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ComfyUI_Pops.py
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ComfyUI_Pops.py
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# !/usr/bin/env python
# -*- coding: UTF-8 -*-
import sys
import os
import torch
from PIL import Image
import numpy as np
import random
from transformers import CLIPTokenizer, CLIPVisionModelWithProjection, CLIPImageProcessor
from diffusers import PriorTransformer, UNet2DConditionModel, KandinskyV22Pipeline
from .model import pops_utils
from .model.pipeline_pops import pOpsPipeline
import folder_paths
from comfy.utils import common_upscale
MAX_SEED = np.iinfo(np.int32).max
dir_path = os.path.dirname(os.path.abspath(__file__))
path_dir = os.path.dirname(dir_path)
file_path = os.path.dirname(path_dir)
device="cuda"
weight_dtype = torch.float16
output_dir = folder_paths.output_directory
def instance_path(path, repo):
if repo == "":
if path == "none":
raise "you need fill repo_id or download model in diffusers directory "
elif path != "none":
model_path = get_local_path(file_path, path)
repo = get_instance_path(model_path)
return repo
paths = []
for search_path in folder_paths.get_folder_paths("diffusers"):
if os.path.exists(search_path):
for root, subdir, files in os.walk(search_path, followlinks=True):
if "model_index.json" in files:
paths.append(os.path.relpath(root, start=search_path))
if paths:
paths = ["none"] + [x for x in paths if x]
else:
paths = ["none", ]
def phi2narry(img):
narry = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
return narry
def narry_list(list_in):
for i in range(len(list_in)):
value = list_in[i]
modified_value = phi2narry(value)
list_in[i] = modified_value
return list_in
def get_instance_path(path):
instance_path = os.path.normpath(path)
if sys.platform == 'win32':
instance_path = instance_path.replace('\\', "/")
return instance_path
def tensor_to_pil(tensor):
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
image = Image.fromarray(image_np, mode='RGB')
return image
def nomarl_upscale_topil(img_tensor, width, height):
samples = img_tensor.movedim(-1, 1)
img = common_upscale(samples, width, height, "nearest-exact", "center")
samples = img.movedim(1, -1)
img_pil = tensor_to_pil(samples)
return img_pil
def get_local_path(file_path, model_path):
path = os.path.join(file_path, "models", "diffusers", model_path)
model_path = os.path.normpath(path)
if sys.platform == 'win32':
model_path = model_path.replace('\\', "/")
return model_path
def process_image(inputs_obj, clip,width, height):
image_caption_suffix = ''
if inputs_obj is not None and isinstance(inputs_obj, str):
if inputs_obj.suffix == '.pth':
image = torch.load(inputs_obj).image_embeds.to(device).to(weight_dtype)
image_caption_suffix = '(embedding)'
else:
raise "input embeds_a must be a pth file"
else:
if isinstance(inputs_obj, Image.Image):
image_pil =inputs_obj
else:
image_pil = Image.new('RGB', (height, width), (255, 255, 255))
image = torch.Tensor(clip(image_pil)['pixel_values'][0]).to(device).unsqueeze(0).to(weight_dtype)
return image, image_caption_suffix
def get_embedding(model,clip,inputs_obj, drop_condition_a, drop_condition_b,prior_seeds,prior_guidance_scale,prior_steps,height,width):
filename_prefix = ''.join(random.choice("0123456789") for _ in range(4))
should_drop_cond = [(drop_condition_a, drop_condition_b)]
image_list, image_pil_list, caption_suffix_list=[],[],[]
for input_path in inputs_obj:
# Process both inputs
image,caption_suffix = process_image(input_path, clip, width, height)
image_list.append(image)
caption_suffix_list.append(caption_suffix)
image_list=[i for i in image_list if i is not None]
input_image_embeds, input_hidden_state = pops_utils.preprocess(image_list[0], image_list[1],
model.image_encoder,
