From b5462877f3039405af5917703cbb80336a93aab2 Mon Sep 17 00:00:00 2001 From: Alexander Dzhoganov Date: Sun, 4 Aug 2024 18:37:23 +0300 Subject: [PATCH] Initial commit --- .gitignore | 1 + LICENSE | 674 +++++++++++++++++++ README.md | 56 ++ __init__.py | 292 ++++++++ examples/flux1dev_2gpu.json | 684 +++++++++++++++++++ examples/flux1dev_sdxl_2gpu.json | 1080 ++++++++++++++++++++++++++++++ examples/nodes.png | Bin 0 -> 175618 bytes examples/sdxl_2gpu.json | 800 ++++++++++++++++++++++ 8 files changed, 3587 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 __init__.py create mode 100644 examples/flux1dev_2gpu.json create mode 100644 examples/flux1dev_sdxl_2gpu.json create mode 100644 examples/nodes.png create mode 100644 examples/sdxl_2gpu.json diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c18dd8d --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +__pycache__/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e72bfdd --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..2fc1c3e --- /dev/null +++ b/README.md @@ -0,0 +1,56 @@ +# ComfyUI-MultiGPU + +### Experimental nodes for using multiple GPUs in a single ComfyUI workflow. + +This extension adds new nodes for model loading that allow you to specify the GPU to use for each model. It monkey patches the memory management of ComfyUI in a hacky way and is neither a comprehensive solution nor a well-tested one. Use at your own risk. + +Note that this does not add parallelism. The workflow steps are still executed sequentially just on different GPUs. Any potential speedup comes from not having to constantly load and unload models from VRAM. + +## Installation + +Clone this repository inside `ComfyUI/custom_nodes/`. + +## Nodes + +![](examples/nodes.png) + +The extension adds new loader nodes corresponding to the default ones. The new nodes have the same functionality but add a new `device` parameter that allows you to specify the GPU to use. + +- `CheckpointLoaderMultiGPU` +- `CLIPLoaderMultiGPU` +- `ControlNetLoaderMultiGPU` +- `DualCLIPLoaderMultiGPU` +- `UNETLoaderMultiGPU` +- `VAELoaderMultiGPU` + +## Example workflows + +All workflows have been tested on a 2x 3090 setup. + +### Loading two SDXL checkpoints on different GPUs + +- [Download](examples/sdxl_2gpu.json) + +This workflow loads two SDXL checkpoints on two different GPUs. The first checkpoint is loaded on GPU 0, and the second checkpoint is loaded on GPU 1. + +### Split FLUX.1-dev across two GPUs + +- [Download](examples/flux1dev_2gpu.json) + +This workflow loads a FLUX.1-dev model and splits it across two GPUs. The UNet model is loaded on GPU 0 while the text encoders and VAE are loaded on GPU 1. + +### FLUX.1-dev and SDXL in the same workflow + +- [Download](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 is loaded on GPU 0, and the SDXL model is loaded on GPU 1. + +## Support + +If you encounter problems, please [open an issue](https://github.com/neuratech-ai/ComfyUI-MultiGPU/issues/new). Attach the workflow if possible. + +## Credits + +Made by [Alexander Dzhoganov](https://github.com/AlexanderDzhoganov). + +For business inquiries, email [sales@neuratech.io](mailto:sales@neuratech.io) or visit [our website](https://neuratech.io/). diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..8a3ee41 --- /dev/null +++ b/__init__.py @@ -0,0 +1,292 @@ +import torch +import folder_paths +import comfy.sd +import comfy.model_management + +current_device = "cuda:0" + + +def get_torch_device_patched(): + global current_device + if ( + not torch.cuda.is_available() + or comfy.model_management.cpu_state == comfy.model_management.CPUState.CPU + ): + return torch.device("cpu") + + return torch.device(current_device) + + +comfy.model_management.get_torch_device = get_torch_device_patched + + +class CheckpointLoaderMultiGPU: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "ckpt_name": (folder_paths.get_filename_list("checkpoints"),), + "device": ([f"cuda:{i}" for i in range(torch.cuda.device_count())],), + } + } + + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, device): + global current_device + current_device = device + + ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) + out = comfy.sd.load_checkpoint_guess_config( + ckpt_path, + output_vae=True, + output_clip=True, + embedding_directory=folder_paths.get_folder_paths("embeddings"), + ) + return out[:3] + + +class UNETLoaderMultiGPU: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "unet_name": (folder_paths.get_filename_list("unet"),), + "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],), + "device": ([f"cuda:{i}" for i in range(torch.cuda.device_count())],), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + CATEGORY = "advanced/loaders" + + def load_unet(self, unet_name, weight_dtype, device): + global current_device + current_device = device + + dtype = None + if weight_dtype == "fp8_e4m3fn": + dtype = torch.float8_e4m3fn + elif weight_dtype == "fp8_e5m2": + dtype = torch.float8_e5m2 + + unet_path = folder_paths.get_full_path("unet", unet_name) + model = comfy.sd.load_unet(unet_path, dtype=dtype) + return (model,) + + +class VAELoaderMultiGPU: + @staticmethod + def vae_list(): + vaes = folder_paths.get_filename_list("vae") + approx_vaes = folder_paths.