+    for name, child in module.named_children():
+        if isinstance(child, nn.Linear):
+            new_lora = LinearLora(
+                in_features=child.in_features,
+                out_features=child.out_features,
+                bias=child.bias,
+                rank=max_rank,
+                scale=scale,
+                dtype=child.weight.dtype,
+                device=child.weight.device,
+            )
+
+            new_lora.weight = child.weight
+            new_lora.bias = child.bias if child.bias is not None else None
+
+            setattr(module, name, new_lora)
+        else:
+            replace_linear_with_lora(
+                module=child,
+                max_rank=max_rank,
+                scale=scale,
+            )
+
+
+class LinearLora(nn.Linear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool,
+        rank: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        lora_bias: bool = True,
+        scale: float = 1.0,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias is not None,
+            device=device,
+            dtype=dtype,
+            *args,
+            **kwargs,
+        )
+
+        assert isinstance(scale, float), "scale must be a float"
+
+        self.scale = scale
+        self.rank = rank
+        self.lora_bias = lora_bias
+        self.dtype = dtype
+        self.device = device
+
+        if rank > (new_rank := min(self.out_features, self.in_features)):
+            self.rank = new_rank
+
+        self.lora_A = nn.Linear(
+            in_features=in_features,
+            out_features=self.rank,
+            bias=False,
+            dtype=dtype,
+            device=device,
+        )
+        self.lora_B = nn.Linear(
+            in_features=self.rank,
+            out_features=out_features,
+            bias=self.lora_bias,
+            dtype=dtype,
+            device=device,
+        )
+
+    def set_scale(self, scale: float) -> None:
+        assert isinstance(scale, float), "scalar value must be a float"
+        self.scale = scale
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        base_out = super().forward(input)
+
+        _lora_out_B = self.lora_B(self.lora_A(input))
+        lora_update = _lora_out_B * self.scale
+
+        return base_out + lora_update
--- a/flux/sampling.py
+++ b/flux/sampling.py
@ -0,0 +1,392 @@
+import math
+from typing import Callable
+
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from PIL import Image
+from torch import Tensor
+
+from .model import Flux
+from .modules.autoencoder import AutoEncoder
+from .modules.conditioner import HFEmbedder
+from .modules.image_embedders import CannyImageEncoder, DepthImageEncoder, ReduxImageEncoder
+from .util import PREFERED_KONTEXT_RESOLUTIONS
+from einops import rearrange, repeat
+
+
+def get_noise(
+    num_samples: int,
+    height: int,
+    width: int,
+    device: torch.device,
+    dtype: torch.dtype,
+    seed: int,
+):
+    return torch.randn(
+        num_samples,
+        16,
+        # allow for packing
+        2 * math.ceil(height / 16),
+        2 * math.ceil(width / 16),
+        dtype=dtype,
+        generator=torch.Generator(device=device).manual_seed(seed),
+    )
+
+
+def prepare(t5: HFEmbedder, clip: HFEmbedder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
+    bs, c, h, w = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    img_ids = torch.zeros(h // 2, w // 2, 3)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    txt = t5(prompt)
+    if txt.shape[0] == 1 and bs > 1:
+        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
+    txt_ids = torch.zeros(bs, txt.shape[1], 3)
+
+    vec = clip(prompt)
+    if vec.shape[0] == 1 and bs > 1:
+        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
+
+    return {
+        "img": img,
+        "img_ids": img_ids.to(img.device),
+        "txt": txt.to(img.device),
+        "txt_ids": txt_ids.to(img.device),
+        "vec": vec.to(img.device),
+    }
+
+
+def prepare_control(
+    t5: HFEmbedder,
+    clip: HFEmbedder,
+    img: Tensor,
+    prompt: str | list[str],
+    ae: AutoEncoder,
+    encoder: DepthImageEncoder | CannyImageEncoder,
+    img_cond_path: str,
+) -> dict[str, Tensor]:
+    # load and encode the conditioning image
+    bs, _, h, w = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img_cond = Image.open(img_cond_path).convert("RGB")
+
+    width = w * 8
+    height = h * 8
+    img_cond = img_cond.resize((width, height), Image.Resampling.LANCZOS)
+    img_cond = np.array(img_cond)
+    img_cond = torch.from_numpy(img_cond).float() / 127.5 - 1.0
+    img_cond = rearrange(img_cond, "h w c -> 1 c h w")
+
+    with torch.no_grad():
+        img_cond = encoder(img_cond)
+        img_cond = ae.encode(img_cond)
+
+    img_cond = img_cond.to(torch.bfloat16)
+    img_cond = rearrange(img_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img_cond.shape[0] == 1 and bs > 1:
+        img_cond = repeat(img_cond, "1 ... -> bs ...", bs=bs)
+
+    return_dict = prepare(t5, clip, img, prompt)
+    return_dict["img_cond"] = img_cond
+    return return_dict
+
+
+def prepare_fill(
+    t5: HFEmbedder,
+    clip: HFEmbedder,
+    img: Tensor,
+    prompt: str | list[str],
+    ae: AutoEncoder,
+    img_cond_path: str,
+    mask_path: str,
+) -> dict[str, Tensor]:
+    # load and encode the conditioning image and the mask
+    bs, _, _, _ = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img_cond = Image.open(img_cond_path).convert("RGB")
+    img_cond = np.array(img_cond)
+    img_cond = torch.from_numpy(img_cond).float() / 127.5 - 1.0
+    img_cond = rearrange(img_cond, "h w c -> 1 c h w")
+
+    mask = Image.open(mask_path).convert("L")
+    mask = np.array(mask)
+    mask = torch.from_numpy(mask).float() / 255.0
+    mask = rearrange(mask, "h w -> 1 1 h w")
+
+    with torch.no_grad():
+        img_cond = img_cond.to(img.device)
+        mask = mask.to(img.device)
+        img_cond = img_cond * (1 - mask)
+        img_cond = ae.encode(img_cond)
+        mask = mask[:, 0, :, :]
+        mask = mask.to(torch.bfloat16)
+        mask = rearrange(
+            mask,
+            "b (h ph) (w pw) -> b (ph pw) h w",
+            ph=8,
+            pw=8,
+        )
+        mask = rearrange(mask, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        if mask.shape[0] == 1 and bs > 1:
+            mask = repeat(mask, "1 ... -> bs ...", bs=bs)
+
+    img_cond = img_cond.to(torch.bfloat16)
+    img_cond = rearrange(img_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img_cond.shape[0] == 1 and bs > 1:
+        img_cond = repeat(img_cond, "1 ... -> bs ...", bs=bs)
+
+    img_cond = torch.cat((img_cond, mask), dim=-1)
+
+    return_dict = prepare(t5, clip, img, prompt)
+    return_dict["img_cond"] = img_cond.to(img.device)
+    return return_dict
+
+
+def prepare_redux(
+    t5: HFEmbedder,
+    clip: HFEmbedder,
+    img: Tensor,
+    prompt: str | list[str],
+    encoder: ReduxImageEncoder,
+    img_cond_path: str,
+) -> dict[str, Tensor]:
+    bs, _, h, w = img.shape
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    img_cond = Image.open(img_cond_path).convert("RGB")
+    with torch.no_grad():
+        img_cond = encoder(img_cond)
+
+    img_cond = img_cond.to(torch.bfloat16)
+    if img_cond.shape[0] == 1 and bs > 1:
+        img_cond = repeat(img_cond, "1 ... -> bs ...", bs=bs)
+
+    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img.shape[0] == 1 and bs > 1:
+        img = repeat(img, "1 ... -> bs ...", bs=bs)
+
+    img_ids = torch.zeros(h // 2, w // 2, 3)
+    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
+    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
+    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+    if isinstance(prompt, str):
+        prompt = [prompt]
+    txt = t5(prompt)
+    txt = torch.cat((txt, img_cond.to(txt)), dim=-2)
+    if txt.shape[0] == 1 and bs > 1:
+        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
+    txt_ids = torch.zeros(bs, txt.shape[1], 3)
+
+    vec = clip(prompt)
+    if vec.shape[0] == 1 and bs > 1:
+        vec = repeat(vec, "1 ... -> bs ...", bs=bs)
+
+    return {
+        "img": img,
+        "img_ids": img_ids.to(img.device),
+        "txt": txt.to(img.device),
+        "txt_ids": txt_ids.to(img.device),
+        "vec": vec.to(img.device),
+    }
+
+
+def prepare_kontext(
+    t5: HFEmbedder,
+    clip: HFEmbedder,
+    prompt: str | list[str],
+    ae: AutoEncoder,
+    img_cond: str,
+    seed: int,
+    device: torch.device,
+    target_width: int | None = None,
+    target_height: int | None = None,
+    bs: int = 1,
+) -> tuple[dict[str, Tensor], int, int]:
+    # load and encode the conditioning image
+    if bs == 1 and not isinstance(prompt, str):
+        bs = len(prompt)
+
+    width, height = img_cond.size
+    aspect_ratio = width / height
+
+    # Kontext is trained on specific resolutions, using one of them is recommended
+    _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
+
+    width = 2 * int(width / 16)
+    height = 2 * int(height / 16)
+
+    img_cond = img_cond.resize((8 * width, 8 * height), Image.Resampling.LANCZOS)
+    img_cond = np.array(img_cond)
+    img_cond = torch.from_numpy(img_cond).float() / 127.5 - 1.0
+    img_cond = rearrange(img_cond, "h w c -> 1 c h w")
+    img_cond_orig = img_cond.clone()
+
+    with torch.no_grad():
+        img_cond = ae.encode(img_cond.to(device))
+
+    img_cond = img_cond.to(torch.bfloat16)
+    img_cond = rearrange(img_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+    if img_cond.shape[0] == 1 and bs > 1:
+        img_cond = repeat(img_cond, "1 ... -> bs ...", bs=bs)
+
+    # image ids are the same as base image with the first dimension set to 1
+    # instead of 0
+    img_cond_ids = torch.zeros(height // 2, width // 2, 3)
+    img_cond_ids[..., 0] = 1
+    img_cond_ids[..., 1] = img_cond_ids[..., 1] + torch.arange(height // 2)[:, None]
+    img_cond_ids[..., 2] = img_cond_ids[..., 2] + torch.arange(width // 2)[None, :]
+    img_cond_ids = repeat(img_cond_ids, "h w c -> b (h w) c", b=bs)
+
+    if target_width is None:
+        target_width = 8 * width
+    if target_height is None:
+        target_height = 8 * height
+
+    img = get_noise(
+        bs,
+        target_height,
+        target_width,
+        device=device,
+        dtype=torch.bfloat16,
+        seed=seed,
+    )
+
+    return_dict = prepare(t5, clip, img, prompt)
+    return_dict["img_cond_seq"] = img_cond
+    return_dict["img_cond_seq_ids"] = img_cond_ids.to(device)
+    return_dict["img_cond_orig"] = img_cond_orig
+    return return_dict, target_height, target_width
+
+
+def time_shift(mu: float, sigma: float, t: Tensor):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+
+def get_lin_function(
+    x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15
+) -> Callable[[float], float]:
+    m = (y2 - y1) / (x2 - x1)
+    b = y1 - m * x1
+    return lambda x: m * x + b
+
+
+def get_schedule(
+    num_steps: int,
+    image_seq_len: int,
+    base_shift: float = 0.5,
+    max_shift: float = 1.15,
+    shift: bool = True,
+) -> list[float]:
+    # extra step for zero
+    timesteps = torch.linspace(1, 0, num_steps + 1)
+
+    # shifting the schedule to favor high timesteps for higher signal images
+    if shift:
+        # estimate mu based on linear estimation between two points
+        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
+        timesteps = time_shift(mu, 1.0, timesteps)
+
+    return timesteps.tolist()
+
+
+def denoise(
+    model: Flux,
+    # model input
+    img: Tensor,
+    img_ids: Tensor,
+    txt: Tensor,
+    txt_ids: Tensor,
+    vec: Tensor,
+    # sampling parameters
+    timesteps: list[float],
+    guidance: float = 4.0,
+    # extra img tokens (channel-wise)
+    img_cond: Tensor | None = None,
+    # extra img tokens (sequence-wise)
+    img_cond_seq: Tensor | None = None,
+    img_cond_seq_ids: Tensor | None = None,
+    callback=None,
+    pipeline=None,
+    loras_slists=None,
+    unpack_latent = None,
+):
+
+    kwargs = {'pipeline': pipeline, 'callback': callback}
+    if callback != None:
+        callback(-1, None, True)
+
+    updated_num_steps= len(timesteps) -1
+    if callback != None:
+        from wgp import update_loras_slists
+        update_loras_slists(model, loras_slists, updated_num_steps)
+        callback(-1, None, True, override_num_inference_steps = updated_num_steps)
+    from mmgp import offload
+    # this is ignored for schnell
+    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
+    for i, (t_curr, t_prev) in enumerate(zip(timesteps[:-1], timesteps[1:])):
+        offload.set_step_no_for_lora(model, i)
+        if pipeline._interrupt:
+            return None
+
+        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
+        img_input = img
+        img_input_ids = img_ids
+        if img_cond is not None:
+            img_input = torch.cat((img, img_cond), dim=-1)
+        if img_cond_seq is not None:
+            assert (
+                img_cond_seq_ids is not None
+            ), "You need to provide either both or neither of the sequence conditioning"
+            img_input = torch.cat((img_input, img_cond_seq), dim=1)
+            img_input_ids = torch.cat((img_input_ids, img_cond_seq_ids), dim=1)
+        pred = model(
+            img=img_input,
+            img_ids=img_input_ids,
+            txt=txt,
+            txt_ids=txt_ids,
+            y=vec,
+            timesteps=t_vec,
+            guidance=guidance_vec,
+            **kwargs
+        )
+        if pred == None: return None
+
+        if img_input_ids is not None:
+            pred = pred[:, : img.shape[1]]
+
+            img += (t_prev - t_curr) * pred
+        if callback is not None:
+            preview = unpack_latent(img).transpose(0,1)
+            callback(i, preview, False)         
+
+
+    return img
+
+
+def unpack(x: Tensor, height: int, width: int) -> Tensor:
+    return rearrange(
+        x,
+        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
+        h=math.ceil(height / 16),
+        w=math.ceil(width / 16),
+        ph=2,
+        pw=2,
+    )
--- a/flux/to_remove/cli.py
+++ b/flux/to_remove/cli.py
@ -0,0 +1,302 @@
+import os
+import re
+import time
+from dataclasses import dataclass
+from glob import iglob
+
+import torch
+from fire import Fire
+from transformers import pipeline
+
+from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
+from flux.util import (
+    check_onnx_access_for_trt,
+    configs,
+    load_ae,
+    load_clip,
+    load_flow_model,
+    load_t5,
+    save_image,
+)
+
+NSFW_THRESHOLD = 0.85
+
+
+@dataclass
+class SamplingOptions:
+    prompt: str
+    width: int
+    height: int
+    num_steps: int
+    guidance: float
+    seed: int | None
+
+
+def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
+    user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the prompt or write a command starting with a slash:\n"
+        "- '/w <width>' will set the width of the generated image\n"
+        "- '/h <height>' will set the height of the generated image\n"
+        "- '/s <seed>' sets the next seed\n"
+        "- '/g <guidance>' sets the guidance (flux-dev only)\n"
+        "- '/n <steps>' sets the number of steps\n"
+        "- '/q' to quit"
+    )
+
+    while (prompt := input(user_question)).startswith("/"):
+        if prompt.startswith("/w"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, width = prompt.split()
+            options.width = 16 * (int(width) // 16)
+            print(
+                f"Setting resolution to {options.width} x {options.height} "
+                f"({options.height * options.width / 1e6:.2f}MP)"
+            )
+        elif prompt.startswith("/h"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, height = prompt.split()
+            options.height = 16 * (int(height) // 16)
+            print(
+                f"Setting resolution to {options.width} x {options.height} "
+                f"({options.height * options.width / 1e6:.2f}MP)"
+            )
+        elif prompt.startswith("/g"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, guidance = prompt.split()
+            options.guidance = float(guidance)
+            print(f"Setting guidance to {options.guidance}")
+        elif prompt.startswith("/s"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, seed = prompt.split()
+            options.seed = int(seed)
+            print(f"Setting seed to {options.seed}")
+        elif prompt.startswith("/n"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, steps = prompt.split()
+            options.num_steps = int(steps)
+            print(f"Setting number of steps to {options.num_steps}")
+        elif prompt.startswith("/q"):
+            print("Quitting")
+            return None
+        else:
+            if not prompt.startswith("/h"):
+                print(f"Got invalid command '{prompt}'\n{usage}")
+            print(usage)
+    if prompt != "":
+        options.prompt = prompt
+    return options
+
+
+@torch.inference_mode()
+def main(
+    name: str = "flux-schnell",
+    width: int = 1360,
+    height: int = 768,
+    seed: int | None = None,
+    prompt: str = (
+        "a photo of a forest with mist swirling around the tree trunks. The word "
+        '"FLUX" is painted over it in big, red brush strokes with visible texture'
+    ),
+    device: str = "cuda" if torch.cuda.is_available() else "cpu",
+    num_steps: int | None = None,
+    loop: bool = False,
+    guidance: float = 2.5,
+    offload: bool = False,
+    output_dir: str = "output",
+    add_sampling_metadata: bool = True,
+    trt: bool = False,
+    trt_transformer_precision: str = "bf16",
+    track_usage: bool = False,
+):
+    """
+    Sample the flux model. Either interactively (set `--loop`) or run for a
+    single image.
+
+    Args:
+        name: Name of the model to load
+        height: height of the sample in pixels (should be a multiple of 16)
+        width: width of the sample in pixels (should be a multiple of 16)
+        seed: Set a seed for sampling
+        output_name: where to save the output image, `{idx}` will be replaced
+            by the index of the sample
+        prompt: Prompt used for sampling
+        device: Pytorch device
+        num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
+        loop: start an interactive session and sample multiple times
+        guidance: guidance value used for guidance distillation
+        add_sampling_metadata: Add the prompt to the image Exif metadata
+        trt: use TensorRT backend for optimized inference
+        trt_transformer_precision: specify transformer precision for inference
+        track_usage: track usage of the model for licensing purposes
+    """
+
+    prompt = prompt.split("|")
+    if len(prompt) == 1:
+        prompt = prompt[0]
+        additional_prompts = None
+    else:
+        additional_prompts = prompt[1:]
+        prompt = prompt[0]
+
+    assert not (
+        (additional_prompts is not None) and loop
