Update: Version 0.1.2

README.md

@@ -7,7 +7,7 @@ This is the official repository for LTX-Video.
 [Website](https://www.lightricks.com/ltxv) |
 [Model](https://huggingface.co/Lightricks/LTX-Video) |
 [Demo](https://fal.ai/models/fal-ai/ltx-video) |
-[Paper (Soon)](https://github.com/Lightricks/LTX-Video) 
+[Paper (Soon)](https://github.com/Lightricks/LTX-Video)
 
 </div>
 
@@ -20,7 +20,11 @@ This is the official repository for LTX-Video.
     - [Installation](#installation)
     - [Inference](#inference)
   - [ComfyUI Integration](#comfyui-integration)
+  - [Diffusers Integration](#diffusers-integration)
 - [Model User Guide](#model-user-guide)
+- [Community Contribution](#community-contribution)
+- [Training](#trining)
+- [Join Us!](#join-us)
 - [Acknowledgement](#acknowledgement)
 
 # Introduction
@@ -60,13 +64,13 @@ source env/bin/activate
 python -m pip install -e .\[inference-script\]
 ```
 
-Then, download the model from [Hugging Face](https://huggingface.co/Lightricks/LTX-Video) 
+Then, download the model from [Hugging Face](https://huggingface.co/Lightricks/LTX-Video)
 
 ```python
-from huggingface_hub import snapshot_download
+from huggingface_hub import hf_hub_download
 