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + sd3_taesd_enc = False + sd3_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + elif v.startswith("taesd3_decoder."): + sd3_taesd_dec = True + elif v.startswith("taesd3_encoder."): + sd3_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + if sd3_taesd_dec and sd3_taesd_enc: + vaes.append("taesd3") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = folder_paths.get_filename_list("vae_approx") + + encoder = next( + filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes) + ) + decoder = next( + filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes) + ) + + enc = comfy.utils.load_torch_file( + folder_paths.get_full_path("vae_approx", encoder) + ) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = comfy.utils.load_torch_file( + folder_paths.get_full_path("vae_approx", decoder) + ) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + sd["vae_shift"] = torch.tensor(0.0) + elif name == "taesd3": + sd["vae_scale"] = torch.tensor(1.5305) + sd["vae_shift"] = torch.tensor(0.0609) + return sd + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "vae_name": (s.vae_list(),), + "device": ([f"cuda:{i}" for i in range(torch.cuda.device_count())],), + } + } + + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + CATEGORY = "loaders" + + # TODO: scale factor? + def load_vae(self, vae_name, device): + global current_device + current_device = device + + if vae_name in ["taesd", "taesdxl", "taesd3"]: + sd = self.load_taesd(vae_name) + else: + vae_path = folder_paths.get_full_path("vae", vae_name) + sd = comfy.utils.load_torch_file(vae_path) + vae = comfy.sd.VAE(sd=sd) + return (vae,) + + +class ControlNetLoaderMultiGPU: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "control_net_name": (folder_paths.get_filename_list("controlnet"),), + "device": ([f"cuda:{i}" for i in range(torch.cuda.device_count())],), + } + } + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + CATEGORY = "loaders" + + def load_controlnet(self, control_net_name, device): + global current_device + current_device = device + + controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) + controlnet = comfy.controlnet.load_controlnet(controlnet_path) + return (controlnet,) + + +class CLIPLoaderMultiGPU: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip_name": (folder_paths.get_filename_list("clip"),), + "type": ( + ["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], + ), + "device": ([f"cuda:{i}" for i in range(torch.cuda.device_count())],), + } + } + + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name, device, type="stable_diffusion"): + global current_device + current_device = device + + if type == "stable_cascade": + clip_type = comfy.sd.CLIPType.STABLE_CASCADE + elif type == "sd3": + clip_type = comfy.sd.CLIPType.SD3 + elif type == "stable_audio": + clip_type = comfy.sd.CLIPType.STABLE_AUDIO + else: + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + + clip_path = folder_paths.get_full_path("clip", clip_name) + clip = comfy.sd.load_clip( + ckpt_paths=[clip_path], + embedding_directory=folder_paths.get_folder_paths("embeddings"), + clip_type=clip_type, + ) + return (clip,) + + +class DualCLIPLoaderMultiGPU: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "clip_name1": (folder_paths.get_filename_list("clip"),), + "clip_name2": (folder_paths.get_filename_list("clip"),), + "type": (["sdxl", "sd3", "flux"],), + "device": ([f"cuda:{i}" for i in range(torch.cuda.device_count())],), + } + } + + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2, type, device): + global current_device + current_device = device + + clip_path1 = folder_paths.get_full_path("clip", clip_name1) + clip_path2 = folder_paths.get_full_path("clip", clip_name2) + if type == "sdxl": + clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION + elif type == "sd3": + clip_type = comfy.sd.CLIPType.SD3 + elif type == "flux": + clip_type = comfy.sd.CLIPType.FLUX + + clip = comfy.sd.load_clip( + ckpt_paths=[clip_path1, clip_path2], + embedding_directory=folder_paths.get_folder_paths("embeddings"), + clip_type=clip_type, + ) + return (clip,) + + +NODE_CLASS_MAPPINGS = { + "CheckpointLoaderMultiGPU": CheckpointLoaderMultiGPU, + "UNETLoaderMultiGPU": UNETLoaderMultiGPU, + "VAELoaderMultiGPU": VAELoaderMultiGPU, + "ControlNetLoaderMultiGPU": ControlNetLoaderMultiGPU, + "CLIPLoaderMultiGPU": CLIPLoaderMultiGPU, + "DualCLIPLoaderMultiGPU": DualCLIPLoaderMultiGPU, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "CheckpointLoaderMultiGPU": "Load Checkpoint (Multi-GPU)", + "UNETLoaderMultiGPU": "Load Diffusion Model (Multi-GPU)", + "VAELoaderMultiGPU": "Load 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