246 lines
12 KiB
Python
246 lines
12 KiB
Python
# !/usr/bin/env python
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# -*- coding: UTF-8 -*-
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import os
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from PIL import Image
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import torch
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import numpy as np
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from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig,FluxTransformer2DModel, AutoencoderKL
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from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig,T5EncoderModel
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from .src.utils import *
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from .scripts.grounding_sam import *
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from .node_utils import cleanup,pil2narry,process_image_with_mask,tensor2pil_upscale
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from .src.pipeline import RFInversionParallelFluxPipeline
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from .src.pipeline_wrapper import RFInversionParallelFluxPipeline as RFInversionParallelFluxPipeline_Wrapper
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from .gradio_demo import generate_image_out,generate_image_in,generate_image_personalize_single,generate_image_reconstruction,generate_image_Composition
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import folder_paths
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MAX_SEED = np.iinfo(np.int32).max
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current_node_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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class Personalize_Anything_Load:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"transformer": (["none"]+folder_paths.get_filename_list("diffusion_models"),),
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"flux_repo": ("STRING", {"default": "F:/test/ComfyUI/models/diffusers/black-forest-labs/FLUX.1-dev"},),
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"quantization":(["none","fp8","nf4"],),
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"quantize_T5":("BOOLEAN",{"default":True}),
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},
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"optional": { "model":("MODEL",),
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"vae":("VAE",),
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}
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}
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RETURN_TYPES = ("MODEL_PERSONALIZE_ANYTHING", )
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RETURN_NAMES = ("model",)
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FUNCTION = "main"
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CATEGORY = "Personalize_Anything"
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def main(self, transformer,flux_repo,quantization,quantize_T5,**kwargs):
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cf_model=kwargs.get("model",None)
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cf_vae=kwargs.get("vae",None)
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flux_repo_local=os.path.join(current_node_path, 'src/FLUX.1-dev')
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if flux_repo:
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if quantization=="none":
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if quantize_T5:
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quant_config = TransformersBitsAndBytesConfig(load_in_8bit=True,) # 8bit default
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text_encoder_2_8bit = T5EncoderModel.from_pretrained(
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flux_repo,
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subfolder="text_encoder_2",
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quantization_config=quant_config,
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torch_dtype=torch.bfloat16,
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)
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pipeline = RFInversionParallelFluxPipeline.from_pretrained(flux_repo,text_encoder_2=text_encoder_2_8bit,torch_dtype=torch.bfloat16,)
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else:
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pipeline = RFInversionParallelFluxPipeline.from_pretrained(flux_repo,torch_dtype=torch.bfloat16,)
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else:
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if quantization=="fp8":
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if quantize_T5:
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quant_config = TransformersBitsAndBytesConfig(load_in_8bit=True,) # 8bit
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text_encoder_2_8bit = T5EncoderModel.from_pretrained(
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flux_repo,
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subfolder="text_encoder_2",
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quantization_config=quant_config,
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torch_dtype=torch.bfloat16,
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)
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transformer = FluxTransformer2DModel.from_pretrained(
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flux_repo,
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subfolder="transformer",
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quantization_config=DiffusersBitsAndBytesConfig(load_in_8bit=True,),
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torch_dtype=torch.bfloat16,
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)
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if quantize_T5:
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pipeline =RFInversionParallelFluxPipeline.from_pretrained(flux_repo,transformer=transformer,text_encoder_2=text_encoder_2_8bit,torch_dtype=torch.bfloat16,)
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else:
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pipeline = RFInversionParallelFluxPipeline.from_pretrained(flux_repo,transformer=transformer, torch_dtype=torch.bfloat16,)
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else: #nf4
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if quantize_T5:
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quant_config = TransformersBitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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)
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text_encoder_2_4bit = T5EncoderModel.from_pretrained(
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flux_repo,
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subfolder="text_encoder_2",
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quantization_config=quant_config,
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torch_dtype=torch.bfloat16,
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)
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transformer = FluxTransformer2DModel.from_pretrained(
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flux_repo,
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subfolder="transformer",
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quantization_config=DiffusersBitsAndBytesConfig(
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load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
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),
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torch_dtype=torch.bfloat16,)
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if quantize_T5:
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pipeline =RFInversionParallelFluxPipeline.from_pretrained(flux_repo,transformer=transformer,text_encoder_2=text_encoder_2_4bit,torch_dtype=torch.bfloat16,)
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else:
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pipeline = RFInversionParallelFluxPipeline.from_pretrained(flux_repo,transformer=transformer, torch_dtype=torch.bfloat16,)
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else:
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if cf_model is None and cf_vae is None:
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if transformer != "none":
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flux_transformer_path = folder_paths.get_full_path("diffusion_models", transformer)
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pipeline = RFInversionParallelFluxPipeline.from_single_file(
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flux_transformer_path,config=flux_repo_local, torch_dtype=torch.bfloat16,)
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else:
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raise Exception("Please select a transformer model")
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else:
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ae_dic=cf_vae.get_sd()
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# vae_path = folder_paths.get_full_path("vae", vae)
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vae_config=os.path.join(flux_repo_local, 'vae')