+    ), "Do not provide additional prompts and set loop to True"
+
+    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
+
+    if name not in configs:
+        available = ", ".join(configs.keys())
+        raise ValueError(f"Got unknown model name: {name}, chose from {available}")
+
+    torch_device = torch.device(device)
+    if num_steps is None:
+        num_steps = 4 if name == "flux-schnell" else 50
+
+    # allow for packing and conversion to latent space
+    height = 16 * (height // 16)
+    width = 16 * (width // 16)
+
+    output_name = os.path.join(output_dir, "img_{idx}.jpg")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        idx = 0
+    else:
+        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
+        if len(fns) > 0:
+            idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
+        else:
+            idx = 0
+
+    if not trt:
+        t5 = load_t5(torch_device, max_length=256 if name == "flux-schnell" else 512)
+        clip = load_clip(torch_device)
+        model = load_flow_model(name, device="cpu" if offload else torch_device)
+        ae = load_ae(name, device="cpu" if offload else torch_device)
+    else:
+        # lazy import to make install optional
+        from flux.trt.trt_manager import ModuleName, TRTManager
+
+        # Check if we need ONNX model access (which requires authentication for FLUX models)
+        onnx_dir = check_onnx_access_for_trt(name, trt_transformer_precision)
+
+        trt_ctx_manager = TRTManager(
+            trt_transformer_precision=trt_transformer_precision,
+            trt_t5_precision=os.getenv("TRT_T5_PRECISION", "bf16"),
+        )
+        engines = trt_ctx_manager.load_engines(
+            model_name=name,
+            module_names={
+                ModuleName.CLIP,
+                ModuleName.TRANSFORMER,
+                ModuleName.T5,
+                ModuleName.VAE,
+            },
+            engine_dir=os.environ.get("TRT_ENGINE_DIR", "./engines"),
+            custom_onnx_paths=onnx_dir or os.environ.get("CUSTOM_ONNX_PATHS", ""),
+            trt_image_height=height,
+            trt_image_width=width,
+            trt_batch_size=1,
+            trt_timing_cache=os.getenv("TRT_TIMING_CACHE_FILE", None),
+            trt_static_batch=False,
+            trt_static_shape=False,
+        )
+
+        ae = engines[ModuleName.VAE].to(device="cpu" if offload else torch_device)
+        model = engines[ModuleName.TRANSFORMER].to(device="cpu" if offload else torch_device)
+        clip = engines[ModuleName.CLIP].to(torch_device)
+        t5 = engines[ModuleName.T5].to(device="cpu" if offload else torch_device)
+
+    rng = torch.Generator(device="cpu")
+    opts = SamplingOptions(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_steps=num_steps,
+        guidance=guidance,
+        seed=seed,
+    )
+
+    if loop:
+        opts = parse_prompt(opts)
+
+    while opts is not None:
+        if opts.seed is None:
+            opts.seed = rng.seed()
+        print(f"Generating with seed {opts.seed}:\n{opts.prompt}")
+        t0 = time.perf_counter()
+
+        # prepare input
+        x = get_noise(
+            1,
+            opts.height,
+            opts.width,
+            device=torch_device,
+            dtype=torch.bfloat16,
+            seed=opts.seed,
+        )
+        opts.seed = None
+        if offload:
+            ae = ae.cpu()
+            torch.cuda.empty_cache()
+            t5, clip = t5.to(torch_device), clip.to(torch_device)
+        inp = prepare(t5, clip, x, prompt=opts.prompt)
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))
+
+        # offload TEs to CPU, load model to gpu
+        if offload:
+            t5, clip = t5.cpu(), clip.cpu()
+            torch.cuda.empty_cache()
+            model = model.to(torch_device)
+
+        # denoise initial noise
+        x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance)
+
+        # offload model, load autoencoder to gpu
+        if offload:
+            model.cpu()
+            torch.cuda.empty_cache()
+            ae.decoder.to(x.device)
+
+        # decode latents to pixel space
+        x = unpack(x.float(), opts.height, opts.width)
+        with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
+            x = ae.decode(x)
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t1 = time.perf_counter()
+
+        fn = output_name.format(idx=idx)
+        print(f"Done in {t1 - t0:.1f}s. Saving {fn}")
+
+        idx = save_image(
+            nsfw_classifier, name, output_name, idx, x, add_sampling_metadata, prompt, track_usage=track_usage
+        )
+
+        if loop:
+            print("-" * 80)
+            opts = parse_prompt(opts)
+        elif additional_prompts:
+            next_prompt = additional_prompts.pop(0)
+            opts.prompt = next_prompt
+        else:
+            opts = None
+
+    if trt:
+        trt_ctx_manager.stop_runtime()
+
+
+if __name__ == "__main__":
+    Fire(main)
--- a/flux/to_remove/cli_control.py
+++ b/flux/to_remove/cli_control.py
@ -0,0 +1,390 @@
+import os
+import re
+import time
+from dataclasses import dataclass
+from glob import iglob
+
+import torch
+from fire import Fire
+from transformers import pipeline
+
+from flux.modules.image_embedders import CannyImageEncoder, DepthImageEncoder
+from flux.sampling import denoise, get_noise, get_schedule, prepare_control, unpack
+from flux.util import configs, load_ae, load_clip, load_flow_model, load_t5, save_image
+
+
+@dataclass
+class SamplingOptions:
+    prompt: str
+    width: int
+    height: int
+    num_steps: int
+    guidance: float
+    seed: int | None
+    img_cond_path: str
+    lora_scale: float | None
+
+
+def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
+    user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the prompt or write a command starting with a slash:\n"
+        "- '/w <width>' will set the width of the generated image\n"
+        "- '/h <height>' will set the height of the generated image\n"
+        "- '/s <seed>' sets the next seed\n"
+        "- '/g <guidance>' sets the guidance (flux-dev only)\n"
+        "- '/n <steps>' sets the number of steps\n"
+        "- '/q' to quit"
+    )
+
+    while (prompt := input(user_question)).startswith("/"):
+        if prompt.startswith("/w"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, width = prompt.split()
+            options.width = 16 * (int(width) // 16)
+            print(
+                f"Setting resolution to {options.width} x {options.height} "
+                f"({options.height * options.width / 1e6:.2f}MP)"
+            )
+        elif prompt.startswith("/h"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, height = prompt.split()
+            options.height = 16 * (int(height) // 16)
+            print(
+                f"Setting resolution to {options.width} x {options.height} "
+                f"({options.height * options.width / 1e6:.2f}MP)"
+            )
+        elif prompt.startswith("/g"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, guidance = prompt.split()
+            options.guidance = float(guidance)
+            print(f"Setting guidance to {options.guidance}")
+        elif prompt.startswith("/s"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, seed = prompt.split()
+            options.seed = int(seed)
+            print(f"Setting seed to {options.seed}")
+        elif prompt.startswith("/n"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, steps = prompt.split()
+            options.num_steps = int(steps)
+            print(f"Setting number of steps to {options.num_steps}")
+        elif prompt.startswith("/q"):
+            print("Quitting")
+            return None
+        else:
+            if not prompt.startswith("/h"):
+                print(f"Got invalid command '{prompt}'\n{usage}")
+            print(usage)
+    if prompt != "":
+        options.prompt = prompt
+    return options
+
+
+def parse_img_cond_path(options: SamplingOptions | None) -> SamplingOptions | None:
+    if options is None:
+        return None
+
+    user_question = "Next conditioning image (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the conditioning image or write a command starting with a slash:\n"
+        "- '/q' to quit"
+    )
+
+    while True:
+        img_cond_path = input(user_question)
+
+        if img_cond_path.startswith("/"):
+            if img_cond_path.startswith("/q"):
+                print("Quitting")
+                return None
+            else:
+                if not img_cond_path.startswith("/h"):
+                    print(f"Got invalid command '{img_cond_path}'\n{usage}")
+                print(usage)
+            continue
+
+        if img_cond_path == "":
+            break
+
+        if not os.path.isfile(img_cond_path) or not img_cond_path.lower().endswith(
+            (".jpg", ".jpeg", ".png", ".webp")
+        ):
+            print(f"File '{img_cond_path}' does not exist or is not a valid image file")
+            continue
+
+        options.img_cond_path = img_cond_path
+        break
+
+    return options
+
+
+def parse_lora_scale(options: SamplingOptions | None) -> tuple[SamplingOptions | None, bool]:
+    changed = False
+
+    if options is None:
+        return None, changed
+
+    user_question = "Next lora scale (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the lora scale or write a command starting with a slash:\n"
+        "- '/q' to quit"
+    )
+
+    while (prompt := input(user_question)).startswith("/"):
+        if prompt.startswith("/q"):
+            print("Quitting")
+            return None, changed
+        else:
+            if not prompt.startswith("/h"):
+                print(f"Got invalid command '{prompt}'\n{usage}")
+            print(usage)
+    if prompt != "":
+        options.lora_scale = float(prompt)
+        changed = True
+    return options, changed
+
+
+@torch.inference_mode()
+def main(
+    name: str,
+    width: int = 1024,
+    height: int = 1024,
+    seed: int | None = None,
+    prompt: str = "a robot made out of gold",
+    device: str = "cuda" if torch.cuda.is_available() else "cpu",
+    num_steps: int = 50,
+    loop: bool = False,
+    guidance: float | None = None,
+    offload: bool = False,
+    output_dir: str = "output",
+    add_sampling_metadata: bool = True,
+    img_cond_path: str = "assets/robot.webp",
+    lora_scale: float | None = 0.85,
+    trt: bool = False,
+    trt_transformer_precision: str = "bf16",
+    track_usage: bool = False,
+    **kwargs: dict | None,
+):
+    """
+    Sample the flux model. Either interactively (set `--loop`) or run for a
+    single image.
+
+    Args:
+        height: height of the sample in pixels (should be a multiple of 16)
+        width: width of the sample in pixels (should be a multiple of 16)
+        seed: Set a seed for sampling
+        output_name: where to save the output image, `{idx}` will be replaced
+            by the index of the sample
+        prompt: Prompt used for sampling
+        device: Pytorch device
+        num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
+        loop: start an interactive session and sample multiple times
+        guidance: guidance value used for guidance distillation
+        add_sampling_metadata: Add the prompt to the image Exif metadata
+        img_cond_path: path to conditioning image (jpeg/png/webp)
+        trt: use TensorRT backend for optimized inference
+        trt_transformer_precision: specify transformer precision for inference
+        track_usage: track usage of the model for licensing purposes
+    """
+    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
+
+    if "lora" in name:
+        assert not trt, "TRT does not support LORA"
+    assert name in [
+        "flux-dev-canny",
+        "flux-dev-depth",
+        "flux-dev-canny-lora",
+        "flux-dev-depth-lora",
+    ], f"Got unknown model name: {name}"
+
+    if guidance is None:
+        if name in ["flux-dev-canny", "flux-dev-canny-lora"]:
+            guidance = 30.0
+        elif name in ["flux-dev-depth", "flux-dev-depth-lora"]:
+            guidance = 10.0
+        else:
+            raise NotImplementedError()
+
+    if name not in configs:
+        available = ", ".join(configs.keys())
+        raise ValueError(f"Got unknown model name: {name}, chose from {available}")
+
+    torch_device = torch.device(device)
+
+    output_name = os.path.join(output_dir, "img_{idx}.jpg")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        idx = 0
+    else:
+        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
+        if len(fns) > 0:
+            idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
+        else:
+            idx = 0
+
+    if name in ["flux-dev-depth", "flux-dev-depth-lora"]:
+        img_embedder = DepthImageEncoder(torch_device)
+    elif name in ["flux-dev-canny", "flux-dev-canny-lora"]:
+        img_embedder = CannyImageEncoder(torch_device)
+    else:
+        raise NotImplementedError()
+
+    if not trt:
+        # init all components
+        t5 = load_t5(torch_device, max_length=512)
+        clip = load_clip(torch_device)
+        model = load_flow_model(name, device="cpu" if offload else torch_device)
+        ae = load_ae(name, device="cpu" if offload else torch_device)
+    else:
+        # lazy import to make install optional
+        from flux.trt.trt_manager import ModuleName, TRTManager
+
+        trt_ctx_manager = TRTManager(
+            trt_transformer_precision=trt_transformer_precision,
+            trt_t5_precision=os.environ.get("TRT_T5_PRECISION", "bf16"),
+        )
+
+        engines = trt_ctx_manager.load_engines(
+            model_name=name,
+            module_names={
+                ModuleName.CLIP,
+                ModuleName.TRANSFORMER,
+                ModuleName.T5,
+                ModuleName.VAE,
+                ModuleName.VAE_ENCODER,
+            },
+            engine_dir=os.environ.get("TRT_ENGINE_DIR", "./engines"),
+            custom_onnx_paths=os.environ.get("CUSTOM_ONNX_PATHS", ""),
+            trt_image_height=height,
+            trt_image_width=width,
+            trt_batch_size=1,
+            trt_static_batch=kwargs.get("static_batch", True),
+            trt_static_shape=kwargs.get("static_shape", True),
+        )
+
+        ae = engines[ModuleName.VAE].to(device="cpu" if offload else torch_device)
+        model = engines[ModuleName.TRANSFORMER].to(device="cpu" if offload else torch_device)
+        clip = engines[ModuleName.CLIP].to(torch_device)
+        t5 = engines[ModuleName.T5].to(device="cpu" if offload else torch_device)
+
+    # set lora scale
+    if "lora" in name and lora_scale is not None:
+        for _, module in model.named_modules():
+            if hasattr(module, "set_scale"):
+                module.set_scale(lora_scale)
+
+    rng = torch.Generator(device="cpu")
+    opts = SamplingOptions(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_steps=num_steps,
+        guidance=guidance,
+        seed=seed,
+        img_cond_path=img_cond_path,
+        lora_scale=lora_scale,
+    )
+
+    if loop:
+        opts = parse_prompt(opts)
+        opts = parse_img_cond_path(opts)
+        if "lora" in name:
+            opts, changed = parse_lora_scale(opts)
+            if changed:
+                # update the lora scale:
+                for _, module in model.named_modules():
+                    if hasattr(module, "set_scale"):
+                        module.set_scale(opts.lora_scale)
+
+    while opts is not None:
+        if opts.seed is None:
+            opts.seed = rng.seed()
+        print(f"Generating with seed {opts.seed}:\n{opts.prompt}")
+        t0 = time.perf_counter()
+
+        # prepare input
+        x = get_noise(
+            1,
+            opts.height,
+            opts.width,
+            device=torch_device,
+            dtype=torch.bfloat16,
+            seed=opts.seed,
+        )
+        opts.seed = None
+        if offload:
+            t5, clip, ae = t5.to(torch_device), clip.to(torch_device), ae.to(torch_device)
+        inp = prepare_control(
+            t5,
+            clip,
+            x,
+            prompt=opts.prompt,
+            ae=ae,
+            encoder=img_embedder,
+            img_cond_path=opts.img_cond_path,
+        )
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))
+
+        # offload TEs and AE to CPU, load model to gpu
+        if offload:
+            t5, clip, ae = t5.cpu(), clip.cpu(), ae.cpu()
+            torch.cuda.empty_cache()
+            model = model.to(torch_device)
+
+        # denoise initial noise
+        x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance)
+
+        # offload model, load autoencoder to gpu
+        if offload:
+            model.cpu()
+            torch.cuda.empty_cache()
+            ae.decoder.to(x.device)
+
+        # decode latents to pixel space
+        x = unpack(x.float(), opts.height, opts.width)
+        with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
+            x = ae.decode(x)
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t1 = time.perf_counter()
+        print(f"Done in {t1 - t0:.1f}s")
+
+        idx = save_image(
+            nsfw_classifier, name, output_name, idx, x, add_sampling_metadata, prompt, track_usage=track_usage
+        )
+
+        if loop:
+            print("-" * 80)
+            opts = parse_prompt(opts)
+            opts = parse_img_cond_path(opts)
+            if "lora" in name:
+                opts, changed = parse_lora_scale(opts)
+                if changed:
+                    # update the lora scale:
+                    for _, module in model.named_modules():
+                        if hasattr(module, "set_scale"):
+                            module.set_scale(opts.lora_scale)
+        else:
+            opts = None
+
+    if trt:
+        trt_ctx_manager.stop_runtime()
+
+
+if __name__ == "__main__":
+    Fire(main)
--- a/flux/to_remove/cli_fill.py
+++ b/flux/to_remove/cli_fill.py
@ -0,0 +1,334 @@
+import os
+import re
+import time
+from dataclasses import dataclass
+from glob import iglob
+
+import torch
+from fire import Fire
+from PIL import Image
+from transformers import pipeline
+
+from flux.sampling import denoise, get_noise, get_schedule, prepare_fill, unpack
+from flux.util import configs, load_ae, load_clip, load_flow_model, load_t5, save_image
+
+
+@dataclass
+class SamplingOptions:
+    prompt: str
+    width: int
+    height: int
+    num_steps: int
+    guidance: float
+    seed: int | None
+    img_cond_path: str
+    img_mask_path: str
+
+
+def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
+    user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the prompt or write a command starting with a slash:\n"
+        "- '/s <seed>' sets the next seed\n"
+        "- '/g <guidance>' sets the guidance (flux-dev only)\n"
+        "- '/n <steps>' sets the number of steps\n"
+        "- '/q' to quit"
+    )
+
+    while (prompt := input(user_question)).startswith("/"):
