124 lines
5.8 KiB
Python
124 lines
5.8 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import numpy as np
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import torch
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import os
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from .model_loader_utils import tensor_upscale
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from .FlashVSR.examples.WanVSR.infer_flashvsr_full import init_pipeline,run_inference
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from .FlashVSR.examples.WanVSR.infer_flashvsr_tiny import init_pipeline_tiny,run_inference_tiny
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import folder_paths
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, io
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import nodes
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MAX_SEED = np.iinfo(np.int32).max
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node_cr_path = os.path.dirname(os.path.abspath(__file__))
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device = torch.device(
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"cuda:0") if torch.cuda.is_available() else torch.device(
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"mps") if torch.backends.mps.is_available() else torch.device(
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"cpu")
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weigths_FlashVSR_current_path = os.path.join(folder_paths.models_dir, "FlashVSR")
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if not os.path.exists(weigths_FlashVSR_current_path):
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os.makedirs(weigths_FlashVSR_current_path)
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folder_paths.add_model_folder_path("FlashVSR", weigths_FlashVSR_current_path) # FlashVSR dir
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class FlashVSR_SM_Model(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="FlashVSR_SM_Model",
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display_name="FlashVSR_SM_Model",
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category="FlashVSR",
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inputs=[
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io.Combo.Input("dit",options= ["none"] + [i for i in folder_paths.get_filename_list("FlashVSR") if "dmd" in i.lower()]),
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io.Combo.Input("proj_pt",options= ["none"] + [i for i in folder_paths.get_filename_list("FlashVSR") if "proj" in i.lower()]),
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io.Combo.Input("vae",options= ["none"] + folder_paths.get_filename_list("vae") ),
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io.Combo.Input("tcd_encoder",options= ["none"] + [i for i in folder_paths.get_filename_list("FlashVSR") if "tcd" in i.lower()] ),
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],
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outputs=[
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io.Custom("FlashVSR_SM_Model").Output(),
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],
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)
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@classmethod
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def execute(cls, dit,proj_pt,vae,tcd_encoder) -> io.NodeOutput:
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dit_path=folder_paths.get_full_path("FlashVSR", dit) if dit != "none" else None
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proj_pt_path=folder_paths.get_full_path("FlashVSR", proj_pt) if proj_pt != "none" else None
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vae_path=folder_paths.get_full_path("vae", vae) if vae != "none" else None
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tcd_encoder_path=folder_paths.get_full_path("FlashVSR", tcd_encoder) if tcd_encoder != "none" else None
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assert dit_path is not None and proj_pt is not None , "Please select the Sdit,proj_pt,checkpoint file"
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assert vae_path is not None or tcd_encoder_path is not None , "Please select the Sdit,proj_pt,checkpoint file"
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if vae_path is None and tcd_encoder_path is not None:
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model=init_pipeline_tiny(proj_pt_path,dit_path, tcd_encoder_path, device="cuda")
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else:
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model=init_pipeline(proj_pt_path,dit_path, vae_path, device="cuda")
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return io.NodeOutput(model)
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class FlashVSR_SM_KSampler(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="FlashVSR_SM_KSampler",
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display_name="FlashVSR_SM_KSampler",
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category="FlashVSR",
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inputs=[
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io.Custom("FlashVSR_SM_Model").Input("model"),
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io.Image.Input("image"),
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io.Combo.Input("emb_pt",options= ["none"] + [i for i in folder_paths.get_filename_list("FlashVSR") if "prompt" in i.lower()]),
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io.Int.Input("width", default=1280, min=128, max=nodes.MAX_RESOLUTION,step=64,display_mode=io.NumberDisplay.number),
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io.Int.Input("height", default=768, min=128, max=nodes.MAX_RESOLUTION,step=64,display_mode=io.NumberDisplay.number),
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io.Int.Input("seed", default=0, min=0, max=MAX_SEED),
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io.Int.Input("scale", default=4, min=1, max=4),
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io.Float.Input("kv_ratio", default=3.5, min=0.0, max=10.0, step=0.1, round=0.01,),
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io.Int.Input("local_range", default=11, min=1,step=1, max=50),
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io.Int.Input("steps", default=1, min=1, max=10000),
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io.Float.Input("cfg", default=1.0, min=0.0, max=100.0, step=0.1, round=0.01,),
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io.Float.Input("sparse_ratio", default=2.0, min=0.0, max=10.0, step=0.1,display_mode=io.NumberDisplay.slider),
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io.Boolean.Input("full_tiled", default=True),
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io.Boolean.Input("color_fix", default=True),
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],
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outputs=[
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io.Image.Output(display_name="images"),
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],
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)
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@classmethod
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def execute(cls, model,image,emb_pt,width,height,seed,scale,kv_ratio,local_range, steps, cfg,sparse_ratio,full_tiled,color_fix) -> io.NodeOutput:
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image=tensor_upscale(image,width, height)
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prompt_path=folder_paths.get_full_path("FlashVSR", emb_pt) if emb_pt != "none" else None
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assert prompt_path is not None , "Please select the emb"
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if hasattr(model,"TCDecoder") :
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print("infer tiny mode")
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images=run_inference_tiny(model,prompt_path,image,seed,scale,kv_ratio,local_range,steps,cfg,sparse_ratio,color_fix )
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else:
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print("infer full mode")
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images=run_inference(model,prompt_path,image,seed,scale,kv_ratio,local_range,steps,cfg,sparse_ratio,full_tiled,color_fix )
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return io.NodeOutput(images.float())
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from aiohttp import web
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from server import PromptServer
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@PromptServer.instance.routes.get("/FlashVSR_SM_Extension")
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async def get_hello(request):
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return web.json_response("FlashVSR_SM_Extension")
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class FlashVSR_SM_Extension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [
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FlashVSR_SM_Model,
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FlashVSR_SM_KSampler,
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]
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async def comfy_entrypoint() -> FlashVSR_SM_Extension: # ComfyUI calls this to load your extension and its nodes.
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return FlashVSR_SM_Extension()
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