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ae = AutoencoderKL.from_single_file(ae_dic,config=vae_config, torch_dtype=torch.bfloat16)
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config_file = os.path.join(flux_repo_local,"transformer/config.json")
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if transformer != "none":
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flux_transformer_path = folder_paths.get_full_path("diffusion_models", transformer)
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transformer_ = FluxTransformer2DModel.from_single_file(
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flux_transformer_path,
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config=config_file,
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torch_dtype=torch.bfloat16,
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)
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else:
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if cf_model is not None:
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cf_state_dict = cf_model.model.diffusion_model.state_dict()
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del cf_model
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cleanup()
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transformer_=FluxTransformer2DModel.from_single_file(cf_state_dict,config=config_file,torch_dtype=torch.bfloat16)
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# unet_config = FluxTransformer2DModel.load_config(config_file)
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# transformer_ = FluxTransformer2DModel.from_config(unet_config).to(torch.bfloat16)
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# transformer_.load_state_dict(cf_state_dict, strict=False)
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del cf_state_dict
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cleanup()
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else:
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raise ValueError("No transformer model found")
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pipeline = RFInversionParallelFluxPipeline_Wrapper.from_pretrained(
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flux_repo_local,vae=ae,transformer=transformer_, torch_dtype=torch.bfloat16,)
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cleanup()
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pipeline.enable_model_cpu_offload()
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return (pipeline,)
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class Personalize_Anything_Sampler:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("MODEL_PERSONALIZE_ANYTHING",),
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"iamge": ("IMAGE",), # B H W C
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"mask": ("MASK",), # B H W
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"prompt": ("STRING", {"default": "A teddy bear", "multiline": True,}),
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"personalize_prompt": ("STRING", {"default": "A teddy bear waving its right hand on a nighttime street, positioned on the left side of the frame, with an empty road on the right.", "multiline": True,}),
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"seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED, "step": 1, "display": "number"}),
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"width": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 16, "display": "number"}),
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"height": ("INT", {"default": 512, "min": 256, "max": 4096, "step": 16, "display": "number"}),
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"timestep": ("INT", {"default": 28, "min": 1, "max": 1024, "step": 1, "display": "number"}),
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"tau": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1, "display": "number"}),
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"shift": ("INT", {"default": 0, "min": -4096, "max": 4096, "step": 1, "display": "number"}),
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"infer_mode":(["inpainting","outpainting","single_personalize","multi_personalize","subject_reconstruction","scene_composition"],),
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},
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"optional": { "bg_image":("IMAGE",),
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"clip": ("CLIP",),
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},
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}
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RETURN_TYPES = ("IMAGE", )
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RETURN_NAMES = ("image",)
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FUNCTION = "main"
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CATEGORY = "Personalize_Anything"
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def main(self,model, iamge,mask,prompt,personalize_prompt,seed,width,height,timestep,tau,shift,infer_mode,**kwargs):
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bg_image=kwargs.get("bg_image")
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cf_clip=kwargs.get("clip")
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if infer_mode=="single_personalize" or infer_mode=="subject_reconstruction" or infer_mode=="scene_composition":
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init_image,mask_pil=process_image_with_mask(iamge,mask,width,height,False) #false
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else:
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init_image,mask_pil=process_image_with_mask(iamge,mask,width,height,True) # outpainting and inpainting need Ture
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if infer_mode=="scene_composition":
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if "[" in prompt:
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prompt=prompt.split("[")
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else:
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raise ValueError("scene_composition need a [ in prompt")
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if isinstance(bg_image, torch.Tensor) :
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bg_image=tensor2pil_upscale(bg_image,width,height)
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else:
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raise ValueError("bg_image must link a image")
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latent_h = height // 16
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latent_w = width // 16
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img_dims = latent_h * latent_w
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mask = create_mask(mask_pil, latent_w, latent_h)
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bg_mask=1-mask
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# inverse
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if infer_mode=="inpainting":
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img=generate_image_in(model,prompt,personalize_prompt, seed, timestep, tau,init_image,mask,height,width,img_dims,device,shift,cf_clip)
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elif infer_mode=="outpainting":
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img=generate_image_out(model,prompt,personalize_prompt, seed, timestep, tau,init_image,mask,height,width,img_dims,device,shift,cf_clip)
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elif infer_mode=="single_personalize":
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img=generate_image_personalize_single(model,prompt,personalize_prompt, seed, timestep, tau, init_image,mask,height,width,img_dims,device,shift,cf_clip)
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elif infer_mode=="multi_personalize":
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pass
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elif infer_mode=="scene_composition":
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img=generate_image_Composition(model,prompt,personalize_prompt, seed, timestep, tau, init_image,bg_image,mask,bg_mask,height,width,img_dims,device,shift,cf_clip)
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else: # subject_reconstruction
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img=generate_image_reconstruction(model,prompt,personalize_prompt, seed, timestep, tau, init_image,mask,height,width,img_dims,device,shift,cf_clip)
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cleanup()
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return (pil2narry(img),)
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NODE_CLASS_MAPPINGS = {
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"Personalize_Anything_Load": Personalize_Anything_Load,
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"Personalize_Anything_Sampler":Personalize_Anything_Sampler,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Personalize_Anything_Load": "Personalize_Anything_Load",
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"Personalize_Anything_Sampler":"Personalize_Anything_Sampler",
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}
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