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Update to examples to include CPU offloads, as well as update readme.md with new examples and their requirements to run.

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John Pollock
2024-12-27 13:22:16 -06:00
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README.md
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@@ -1,6 +1,6 @@
# ComfyUI-MultiGPU
## Experimental nodes for using multiple GPUs and offloading model components to the CPU in a single ComfyUI workflow
## Experimental nodes for using multiple GPUs as well as offloading model components to the CPU in a single ComfyUI workflow
This extension adds device selection capabilities to model loading nodes in ComfyUI. It monkey patches the memory management of ComfyUI in a hacky way and is neither a comprehensive solution is nor is it well-tested on any edge-case CUDA/CPU solutions. **Use at your own risk.**
@@ -56,17 +56,22 @@ This workflow loads a FLUX.1-dev model and splits its components across two GPUs
### Split FLUX.1-dev between the CPU and a single GPU
- [examples/flux1dev_cpu_1gpu_GGUF.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/flux1dev_cpu_1gpu_GGUF.json)
This workflow demonstrates splitting a quantized, GGUF FLUX.1-dev model between a CPU and a single GPU. The UNet model is loaded on the GPU, while the VAE and text encoders are handled by the CPU.
This workflow demonstrates splitting a quantized, GGUF FLUX.1-dev model between a CPU and a single GPU. The UNet model is loaded on the GPU, while the VAE and text encoders are handled by the CPU. Requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF).
### Using GGUF quantized models across GPUs
- [examples/flux1dev_2gpu_GGUF.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/flux1dev_2gpu_GGUF.json)
This workflow demonstrates using quantized GGUF models split across multiple GPUs for reduced VRAM usage with the UNet on GPU 1, VAE and text encoders on GPU 0.
This workflow demonstrates using quantized GGUF models split across multiple GPUs for reduced VRAM usage with the UNet on GPU 1, VAE and text encoders on GPU 0. Requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF).
### Using GGUF quantized models across a CPU and a single GPU for video generation
- [examples/hunyuan_cpu_1gpu_GGUF.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/hunyuan_cpu_1gpu_GGUF.json)
This workflow demonstrates using quantized GGUF models for Hunyan Video split across the CPU and one GPU. In this instance, a quantized video model's UNet and VAE are on GPU 0, whereas a split of one standard and one GGUF model text encoder are on the CPU. Requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF).
### Using GGUF quantized models across GPUs for video generation
- [examples/hunyuan_2gpu_GGUF.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/hunyuan_2gpu_GGUF.json)
This workflow demonstrates using quantized GGUF models for Hunyan Video split across multiple GPUs with the FastVideo LoRA. In this instance, the video model is on GPU 0 whereas the VAE and text encoders are on GPU 1.
This workflow demonstrates using quantized GGUF models for Hunyan Video split across multiple GPUs. In this instance, a quantized video model's UNet is on GPU 0 whereas the VAE and text encoders are on GPU 1. Requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF).
### Loading two SDXL checkpoints on different GPUs
@@ -78,20 +83,16 @@ This workflow loads two SDXL checkpoints on two different GPUs. The first checkp
- [examples/flux1dev_sdxl_2gpu.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/flux1dev_sdxl_2gpu.json)
This workflow loads a FLUX.1-dev model and an SDXL model in the same workflow. The FLUX.1-dev model has its UNet on GPU 1 with VAE and text encoders on GPU 0, while the SDXL model uses separate allocations on GPU 0.
### EXPERIMENTAL - USE AT YOUR OWN RISK
### Image to Prompt to Image to Video Generation Pipeline
These workflows combine multiple features and non-core loaders types and, as provided, require significant VRAM to execute. They are provided as examples of what's possible but may require adjustment for your specific setup.
#### Image to Prompt to Image to Video Generation Pipeline
- [examples/florence2_flux1dev_ltxv_cpu_2gpu_GGUF.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/florence2_flux1dev_ltxv_cpu_2gpu_GGUF.json)
- [examples/florence2_flux1dev_ltxv_cpu_2gpu.json](https://github.com/pollockjj/ComfyUI-MultiGPU/blob/main/examples/florence2_flux1dev_ltxv_cpu_2gpu.json)
This workflow creates an img2txt2img2vid video generation pipeline by:
1. Loading the Florence2 model on the CPU and providing a starting image for analysis and generating a text response
2. Loading FLUX.1 Dev UNET on GPU 1, with CLIP and VAE on the CPU and generating an image using the Florence2 text as a prompt
3. Loading the LTX Video UNet and VAE on GPU 2, and LTX-encoded CLIP on the CPU, and taking the resulting FLUX.1 image and provide it as the starting image for an LTX Video image-to-video generation
4. Generate a 5 second video based on the provided image
All models are distributed across available the available CPU and GPUs with no model reloading on dual 3090s
All models are distributed across available the available CPU and GPUs with no model reloading on dual 3090s. Requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF) and [ComfyUI-LTXVideo](https://github.com/Lightricks/ComfyUI-LTXVideo)
#### LLM-Guided Video Generation
@@ -100,7 +101,7 @@ This workflow demonstrates:
1. Using a local LLM (loaded on first GPU via llama.cpp) to take a text suggestion and craft an LTX Video promot
2. Feeding the enhanced prompt to LTXVideo (loaded on second GPU) for video generation
Requires appropriate LLM and LTXVideo models.
Requires appropriate LLM. Requires [ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF).
## Support
examples/florence2_flux1dev_ltxv_cpu_2gpu_GGUF.json → examples/florence2_flux1dev_ltxv_cpu_2gpu.json
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examples/flux1dev_2gpu.json
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examples/flux1dev_2gpu_GGUF.json
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examples/hunyuan_2gpu_GGUF.json
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examples/llamacpp_ltxv_2gpu_GGUF.json
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examples/sdxl_2gpu.json
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