Update README.md

README.md

@@ -8,14 +8,33 @@ base_model:
 
 [![View Code](https://img.shields.io/badge/📂_View_Code-GitHub-181717?style=for-the-badge&logo=github)](https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler)
 
-A Non official custom nodes for ComfyUI that enables Upscale Video generation using [SeedVR2](https://github.com/ByteDance-Seed/SeedVR).
+A Non official custom nodes for ComfyUI that enables Upscale Video/Images generation using [SeedVR2](https://github.com/ByteDance-Seed/SeedVR).
 
-<video width="700px" controls>
-  <source src="https://github.com/user-attachments/assets/8fbd6c1f-4246-4dbe-8819-4e684490c5f2" type="video/mp4">
-  Your browser does not support the video tag.
-</video>
+<img src="docs/demo_01.jpg">
+<img src="docs/demo_02.jpg">
 
-<img src="docs/usage.png" width="700px">
+<img src="docs/usage.png">
+
+## 🆙 Todo
+
+- Fixed unloading the 3B model when the process is finished (sorry about that, I'm trying to find out what's going on)
+
+## 🚀 Updates
+
+**2025.06.24**
+
+- 🚀 Speed up the process until x4 (see new benchmark)
+
+**2025.06.22**
+
+- 💪 FP8 compatibility !
+- 🚀 Speed Up all Process
+- 🚀 less VRAM consumption (Stay high, batch_size=1 for RTX4090 max, I'm trying to fix that)
+- 🛠️ Better benchmark coming soon
+
+**2025.06.20**
+
+- 🛠️ Initial push
 
 ## Features
 
@@ -25,6 +44,7 @@ A Non official custom nodes for ComfyUI that enables Upscale Video generation us
 
 ## Requirements
 
+- A Huge VRAM capabilities is better, from my test, even the 3B version need a lot of VRAM at least 18GB.
 - Last ComfyUI version with python 3.12.9 (may be works with older versions but I haven't test it)
 
 ## Installation
@@ -73,36 +93,56 @@ python_embeded\python.exe -m pip install -r flash_attn
 
 <img src="docs/node.png" width="100%">
 
+2. things to know
+
+**temporal consistency** : at least a batch_size of 5 is required to activate temporal consistency
+
 2. Configure the node parameters:
 
    - `model`: Select your 3B or 7B model
    - `seed`: a seed but it generate another seed from this one
    - `new_width`: New desired Width, will keep ration on height
    - `cfg_scale`:
-   - `batch_size`: VERY IMPORTANT!, this model consume a lot of VRAM, All your VRAM, even for the 3B model, so for GPU under 24GB VRAM keep this value Low, good values are [1,5,9,13,...]
-   - `vram_mode`: It will try to help with VRAM, but 'auto' is good
+   - `batch_size`: VERY IMPORTANT!, this model consume a lot of VRAM, All your VRAM, even for the 3B model, so for GPU under 24GB VRAM keep this value Low, good value is "1" without temporal consistency
+   - `preserve_vram`: for VRAM < 24GB, If true, It will unload unused models during process, longer but works, otherwise probably OOM with
 
