[V1] Docs Update (#402)

README.md

@@ -19,11 +19,11 @@ With FastVideo's optimizations, you can achieve more than 3x inference improveme
 
 FastVideo has the following features:
 - State-of-the-art performance optimizations for inference
-  - [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
-  - TeaCache
-  - Sage Attention
+  - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
+  - [TeaCache](https://arxiv.org/pdf/2411.19108)
+  - [Sage Attention](https://arxiv.org/abs/2410.02367)
 - Cutting edge models
-  - Wan2.1 T2V, I2v
+  - Wan2.1 T2V, I2V
   - HunyuanVideo
   - FastHunyuan: consistency distilled video diffusion models for 8x inference speedup.
   - StepVideo T2V

docs/source/index.md

@@ -32,11 +32,11 @@ With FastVideo's optimizations, you can achieve more than 3x inference improveme
 
 FastVideo has the following features:
 - State-of-the-art performance optimizations for inference
-  - [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
-  - TeaCache
-  - Sage Attention
+  - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
+  - [TeaCache](https://arxiv.org/pdf/2411.19108)
+  - [Sage Attention](https://arxiv.org/abs/2410.02367)
 - Cutting edge models
-  - Wan2.1 T2V, I2v
+  - Wan2.1 T2V, I2V
   - HunyuanVideo
   - FastHunyuan: consistency distilled video diffusion models for 8x inference speedup.
   - StepVideo T2V

docs/source/inference/configuration.md

@@ -0,0 +1,77 @@
+(inference-configuration)=
+# Configuration
+
+## Multi-GPU Setup
+
+FastVideo automatically distributes the generation process when multiple GPUs are specified:
+
+```python
+# Will use 4 GPUs in parallel for faster generation
+generator = VideoGenerator.from_pretrained(
+    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+    num_gpus=4,
+)
+```
+
+## Customizing Generation
+
+- `PipelineConfig`: Initialization time parameters
+- `SamplingParam`: Generation time parameters
+
+You can customize various parameters when generating videos using the `PipelineConfig` and `SamplingParam` class:
+
+```python
+from fastvideo import VideoGenerator, SamplingParam, PipelineConfig
+
+def main():
+    model_name = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+    config = PipelineConfig.from_pretrained(model_name)
+    config.vae_precision = "fp16"
+    config.use_cpu_offload = True
+
+    # Create the generator
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        num_gpus=1,
+        pipeline_config=config
+    )
+
+    # Create and customize sampling parameters
+    sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+    
+    # How many frames to generate
+    sampling_param.num_frames = 45
+    
+    # Video resolution (width, height)
+    sampling_param.width = 1024
+    sampling_param.height = 576
+    
+    # How many steps we denoise the video (higher = better quality, slower generation)
+    sampling_param.num_inference_steps = 30
+    
+    # How strongly the video conforms to the prompt (higher = more faithful to prompt)
+    sampling_param.guidance_scale = 7.5
+    
+    # Random seed for reproducibility
+    sampling_param.seed = 42  # Optional, leave unset for random results
+
+    # Generate video with custom parameters
+    prompt = "A beautiful sunset over a calm ocean, with gentle waves."
+    video = generator.generate_video(
+        prompt, 
+        sampling_param=sampling_param, 
+        output_path="my_videos/",  # Controls where videos are saved
+        return_frames=True,  # Also return frames from this call (defaults to False)
+        save_video=True
+    )
+
+    # If return_frames=True, video contains the generated frames as a NumPy array
+    print(f"Generated {len(video)} frames")
+
+if __name__ == '__main__':
+    main()
+```
+
+## Performance Optimization
+
+For configuring optimizations, please see our [optimizations guide](#inference-optimizations)

docs/source/inference/inference_quick_start.md

@@ -2,19 +2,11 @@
 
 This page contains step-by-step instructions to get you quickly started with video generation using FastVideo.
 
-## Table of Contents
-- [Generating Your First Video](#generating-your-first-video)
-- [Customizing Generation](#customizing-generation)
-- [Available Models](#available-models)
-- [Image-to-Video Generation](#image-to-video-generation)
-- [Troubleshooting](#troubleshooting)
-- [Advanced Configuration](#advanced-configuration)
-- [Next Steps](#next-steps)
-
-## Software Requirements
+## Requirements
 - **OS**: Linux (Tested on Ubuntu 22.04+)
 - **Python**: 3.10-3.12
 - **CUDA**: 12.4
+- **GPU**: At least one NVIDIA GPU
 
 ## Installation
 
@@ -78,18 +70,25 @@ You can generate a video starting from an initial image:
 ```python
 from fastvideo import VideoGenerator, SamplingParam
 
-# Create the generator
-generator = VideoGenerator.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+def main():
+    # Create the generator
+    model_name = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+    generator = VideoGenerator.from_pretrained(model_name, num_gpus=1)
 
-# Set up parameters with an initial image
-sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
-sampling_param.image_path = "path/to/your/image.jpg"
-sampling_param.num_frames = 24
-sampling_param.image_strength = 0.8  # How much to preserve the original image (0-1)
+    # Set up parameters with an initial image
+    sampling_param = SamplingParam.from_pretrained(model_name)
+    sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    sampling_param.num_frames = 107
+    sampling_param.image_strength = 0.8  # How much to preserve the original image (0-1)
 
-# Generate video based on the image
-prompt = "A photograph coming to life with gentle movement"
-video = generator.generate_video(prompt, sampling_param=sampling_param)
+    # Generate video based on the image
+    prompt = "A photograph coming to life with gentle movement"
+    generator.generate_video(prompt, sampling_param=sampling_param,
+                             output_path="my_videos/",
+                             save_video=True)
+
+if __name__ == '__main__':
+    main()
 ```
 
 ## Troubleshooting
@@ -100,7 +99,7 @@ Common issues and their solutions:
 If you encounter CUDA out of memory errors:
 - Reduce `num_frames` or video resolution
 - Enable memory optimization with `enable_model_cpu_offload`
-- Try a smaller model or use quantized versions
+- Try a smaller model or use distilled versions
 - Use `num_gpus` > 1 if multiple GPUs are available
 
 ### Slow Generation
@@ -116,14 +115,10 @@ If the generated video doesn't match your prompt:
 - Experiment with different random seeds
 - Try a different model
 
-## Advanced Configuration
-
-## Optimizations
-
 ## Next Steps
 
-- Explore the [API Reference](../api/index.md) for detailed documentation
-- Learn about [Advanced Inference Options](../inference/overview_back.md)
-- See [Examples](../examples/index.md) for more usage scenarios
-- Check out the [Model Training](../training/overview.md) guide to fine-tune models
-- Join our [Community Discord](https://discord.gg/fastvideo) for support and sharing
+- Learn about [Advanced Inference Configurations](#inference-configuration)
+- Learn about using [Optimizations](#inference-optimizations)
+- See [Examples](../examples/examples_inference_index.md) for more usage scenarios
+- Join our [Community Discord](https://discord.gg/JA7cksDz86).
+- Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-2zf6ru791-sRwI9lPIUJQq1mIeB_yjJg).

docs/source/inference/optimizations.md

@@ -1,3 +1,4 @@
+(inference-optimizations)=
 # Optimizations
 
 This page describes the various options for speeding up generation times in FastVideo.