 model_path = 'PATH'   # The local directory to save downloaded checkpoint
-snapshot_download("Lightricks/LTX-Video", local_dir=model_path, local_dir_use_symlinks=False, repo_type='model')
+hf_hub_download(repo_id="Lightricks/LTX-Video", filename="ltx-video-2b-v0.9.1.safetensors", local_dir=model_path, local_dir_use_symlinks=False, repo_type='model')
 ```
 
 ### Inference
@@ -113,7 +117,68 @@ When writing prompts, focus on detailed, chronological descriptions of actions a
 * Guidance Scale: 3-3.5 are the recommended values
 * Inference Steps: More steps (40+) for quality, fewer steps (20-30) for speed
 
-## More to come...
+## Community Contribution
+
+### ComfyUI-LTXTricks 🛠️
+
+A community project providing additional nodes for enhanced control over the LTX Video model. It includes implementations of advanced techniques like RF-Inversion, RF-Edit, FlowEdit, and more. These nodes enable workflows such as Image and Video to Video (I+V2V), enhanced sampling via Spatiotemporal Skip Guidance (STG), and interpolation with precise frame settings.
+
+- **Repository:** [ComfyUI-LTXTricks](https://github.com/logtd/ComfyUI-LTXTricks)
+- **Features:**
+  - 🔄 **RF-Inversion:** Implements [RF-Inversion](https://rf-inversion.github.io/) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_inversion.json).
+  - ✂️ **RF-Edit:** Implements [RF-Solver-Edit](https://github.com/wangjiangshan0725/RF-Solver-Edit) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_rf_edit.json).
+  - 🌊 **FlowEdit:** Implements [FlowEdit](https://github.com/fallenshock/FlowEdit) with an [example workflow here](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_flow_edit.json).
+  - 🎥 **I+V2V:** Enables Video to Video with a reference image. [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_iv2v.json).
+  - ✨ **Enhance:** Partial implementation of [STGuidance](https://junhahyung.github.io/STGuidance/). [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltxv_stg.json).
+  - 🖼️ **Interpolation and Frame Setting:** Nodes for precise control of latents per frame. [Example workflow](https://github.com/logtd/ComfyUI-LTXTricks/blob/main/example_workflows/example_ltx_interpolation.json).
+
+
+### LTX-VideoQ8 🎱
+
+**LTX-VideoQ8** is an 8-bit optimized version of [LTX-Video](https://github.com/Lightricks/LTX-Video), designed for faster performance on NVIDIA ADA GPUs.
+
+- **Repository:** [LTX-VideoQ8](https://github.com/KONAKONA666/LTX-Video)
+- **Features:**
+  - 🚀 Up to 3X speed-up with no accuracy loss
+  - 🎥 Generate 720x480x121 videos in under a minute on RTX 4060 (8GB VRAM)
+  - 🛠️ Fine-tune 2B transformer models with precalculated latents
+- **Community Discussion:** [Reddit Thread](https://www.reddit.com/r/StableDiffusion/comments/1h79ks2/fast_ltx_video_on_rtx_4060_and_other_ada_gpus/)
+
+### Your Contribution
+
+...is welcome! If you have a project or tool that integrates with LTX-Video,
+please let us know by opening an issue or pull request.
+
+# Training
+
+## Diffusers
+
+Diffusers implemented [LoRA support](https://github.com/huggingface/diffusers/pull/10228),
+with a training script for fine-tuning.
+More information and training script in
+[finetrainers](https://github.com/a-r-r-o-w/finetrainers?tab=readme-ov-file#training).
+
+## Diffusion-Pipe
+
+An experimental training framework with pipeline parallelism, enabling fine-tuning of large models like **LTX-Video** across multiple GPUs.
+
+- **Repository:** [Diffusion-Pipe](https://github.com/tdrussell/diffusion-pipe)
+- **Features:**
+  - 🛠️ Full fine-tune support for LTX-Video using LoRA
+  - 📊 Useful metrics logged to Tensorboard
+  - 🔄 Training state checkpointing and resumption
+  - ⚡ Efficient pre-caching of latents and text embeddings for multi-GPU setups
+
+
+# Join Us 🚀
+
+Want to work on cutting-edge AI research and make a real impact on millions of users worldwide?
+
+At **Lightricks**, an AI-first company, we’re revolutionizing how visual content is created.
+
+If you are passionate about AI, computer vision, and video generation, we would love to hear from you!
+
+Please visit our [careers page](https://careers.lightricks.com/careers?query=&office=all&department=R%26D) for more information.
 
 # Acknowledgement
 

inference.py

@@ -1,5 +1,4 @@
 import argparse
-import json
 import os
 import random
 from datetime import datetime
@@ -8,7 +7,6 @@
 
 import imageio
 import numpy as np
-from safetensors import safe_open
 import torch
 import torch.nn.functional as F
 from PIL import Image
@@ -22,41 +20,18 @@
 from ltx_video.pipelines.pipeline_ltx_video import LTXVideoPipeline
 from ltx_video.schedulers.rf import RectifiedFlowScheduler
 from ltx_video.utils.conditioning_method import ConditioningMethod
-
+from ltx_video.utils.skip_layer_strategy import SkipLayerStrategy
 
 MAX_HEIGHT = 720
 MAX_WIDTH = 1280
 MAX_NUM_FRAMES = 257
 
 
-def load_vae(vae_config, ckpt):
-    vae = CausalVideoAutoencoder.from_config(vae_config)
-    vae_state_dict = {
-        key.replace("vae.", ""): value
-        for key, value in ckpt.items()
-        if key.startswith("vae.")
-    }
-    vae.load_state_dict(vae_state_dict)
-    if torch.cuda.is_available():
-        vae = vae.cuda()
-    return vae.to(torch.bfloat16)
-
-
-def load_transformer(transformer_config, ckpt):
-    transformer = Transformer3DModel.from_config(transformer_config)
-    transformer_state_dict = {
-        key.replace("model.diffusion_model.", ""): value
-        for key, value in ckpt.items()
-        if key.startswith("model.diffusion_model.")
-    }
-    transformer.load_state_dict(transformer_state_dict, strict=True)
+def get_total_gpu_memory():
     if torch.cuda.is_available():
-        transformer = transformer.cuda()