+        if prompt.startswith("/g"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, guidance = prompt.split()
+            options.guidance = float(guidance)
+            print(f"Setting guidance to {options.guidance}")
+        elif prompt.startswith("/s"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, seed = prompt.split()
+            options.seed = int(seed)
+            print(f"Setting seed to {options.seed}")
+        elif prompt.startswith("/n"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, steps = prompt.split()
+            options.num_steps = int(steps)
+            print(f"Setting number of steps to {options.num_steps}")
+        elif prompt.startswith("/q"):
+            print("Quitting")
+            return None
+        else:
+            if not prompt.startswith("/h"):
+                print(f"Got invalid command '{prompt}'\n{usage}")
+            print(usage)
+    if prompt != "":
+        options.prompt = prompt
+    return options
+
+
+def parse_img_cond_path(options: SamplingOptions | None) -> SamplingOptions | None:
+    if options is None:
+        return None
+
+    user_question = "Next conditioning image (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the conditioning image or write a command starting with a slash:\n"
+        "- '/q' to quit"
+    )
+
+    while True:
+        img_cond_path = input(user_question)
+
+        if img_cond_path.startswith("/"):
+            if img_cond_path.startswith("/q"):
+                print("Quitting")
+                return None
+            else:
+                if not img_cond_path.startswith("/h"):
+                    print(f"Got invalid command '{img_cond_path}'\n{usage}")
+                print(usage)
+            continue
+
+        if img_cond_path == "":
+            break
+
+        if not os.path.isfile(img_cond_path) or not img_cond_path.lower().endswith(
+            (".jpg", ".jpeg", ".png", ".webp")
+        ):
+            print(f"File '{img_cond_path}' does not exist or is not a valid image file")
+            continue
+        else:
+            with Image.open(img_cond_path) as img:
+                width, height = img.size
+
+            if width % 32 != 0 or height % 32 != 0:
+                print(f"Image dimensions must be divisible by 32, got {width}x{height}")
+                continue
+
+        options.img_cond_path = img_cond_path
+        break
+
+    return options
+
+
+def parse_img_mask_path(options: SamplingOptions | None) -> SamplingOptions | None:
+    if options is None:
+        return None
+
+    user_question = "Next conditioning mask (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the conditioning mask or write a command starting with a slash:\n"
+        "- '/q' to quit"
+    )
+
+    while True:
+        img_mask_path = input(user_question)
+
+        if img_mask_path.startswith("/"):
+            if img_mask_path.startswith("/q"):
+                print("Quitting")
+                return None
+            else:
+                if not img_mask_path.startswith("/h"):
+                    print(f"Got invalid command '{img_mask_path}'\n{usage}")
+                print(usage)
+            continue
+
+        if img_mask_path == "":
+            break
+
+        if not os.path.isfile(img_mask_path) or not img_mask_path.lower().endswith(
+            (".jpg", ".jpeg", ".png", ".webp")
+        ):
+            print(f"File '{img_mask_path}' does not exist or is not a valid image file")
+            continue
+        else:
+            with Image.open(img_mask_path) as img:
+                width, height = img.size
+
+            if width % 32 != 0 or height % 32 != 0:
+                print(f"Image dimensions must be divisible by 32, got {width}x{height}")
+                continue
+            else:
+                with Image.open(options.img_cond_path) as img_cond:
+                    img_cond_width, img_cond_height = img_cond.size
+
+                if width != img_cond_width or height != img_cond_height:
+                    print(
+                        f"Mask dimensions must match conditioning image, got {width}x{height} and {img_cond_width}x{img_cond_height}"
+                    )
+                    continue
+
+        options.img_mask_path = img_mask_path
+        break
+
+    return options
+
+
+@torch.inference_mode()
+def main(
+    seed: int | None = None,
+    prompt: str = "a white paper cup",
+    device: str = "cuda" if torch.cuda.is_available() else "cpu",
+    num_steps: int = 50,
+    loop: bool = False,
+    guidance: float = 30.0,
+    offload: bool = False,
+    output_dir: str = "output",
+    add_sampling_metadata: bool = True,
+    img_cond_path: str = "assets/cup.png",
+    img_mask_path: str = "assets/cup_mask.png",
+    track_usage: bool = False,
+):
+    """
+    Sample the flux model. Either interactively (set `--loop`) or run for a
+    single image. This demo assumes that the conditioning image and mask have
+    the same shape and that height and width are divisible by 32.
+
+    Args:
+        seed: Set a seed for sampling
+        output_name: where to save the output image, `{idx}` will be replaced
+            by the index of the sample
+        prompt: Prompt used for sampling
+        device: Pytorch device
+        num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
+        loop: start an interactive session and sample multiple times
+        guidance: guidance value used for guidance distillation
+        add_sampling_metadata: Add the prompt to the image Exif metadata
+        img_cond_path: path to conditioning image (jpeg/png/webp)
+        img_mask_path: path to conditioning mask (jpeg/png/webp)
+        track_usage: track usage of the model for licensing purposes
+    """
+    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
+
+    name = "flux-dev-fill"
+    if name not in configs:
+        available = ", ".join(configs.keys())
+        raise ValueError(f"Got unknown model name: {name}, chose from {available}")
+
+    torch_device = torch.device(device)
+
+    output_name = os.path.join(output_dir, "img_{idx}.jpg")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        idx = 0
+    else:
+        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
+        if len(fns) > 0:
+            idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
+        else:
+            idx = 0
+
+    # init all components
+    t5 = load_t5(torch_device, max_length=128)
+    clip = load_clip(torch_device)
+    model = load_flow_model(name, device="cpu" if offload else torch_device)
+    ae = load_ae(name, device="cpu" if offload else torch_device)
+
+    rng = torch.Generator(device="cpu")
+    with Image.open(img_cond_path) as img:
+        width, height = img.size
+    opts = SamplingOptions(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_steps=num_steps,
+        guidance=guidance,
+        seed=seed,
+        img_cond_path=img_cond_path,
+        img_mask_path=img_mask_path,
+    )
+
+    if loop:
+        opts = parse_prompt(opts)
+        opts = parse_img_cond_path(opts)
+
+        with Image.open(opts.img_cond_path) as img:
+            width, height = img.size
+        opts.height = height
+        opts.width = width
+
+        opts = parse_img_mask_path(opts)
+
+    while opts is not None:
+        if opts.seed is None:
+            opts.seed = rng.seed()
+        print(f"Generating with seed {opts.seed}:\n{opts.prompt}")
+        t0 = time.perf_counter()
+
+        # prepare input
+        x = get_noise(
+            1,
+            opts.height,
+            opts.width,
+            device=torch_device,
+            dtype=torch.bfloat16,
+            seed=opts.seed,
+        )
+        opts.seed = None
+        if offload:
+            t5, clip, ae = t5.to(torch_device), clip.to(torch_device), ae.to(torch_device)
+        inp = prepare_fill(
+            t5,
+            clip,
+            x,
+            prompt=opts.prompt,
+            ae=ae,
+            img_cond_path=opts.img_cond_path,
+            mask_path=opts.img_mask_path,
+        )
+
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))
+
+        # offload TEs and AE to CPU, load model to gpu
+        if offload:
+            t5, clip, ae = t5.cpu(), clip.cpu(), ae.cpu()
+            torch.cuda.empty_cache()
+            model = model.to(torch_device)
+
+        # denoise initial noise
+        x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance)
+
+        # offload model, load autoencoder to gpu
+        if offload:
+            model.cpu()
+            torch.cuda.empty_cache()
+            ae.decoder.to(x.device)
+
+        # decode latents to pixel space
+        x = unpack(x.float(), opts.height, opts.width)
+        with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
+            x = ae.decode(x)
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t1 = time.perf_counter()
+        print(f"Done in {t1 - t0:.1f}s")
+
+        idx = save_image(
+            nsfw_classifier, name, output_name, idx, x, add_sampling_metadata, prompt, track_usage=track_usage
+        )
+
+        if loop:
+            print("-" * 80)
+            opts = parse_prompt(opts)
+            opts = parse_img_cond_path(opts)
+
+            with Image.open(opts.img_cond_path) as img:
+                width, height = img.size
+            opts.height = height
+            opts.width = width
+
+            opts = parse_img_mask_path(opts)
+        else:
+            opts = None
+
+
+if __name__ == "__main__":
+    Fire(main)
--- a/flux/to_remove/cli_kontext.py
+++ b/flux/to_remove/cli_kontext.py
@ -0,0 +1,368 @@
+import os
+import re
+import time
+from dataclasses import dataclass
+from glob import iglob
+
+import torch
+from fire import Fire
+
+from flux.content_filters import PixtralContentFilter
+from flux.sampling import denoise, get_schedule, prepare_kontext, unpack
+from flux.util import (
+    aspect_ratio_to_height_width,
+    check_onnx_access_for_trt,
+    load_ae,
+    load_clip,
+    load_flow_model,
+    load_t5,
+    save_image,
+)
+
+
+@dataclass
+class SamplingOptions:
+    prompt: str
+    width: int | None
+    height: int | None
+    num_steps: int
+    guidance: float
+    seed: int | None
+    img_cond_path: str
+
+
+def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
+    user_question = "Next prompt (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the prompt or write a command starting with a slash:\n"
+        "- '/ar <width>:<height>' will set the aspect ratio of the generated image\n"
+        "- '/s <seed>' sets the next seed\n"
+        "- '/g <guidance>' sets the guidance (flux-dev only)\n"
+        "- '/n <steps>' sets the number of steps\n"
+        "- '/q' to quit"
+    )
+
+    while (prompt := input(user_question)).startswith("/"):
+        if prompt.startswith("/ar"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, ratio_prompt = prompt.split()
+            if ratio_prompt == "auto":
+                options.width = None
+                options.height = None
+                print("Setting resolution to input image resolution.")
+            else:
+                options.width, options.height = aspect_ratio_to_height_width(ratio_prompt)
+                print(f"Setting resolution to {options.width} x {options.height}.")
+        elif prompt.startswith("/h"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, height = prompt.split()
+            if height == "auto":
+                options.height = None
+            else:
+                options.height = 16 * (int(height) // 16)
+            if options.height is not None and options.width is not None:
+                print(
+                    f"Setting resolution to {options.width} x {options.height} "
+                    f"({options.height * options.width / 1e6:.2f}MP)"
+                )
+            else:
+                print(f"Setting resolution to {options.width} x {options.height}.")
+        elif prompt.startswith("/g"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, guidance = prompt.split()
+            options.guidance = float(guidance)
+            print(f"Setting guidance to {options.guidance}")
+        elif prompt.startswith("/s"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, seed = prompt.split()
+            options.seed = int(seed)
+            print(f"Setting seed to {options.seed}")
+        elif prompt.startswith("/n"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, steps = prompt.split()
+            options.num_steps = int(steps)
+            print(f"Setting number of steps to {options.num_steps}")
+        elif prompt.startswith("/q"):
+            print("Quitting")
+            return None
+        else:
+            if not prompt.startswith("/h"):
+                print(f"Got invalid command '{prompt}'\n{usage}")
+            print(usage)
+    if prompt != "":
+        options.prompt = prompt
+    return options
+
+
+def parse_img_cond_path(options: SamplingOptions | None) -> SamplingOptions | None:
+    if options is None:
+        return None
+
+    user_question = "Next input image (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write a path to an image directly, leave this field empty "
+        "to repeat the last input image or write a command starting with a slash:\n"
+        "- '/q' to quit\n\n"
+        "The input image will be edited by FLUX.1 Kontext creating a new image based"
+        "on your instruction prompt."
+    )
+
+    while True:
+        img_cond_path = input(user_question)
+
+        if img_cond_path.startswith("/"):
+            if img_cond_path.startswith("/q"):
+                print("Quitting")
+                return None
+            else:
+                if not img_cond_path.startswith("/h"):
+                    print(f"Got invalid command '{img_cond_path}'\n{usage}")
+                print(usage)
+            continue
+
+        if img_cond_path == "":
+            break
+
+        if not os.path.isfile(img_cond_path) or not img_cond_path.lower().endswith(
+            (".jpg", ".jpeg", ".png", ".webp")
+        ):
+            print(f"File '{img_cond_path}' does not exist or is not a valid image file")
+            continue
+
+        options.img_cond_path = img_cond_path
+        break
+
+    return options
+
+
+@torch.inference_mode()
+def main(
+    name: str = "flux-dev-kontext",
+    aspect_ratio: str | None = None,
+    seed: int | None = None,
+    prompt: str = "replace the logo with the text 'Black Forest Labs'",
+    device: str = "cuda" if torch.cuda.is_available() else "cpu",
+    num_steps: int = 30,
+    loop: bool = False,
+    guidance: float = 2.5,
+    offload: bool = False,
+    output_dir: str = "output",
+    add_sampling_metadata: bool = True,
+    img_cond_path: str = "assets/cup.png",
+    trt: bool = False,
+    trt_transformer_precision: str = "bf16",
+    track_usage: bool = False,
+):
+    """
+    Sample the flux model. Either interactively (set `--loop`) or run for a
+    single image.
+
+    Args:
+        height: height of the sample in pixels (should be a multiple of 16), None
+            defaults to the size of the conditioning
+        width: width of the sample in pixels (should be a multiple of 16), None
+            defaults to the size of the conditioning
+        seed: Set a seed for sampling
+        output_name: where to save the output image, `{idx}` will be replaced
+            by the index of the sample
+        prompt: Prompt used for sampling
+        device: Pytorch device
+        num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
+        loop: start an interactive session and sample multiple times
+        guidance: guidance value used for guidance distillation
+        add_sampling_metadata: Add the prompt to the image Exif metadata
+        img_cond_path: path to conditioning image (jpeg/png/webp)
+        trt: use TensorRT backend for optimized inference
+        track_usage: track usage of the model for licensing purposes
+    """
+    assert name == "flux-dev-kontext", f"Got unknown model name: {name}"
+
+    torch_device = torch.device(device)
+
+    output_name = os.path.join(output_dir, "img_{idx}.jpg")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        idx = 0
+    else:
+        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
+        if len(fns) > 0:
+            idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
+        else:
+            idx = 0
+
+    if aspect_ratio is None:
+        width = None
+        height = None
+    else:
+        width, height = aspect_ratio_to_height_width(aspect_ratio)
+
+    if not trt:
+        t5 = load_t5(torch_device, max_length=512)
+        clip = load_clip(torch_device)
+        model = load_flow_model(name, device="cpu" if offload else torch_device)
+    else:
+        # lazy import to make install optional
+        from flux.trt.trt_manager import ModuleName, TRTManager
+
+        # Check if we need ONNX model access (which requires authentication for FLUX models)
+        onnx_dir = check_onnx_access_for_trt(name, trt_transformer_precision)
+
+        trt_ctx_manager = TRTManager(
+            trt_transformer_precision=trt_transformer_precision,
+            trt_t5_precision=os.environ.get("TRT_T5_PRECISION", "bf16"),
+        )
+        engines = trt_ctx_manager.load_engines(
+            model_name=name,
+            module_names={
+                ModuleName.CLIP,
+                ModuleName.TRANSFORMER,
+                ModuleName.T5,
+            },
+            engine_dir=os.environ.get("TRT_ENGINE_DIR", "./engines"),
+            custom_onnx_paths=onnx_dir or os.environ.get("CUSTOM_ONNX_PATHS", ""),
+            trt_image_height=height,
+            trt_image_width=width,
+            trt_batch_size=1,
+            trt_timing_cache=os.getenv("TRT_TIMING_CACHE_FILE", None),
+            trt_static_batch=False,
+            trt_static_shape=False,
+        )
+
+        model = engines[ModuleName.TRANSFORMER].to(device="cpu" if offload else torch_device)
+        clip = engines[ModuleName.CLIP].to(torch_device)
+        t5 = engines[ModuleName.T5].to(device="cpu" if offload else torch_device)
+
+    ae = load_ae(name, device="cpu" if offload else torch_device)
+    content_filter = PixtralContentFilter(torch.device("cpu"))
+
+    rng = torch.Generator(device="cpu")
+    opts = SamplingOptions(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_steps=num_steps,
+        guidance=guidance,
+        seed=seed,
+        img_cond_path=img_cond_path,
+    )
+
+    if loop:
+        opts = parse_prompt(opts)
+        opts = parse_img_cond_path(opts)
+
+    while opts is not None:
+        if opts.seed is None:
+            opts.seed = rng.seed()
+        print(f"Generating with seed {opts.seed}:\n{opts.prompt}")
+        t0 = time.perf_counter()
+
+        if content_filter.test_txt(opts.prompt):
+            print("Your prompt has been automatically flagged. Please choose another prompt.")
+            if loop:
+                print("-" * 80)
+                opts = parse_prompt(opts)
+                opts = parse_img_cond_path(opts)