model.prior.clip_mean.detach(),
model.prior.clip_std.detach(),
should_drop_cond=should_drop_cond)
captions = [f"objects{caption_suffix_list[0]}", f"textures{caption_suffix_list[1]}"]
negative_input_embeds = torch.zeros_like(input_image_embeds)
negative_hidden_states = torch.zeros_like(input_hidden_state)
img_emb =model(input_embeds=input_image_embeds, input_hidden_states=input_hidden_state,
negative_input_embeds=negative_input_embeds,
negative_input_hidden_states=negative_hidden_states,
num_inference_steps=prior_steps,
num_images_per_prompt=1,
guidance_scale=prior_guidance_scale,
generator=torch.Generator(device=device).manual_seed(prior_seeds))
img_emb_file = os.path.join(output_dir, f"infer_binary{filename_prefix}_s_{prior_seeds}_cfg_{prior_guidance_scale}_img_emb.pth")
if not os.path.exists(img_emb_file):
torch.save(img_emb, img_emb_file)
positive_emb=img_emb.image_embeds
negative_emb = img_emb.negative_image_embeds
return positive_emb, negative_emb,input_hidden_state,img_emb_file
def get_embedding_instruct(model,clip,tokenizer, inputs_obj, texts,prior_guidance_scale, prior_seeds, prior_steps,height,width):
#print(texts, type(texts))
image_a, caption_suffix_a = process_image(inputs_obj,clip,width, height)
text_inputs = tokenizer(text=texts,padding="max_length",max_length=tokenizer.model_max_length,truncation=True,return_tensors="pt",)
mask = text_inputs.attention_mask.bool() # [0]
text_encoder_output = model.text_encoder(text_inputs.input_ids.to(device))
text_encoder_hidden_states = text_encoder_output.last_hidden_state
text_encoder_concat = text_encoder_hidden_states[:, :mask.sum().item()]
#
input_image_embeds, input_hidden_state = pops_utils.preprocess(image_a, None,
model.image_encoder,
model.prior.clip_mean.detach(),
model.prior.clip_std.detach(),
concat_hidden_states=text_encoder_concat)
negative_input_embeds = torch.zeros_like(input_image_embeds)
negative_hidden_states = torch.zeros_like(input_hidden_state)
# for scale in prior_guidance_scale:
img_emb = model(input_embeds=input_image_embeds, input_hidden_states=input_hidden_state,
negative_input_embeds=negative_input_embeds,
negative_input_hidden_states=negative_hidden_states,
num_inference_steps=prior_steps,
num_images_per_prompt=1,
guidance_scale=prior_guidance_scale,
generator=torch.Generator(device=device).manual_seed(prior_seeds))
img_emb_file = os.path.join(output_dir, f"{texts}_s_{prior_seeds}_cfg_{prior_guidance_scale}_img_emb.pth")
if not os.path.exists(img_emb_file):
torch.save(img_emb, img_emb_file)
positive_emb = img_emb.image_embeds
negative_emb = img_emb.negative_image_embeds
return positive_emb, negative_emb,input_hidden_state,img_emb_file
class Pops_Repo_Loader:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"local_prior": (paths,),
"prior_repo": ("STRING", {"default": "kandinsky-community/kandinsky-2-2-prior"}),
"local_decoder": (paths,),
"decoder_repo": ("STRING", {"default": "kandinsky-community/kandinsky-2-2-decoder"}),
"pops_ckpt": (["none"] + folder_paths.get_filename_list("checkpoints"),),
"function_type": (["Binary","instruct",],),
}
}
RETURN_TYPES = ("MODEL","CLIP","VAE","MODEL",)
RETURN_NAMES = ("model","clip","vae","tokenizer",)
FUNCTION = "pops_repo_loader"
CATEGORY = "Pops"
def pops_repo_loader(self, local_prior, prior_repo, local_decoder, decoder_repo, pops_ckpt,function_type):
kandinsky_prior_repo = instance_path(local_prior, prior_repo)
kandinsky_decoder_repo = instance_path(local_decoder, decoder_repo)
Pops_ckpt = folder_paths.get_full_path("checkpoints", pops_ckpt)
#clip = CLIPImageProcessor.from_pretrained(kandinsky_prior_repo,subfolder='image_processor')
clip = CLIPImageProcessor()
prior = PriorTransformer.from_pretrained(kandinsky_prior_repo, subfolder="prior", use_safetensors=True)