-    return transformer
-
-
-def load_scheduler(scheduler_config):
-    return RectifiedFlowScheduler.from_config(scheduler_config)
+        total_memory = torch.cuda.get_device_properties(0).total_memory / (1024**3)
+        return total_memory
+    return None
 
 
 def load_image_to_tensor_with_resize_and_crop(
@@ -204,6 +179,30 @@ def main():
         default=3,
         help="Guidance scale for the pipeline",
     )
+    parser.add_argument(
+        "--stg_scale",
+        type=float,
+        default=1,
+        help="Spatiotemporal guidance scale for the pipeline. 0 to disable STG.",
+    )
+    parser.add_argument(
+        "--stg_rescale",
+        type=float,
+        default=0.7,
+        help="Spatiotemporal guidance rescaling scale for the pipeline. 1 to disable rescale.",
+    )
+    parser.add_argument(
+        "--stg_mode",
+        type=str,
+        default="stg_a",
+        help="Spatiotemporal guidance mode for the pipeline. Can be either stg_a or stg_r.",
+    )
+    parser.add_argument(
+        "--stg_skip_layers",
+        type=str,
+        default="19",
+        help="Attention layers to skip for spatiotemporal guidance. Comma separated list of integers.",
+    )
     parser.add_argument(
         "--image_cond_noise_scale",
         type=float,
@@ -233,9 +232,24 @@ def main():
     )
 
     parser.add_argument(
-        "--bfloat16",
-        action="store_true",
-        help="Denoise in bfloat16",
+        "--precision",
+        choices=["bfloat16", "mixed_precision"],
+        default="bfloat16",
+        help="Sets the precision for the transformer and tokenizer. Default is bfloat16. If 'mixed_precision' is enabled, it moves to mixed-precision.",
+    )
+
+    # VAE noise augmentation
+    parser.add_argument(
+        "--decode_timestep",
+        type=float,
+        default=0.05,
+        help="Timestep for decoding noise",
+    )
+    parser.add_argument(
+        "--decode_noise_scale",
+        type=float,
+        default=0.025,
+        help="Noise level for decoding noise",
     )
 
     # Prompts
@@ -251,6 +265,12 @@ def main():
         help="Negative prompt for undesired features",
     )
 
+    parser.add_argument(
+        "--offload_to_cpu",
+        action="store_true",
+        help="Offloading unnecessary computations to CPU.",
+    )
+
     logger = logging.get_logger(__name__)
 
     args = parser.parse_args()
@@ -259,6 +279,8 @@ def main():
 
     seed_everething(args.seed)
 
+    offload_to_cpu = False if not args.offload_to_cpu else get_total_gpu_memory() < 30
+
     output_dir = (
         Path(args.output_path)
         if args.output_path
@@ -301,35 +323,36 @@ def main():
     else:
         media_items = None
 
-    # Paths for the separate mode directories
     ckpt_path = Path(args.ckpt_path)
-    ckpt = {}
-    with safe_open(ckpt_path, framework="pt", device="cpu") as f:
-        metadata = f.metadata()
-        for k in f.keys():
-            ckpt[k] = f.get_tensor(k)
-
-    configs = json.loads(metadata["config"])
-    vae_config = configs["vae"]
-    transformer_config = configs["transformer"]
-    scheduler_config = configs["scheduler"]
-
-    # Load models
-    vae = load_vae(vae_config, ckpt)
-    transformer = load_transformer(transformer_config, ckpt)
-    scheduler = load_scheduler(scheduler_config)
-    patchifier = SymmetricPatchifier(patch_size=1)
+    vae = CausalVideoAutoencoder.from_pretrained(ckpt_path)
+    transformer = Transformer3DModel.from_pretrained(ckpt_path)
+    scheduler = RectifiedFlowScheduler.from_pretrained(ckpt_path)
+
     text_encoder = T5EncoderModel.from_pretrained(
         "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="text_encoder"
     )
-    if torch.cuda.is_available():
-        text_encoder = text_encoder.to("cuda")
+    patchifier = SymmetricPatchifier(patch_size=1)
     tokenizer = T5Tokenizer.from_pretrained(
         "PixArt-alpha/PixArt-XL-2-1024-MS", subfolder="tokenizer"
     )
 
-    if args.bfloat16 and transformer.dtype != torch.bfloat16:
+    if torch.cuda.is_available():
+        transformer = transformer.cuda()
+        vae = vae.cuda()
+        text_encoder = text_encoder.cuda()
+
+    vae = vae.to(torch.bfloat16)
+    if args.precision == "bfloat16" and transformer.dtype != torch.bfloat16:
         transformer = transformer.to(torch.bfloat16)
+    text_encoder = text_encoder.to(torch.bfloat16)
+
+    # Set spatiotemporal guidance
+    skip_block_list = [int(x.strip()) for x in args.stg_skip_layers.split(",")]
+    skip_layer_strategy = (
+        SkipLayerStrategy.Attention
+        if args.stg_mode.lower() == "stg_a"
+        else SkipLayerStrategy.Residual
+    )
 
     # Use submodels for the pipeline
     submodel_dict = {
@@ -362,6 +385,11 @@ def main():
         num_inference_steps=args.num_inference_steps,
         num_images_per_prompt=args.num_images_per_prompt,
         guidance_scale=args.guidance_scale,
+        skip_layer_strategy=skip_layer_strategy,
+        skip_block_list=skip_block_list,
+        stg_scale=args.stg_scale,
+        do_rescaling=args.stg_rescale != 1,
+        rescaling_scale=args.stg_rescale,
         generator=generator,
         output_type="pt",
         callback_on_step_end=None,
@@ -378,7 +406,10 @@ def main():
             else ConditioningMethod.UNCONDITIONAL
         ),
         image_cond_noise_scale=args.image_cond_noise_scale,
-        mixed_precision=not args.bfloat16,
+        decode_timestep=args.decode_timestep,
+        decode_noise_scale=args.decode_noise_scale,
+        mixed_precision=(args.precision == "mixed_precision"),
+        offload_to_cpu=offload_to_cpu,
     ).images
 
     # Crop the padded images to the desired resolution and number of frames