+            else:
+                opts = None
+            continue
+        if content_filter.test_image(opts.img_cond_path):
+            print("Your input image has been automatically flagged. Please choose another image.")
+            if loop:
+                print("-" * 80)
+                opts = parse_prompt(opts)
+                opts = parse_img_cond_path(opts)
+            else:
+                opts = None
+            continue
+
+        if offload:
+            t5, clip, ae = t5.to(torch_device), clip.to(torch_device), ae.to(torch_device)
+        inp, height, width = prepare_kontext(
+            t5=t5,
+            clip=clip,
+            prompt=opts.prompt,
+            ae=ae,
+            img_cond_path=opts.img_cond_path,
+            target_width=opts.width,
+            target_height=opts.height,
+            bs=1,
+            seed=opts.seed,
+            device=torch_device,
+        )
+        from safetensors.torch import save_file
+
+        save_file({k: v.cpu().contiguous() for k, v in inp.items()}, "output/noise.sft")
+        inp.pop("img_cond_orig")
+        opts.seed = None
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))
+
+        # offload TEs and AE to CPU, load model to gpu
+        if offload:
+            t5, clip, ae = t5.cpu(), clip.cpu(), ae.cpu()
+            torch.cuda.empty_cache()
+            model = model.to(torch_device)
+
+        # denoise initial noise
+        t00 = time.time()
+        x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance)
+        torch.cuda.synchronize()
+        t01 = time.time()
+        print(f"Denoising took {t01 - t00:.3f}s")
+
+        # offload model, load autoencoder to gpu
+        if offload:
+            model.cpu()
+            torch.cuda.empty_cache()
+            ae.decoder.to(x.device)
+
+        # decode latents to pixel space
+        x = unpack(x.float(), height, width)
+        with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
+            ae_dev_t0 = time.perf_counter()
+            x = ae.decode(x)
+            torch.cuda.synchronize()
+            ae_dev_t1 = time.perf_counter()
+            print(f"AE decode took {ae_dev_t1 - ae_dev_t0:.3f}s")
+
+        if content_filter.test_image(x.cpu()):
+            print(
+                "Your output image has been automatically flagged. Choose another prompt/image or try again."
+            )
+            if loop:
+                print("-" * 80)
+                opts = parse_prompt(opts)
+                opts = parse_img_cond_path(opts)
+            else:
+                opts = None
+            continue
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t1 = time.perf_counter()
+        print(f"Done in {t1 - t0:.1f}s")
+
+        idx = save_image(
+            None, name, output_name, idx, x, add_sampling_metadata, prompt, track_usage=track_usage
+        )
+
+        if loop:
+            print("-" * 80)
+            opts = parse_prompt(opts)
+            opts = parse_img_cond_path(opts)
+        else:
+            opts = None
+
+
+if __name__ == "__main__":
+    Fire(main)
--- a/flux/to_remove/cli_redux.py
+++ b/flux/to_remove/cli_redux.py
@ -0,0 +1,290 @@
+import os
+import re
+import time
+from dataclasses import dataclass
+from glob import iglob
+
+import torch
+from fire import Fire
+from transformers import pipeline
+
+from flux.modules.image_embedders import ReduxImageEncoder
+from flux.sampling import denoise, get_noise, get_schedule, prepare_redux, unpack
+from flux.util import (
+    get_checkpoint_path,
+    load_ae,
+    load_clip,
+    load_flow_model,
+    load_t5,
+    save_image,
+)
+
+
+@dataclass
+class SamplingOptions:
+    prompt: str
+    width: int
+    height: int
+    num_steps: int
+    guidance: float
+    seed: int | None
+    img_cond_path: str
+
+
+def parse_prompt(options: SamplingOptions) -> SamplingOptions | None:
+    user_question = "Write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Leave this field empty to do nothing "
+        "or write a command starting with a slash:\n"
+        "- '/w <width>' will set the width of the generated image\n"
+        "- '/h <height>' will set the height of the generated image\n"
+        "- '/s <seed>' sets the next seed\n"
+        "- '/g <guidance>' sets the guidance (flux-dev only)\n"
+        "- '/n <steps>' sets the number of steps\n"
+        "- '/q' to quit"
+    )
+
+    while (prompt := input(user_question)).startswith("/"):
+        if prompt.startswith("/w"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, width = prompt.split()
+            options.width = 16 * (int(width) // 16)
+            print(
+                f"Setting resolution to {options.width} x {options.height} "
+                f"({options.height * options.width / 1e6:.2f}MP)"
+            )
+        elif prompt.startswith("/h"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, height = prompt.split()
+            options.height = 16 * (int(height) // 16)
+            print(
+                f"Setting resolution to {options.width} x {options.height} "
+                f"({options.height * options.width / 1e6:.2f}MP)"
+            )
+        elif prompt.startswith("/g"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, guidance = prompt.split()
+            options.guidance = float(guidance)
+            print(f"Setting guidance to {options.guidance}")
+        elif prompt.startswith("/s"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, seed = prompt.split()
+            options.seed = int(seed)
+            print(f"Setting seed to {options.seed}")
+        elif prompt.startswith("/n"):
+            if prompt.count(" ") != 1:
+                print(f"Got invalid command '{prompt}'\n{usage}")
+                continue
+            _, steps = prompt.split()
+            options.num_steps = int(steps)
+            print(f"Setting number of steps to {options.num_steps}")
+        elif prompt.startswith("/q"):
+            print("Quitting")
+            return None
+        else:
+            if not prompt.startswith("/h"):
+                print(f"Got invalid command '{prompt}'\n{usage}")
+            print(usage)
+    return options
+
+
+def parse_img_cond_path(options: SamplingOptions | None) -> SamplingOptions | None:
+    if options is None:
+        return None
+
+    user_question = "Next conditioning image (write /h for help, /q to quit and leave empty to repeat):\n"
+    usage = (
+        "Usage: Either write your prompt directly, leave this field empty "
+        "to repeat the conditioning image or write a command starting with a slash:\n"
+        "- '/q' to quit"
+    )
+
+    while True:
+        img_cond_path = input(user_question)
+
+        if img_cond_path.startswith("/"):
+            if img_cond_path.startswith("/q"):
+                print("Quitting")
+                return None
+            else:
+                if not img_cond_path.startswith("/h"):
+                    print(f"Got invalid command '{img_cond_path}'\n{usage}")
+                print(usage)
+            continue
+
+        if img_cond_path == "":
+            break
+
+        if not os.path.isfile(img_cond_path) or not img_cond_path.lower().endswith(
+            (".jpg", ".jpeg", ".png", ".webp")
+        ):
+            print(f"File '{img_cond_path}' does not exist or is not a valid image file")
+            continue
+
+        options.img_cond_path = img_cond_path
+        break
+
+    return options
+
+
+@torch.inference_mode()
+def main(
+    name: str = "flux-dev",
+    width: int = 1360,
+    height: int = 768,
+    seed: int | None = None,
+    device: str = "cuda" if torch.cuda.is_available() else "cpu",
+    num_steps: int | None = None,
+    loop: bool = False,
+    guidance: float = 2.5,
+    offload: bool = False,
+    output_dir: str = "output",
+    add_sampling_metadata: bool = True,
+    img_cond_path: str = "assets/robot.webp",
+    track_usage: bool = False,
+):
+    """
+    Sample the flux model. Either interactively (set `--loop`) or run for a
+    single image.
+
+    Args:
+        name: Name of the base model to use (either 'flux-dev' or 'flux-schnell')
+        height: height of the sample in pixels (should be a multiple of 16)
+        width: width of the sample in pixels (should be a multiple of 16)
+        seed: Set a seed for sampling
+        device: Pytorch device
+        num_steps: number of sampling steps (default 4 for schnell, 50 for guidance distilled)
+        loop: start an interactive session and sample multiple times
+        guidance: guidance value used for guidance distillation
+        offload: offload models to CPU when not in use
+        output_dir: where to save the output images
+        add_sampling_metadata: Add the prompt to the image Exif metadata
+        img_cond_path: path to conditioning image (jpeg/png/webp)
+        track_usage: track usage of the model for licensing purposes
+    """
+
+    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
+
+    if name not in (available := ["flux-dev", "flux-schnell"]):
+        raise ValueError(f"Got unknown model name: {name}, chose from {available}")
+
+    torch_device = torch.device(device)
+    if num_steps is None:
+        num_steps = 4 if name == "flux-schnell" else 50
+
+    output_name = os.path.join(output_dir, "img_{idx}.jpg")
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+        idx = 0
+    else:
+        fns = [fn for fn in iglob(output_name.format(idx="*")) if re.search(r"img_[0-9]+\.jpg$", fn)]
+        if len(fns) > 0:
+            idx = max(int(fn.split("_")[-1].split(".")[0]) for fn in fns) + 1
+        else:
+            idx = 0
+
+    # init all components
+    t5 = load_t5(torch_device, max_length=256 if name == "flux-schnell" else 512)
+    clip = load_clip(torch_device)
+    model = load_flow_model(name, device="cpu" if offload else torch_device)
+    ae = load_ae(name, device="cpu" if offload else torch_device)
+
+    # Download and initialize the Redux adapter
+    redux_path = str(
+        get_checkpoint_path("black-forest-labs/FLUX.1-Redux-dev", "flux1-redux-dev.safetensors", "FLUX_REDUX")
+    )
+    img_embedder = ReduxImageEncoder(torch_device, redux_path=redux_path)
+
+    rng = torch.Generator(device="cpu")
+    prompt = ""
+    opts = SamplingOptions(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_steps=num_steps,
+        guidance=guidance,
+        seed=seed,
+        img_cond_path=img_cond_path,
+    )
+
+    if loop:
+        opts = parse_prompt(opts)
+        opts = parse_img_cond_path(opts)
+
+    while opts is not None:
+        if opts.seed is None:
+            opts.seed = rng.seed()
+        print(f"Generating with seed {opts.seed}:\n{opts.prompt}")
+        t0 = time.perf_counter()
+
+        # prepare input
+        x = get_noise(
+            1,
+            opts.height,
+            opts.width,
+            device=torch_device,
+            dtype=torch.bfloat16,
+            seed=opts.seed,
+        )
+        opts.seed = None
+        if offload:
+            ae = ae.cpu()
+            torch.cuda.empty_cache()
+            t5, clip = t5.to(torch_device), clip.to(torch_device)
+        inp = prepare_redux(
+            t5,
+            clip,
+            x,
+            prompt=opts.prompt,
+            encoder=img_embedder,
+            img_cond_path=opts.img_cond_path,
+        )
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(name != "flux-schnell"))
+
+        # offload TEs to CPU, load model to gpu
+        if offload:
+            t5, clip = t5.cpu(), clip.cpu()
+            torch.cuda.empty_cache()
+            model = model.to(torch_device)
+
+        # denoise initial noise
+        x = denoise(model, **inp, timesteps=timesteps, guidance=opts.guidance)
+
+        # offload model, load autoencoder to gpu
+        if offload:
+            model.cpu()
+            torch.cuda.empty_cache()
+            ae.decoder.to(x.device)
+
+        # decode latents to pixel space
+        x = unpack(x.float(), opts.height, opts.width)
+        with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
+            x = ae.decode(x)
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t1 = time.perf_counter()
+        print(f"Done in {t1 - t0:.1f}s")
+
+        idx = save_image(
+            nsfw_classifier, name, output_name, idx, x, add_sampling_metadata, prompt, track_usage=track_usage
+        )
+
+        if loop:
+            print("-" * 80)
+            opts = parse_prompt(opts)
+            opts = parse_img_cond_path(opts)
+        else:
+            opts = None
+
+
+if __name__ == "__main__":
+    Fire(main)
--- a/flux/to_remove/content_filters.py
+++ b/flux/to_remove/content_filters.py
@ -0,0 +1,171 @@
+import torch
+from einops import rearrange
+from PIL import Image
+from transformers import AutoProcessor, LlavaForConditionalGeneration, pipeline
+
+PROMPT_IMAGE_INTEGRITY = """
+Task: Analyze an image to identify potential copyright concerns or depictions of public figures.
+
+Output: Respond with only "yes" or "no"
+
+Criteria for "yes":
+- The image contains a recognizable character from copyrighted media (movies, TV, comics, games, etc.)
+- The image displays a trademarked logo or brand
+- The image depicts a recognizable public figure (celebrities, politicians, athletes, influencers, historical figures, etc.)
+
+Criteria for "no":
+- All other cases
+- When you cannot identify the specific copyrighted work or named individual
+
+Critical Requirements:
+1. You must be able to name the exact copyrighted work or specific person depicted
+2. General references to demographics or characteristics are not sufficient
+3. Base your decision solely on visual content, not interpretation
+4. Provide only the one-word answer: "yes" or "no"
+""".strip()
+
+
+PROMPT_IMAGE_INTEGRITY_FOLLOW_UP = "Does this image have copyright concerns or includes public figures?"
+
+PROMPT_TEXT_INTEGRITY = """
+Task: Analyze a text prompt to identify potential copyright concerns or requests to depict living public figures.
+
+Output: Respond with only "yes" or "no"
+
+Criteria for "Yes":
+- The prompt explicitly names a character from copyrighted media (movies, TV, comics, games, etc.)
+- The prompt explicitly mentions a trademarked logo or brand
+- The prompt names or describes a specific living public figure (celebrities, politicians, athletes, influencers, etc.)
+
+Criteria for "No":
+- All other cases
+- When you cannot identify the specific copyrighted work or named individual
+
+Critical Requirements:
+1. You must be able to name the exact copyrighted work or specific person referenced
+2. General demographic descriptions or characteristics are not sufficient
+3. Analyze only the prompt text, not potential image outcomes
+4. Provide only the one-word answer: "yes" or "no"
+
+The prompt to check is:
+-----
+{prompt}
+-----
+
+Does this prompt have copyright concerns or includes public figures?
+""".strip()
+
+
+class PixtralContentFilter(torch.nn.Module):
+    def __init__(
+        self,
+        device: torch.device = torch.device("cpu"),
+        nsfw_threshold: float = 0.85,
+    ):
+        super().__init__()
+
+        model_id = "mistral-community/pixtral-12b"
+        self.processor = AutoProcessor.from_pretrained(model_id)
+        self.model = LlavaForConditionalGeneration.from_pretrained(model_id, device_map=device)
+
+        self.yes_token, self.no_token = self.processor.tokenizer.encode(["yes", "no"])
+
+        self.nsfw_classifier = pipeline(
+            "image-classification", model="Falconsai/nsfw_image_detection", device=device
+        )
+        self.nsfw_threshold = nsfw_threshold
+
+    def yes_no_logit_processor(
+        self, input_ids: torch.LongTensor, scores: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        """
+        Sets all tokens but yes/no to the minimum.
+        """
+        scores_yes_token = scores[:, self.yes_token].clone()
+        scores_no_token = scores[:, self.no_token].clone()
+        scores_min = scores.min()
+        scores[:, :] = scores_min - 1
+        scores[:, self.yes_token] = scores_yes_token
+        scores[:, self.no_token] = scores_no_token
+        return scores
+
+    def test_image(self, image: Image.Image | str | torch.Tensor) -> bool:
+        if isinstance(image, torch.Tensor):
+            image = rearrange(image[0].clamp(-1.0, 1.0), "c h w -> h w c")
+            image = Image.fromarray((127.5 * (image + 1.0)).cpu().byte().numpy())
+        elif isinstance(image, str):
+            image = Image.open(image)
+
+        classification = next(c for c in self.nsfw_classifier(image) if c["label"] == "nsfw")
+        if classification["score"] > self.nsfw_threshold:
+            return True
+
+        # 512^2 pixels are enough for checking
+        w, h = image.size
+        f = (512**2 / (w * h)) ** 0.5
+        image = image.resize((int(f * w), int(f * h)))
+
+        chat = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "content": PROMPT_IMAGE_INTEGRITY,
+                    },
+                    {
+                        "type": "image",
+                        "image": image,
+                    },
+                    {
+                        "type": "text",
+                        "content": PROMPT_IMAGE_INTEGRITY_FOLLOW_UP,
+                    },
+                ],
+            }
+        ]
+
+        inputs = self.processor.apply_chat_template(
+            chat,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        ).to(self.model.device)
+
+        generate_ids = self.model.generate(
+            **inputs,
+            max_new_tokens=1,
+            logits_processor=[self.yes_no_logit_processor],
+            do_sample=False,
+        )
+        return generate_ids[0, -1].item() == self.yes_token
+
+    def test_txt(self, txt: str) -> bool:
+        chat = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "content": PROMPT_TEXT_INTEGRITY.format(prompt=txt),
+                    },
+                ],
+            }
+        ]
+
+        inputs = self.processor.apply_chat_template(
+            chat,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        ).to(self.model.device)
+
+        generate_ids = self.model.generate(
+            **inputs,
+            max_new_tokens=1,
+            logits_processor=[self.yes_no_logit_processor],
+            do_sample=False,
+        )
+        return generate_ids[0, -1].item() == self.yes_token
--- a/flux/util.py
+++ b/flux/util.py
@ -0,0 +1,702 @@
+import getpass
+import math
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+import requests
+import torch
+from einops import rearrange
+from huggingface_hub import hf_hub_download, login
+from PIL import ExifTags, Image
+from safetensors.torch import load_file as load_sft
+
+from flux.model import Flux, FluxLoraWrapper, FluxParams
+from flux.modules.autoencoder import AutoEncoder, AutoEncoderParams
+from flux.modules.conditioner import HFEmbedder
+
+CHECKPOINTS_DIR = Path("checkpoints")
+CHECKPOINTS_DIR.mkdir(exist_ok=True)
+BFL_API_KEY = os.getenv("BFL_API_KEY")
+