 ## Performance
 
-1. **NVIDIA H100 93GB VRAM**
-
-3B or 7B spike to 90+GB VRAM!! but fast!!
-
-- 3B Model, 97 images, from 512x768 to 1280x1920, batch_size=50 => Prompt executed in 338.63 seconds
-- 3B Model, 97 images, from 512x768 to 1280x1920, batch_size=10 => Prompt executed in 540.22 seconds
-- 3B Model, 97 images, from 512x768 to 720x1080, batch_size=10 => Prompt executed in 183.64 seconds
-- 7B Model, 50 images, 512x768 to 1080x1620, batch_size=50, Prompt executed in 166.89 seconds
-- 7B Model, 97 images, 512x768 to 1080x1620, batch_size=97, Prompt executed in 146.72 seconds
-- 7B Model, 200 images, 512x768 to 1080x1620, batch_size=200, Prompt executed in 266.14 seconds
-
-2. **NVIDIA RTX4090 24GB VRAM**
-
-- 3B Model, 20 images, from 512x768 to 1080x1620, batch_size=1, Prompt executed in 1022.26 seconds
--
+**NVIDIA H100 93GB VRAM** (values in parentheses are from the previous benchmark):
+
+| Images | Resolution          | Batch Size | Time fp8 (s)     | FPS fp8     | Time fp16 (s)    | FPS fp16    |
+| ------ | ------------------- | ---------- | ---------------- | ----------- | ---------------- | ----------- |
+| 3      | 512×768 → 1080×1620 | 1          | 10.18 (58.10)    | 0.29 (0.05) | 10.67 (60.13)    | 0.28 (0.05) |
+| 15     | 512×768 → 1080×1620 | 5          | 26.71 (135.63)   | 0.56 (0.11) | 27.75 (144.18)   | 0.54 (0.10) |
+| 27     | 512×768 → 1080×1620 | 9          | 33.97 (163.22)   | 0.79 (0.17) | 35.08 (177.61)   | 0.77 (0.15) |
+| 39     | 512×768 → 1080×1620 | 13         | 41.01 (189.36)   | 0.95 (0.21) | 42.08 (210.11)   | 0.93 (0.19) |
+| 51     | 512×768 → 1080×1620 | 17         | 48.12 (215.80)   | 1.06 (0.24) | 49.44 (242.64)   | 1.03 (0.21) |
+| 63     | 512×768 → 1080×1620 | 21         | 55.40 (241.79)   | 1.14 (0.26) | 56.70 (275.55)   | 1.11 (0.23) |
+| 75     | 512×768 → 1080×1620 | 25         | 62.60 (267.93)   | 1.20 (0.28) | 63.80 (308.51)   | 1.18 (0.24) |
+| 123    | 512×768 → 1080×1620 | 41         | 91.38 (373.60)   | 1.35 (0.33) | 92.90 (440.01)   | 1.32 (0.28) |
+| 243    | 512×768 → 1080×1620 | 81         | 164.25 (642.20)  | 1.48 (0.38) | 166.09 (780.20)  | 1.46 (0.31) |
+| 363    | 512×768 → 1080×1620 | 121        | 238.18 (913.61)  | 1.52 (0.40) | 239.80 (1114.32) | 1.51 (0.33) |
+| 453    | 512×768 → 1080×1620 | 151        | 296.52 (1132.01) | 1.53 (0.40) | 298.65 (1384.86) | 1.52 (0.33) |
+| 633    | 512×768 → 1080×1620 | 211        | 406.65 (1541.09) | 1.56 (0.41) | 409.44 (1887.62) | 1.55 (0.34) |
+| 903    | 512×768 → 1080×1620 | 301        | OOM (OOM)        | OOM (OOM)   | OOM (OOM)        | OOM (OOM)   |
+
+**NVIDIA RTX4090 24GB VRAM** (preserved_vram=off)
+| Model | Images | Resolution | Batch Size | Time (seconds) | FPS | Note |
+| ------------------------- | ------ | ------------------- | ---------- | -------------- | --- | --- |
+| 3B fp8 | 5 | 512x768 → 1080x1620 | 1 | 22.52 | 0.22 | |
+| 3B fp16 | 5 | 512x768 → 1080x1620 | 1 | 27.84 | 0.18 | |
+| 7B fp8 | 5 | 512x768 → 1080x1620 | 1 | 75.51 | 0.07 | |
+| 7B fp16 | 5 | 512x768 → 1080x1620 | 1 | 78.93 | 0.06 | |
+| 3B fp8 | 10 | 512x768 → 1080x1620 | 5 | 39.75 | 0.15 | preserve_memory=on|
+| 3B fp8 | 20 | 512x768 → 1080x1620 | 1 | 65.40 | 0.31 | |
+| 3B fp16 | 20 | 512x768 → 1080x1620 | 1 | 91.12 | 0.22 | |
+| 3B fp8 | 20 | 512x768 → 1280x1920 | 1 | 89.10 | 0.22 | |
+| 3B fp8 | 20 | 512x768 → 1480x2220 | 1 | 136.08| 0.15 | |
+| 3B fp8 | 20 | 512x768 → 1620x2430 | 1 | 191.28 | 0.10 | preserve_memory=on without GPU overload so longer 320sec |
 
 ## Limitations
 
-- Use a lot of VRAM, it will take alllllll!!!!
+- Use a lot of VRAM, it will take all!!
 - Processing speed depends on GPU capabilities
 
 ## Credits
@@ -114,3 +154,4 @@ python_embeded\python.exe -m pip install -r flash_attn
 - The code in this repository is released under the MIT license as found in the [LICENSE file](LICENSE).
 
 
+