+os.environ.setdefault("TRT_ENGINE_DIR", str(CHECKPOINTS_DIR / "trt_engines"))
+(CHECKPOINTS_DIR / "trt_engines").mkdir(exist_ok=True)
+
+
+def ensure_hf_auth():
+    hf_token = os.environ.get("HF_TOKEN")
+    if hf_token:
+        print("Trying to authenticate to HuggingFace with the HF_TOKEN environment variable.")
+        try:
+            login(token=hf_token)
+            print("Successfully authenticated with HuggingFace using HF_TOKEN")
+            return True
+        except Exception as e:
+            print(f"Warning: Failed to authenticate with HF_TOKEN: {e}")
+
+    if os.path.exists(os.path.expanduser("~/.cache/huggingface/token")):
+        print("Already authenticated with HuggingFace")
+        return True
+
+    return False
+
+
+def prompt_for_hf_auth():
+    try:
+        token = getpass.getpass("HF Token (hidden input): ").strip()
+        if not token:
+            print("No token provided. Aborting.")
+            return False
+
+        login(token=token)
+        print("Successfully authenticated!")
+        return True
+    except KeyboardInterrupt:
+        print("\nAuthentication cancelled by user.")
+        return False
+    except Exception as auth_e:
+        print(f"Authentication failed: {auth_e}")
+        print("Tip: You can also run 'huggingface-cli login' or set HF_TOKEN environment variable")
+        return False
+
+
+def get_checkpoint_path(repo_id: str, filename: str, env_var: str) -> Path:
+    """Get the local path for a checkpoint file, downloading if necessary."""
+    # if os.environ.get(env_var) is not None:
+    #     local_path = os.environ[env_var]
+    #     if os.path.exists(local_path):
+    #         return Path(local_path)
+
+    #     print(
+    #         f"Trying to load model {repo_id}, {filename} from environment "
+    #         f"variable {env_var}. But file {local_path} does not exist. "
+    #         "Falling back to default location."
+    #     )
+
+    # # Create a safe directory name from repo_id
+    # safe_repo_name = repo_id.replace("/", "_")
+    # checkpoint_dir = CHECKPOINTS_DIR / safe_repo_name
+    # checkpoint_dir.mkdir(exist_ok=True)
+
+    # local_path = checkpoint_dir / filename
+
+    local_path = filename
+    from mmgp import offload
+
+    if False:
+        print(f"Downloading {filename} from {repo_id} to {local_path}")
+        try:
+            ensure_hf_auth()
+            hf_hub_download(repo_id=repo_id, filename=filename, local_dir=checkpoint_dir)
+        except Exception as e:
+            if "gated repo" in str(e).lower() or "restricted" in str(e).lower():
+                print(f"\nError: Cannot access {repo_id} -- this is a gated repository.")
+
+                # Try one more time to authenticate
+                if prompt_for_hf_auth():
+                    # Retry the download after authentication
+                    print("Retrying download...")
+                    hf_hub_download(repo_id=repo_id, filename=filename, local_dir=checkpoint_dir)
+                else:
+                    print("Authentication failed or cancelled.")
+                    print("You can also run 'huggingface-cli login' or set HF_TOKEN environment variable")
+                    raise RuntimeError(f"Authentication required for {repo_id}")
+            else:
+                raise e
+
+    return local_path
+
+
+def download_onnx_models_for_trt(model_name: str, trt_transformer_precision: str = "bf16") -> str | None:
+    """Download ONNX models for TRT to our checkpoints directory"""
+    onnx_repo_map = {
+        "flux-dev": "black-forest-labs/FLUX.1-dev-onnx",
+        "flux-schnell": "black-forest-labs/FLUX.1-schnell-onnx",
+        "flux-dev-canny": "black-forest-labs/FLUX.1-Canny-dev-onnx",
+        "flux-dev-depth": "black-forest-labs/FLUX.1-Depth-dev-onnx",
+        "flux-dev-redux": "black-forest-labs/FLUX.1-Redux-dev-onnx",
+        "flux-dev-fill": "black-forest-labs/FLUX.1-Fill-dev-onnx",
+        "flux-dev-kontext": "black-forest-labs/FLUX.1-Kontext-dev-onnx",
+    }
+
+    if model_name not in onnx_repo_map:
+        return None  # No ONNX repository required for this model
+
+    repo_id = onnx_repo_map[model_name]
+    safe_repo_name = repo_id.replace("/", "_")
+    onnx_dir = CHECKPOINTS_DIR / safe_repo_name
+
+    # Map of module names to their ONNX file paths (using specified precision)
+    onnx_file_map = {
+        "clip": "clip.opt/model.onnx",
+        "transformer": f"transformer.opt/{trt_transformer_precision}/model.onnx",
+        "transformer_data": f"transformer.opt/{trt_transformer_precision}/backbone.onnx_data",
+        "t5": "t5.opt/model.onnx",
+        "t5_data": "t5.opt/backbone.onnx_data",
+        "vae": "vae.opt/model.onnx",
+    }
+
+    # If all files exist locally, return the custom_onnx_paths format
+    if onnx_dir.exists():
+        all_files_exist = True
+        custom_paths = []
+        for module, onnx_file in onnx_file_map.items():
+            if module.endswith("_data"):
+                continue  # Skip data files
+            local_path = onnx_dir / onnx_file
+            if not local_path.exists():
+                all_files_exist = False
+                break
+            custom_paths.append(f"{module}:{local_path}")
+
+        if all_files_exist:
+            print(f"ONNX models ready in {onnx_dir}")
+            return ",".join(custom_paths)
+
+    # If not all files exist, download them
+    print(f"Downloading ONNX models from {repo_id} to {onnx_dir}")
+    print(f"Using transformer precision: {trt_transformer_precision}")
+    onnx_dir.mkdir(exist_ok=True)
+
+    # Download all ONNX files
+    for module, onnx_file in onnx_file_map.items():
+        local_path = onnx_dir / onnx_file
+        if local_path.exists():
+            continue  # Already downloaded
+
+        # Create parent directories
+        local_path.parent.mkdir(parents=True, exist_ok=True)
+
+        try:
+            print(f"Downloading {onnx_file}")
+            hf_hub_download(repo_id=repo_id, filename=onnx_file, local_dir=onnx_dir)
+        except Exception as e:
+            if "does not exist" in str(e).lower() or "not found" in str(e).lower():
+                continue
+            elif "gated repo" in str(e).lower() or "restricted" in str(e).lower():
+                print(f"Cannot access {repo_id} - requires license acceptance")
+                print("Please follow these steps:")
+                print(f"   1. Visit: https://huggingface.co/{repo_id}")
+                print("   2. Log in to your HuggingFace account")
+                print("   3. Accept the license terms and conditions")
+                print("   4. Then retry this command")
+                raise RuntimeError(f"License acceptance required for {model_name}")
+            else:
+                # Re-raise other errors
+                raise
+
+    print(f"ONNX models ready in {onnx_dir}")
+
+    # Return the custom_onnx_paths format that TRT expects: "module1:path1,module2:path2"
+    # Note: Only return the actual module paths, not the data file
+    custom_paths = []
+    for module, onnx_file in onnx_file_map.items():
+        if module.endswith("_data"):
+            continue  # Skip the data file in the return paths
+        full_path = onnx_dir / onnx_file
+        if full_path.exists():
+            custom_paths.append(f"{module}:{full_path}")
+
+    return ",".join(custom_paths)
+
+
+def check_onnx_access_for_trt(model_name: str, trt_transformer_precision: str = "bf16") -> str | None:
+    """Check ONNX access and download models for TRT - returns ONNX directory path"""
+    return download_onnx_models_for_trt(model_name, trt_transformer_precision)
+
+
+def track_usage_via_api(name: str, n=1) -> None:
+    """
+    Track usage of licensed models via the BFL API for commercial licensing compliance.
+
+    For more information on licensing BFL's models for commercial use and usage reporting,
+    see the README.md or visit: https://dashboard.bfl.ai/licensing/subscriptions?showInstructions=true
+    """
+    assert BFL_API_KEY is not None, "BFL_API_KEY is not set"
+
+    model_slug_map = {
+        "flux-dev": "flux-1-dev",
+        "flux-dev-kontext": "flux-1-kontext-dev",
+        "flux-dev-fill": "flux-tools",
+        "flux-dev-depth": "flux-tools",
+        "flux-dev-canny": "flux-tools",
+        "flux-dev-canny-lora": "flux-tools",
+        "flux-dev-depth-lora": "flux-tools",
+        "flux-dev-redux": "flux-tools",
+    }
+
+    if name not in model_slug_map:
+        print(f"Skipping tracking usage for {name}, as it cannot be tracked. Please check the model name.")
+        return
+
+    model_slug = model_slug_map[name]
+    url = f"https://api.bfl.ai/v1/licenses/models/{model_slug}/usage"
+    headers = {"x-key": BFL_API_KEY, "Content-Type": "application/json"}
+    payload = {"number_of_generations": n}
+
+    response = requests.post(url, headers=headers, json=payload)
+    if response.status_code != 200:
+        raise Exception(f"Failed to track usage: {response.status_code} {response.text}")
+    else:
+        print(f"Successfully tracked usage for {name} with {n} generations")
+
+
+def save_image(
+    nsfw_classifier,
+    name: str,
+    output_name: str,
+    idx: int,
+    x: torch.Tensor,
+    add_sampling_metadata: bool,
+    prompt: str,
+    nsfw_threshold: float = 0.85,
+    track_usage: bool = False,
+) -> int:
+    fn = output_name.format(idx=idx)
+    print(f"Saving {fn}")
+    # bring into PIL format and save
+    x = x.clamp(-1, 1)
+    x = rearrange(x[0], "c h w -> h w c")
+    img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
+    
+    if nsfw_classifier is not None:
+        nsfw_score = [x["score"] for x in nsfw_classifier(img) if x["label"] == "nsfw"][0]
+    else:
+        nsfw_score = nsfw_threshold - 1.0
+
+    if nsfw_score < nsfw_threshold:
+        exif_data = Image.Exif()
+        if name in ["flux-dev", "flux-schnell"]:
+            exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
+        else:
+            exif_data[ExifTags.Base.Software] = "AI generated;img2img;flux"
+        exif_data[ExifTags.Base.Make] = "Black Forest Labs"
+        exif_data[ExifTags.Base.Model] = name
+        if add_sampling_metadata:
+            exif_data[ExifTags.Base.ImageDescription] = prompt
+        img.save(fn, exif=exif_data, quality=95, subsampling=0)
+        if track_usage:
+            track_usage_via_api(name, 1)
+        idx += 1
+    else:
+        print("Your generated image may contain NSFW content.")
+
+    return idx
+
+
+@dataclass
+class ModelSpec:
+    params: FluxParams
+    ae_params: AutoEncoderParams
+    repo_id: str
+    repo_flow: str
+    repo_ae: str
+    lora_repo_id: str | None = None
+    lora_filename: str | None = None
+
+
+configs = {
+    "flux-dev": ModelSpec(
+        repo_id="",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=64,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-schnell": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-schnell",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=64,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=False,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-canny": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-Canny-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=128,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-canny-lora": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        lora_repo_id="black-forest-labs/FLUX.1-Canny-dev-lora",
+        lora_filename="flux1-canny-dev-lora.safetensors",
+        params=FluxParams(
+            in_channels=128,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-depth": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-Depth-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=128,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-depth-lora": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        lora_repo_id="black-forest-labs/FLUX.1-Depth-dev-lora",
+        lora_filename="flux1-depth-dev-lora.safetensors",
+        params=FluxParams(
+            in_channels=128,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-redux": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-Redux-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=64,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-fill": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-Fill-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=384,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+    "flux-dev-kontext": ModelSpec(
+        repo_id="black-forest-labs/FLUX.1-Kontext-dev",
+        repo_flow="",
+        repo_ae="ckpts/flux_vae.safetensors",
+        params=FluxParams(
+            in_channels=64,
+            out_channels=64,
+            vec_in_dim=768,
+            context_in_dim=4096,
+            hidden_size=3072,
+            mlp_ratio=4.0,
+            num_heads=24,
+            depth=19,
+            depth_single_blocks=38,
+            axes_dim=[16, 56, 56],
+            theta=10_000,
+            qkv_bias=True,
+            guidance_embed=True,
+        ),
+        ae_params=AutoEncoderParams(
+            resolution=256,
+            in_channels=3,
+            ch=128,
+            out_ch=3,
+            ch_mult=[1, 2, 4, 4],
+            num_res_blocks=2,
+            z_channels=16,
+            scale_factor=0.3611,
+            shift_factor=0.1159,
+        ),
+    ),
+}
+
+
+PREFERED_KONTEXT_RESOLUTIONS = [
+    (672, 1568),
+    (688, 1504),
+    (720, 1456),
+    (752, 1392),
+    (800, 1328),
+    (832, 1248),
+    (880, 1184),
+    (944, 1104),
+    (1024, 1024),
+    (1104, 944),
+    (1184, 880),
+    (1248, 832),
+    (1328, 800),
+    (1392, 752),
+    (1456, 720),
+    (1504, 688),
+    (1568, 672),
+]
+
+
+def aspect_ratio_to_height_width(aspect_ratio: str, area: int = 1024**2) -> tuple[int, int]:
+    width = float(aspect_ratio.split(":")[0])
+    height = float(aspect_ratio.split(":")[1])
+    ratio = width / height
+    width = round(math.sqrt(area * ratio))
+    height = round(math.sqrt(area / ratio))
+    return 16 * (width // 16), 16 * (height // 16)
+
+
+def print_load_warning(missing: list[str], unexpected: list[str]) -> None:
+    if len(missing) > 0 and len(unexpected) > 0:
+        print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing))
+        print("\n" + "-" * 79 + "\n")
+        print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected))
+    elif len(missing) > 0:
+        print(f"Got {len(missing)} missing keys:\n\t" + "\n\t".join(missing))
+    elif len(unexpected) > 0:
+        print(f"Got {len(unexpected)} unexpected keys:\n\t" + "\n\t".join(unexpected))
+
+
+def load_flow_model(name: str, model_filename, device: str | torch.device = "cuda", verbose: bool = True) -> Flux:
+    # Loading Flux
+    config = configs[name]
+
+    ckpt_path =  model_filename #config.repo_flow
+
+    with torch.device("meta"):
+        if config.lora_repo_id is not None and config.lora_filename is not None:
+            model = FluxLoraWrapper(params=config.params).to(torch.bfloat16)
+        else:
+            model = Flux(config.params).to(torch.bfloat16)
+
+    # print(f"Loading checkpoint: {ckpt_path}")
+    from mmgp import offload
+    offload.load_model_data(model, model_filename )
+
+    # # load_sft doesn't support torch.device
+    # sd = load_sft(ckpt_path, device=str(device))
+    # sd = optionally_expand_state_dict(model, sd)
+    # missing, unexpected = model.load_state_dict(sd, strict=False, assign=True)
+    # if verbose:
+    #     print_load_warning(missing, unexpected)
+
+    # if config.lora_repo_id is not None and config.lora_filename is not None:
+    #     print("Loading LoRA")
+    #     lora_path = str(get_checkpoint_path(config.lora_repo_id, config.lora_filename, "FLUX_LORA"))
+    #     lora_sd = load_sft(lora_path, device=str(device))
+    #     # loading the lora params + overwriting scale values in the norms
+    #     missing, unexpected = model.load_state_dict(lora_sd, strict=False, assign=True)
+    #     if verbose:
+    #         print_load_warning(missing, unexpected)
+    return model
+
+
+def load_t5(device: str | torch.device = "cuda", text_encoder_filename = None, max_length: int = 512) -> HFEmbedder:
+    # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
+    return HFEmbedder("",text_encoder_filename, max_length=max_length, torch_dtype=torch.bfloat16).to(device)
+
+
+def load_clip(device: str | torch.device = "cuda") -> HFEmbedder:
+    return HFEmbedder("ckpts/clip_vit_large_patch14", "", max_length=77, torch_dtype=torch.bfloat16, is_clip  =True).to(device)
+
+
+def load_ae(name: str, device: str | torch.device = "cuda") -> AutoEncoder:
+    config = configs[name]
+    ckpt_path = str(get_checkpoint_path(config.repo_id, config.repo_ae, "FLUX_AE"))
+
+    # Loading the autoencoder
+    with torch.device("meta"):
+        ae = AutoEncoder(config.ae_params)
+
+    # print(f"Loading AE checkpoint: {ckpt_path}")
+    sd = load_sft(ckpt_path, device=str(device))
+    missing, unexpected = ae.load_state_dict(sd, strict=False, assign=True)
+    print_load_warning(missing, unexpected)
+    return ae
+
+
+def optionally_expand_state_dict(model: torch.nn.Module, state_dict: dict) -> dict:
+    """
+    Optionally expand the state dict to match the model's parameters shapes.
+    """
+    for name, param in model.named_parameters():
+        if name in state_dict:
+            if state_dict[name].shape != param.shape:
+                print(
+                    f"Expanding '{name}' with shape {state_dict[name].shape} to model parameter with shape {param.shape}."
+                )
+                # expand with zeros:
+                expanded_state_dict_weight = torch.zeros_like(param, device=state_dict[name].device)
+                slices = tuple(slice(0, dim) for dim in state_dict[name].shape)
+                expanded_state_dict_weight[slices] = state_dict[name]
+                state_dict[name] = expanded_state_dict_weight
+
+    return state_dict
+
+
--- a/loras_flux/readme.txt
+++ b/loras_flux/readme.txt
@ -0,0 +1 @@
+flux loras go here
--- a/ltx_video/ltxv.py
+++ b/ltx_video/ltxv.py
@ -149,6 +149,7 @@ class LTXV:
        self,
        model_filepath: str,
        text_encoder_filepath: str,
+        model_def,
        dtype = torch.bfloat16,
        VAE_dtype = torch.bfloat16, 
        mixed_precision_transformer = False
@ -157,8 +158,8 @@ class LTXV:
        # if dtype == torch.float16:
        dtype  = torch.bfloat16
        self.mixed_precision_transformer = mixed_precision_transformer
-        self.distilled = any("lora" in name for name in model_filepath)
-        model_filepath = [name for name in model_filepath if not "lora" in name ]
+        self.model_def = model_def
+        self.pipeline_config = model_def["LTXV_config"]        
        # with safe_open(ckpt_path, framework="pt") as f:
        #     metadata = f.metadata()
        #     config_str = metadata.get("config")
@ -220,11 +221,11 @@ class LTXV:
            prompt_enhancer_llm_model = None
            prompt_enhancer_llm_tokenizer = None

-        if prompt_enhancer_image_caption_model != None:
-            pipe["prompt_enhancer_image_caption_model"] = prompt_enhancer_image_caption_model
-            prompt_enhancer_image_caption_model._model_dtype = torch.float
+        # if prompt_enhancer_image_caption_model != None:
+        #     pipe["prompt_enhancer_image_caption_model"] = prompt_enhancer_image_caption_model
+        #     prompt_enhancer_image_caption_model._model_dtype = torch.float
        
-            pipe["prompt_enhancer_llm_model"] = prompt_enhancer_llm_model
+        #     pipe["prompt_enhancer_llm_model"] = prompt_enhancer_llm_model

        # offload.profile(pipe, profile_no=5, extraModelsToQuantize = None, quantizeTransformer = False, budgets = { "prompt_enhancer_llm_model" : 10000, "prompt_enhancer_image_caption_model" : 10000, "vae" : 3000, "*" : 100 }, verboseLevel=2)

@ -299,14 +300,10 @@ class LTXV:
            conditioning_media_paths = None
            conditioning_start_frames = None

-        if self.distilled :
-            pipeline_config = "ltx_video/configs/ltxv-13b-0.9.7-distilled.yaml"
-        else:
-            pipeline_config = "ltx_video/configs/ltxv-13b-0.9.7-dev.yaml"
        # check if pipeline_config is a file
-        if not os.path.isfile(pipeline_config):
-            raise ValueError(f"Pipeline config file {pipeline_config} does not exist")
-        with open(pipeline_config, "r") as f:
+            if not os.path.isfile(self.pipeline_config):
+                raise ValueError(f"Pipeline config file {self.pipeline_config} does not exist")
+        with open(self.pipeline_config, "r") as f:
            pipeline_config = yaml.safe_load(f)


@ -520,7 +517,7 @@ def get_media_num_frames(media_path: str) -> int:
        return media_path.shape[1]
    elif isinstance(media_path, str) and any( media_path.lower().endswith(ext) for ext in [".mp4", ".avi", ".mov", ".mkv"]):
        reader = imageio.get_reader(media_path)
-        return min(reader.count_frames(), max_frames)
+        return min(reader.count_frames(), 0) # to do
    else:
        raise Exception("video format not supported")

@ -564,6 +561,3 @@ def load_media_file(
        raise Exception("video format not supported")
    return media_tensor

-
-if __name__ == "__main__":
-    main()
--- a/postprocessing/film_grain.py
+++ b/postprocessing/film_grain.py
@ -0,0 +1,21 @@
+# Thanks to https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/film_grain.py
+import torch
+
+def add_film_grain(images: torch.Tensor, grain_intensity: float = 0, saturation: float = 0.5):
+    device = images.device 
+
+    images = images.permute(1, 2 ,3 ,0)
+    images.add_(1.).div_(2.)
+    grain = torch.randn_like(images, device=device)
+    grain[:, :, :, 0] *= 2
+    grain[:, :, :, 2] *= 3
+    grain = grain * saturation + grain[:, :, :, 1].unsqueeze(3).repeat(
+        1, 1, 1, 3
+    ) * (1 - saturation)
+
+    # Blend the grain with the image
+    noised_images = images + grain_intensity * grain
+    noised_images.clamp_(0, 1)
+    noised_images.sub_(.5).mul_(2.)
+    noised_images = noised_images.permute(3, 0, 1 ,2)
+    return noised_images
--- a/preprocessing/depth_anything_v2/layers/attention.py
+++ b/preprocessing/depth_anything_v2/layers/attention.py
@ -15,13 +15,7 @@ from torch import nn

 logger = logging.getLogger("dinov2")

-try:
-    from xformers.ops import memory_efficient_attention, unbind, fmha
-
-    XFORMERS_AVAILABLE = True
-except ImportError:
-    logger.warning("xFormers not available")
-    XFORMERS_AVAILABLE = False
+XFORMERS_AVAILABLE = False


 class Attention(nn.Module):
--- a/preprocessing/depth_anything_v2/layers/block.py
+++ b/preprocessing/depth_anything_v2/layers/block.py
@ -23,14 +23,7 @@ from .mlp import Mlp
 logger = logging.getLogger("dinov2")


-try:
-    from xformers.ops import fmha
-    from xformers.ops import scaled_index_add, index_select_cat
-
-    XFORMERS_AVAILABLE = True
-except ImportError:
-    # logger.warning("xFormers not available")
-    XFORMERS_AVAILABLE = False
+XFORMERS_AVAILABLE = False


 class Block(nn.Module):
--- a/preprocessing/matanyone/app.py
+++ b/preprocessing/matanyone/app.py
@ -65,6 +65,7 @@ def get_frames_from_image(image_input, image_state):
    Return 
        [[0:nearest_frame], [nearest_frame:], nearest_frame]
    """
+    load_sam()

    user_name = time.time()
    frames = [image_input] * 2  # hardcode: mimic a video with 2 frames
@ -89,7 +90,7 @@ def get_frames_from_image(image_input, image_state):
                        gr.update(visible=True), gr.update(visible=True), \
                        gr.update(visible=True), gr.update(visible=True),\
                        gr.update(visible=True), gr.update(visible=True), \
-                        gr.update(visible=True), gr.update(visible=False), \
+                        gr.update(visible=True), gr.update(value="", visible=True),  gr.update(visible=False), \
                        gr.update(visible=False), gr.update(visible=True), \
                        gr.update(visible=True)

@ -103,6 +104,8 @@ def get_frames_from_video(video_input, video_state):
        [[0:nearest_frame], [nearest_frame:], nearest_frame]
    """

+    load_sam()
+
    while model == None:
        time.sleep(1)
        
@ -273,6 +276,20 @@ def save_video(frames, output_path, fps):

    return output_path

+def mask_to_xyxy_box(mask):
+    rows, cols = np.where(mask == 255)
+    xmin = min(cols)
+    xmax = max(cols) + 1
+    ymin = min(rows)
+    ymax = max(rows) + 1
+    xmin = max(xmin, 0)
+    ymin = max(ymin, 0)
+    xmax = min(xmax, mask.shape[1])
+    ymax = min(ymax, mask.shape[0])
+    box = [xmin, ymin, xmax, ymax]
+    box = [int(x) for x in box]
+    return box
+
 # image matting
 def image_matting(video_state, interactive_state, mask_dropdown, erode_kernel_size, dilate_kernel_size, refine_iter):
    matanyone_processor = InferenceCore(matanyone_model, cfg=matanyone_model.cfg)
@ -320,9 +337,17 @@ def image_matting(video_state, interactive_state, mask_dropdown, erode_kernel_si
    foreground = output_frames

    foreground_output = Image.fromarray(foreground[-1])
-    alpha_output = Image.fromarray(alpha[-1][:,:,0])
-
-    return foreground_output, gr.update(visible=True) 
+    alpha_output = alpha[-1][:,:,0]
+    frame_temp = alpha_output.copy()
+    alpha_output[frame_temp > 127] = 0
+    alpha_output[frame_temp <= 127] = 255
+    bbox_info = mask_to_xyxy_box(alpha_output)
+    h = alpha_output.shape[0]
+    w = alpha_output.shape[1]
+    bbox_info = [str(int(bbox_info[0]/ w * 100 )), str(int(bbox_info[1]/ h * 100 )),  str(int(bbox_info[2]/ w * 100 )), str(int(bbox_info[3]/ h * 100 )) ]
+    bbox_info = ":".join(bbox_info)
+    alpha_output = Image.fromarray(alpha_output)
+    return foreground_output, alpha_output, bbox_info, gr.update(visible=True), gr.update(visible=True) 

 # video matting
 def video_matting(video_state, end_slider, matting_type, interactive_state, mask_dropdown, erode_kernel_size, dilate_kernel_size):
@ -469,6 +494,13 @@ def restart():
        gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), \
        gr.update(visible=False), gr.update(visible=False, choices=[], value=[]), "", gr.update(visible=False)

+def load_sam():
+    global model_loaded
+    global model
+    global matanyone_model 
+    model.samcontroler.sam_controler.model.to(arg_device)
+    matanyone_model.to(arg_device)
+
 def load_unload_models(selected):
    global model_loaded
    global model
@ -476,8 +508,7 @@ def load_unload_models(selected):
    if selected:
        # print("Matanyone Tab Selected")
        if model_loaded:
-            model.samcontroler.sam_controler.model.to(arg_device)
-            matanyone_model.to(arg_device)
+            load_sam()
        else:
            # args, defined in track_anything.py
            sam_checkpoint_url_dict = {
@ -522,12 +553,16 @@ def export_to_vace_video_input(foreground_video_output):

 def export_image(image_refs, image_output):
    gr.Info("Masked Image transferred to Current Video")
-    # return "MV#" + str(time.time()), foreground_video_output, alpha_video_output
    if image_refs == None:
        image_refs =[]
    image_refs.append( image_output)
    return image_refs

+def export_image_mask(image_input, image_mask):
+    gr.Info("Input Image & Mask transferred to Current Video")
+    return Image.fromarray(image_input), image_mask
+
+
 def export_to_current_video_engine(model_type, foreground_video_output, alpha_video_output):
    gr.Info("Original Video and Full Mask have been transferred")
    # return "MV#" + str(time.time()), foreground_video_output, alpha_video_output
@ -543,7 +578,7 @@ def teleport_to_video_tab(tab_state):
    return gr.Tabs(selected="video_gen")


-def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, vace_image_refs):
+def display(tabs, tab_state, model_choice, vace_video_input, vace_image_input, vace_video_mask, vace_image_mask, vace_image_refs):
    # my_tab.select(fn=load_unload_models, inputs=[], outputs=[])

    media_url = "https://github.com/pq-yang/MatAnyone/releases/download/media/"
@ -677,7 +712,7 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                                foreground_output_button = gr.Button(value="Black & White Video Output", visible=False, elem_classes="new_button")
                            with gr.Column(scale=2):
                                alpha_video_output = gr.Video(label="B & W Mask Video Output", visible=False, elem_classes="video")
-                                alpha_output_button = gr.Button(value="Alpha Mask Output", visible=False, elem_classes="new_button")
+                                export_image_mask_btn = gr.Button(value="Alpha Mask Output", visible=False, elem_classes="new_button")
                        with gr.Row():
                            with gr.Row(visible= False):
                                export_to_vace_video_14B_btn = gr.Button("Export to current Video Input Video For Inpainting", visible= False)
@ -696,7 +731,7 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                    ],
                    outputs=[video_state, video_info, template_frame,
                            image_selection_slider, end_selection_slider,  track_pause_number_slider, point_prompt, matting_type, clear_button_click, add_mask_button, matting_button, template_frame,
-                            foreground_video_output, alpha_video_output, foreground_output_button, alpha_output_button, mask_dropdown, step2_title]
+                            foreground_video_output, alpha_video_output, foreground_output_button, export_image_mask_btn, mask_dropdown, step2_title]
                )   

                # second step: select images from slider
@ -755,7 +790,7 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                        foreground_video_output, alpha_video_output,
                        template_frame,
                        image_selection_slider, end_selection_slider, track_pause_number_slider,point_prompt, export_to_vace_video_14B_btn, export_to_current_video_engine_btn, matting_type, clear_button_click, 
-                        add_mask_button, matting_button, template_frame, foreground_video_output, alpha_video_output, remove_mask_button, foreground_output_button, alpha_output_button, mask_dropdown, video_info, step2_title
+                        add_mask_button, matting_button, template_frame, foreground_video_output, alpha_video_output, remove_mask_button, foreground_output_button, export_image_mask_btn, mask_dropdown, video_info, step2_title
                    ],
                    queue=False,
                    show_progress=False)
@ -770,7 +805,7 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                        foreground_video_output, alpha_video_output,
                        template_frame,
                        image_selection_slider , end_selection_slider, track_pause_number_slider,point_prompt, export_to_vace_video_14B_btn, export_to_current_video_engine_btn, matting_type, clear_button_click, 
-                        add_mask_button, matting_button, template_frame, foreground_video_output, alpha_video_output, remove_mask_button, foreground_output_button, alpha_output_button, mask_dropdown, video_info, step2_title
+                        add_mask_button, matting_button, template_frame, foreground_video_output, alpha_video_output, remove_mask_button, foreground_output_button, export_image_mask_btn, mask_dropdown, video_info, step2_title
                    ],
                    queue=False,
                    show_progress=False)
@ -872,15 +907,19 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                    # output image
                    with gr.Row(equal_height=True):
                        foreground_image_output = gr.Image(type="pil", label="Foreground Output", visible=False, elem_classes="image")
+                        alpha_image_output = gr.Image(type="pil", label="Mask", visible=False, elem_classes="image")
+                    with gr.Row(equal_height=True):
+                        bbox_info = gr.Text(label ="Mask BBox Info (Left:Top:Right:Bottom)", interactive= False)
                    with gr.Row():
-                        with gr.Row():
-                            export_image_btn = gr.Button(value="Add to current Reference Images", visible=False, elem_classes="new_button")
-                    with gr.Column(scale=2, visible= False):
-                        alpha_image_output = gr.Image(type="pil", label="Alpha Output", visible=False, elem_classes="image")
-                        alpha_output_button = gr.Button(value="Alpha Mask Output", visible=False, elem_classes="new_button")
+                        # with gr.Row():
+                        export_image_btn = gr.Button(value="Add to current Reference Images", visible=False, elem_classes="new_button")
+                    # with gr.Column(scale=2, visible= True):
+                        export_image_mask_btn = gr.Button(value="Set to Control Image & Mask", visible=False, elem_classes="new_button")

                export_image_btn.click(  fn=export_image, inputs= [vace_image_refs, foreground_image_output], outputs= [vace_image_refs]).then( #video_prompt_video_guide_trigger, 
                    fn=teleport_to_video_tab, inputs= [tab_state], outputs= [tabs])
+                export_image_mask_btn.click(  fn=export_image_mask, inputs= [image_input, alpha_image_output], outputs= [vace_image_input, vace_image_mask]).then( #video_prompt_video_guide_trigger, 
+                    fn=teleport_to_video_tab, inputs= [tab_state], outputs= [tabs])

                # first step: get the image information 
                extract_frames_button.click(
@ -890,9 +929,17 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                    ],
                    outputs=[image_state, image_info, template_frame,
                            image_selection_slider, track_pause_number_slider,point_prompt, clear_button_click, add_mask_button, matting_button, template_frame,
-                            foreground_image_output, alpha_image_output, export_image_btn, alpha_output_button, mask_dropdown, step2_title]
+                            foreground_image_output, alpha_image_output, bbox_info, export_image_btn, export_image_mask_btn, mask_dropdown, step2_title]
                )   

+                # points clear
+                clear_button_click.click(
+                    fn = clear_click,
+                    inputs = [image_state, click_state,],
+                    outputs = [template_frame,click_state],
+                )
+
+
                # second step: select images from slider
                image_selection_slider.release(fn=select_image_template, 
                                            inputs=[image_selection_slider, image_state, interactive_state], 
@ -925,7 +972,7 @@ def display(tabs, tab_state, model_choice, vace_video_input, vace_video_mask, va
                matting_button.click(
                    fn=image_matting,
                    inputs=[image_state, interactive_state, mask_dropdown, erode_kernel_size, dilate_kernel_size, image_selection_slider],
-                    outputs=[foreground_image_output, export_image_btn]
+                    outputs=[foreground_image_output, alpha_image_output,bbox_info, export_image_btn, export_image_mask_btn]
                )


--- a/wan/any2video.py
+++ b/wan/any2video.py
@ -61,6 +61,7 @@ class WanAny2V:
        checkpoint_dir,
        model_filename = None,
        model_type = None, 
+        model_def = None,
        base_model_type = None,
        text_encoder_filename = None,
        quantizeTransformer = False,
@ -75,7 +76,8 @@ class WanAny2V:
        self.dtype = dtype
        self.num_train_timesteps = config.num_train_timesteps
        self.param_dtype = config.param_dtype
-
+        self.model_def = model_def
+        self.image_outputs = model_def.get("image_outputs", False)
        self.text_encoder = T5EncoderModel(
            text_len=config.text_len,
            dtype=config.t5_dtype,
@ -106,18 +108,18 @@ class WanAny2V:
        #     config = json.load(f)
        # from mmgp import safetensors2
        # sd = safetensors2.torch_load_file(xmodel_filename)
-
+        # model_filename = "c:/temp/flf/diffusion_pytorch_model-00001-of-00007.safetensors"
        base_config_file = f"configs/{base_model_type}.json"
-        forcedConfigPath = base_config_file if len(model_filename) > 1 or base_model_type in ["flf2v_720p"]  else None
+        forcedConfigPath = base_config_file if len(model_filename) > 1 else None
+        # forcedConfigPath = base_config_file = f"configs/flf2v_720p.json"
        # model_filename[1] = xmodel_filename
        self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel,do_quantize= quantizeTransformer and not save_quantized, writable_tensors= False, defaultConfigPath=base_config_file , forcedConfigPath= forcedConfigPath)
        # self.model = offload.load_model_data(self.model, xmodel_filename )
        # offload.load_model_data(self.model, "c:/temp/Phantom-Wan-1.3B.pth")
-        # self.model.to(torch.bfloat16)
-        # self.model.cpu()
        self.model.lock_layers_dtypes(torch.float32 if mixed_precision_transformer else dtype)
        offload.change_dtype(self.model, dtype, True)
-        # offload.save_model(self.model, "multitalkbf16.safetensors", config_file_path=base_config_file, filter_sd=sd)
+        # offload.save_model(self.model, "flf2v_720p.safetensors", config_file_path=base_config_file)
+        # offload.save_model(self.model, "flf2v_quanto_int8_fp16_720p.safetensors", do_quantize= True, config_file_path=base_config_file)
        # offload.save_model(self.model, "multitalk_quanto_fp16.safetensors", do_quantize= True, config_file_path=base_config_file, filter_sd=sd)

        # offload.save_model(self.model, "wan2.1_selforcing_fp16.safetensors", config_file_path=base_config_file)
@ -126,7 +128,7 @@ class WanAny2V:
        self.model.eval().requires_grad_(False)
        if save_quantized:
            from wgp import save_quantized_model
-            save_quantized_model(self.model, model_type, model_filename[1 if base_model_type=="fantasy" else 0], dtype, base_config_file)
+            save_quantized_model(self.model, model_type, model_filename[0], dtype, base_config_file)

        self.sample_neg_prompt = config.sample_neg_prompt

@ -208,7 +210,7 @@ class WanAny2V:

            if refs is not None:
                length = len(refs)
-                mask_pad = torch.zeros_like(mask[:, :length, :, :])
+                mask_pad = torch.zeros(mask.shape[0], length, *mask.shape[-2:], dtype=mask.dtype, device=mask.device)
                mask = torch.cat((mask_pad, mask), dim=1)
            result_masks.append(mask)
        return result_masks
@ -327,20 +329,6 @@ class WanAny2V:
            self.background_mask = [ item if item != None else self.background_mask[0] for item in self.background_mask ] # deplicate background mask with double control net since first controlnet image ref modifed by ref
        return src_video, src_mask, src_ref_images

-    def decode_latent(self, zs, ref_images=None, tile_size= 0 ):
-        if ref_images is None:
-            ref_images = [None] * len(zs)
-        # else:
-        #     assert len(zs) == len(ref_images)
-
-        trimed_zs = []
-        for z, refs in zip(zs, ref_images):
-            if refs is not None:
-                z = z[:, len(refs):, :, :]
-            trimed_zs.append(z)
-
-        return self.vae.decode(trimed_zs, tile_size= tile_size)
-
    def get_vae_latents(self, ref_images, device, tile_size= 0):
        ref_vae_latents = []
        for ref_image in ref_images:
@ -366,6 +354,7 @@ class WanAny2V:
        height = 720,
        fit_into_canvas = True,
        frame_num=81,
+        batch_size = 1,
        shift=5.0,
        sample_solver='unipc',
        sampling_steps=50,
@ -397,6 +386,7 @@ class WanAny2V:
        NAG_alpha = 0.5,
        offloadobj = None,
        apg_switch = False,
+        speakers_bboxes = None,
        **bbargs
                ):
        
@ -478,7 +468,7 @@ class WanAny2V:
            if input_frames != None:
                _ , preframes_count, height, width = input_frames.shape
                lat_h, lat_w = height // self.vae_stride[1], width // self.vae_stride[2]                                    
-                clip_context = self.clip.visual([input_frames[:, -1:]]) #.to(self.param_dtype)
+                clip_context = self.clip.visual([input_frames[:, -1:]]) if model_type != "flf2v_720p" else self.clip.visual([input_frames[:, -1:], input_frames[:, -1:]])
                input_frames = input_frames.to(device=self.device).to(dtype= self.VAE_dtype)
                enc =  torch.concat( [input_frames, torch.zeros( (3, frame_num-preframes_count, height, width), 
                                     device=self.device, dtype= self.VAE_dtype)], 
@ -488,7 +478,7 @@ class WanAny2V:
                preframes_count = 1
                image_start = TF.to_tensor(image_start)
                any_end_frame = image_end != None 
-                add_frames_for_end_image = any_end_frame and model_type not in ["fun_inp_1.3B", "fun_inp", "i2v_720p"]
+                add_frames_for_end_image = any_end_frame and model_type == "i2v"
                if any_end_frame:
                    image_end = TF.to_tensor(image_end) 
                    if add_frames_for_end_image:
@ -517,8 +507,8 @@ class WanAny2V:
                    img_interpolated2 = resize_lanczos(image_end, h, w).sub_(0.5).div_(0.5).unsqueeze(0).transpose(0,1).to(self.device) #, self.dtype
                    image_end = resize_lanczos(image_end, clip_image_size, clip_image_size)
                    image_end = image_end.sub_(0.5).div_(0.5).to(self.device) #, self.dtype
-                if image_end != None and model_type == "flf2v_720p":
-                    clip_context = self.clip.visual([image_start[:, None, :, :], image_end[:, None, :, :]])
+                if model_type == "flf2v_720p":                    
+                    clip_context = self.clip.visual([image_start[:, None, :, :], image_end[:, None, :, :] if image_end != None else image_start[:, None, :, :]])
                else:
                    clip_context = self.clip.visual([image_start[:, None, :, :]])

@ -554,8 +544,8 @@ class WanAny2V:
            overlapped_latents_frames_num = int(1 + (preframes_count-1) // 4)
            if overlapped_latents != None:
                # disabled because looks worse
-                if False and overlapped_latents_frames_num > 1: lat_y[:, 1:overlapped_latents_frames_num]  = overlapped_latents[:, 1:]
-                extended_overlapped_latents = lat_y[:, :overlapped_latents_frames_num].clone() 
+                if False and overlapped_latents_frames_num > 1: lat_y[:, :, 1:overlapped_latents_frames_num]  = overlapped_latents[:, 1:]
+                extended_overlapped_latents = lat_y[:, :overlapped_latents_frames_num].clone().unsqueeze(0)
            y = torch.concat([msk, lat_y])
            lat_y = None
            kwargs.update({'clip_fea': clip_context, 'y': y})
@ -586,7 +576,7 @@ class WanAny2V:
                    overlapped_frames_num = (overlapped_latents_frames_num-1) * 4 + 1
                else: 
                    overlapped_latents_frames_num = overlapped_frames_num  = 0
-                if len(keep_frames_parsed) == 0  or  (overlapped_frames_num + len(keep_frames_parsed)) == input_frames.shape[1] and all(keep_frames_parsed) : keep_frames_parsed = [] 
+                if len(keep_frames_parsed) == 0  or self.image_outputs or  (overlapped_frames_num + len(keep_frames_parsed)) == input_frames.shape[1] and all(keep_frames_parsed) : keep_frames_parsed = [] 
                injection_denoising_step = int(sampling_steps * (1. - denoising_strength) )
                latent_keep_frames = []
                if source_latents.shape[1] < lat_frames or len(keep_frames_parsed) > 0:
@ -609,6 +599,7 @@ class WanAny2V:
                input_ref_images = self.get_vae_latents(input_ref_images, self.device)
                input_ref_images_neg = torch.zeros_like(input_ref_images)
                ref_images_count = input_ref_images.shape[1] if input_ref_images != None else 0
+                trim_frames = input_ref_images.shape[1]

        # Vace
        if vace :
@ -633,8 +624,8 @@ class WanAny2V:
            context_scale = context_scale if context_scale != None else [1.0] * len(z)
            kwargs.update({'vace_context' : z, 'vace_context_scale' : context_scale, "ref_images_count": ref_images_count })
            if overlapped_latents != None :
-                overlapped_latents_size = overlapped_latents.shape[1]
-                extended_overlapped_latents = z[0][0:16, 0:overlapped_latents_size + ref_images_count].clone()
+                overlapped_latents_size = overlapped_latents.shape[2]
+                extended_overlapped_latents = z[0][:16, :overlapped_latents_size + ref_images_count].clone().unsqueeze(0)

            target_shape = list(z0[0].shape)
            target_shape[0] = int(target_shape[0] / 2)
@ -649,7 +640,7 @@ class WanAny2V:
            from wan.multitalk.multitalk import get_target_masks
            audio_proj = [audio.to(self.dtype) for audio in audio_proj]
            human_no = len(audio_proj[0])
-            token_ref_target_masks = get_target_masks(human_no, lat_h, lat_w, height, width, face_scale = 0.05, bbox = None).to(self.dtype) if human_no > 1 else None
+            token_ref_target_masks = get_target_masks(human_no, lat_h, lat_w, height, width, face_scale = 0.05, bbox = speakers_bboxes).to(self.dtype) if human_no > 1 else None

        if fantasy and audio_proj != None:
            kwargs.update({ "audio_proj": audio_proj.to(self.dtype), "audio_context_lens": audio_context_lens, }) 
@ -658,8 +649,8 @@ class WanAny2V:
        if self._interrupt:
            return None

+        expand_shape = [batch_size] + [-1] * len(target_shape)
        # Ropes
-        batch_size = 1
        if target_camera != None:
            shape = list(target_shape[1:])
            shape[0] *= 2
@ -692,20 +683,20 @@ class WanAny2V:
        # init denoising
        updated_num_steps= len(timesteps)
        if callback != None:
-            from wgp import update_loras_slists
+            from wan.utils.utils import update_loras_slists
            update_loras_slists(self.model, loras_slists, updated_num_steps)
            callback(-1, None, True, override_num_inference_steps = updated_num_steps)

        if sample_scheduler != None:
            scheduler_kwargs = {} if isinstance(sample_scheduler, FlowMatchScheduler) else {"generator": seed_g}
-
-        latents = torch.randn( *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
+        # b, c, lat_f, lat_h, lat_w
+        latents = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
        if apg_switch != 0:  
            apg_momentum = -0.75
            apg_norm_threshold = 55
            text_momentumbuffer  = MomentumBuffer(apg_momentum) 
            audio_momentumbuffer = MomentumBuffer(apg_momentum) 
-
+        # self.image_outputs = False
        # denoising
        for i, t in enumerate(tqdm(timesteps)):
            offload.set_step_no_for_lora(self.model, i)
@ -715,36 +706,36 @@ class WanAny2V:

            if denoising_strength < 1 and input_frames != None and i <= injection_denoising_step:
                sigma = t / 1000
-                noise = torch.randn( *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
+                noise = torch.randn(batch_size, *target_shape, dtype=torch.float32, device=self.device, generator=seed_g)
                if inject_from_start:
                    new_latents = latents.clone()
-                    new_latents[:, :source_latents.shape[1] ] = noise[:, :source_latents.shape[1] ] * sigma + (1 - sigma) * source_latents
+                    new_latents[:,:, :source_latents.shape[1] ] = noise[:, :, :source_latents.shape[1] ] * sigma + (1 - sigma) * source_latents.unsqueeze(0)
                    for latent_no, keep_latent in enumerate(latent_keep_frames):
                        if not keep_latent:
-                            new_latents[:, latent_no:latent_no+1 ] = latents[:, latent_no:latent_no+1]
+                            new_latents[:, :, latent_no:latent_no+1 ] = latents[:, :, latent_no:latent_no+1]
                    latents = new_latents
                    new_latents = None
                else:
-                    latents = noise * sigma + (1 - sigma) * source_latents
+                    latents = noise * sigma + (1 - sigma) * source_latents.unsqueeze(0)
                noise = None

            if extended_overlapped_latents != None:
                latent_noise_factor = t / 1000
-                latents[:, 0:extended_overlapped_latents.shape[1]]   = extended_overlapped_latents  * (1.0 - latent_noise_factor) + torch.randn_like(extended_overlapped_latents ) * latent_noise_factor 
+                latents[:, :, :extended_overlapped_latents.shape[2]]   = extended_overlapped_latents  * (1.0 - latent_noise_factor) + torch.randn_like(extended_overlapped_latents ) * latent_noise_factor 
                if vace:
                    overlap_noise_factor = overlap_noise / 1000 
                    for zz in z:
-                        zz[0:16, ref_images_count:extended_overlapped_latents.shape[1] ]   = extended_overlapped_latents[:, ref_images_count:]  * (1.0 - overlap_noise_factor) + torch.randn_like(extended_overlapped_latents[:, ref_images_count:] ) * overlap_noise_factor 
+                        zz[0:16, ref_images_count:extended_overlapped_latents.shape[2] ]   = extended_overlapped_latents[0, :, ref_images_count:]  * (1.0 - overlap_noise_factor) + torch.randn_like(extended_overlapped_latents[0, :, ref_images_count:] ) * overlap_noise_factor 

            if target_camera != None:
-                latent_model_input = torch.cat([latents, source_latents], dim=1)
+                latent_model_input = torch.cat([latents, source_latents.unsqueeze(0).expand(*expand_shape)], dim=2) # !!!!
            else:
                latent_model_input = latents

            if phantom:
                gen_args = {
-                    "x" : ([ torch.cat([latent_model_input[:,:-ref_images_count], input_ref_images], dim=1) ] * 2 + 
-                        [ torch.cat([latent_model_input[:,:-ref_images_count], input_ref_images_neg], dim=1)]),
+                    "x" : ([ torch.cat([latent_model_input[:,:, :-ref_images_count], input_ref_images.unsqueeze(0).expand(*expand_shape)], dim=2) ] * 2 + 
+                        [ torch.cat([latent_model_input[:,:, :-ref_images_count], input_ref_images_neg.unsqueeze(0).expand(*expand_shape)], dim=2)]),
                    "context": [context, context_null, context_null] ,
                }
            elif fantasy:
@ -753,7 +744,7 @@ class WanAny2V:
                    "context" : [context, context_null, context_null],
                    "audio_scale": [audio_scale, None, None ]
                }
-            elif multitalk:
+            elif multitalk and audio_proj != None:
                gen_args = {
                    "x" : [latent_model_input, latent_model_input, latent_model_input],
                    "context" : [context, context_null, context_null],
@ -832,38 +823,41 @@ class WanAny2V:
            if sample_solver == "euler":
                dt = timesteps[i] if i == len(timesteps)-1 else (timesteps[i] - timesteps[i + 1])
                dt = dt / self.num_timesteps
-                latents = latents - noise_pred * dt[:, None, None, None]
+                latents = latents - noise_pred * dt[:, None, None, None, None]
            else:
-                temp_x0 = sample_scheduler.step(
-                    noise_pred[:, :target_shape[1]].unsqueeze(0),
+                latents = sample_scheduler.step(
+                    noise_pred[:, :, :target_shape[1]],
                    t,
-                    latents.unsqueeze(0),
+                    latents,
                    **scheduler_kwargs)[0]
-                latents = temp_x0.squeeze(0)
-                del temp_x0

            if callback is not None:
-                callback(i, latents, False)         
+                latents_preview = latents
+                if vace and ref_images_count > 0: latents_preview = latents_preview[:, :, ref_images_count: ] 
+                if trim_frames > 0:  latents_preview=  latents_preview[:, :,:-trim_frames]
+                if len(latents_preview) > 1: latents_preview = latents_preview.transpose(0,2)
+                callback(i, latents_preview[0], False)
+                latents_preview = None

-        x0 = [latents]
+        if vace and ref_images_count > 0: latents = latents[:, :, ref_images_count:]
+        if trim_frames > 0:  latents=  latents[:, :,:-trim_frames]
+        if return_latent_slice != None:
+            latent_slice = latents[:, :, return_latent_slice].clone()
+
+        x0 =latents.unbind(dim=0)

        if chipmunk:
            self.model.release_chipmunk() # need to add it at every exit when in prod

-        if return_latent_slice != None:
-            latent_slice = latents[:, return_latent_slice].clone()
-        if vace:
-            # vace post processing
-            videos = self.decode_latent(x0, input_ref_images, VAE_tile_size)
-        else:
-            if phantom and input_ref_images != None:
-                trim_frames = input_ref_images.shape[1]
-            if trim_frames > 0: x0 = [x0_[:,:-trim_frames] for x0_ in x0]
-            videos = self.vae.decode(x0, VAE_tile_size)
+        videos = self.vae.decode(x0, VAE_tile_size)

+        if self.image_outputs:
+            videos = torch.cat(videos, dim=1) if len(videos) > 1 else videos[0]
+        else:
+            videos = videos[0] # return only first video     
        if return_latent_slice != None:
-            return { "x" : videos[0], "latent_slice" : latent_slice }
-        return videos[0]
+            return { "x" : videos, "latent_slice" : latent_slice }
+        return videos

    def adapt_vace_model(self):
        model = self.model
--- a/wan/diffusion_forcing.py
+++ b/wan/diffusion_forcing.py
@ -19,7 +19,7 @@ from wan.utils.utils import calculate_new_dimensions
 from .utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
                               get_sampling_sigmas, retrieve_timesteps)
 from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
-from wgp import update_loras_slists
+from wan.utils.utils import update_loras_slists

 class DTT2V:

@ -31,6 +31,7 @@ class DTT2V:
        rank=0,
        model_filename = None,
        model_type = None,
+        model_def = None,
        base_model_type = None,
        save_quantized = False,
        text_encoder_filename = None,
@ -53,6 +54,8 @@ class DTT2V:
            checkpoint_path=text_encoder_filename,
            tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
            shard_fn= None)
+        self.model_def = model_def
+        self.image_outputs = model_def.get("image_outputs", False)

        self.vae_stride = config.vae_stride
        self.patch_size = config.patch_size 
@ -202,6 +205,7 @@ class DTT2V:
        width: int = 832,
        fit_into_canvas = True,
        frame_num: int = 97,
+        batch_size = 1,
        sampling_steps: int = 50,
        shift: float = 1.0,
        guide_scale: float = 5.0,
@ -224,8 +228,9 @@ class DTT2V:
        generator = torch.Generator(device=self.device)
        generator.manual_seed(seed)
        self._guidance_scale = guide_scale
-        frame_num = max(17, frame_num) # must match causal_block_size for value of 5
-        frame_num = int( round( (frame_num - 17) / 20)* 20 + 17 )
+        if frame_num > 1:
+            frame_num = max(17, frame_num) # must match causal_block_size for value of 5
+            frame_num = int( round( (frame_num - 17) / 20)* 20 + 17 )

        if ar_step == 0: 
            causal_block_size = 1
@ -297,12 +302,12 @@ class DTT2V:
                    prefix_video = prefix_video[:, : predix_video_latent_length]

        base_num_frames_iter = latent_length
-        latent_shape = [16, base_num_frames_iter, latent_height, latent_width]
+        latent_shape = [batch_size, 16, base_num_frames_iter, latent_height, latent_width]
        latents = self.prepare_latents(
            latent_shape, dtype=torch.float32, device=self.device, generator=generator
        )
        if prefix_video is not None:
-            latents[:, :predix_video_latent_length] = prefix_video.to(torch.float32)
+            latents[:, :, :predix_video_latent_length] = prefix_video.to(torch.float32)
        step_matrix, _, step_update_mask, valid_interval = self.generate_timestep_matrix(
            base_num_frames_iter,
            init_timesteps,
@ -340,7 +345,7 @@ class DTT2V:
        else:
            self.model.enable_cache = None
        from mmgp import offload
-        freqs = get_rotary_pos_embed(latents.shape[1 :], enable_RIFLEx= False) 
+        freqs = get_rotary_pos_embed(latents.shape[2 :], enable_RIFLEx= False) 
        kwrags = {
            "freqs" :freqs,
            "fps" : fps_embeds,
@ -358,15 +363,15 @@ class DTT2V:
            update_mask_i = step_update_mask[i]
            valid_interval_start, valid_interval_end = valid_interval[i]
            timestep = timestep_i[None, valid_interval_start:valid_interval_end].clone()
-            latent_model_input = latents[:, valid_interval_start:valid_interval_end, :, :].clone()
+            latent_model_input = latents[:, :, valid_interval_start:valid_interval_end, :, :].clone()
            if overlap_noise > 0 and valid_interval_start < predix_video_latent_length:
                noise_factor = 0.001 * overlap_noise
                timestep_for_noised_condition = overlap_noise
-                latent_model_input[:, valid_interval_start:predix_video_latent_length] = (
-                    latent_model_input[:, valid_interval_start:predix_video_latent_length]
+                latent_model_input[:, :, valid_interval_start:predix_video_latent_length] = (
+                    latent_model_input[:, :, valid_interval_start:predix_video_latent_length]
                    * (1.0 - noise_factor)
                    + torch.randn_like(
-                        latent_model_input[:, valid_interval_start:predix_video_latent_length]
+                        latent_model_input[:, :, valid_interval_start:predix_video_latent_length]
                    )
                    * noise_factor
                )
@ -417,18 +422,27 @@ class DTT2V:
                    del noise_pred_cond, noise_pred_uncond
            for idx in range(valid_interval_start, valid_interval_end):
                if update_mask_i[idx].item():
-                    latents[:, idx] = sample_schedulers[idx].step(
-                        noise_pred[:, idx - valid_interval_start],
+                    latents[:, :, idx] = sample_schedulers[idx].step(
+                        noise_pred[:, :, idx - valid_interval_start],
                        timestep_i[idx],
-                        latents[:, idx],
+                        latents[:, :, idx],
                        return_dict=False,
                        generator=generator,
                    )[0]
                    sample_schedulers_counter[idx] += 1
            if callback is not None:
-                callback(i, latents.squeeze(0), False)         
+                latents_preview = latents
+                if len(latents_preview) > 1: latents_preview = latents_preview.transpose(0,2)
+                callback(i, latents_preview[0], False)
+                latents_preview = None

-        x0 = latents.unsqueeze(0)
-        videos = [self.vae.decode(x0, tile_size= VAE_tile_size)[0]]
-        output_video = videos[0].clamp(-1, 1).cpu()  # c, f, h, w
-        return output_video
+        x0 =latents.unbind(dim=0)
+
+        videos = self.vae.decode(x0, VAE_tile_size)
+
+        if self.image_outputs:
+            videos = torch.cat(videos, dim=1) if len(videos) > 1 else videos[0]
+        else:
+            videos = videos[0] # return only first video     
+
+        return videos
--- a/wan/modules/attention.py
+++ b/wan/modules/attention.py
@ -185,7 +185,7 @@ def pay_attention(
    q,k,v = qkv_list
    qkv_list.clear()
    out_dtype = q.dtype
-    if  q.dtype == torch.bfloat16 and not bfloat16_supported:
+    if q.dtype == torch.bfloat16 and not bfloat16_supported:
        q = q.to(torch.float16)
        k = k.to(torch.float16)
        v = v.to(torch.float16)
@ -194,7 +194,9 @@ def pay_attention(

    q = q.to(v.dtype)
    k = k.to(v.dtype)
-
+    batch = len(q)
+    if len(k) != batch: k = k.expand(batch, -1, -1, -1)
+    if len(v) != batch: v = v.expand(batch, -1, -1, -1)
    if attn == "chipmunk":
        from src.chipmunk.modules import SparseDiffMlp, SparseDiffAttn
        from src.chipmunk.util import LayerCounter, GLOBAL_CONFIG
--- a/wan/modules/model.py
+++ b/wan/modules/model.py
@ -33,9 +33,10 @@ def sinusoidal_embedding_1d(dim, position):


 def reshape_latent(latent, latent_frames):
-    if latent_frames == latent.shape[0]:
-        return latent
-    return latent.reshape(latent_frames, -1, latent.shape[-1] )
+    return latent.reshape(latent.shape[0], latent_frames, -1, latent.shape[-1] )
+
+def restore_latent_shape(latent):
+    return latent.reshape(latent.shape[0], -1, latent.shape[-1] )


 def identify_k( b: float, d: int, N: int):
@ -493,7 +494,7 @@ class WanAttentionBlock(nn.Module):
        x_mod = reshape_latent(x_mod , latent_frames)
        x_mod *= 1 + e[1]
        x_mod += e[0]
-        x_mod = reshape_latent(x_mod , 1)
+        x_mod = restore_latent_shape(x_mod)
        if cam_emb != None:
            cam_emb = self.cam_encoder(cam_emb)
            cam_emb = cam_emb.repeat(1, 2, 1)
@ -510,7 +511,7 @@ class WanAttentionBlock(nn.Module):

        x, y = reshape_latent(x , latent_frames), reshape_latent(y , latent_frames)
        x.addcmul_(y, e[2])
-        x, y = reshape_latent(x , 1), reshape_latent(y , 1)
+        x, y = restore_latent_shape(x), restore_latent_shape(y)
        del y
        y = self.norm3(x)
        y = y.to(attention_dtype)
@ -542,7 +543,7 @@ class WanAttentionBlock(nn.Module):
        y = reshape_latent(y , latent_frames)
        y *= 1 + e[4]
        y += e[3]
-        y = reshape_latent(y , 1)
+        y = restore_latent_shape(y)
        y = y.to(attention_dtype)

        ffn = self.ffn[0]
@ -562,7 +563,7 @@ class WanAttentionBlock(nn.Module):
        y = y.to(dtype)
        x, y = reshape_latent(x , latent_frames), reshape_latent(y , latent_frames)
        x.addcmul_(y, e[5])
-        x, y = reshape_latent(x , 1), reshape_latent(y , 1)
+        x, y = restore_latent_shape(x), restore_latent_shape(y)

        if hints_processed is not None:
            for hint, scale in zip(hints_processed, context_scale):
@ -669,6 +670,8 @@ class VaceWanAttentionBlock(WanAttentionBlock):
        hints[0] = None
        if self.block_id == 0:
            c = self.before_proj(c)
+            bz = x.shape[0]
+            if bz > c.shape[0]: c = c.repeat(bz, 1, 1 )
            c += x
        c = super().forward(c, **kwargs)
        c_skip = self.after_proj(c)
@ -707,7 +710,7 @@ class Head(nn.Module):
        x = reshape_latent(x , latent_frames)
        x *= (1 + e[1])
        x += e[0]
-        x = reshape_latent(x , 1)
+        x = restore_latent_shape(x)
        x= x.to(self.head.weight.dtype)
        x = self.head(x)
        return x
@ -1163,10 +1166,14 @@ class WanModel(ModelMixin, ConfigMixin):
                last_x_idx = i
            else:
                # image source
+                bz = len(x)
                if y is not None:
-                    x = torch.cat([x, y], dim=0)
+                    y = y.unsqueeze(0)        
+                    if bz > 1: y = y.expand(bz, -1, -1, -1, -1)
+                    x = torch.cat([x, y], dim=1)
                # embeddings
-                x = self.patch_embedding(x.unsqueeze(0)).to(modulation_dtype)
+                # x = self.patch_embedding(x.unsqueeze(0)).to(modulation_dtype)
+                x = self.patch_embedding(x).to(modulation_dtype)
                grid_sizes = x.shape[2:]
                if chipmunk:
                    x = x.unsqueeze(-1)
@ -1204,7 +1211,7 @@ class WanModel(ModelMixin, ConfigMixin):
        )  # b, dim        
        e0 = self.time_projection(e).unflatten(1, (6, self.dim)).to(e.dtype)

-        if self.inject_sample_info:
+        if self.inject_sample_info and fps!=None:
            fps = torch.tensor(fps, dtype=torch.long, device=device)

            fps_emb = self.fps_embedding(fps).to(e.dtype) 
@ -1402,7 +1409,7 @@ class WanModel(ModelMixin, ConfigMixin):
            x_list[i] = self.unpatchify(x, grid_sizes)
            del x

-        return [x[0].float() for x in x_list]
+        return [x.float() for x in x_list]

    def unpatchify(self, x, grid_sizes):
        r"""
@ -1427,7 +1434,10 @@ class WanModel(ModelMixin, ConfigMixin):
            u = torch.einsum('fhwpqrc->cfphqwr', u)
            u = u.reshape(c, *[i * j for i, j in zip(grid_sizes, self.patch_size)])
            out.append(u)
-        return out
+        if len(x) == 1:
+            return out[0].unsqueeze(0)
+        else:
+            return torch.stack(out, 0)

    def init_weights(self):
        r"""
--- a/wan/multitalk/attention.py
+++ b/wan/multitalk/attention.py
@ -333,7 +333,7 @@ class SingleStreamMutiAttention(SingleStreamAttention):

        human1 = normalize_and_scale(x_ref_attn_map[0], (human1_min_value, human1_max_value), (self.rope_h1[0], self.rope_h1[1]))
        human2 = normalize_and_scale(x_ref_attn_map[1], (human2_min_value, human2_max_value), (self.rope_h2[0], self.rope_h2[1]))
-        back   = torch.full((x_ref_attn_map.size(1),), self.rope_bak, dtype=human1.dtype).to(human1.device)
+        back   = torch.full((x_ref_attn_map.size(1),), self.rope_bak, dtype=human1.dtype, device=human1.device)
        max_indices = x_ref_attn_map.argmax(dim=0)
        normalized_map = torch.stack([human1, human2, back], dim=1)
        normalized_pos = normalized_map[range(x_ref_attn_map.size(1)), max_indices] # N 
@ -351,7 +351,7 @@ class SingleStreamMutiAttention(SingleStreamAttention):
        if self.qk_norm:
            encoder_k = self.add_k_norm(encoder_k)

-        per_frame = torch.zeros(N_a, dtype=encoder_k.dtype).to(encoder_k.device)
+        per_frame = torch.zeros(N_a, dtype=encoder_k.dtype, device=encoder_k.device)
        per_frame[:per_frame.size(0)//2] = (self.rope_h1[0] + self.rope_h1[1]) / 2
        per_frame[per_frame.size(0)//2:] = (self.rope_h2[0] + self.rope_h2[1]) / 2
        encoder_pos = torch.concat([per_frame]*N_t, dim=0)
--- a/wan/multitalk/multitalk.py
+++ b/wan/multitalk/multitalk.py
@ -184,6 +184,7 @@ def get_full_audio_embeddings(audio_guide1 = None, audio_guide2 = None, combinat


 def get_window_audio_embeddings(full_audio_embs, audio_start_idx=0, clip_length = 81, vae_scale = 4, audio_window = 5):
+    if full_audio_embs == None: return None
    HUMAN_NUMBER = len(full_audio_embs)
    audio_end_idx = audio_start_idx + clip_length
    indices = (torch.arange(2 * 2 + 1) - 2) * 1 
@ -271,6 +272,34 @@ def timestep_transform(
    new_t = new_t * num_timesteps
    return new_t

+def parse_speakers_locations(speakers_locations):
+    bbox = {}
+    if speakers_locations is None or len(speakers_locations) == 0:
+        return None, ""
+    speakers = speakers_locations.split(" ")
+    if len(speakers) !=2:
+        error= "Two speakers locations should be defined"
+        return "", error
+    
+    for i, speaker in enumerate(speakers):
+        location = speaker.strip().split(":")
+        if len(location) not in (2,4):
+            error = f"Invalid Speaker Location '{location}'. A Speaker Location should be defined in the format Left:Right or usuing a BBox Left:Top:Right:Bottom"
+            return "", error
+        try:
+            good = False
+            location_float = [ float(val) for val in location]
+            good = all( 0 <= val <= 100 for val in location_float)
+        except:
+            pass
+        if not good:
+            error = f"Invalid Speaker Location '{location}'. Each number should be between 0 and 100."
+            return "", error
+        if len(location_float) == 2:
+            location_float = [location_float[0], 0, location_float[1], 100]
+        bbox[f"human{i}"] = location_float
+    return bbox, ""
+

 # construct human mask
 def get_target_masks(HUMAN_NUMBER, lat_h, lat_w, src_h, src_w, face_scale = 0.05, bbox = None):
@ -285,7 +314,9 @@ def get_target_masks(HUMAN_NUMBER, lat_h, lat_w, src_h, src_w, face_scale = 0.05
            assert len(bbox) == HUMAN_NUMBER, f"The number of target bbox should be the same with cond_audio"
            background_mask = torch.zeros([src_h, src_w])
            for _, person_bbox in bbox.items():
-                x_min, y_min, x_max, y_max = person_bbox
+                y_min, x_min, y_max, x_max = person_bbox
+                x_min, y_min, x_max, y_max = max(x_min,5), max(y_min, 5), min(x_max,95), min(y_max,95)                
+                x_min, y_min, x_max, y_max =  int(src_h * x_min / 100), int(src_w * y_min / 100), int(src_h * x_max / 100), int(src_w * y_max / 100)
                human_mask = torch.zeros([src_h, src_w])
                human_mask[int(x_min):int(x_max), int(y_min):int(y_max)] = 1
                background_mask += human_mask
@ -305,7 +336,7 @@ def get_target_masks(HUMAN_NUMBER, lat_h, lat_w, src_h, src_w, face_scale = 0.05
            human_masks = [human_mask1, human_mask2]
        background_mask = torch.where(background_mask > 0, torch.tensor(0), torch.tensor(1))
        human_masks.append(background_mask)
-    
+    # toto = Image.fromarray(human_masks[2].mul_(255).unsqueeze(-1).repeat(1,1,3).to(torch.uint8).cpu().numpy())
    ref_target_masks = torch.stack(human_masks, dim=0) #.to(self.device)
    # resize and centercrop for ref_target_masks 
    # ref_target_masks = resize_and_centercrop(ref_target_masks, (target_h, target_w))
--- a/wan/multitalk/multitalk_utils.py
+++ b/wan/multitalk/multitalk_utils.py
@ -128,7 +128,7 @@ def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, spli

    _, seq_lens, heads, _ = visual_q.shape
    class_num, _ = ref_target_masks.shape
-    x_ref_attn_maps = torch.zeros(class_num, seq_lens).to(visual_q.device).to(visual_q.dtype)
+    x_ref_attn_maps = torch.zeros(class_num, seq_lens, dtype=visual_q.dtype, device=visual_q.device)

    split_chunk = heads // split_num
    
--- a/wan/utils/basic_flowmatch.py
+++ b/wan/utils/basic_flowmatch.py
@ -53,7 +53,7 @@ class FlowMatchScheduler():
        else:
            sigma_ = self.sigmas[timestep_id + 1].reshape(-1, 1, 1, 1)
        prev_sample = sample + model_output * (sigma_ - sigma)
-        return prev_sample
+        return [prev_sample]

    def add_noise(self, original_samples, noise, timestep):
        """
--- a/wan/utils/utils.py
+++ b/wan/utils/utils.py
@ -5,7 +5,8 @@ import os
 import os.path as osp
 import torchvision.transforms.functional as TF
 import torch.nn.functional as F
-
+import cv2
+import tempfile
 import imageio
 import torch
 import decord
@ -33,6 +34,21 @@ def seed_everything(seed: int):
    if torch.backends.mps.is_available():
        torch.mps.manual_seed(seed)

+def expand_slist(slist, num_inference_steps ):
+    new_slist= []
+    inc =  len(slist) / num_inference_steps 
+    pos = 0
+    for i in range(num_inference_steps):
+        new_slist.append(slist[ int(pos)])
+        pos += inc
+    return new_slist
+
+def update_loras_slists(trans, slists, num_inference_steps ):
+    from mmgp import offload
+    slists = [ expand_slist(slist, num_inference_steps ) if isinstance(slist, list) else slist for slist in slists ]
+    nos = [str(l) for l in range(len(slists))]
+    offload.activate_loras(trans, nos, slists ) 
+
 def resample(video_fps, video_frames_count, max_target_frames_count, target_fps, start_target_frame ):
    import math

@ -101,6 +117,29 @@ def get_video_frame(file_name, frame_no):
    img = Image.fromarray(frame.numpy().astype(np.uint8))
    return img

+def convert_image_to_video(image):
+    if image is None:
+        return None
+    
+    # Convert PIL/numpy image to OpenCV format if needed
+    if isinstance(image, np.ndarray):
+        # Gradio images are typically RGB, OpenCV expects BGR
+        img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    else:
+        # Handle PIL Image
+        img_array = np.array(image)
+        img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+    
+    height, width = img_bgr.shape[:2]
+    
+    # Create temporary video file (auto-cleaned by Gradio)
+    with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_video:
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        out = cv2.VideoWriter(temp_video.name, fourcc, 30.0, (width, height))
+        out.write(img_bgr)
+        out.release()
+        return temp_video.name
+    
 def resize_lanczos(img, h, w):
    img = Image.fromarray(np.clip(255. * img.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8))
    img = img.resize((w,h), resample=Image.Resampling.LANCZOS) 
@ -454,10 +493,10 @@ def extract_audio_tracks(source_video, verbose=False, query_only= False):
        
    except ffmpeg.Error as e:
        print(f"FFmpeg error during audio extraction: {e}")
-        return []
+        return 0 if query_only else []
    except Exception as e:
        print(f"Error during audio extraction: {e}")
-        return []
+        return 0 if query_only else []

 def combine_video_with_audio_tracks(target_video, audio_tracks, output_video, verbose=False):
    """
--- a/wgp.py
+++ b/wgp.py