prior_state_dict = torch.load(Pops_ckpt, map_location=torch.device('cuda'))
prior.load_state_dict(prior_state_dict, strict=False)
prior.eval()
prior = prior.to(weight_dtype)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(kandinsky_prior_repo, subfolder='image_encoder',
torch_dtype=torch.float16).eval()
# Freeze text_encoder and image_encoder
image_encoder.requires_grad_(False)
model = pOpsPipeline.from_pretrained(kandinsky_prior_repo,
prior=prior,
image_encoder=image_encoder,
torch_dtype=torch.float16).to(device)
# Load decoder model f
unet = UNet2DConditionModel.from_pretrained(kandinsky_decoder_repo,
subfolder='unet').to(torch.float16).to(device)
vae = KandinskyV22Pipeline.from_pretrained(kandinsky_decoder_repo, unet=unet,
torch_dtype=torch.float16).to(device)
if function_type=="instruct":
tokenizer = CLIPTokenizer.from_pretrained(kandinsky_prior_repo, subfolder='tokenizer')
else:
tokenizer =None
return (model,clip,vae,tokenizer,)
class Pops_Sampler:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model":("MODEL",),
"clip": ("CLIP",),
"texts": ("STRING",{"default": "smooth"}),
"drop_condition_a": ("BOOLEAN", {"default": False},),
"drop_condition_b": ("BOOLEAN", {"default": False},),
"prior_guidance_scale": (
"FLOAT", {"default": 1.0, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
"seed": ("INT", {"default": 2, "min": 1, "max": MAX_SEED}),
"prior_steps": ("INT", {"default": 25, "min": 1, "max": 4096}),
"height": ("INT", {"default": 768, "min": 256, "max": 4096,"step": 64}),
"width": ("INT", {"default": 768, "min": 256, "max": 4096,"step": 64}),
"use_mean": ("BOOLEAN", {"default": False},),},
"optional": {
"tokenizer": ("MODEL",),
"image_a": ("IMAGE",),
"image_b": ("IMAGE",),
"embeds_a": ("STRING", {"forceInput": True},),
"embeds_b": ("STRING", {"forceInput": True},),
}
}
RETURN_TYPES = ("CONDITIONING", "CONDITIONING","STRING",)
RETURN_NAMES = ("positive_emb", "ng_image_embeds","img_emb_file",)
FUNCTION = "pops_sampler"
CATEGORY = "Pops"
def pops_sampler(self, model,clip,texts
, drop_condition_a,drop_condition_b,prior_guidance_scale,seed,prior_steps,height,width,use_mean,**kwargs):
tokenizer=kwargs.get("tokenizer")
image_a = kwargs.get("image_a")
image_b = kwargs.get("image_b")
if isinstance(image_a, torch.Tensor):
image_a = nomarl_upscale_topil(image_a, width, height)
if isinstance(image_b, torch.Tensor):
image_b = nomarl_upscale_topil(image_b, width, height)
embeds_a = kwargs.get("embeds_a")
embeds_b = kwargs.get("embeds_b")
inputs_obj=[image_a,image_b, embeds_a,embeds_b]
inputs_obj = [i for i in inputs_obj if i is not None]
if tokenizer:
inputs_obj=inputs_obj[0]
positive_emb, negative_emb,input_hidden_state,img_emb_file= get_embedding_instruct(model,tokenizer,clip, inputs_obj,texts,
prior_guidance_scale, seed, prior_steps,height,width)
else:
if len(inputs_obj)>2:
inputs_obj = random.choices(inputs_obj,k=2)
elif len(inputs_obj)<2:
inputs_obj=inputs_obj.append([None])
else:
pass
positive_emb, negative_emb,input_hidden_state,img_emb_file= get_embedding(model,clip,inputs_obj,drop_condition_a, drop_condition_b,seed,prior_guidance_scale,prior_steps,height,width)
if use_mean:
mean_emb = 0.5 * input_hidden_state[:, 0] + 0.5 * input_hidden_state[:, 1]
mean_emb = (mean_emb * model.prior.clip_std) + model.prior.clip_mean
negative_emb = model.get_zero_embed(mean_emb.shape[0], device=mean_emb.device)
positive_emb=mean_emb
return (positive_emb, negative_emb,img_emb_file,)
class Pops_Decoder:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"vae": ("VAE",),
"positive_emb": ("CONDITIONING",),
"negative_emb": ("CONDITIONING",),
"seed": ("INT", {"default": 2, "min": 1, "max": MAX_SEED}),
"steps": ("INT", {"default": 25, "min": 1, "max": 4096}),
"guidance_scale": (
"FLOAT", {"default": 1.0, "min": 0.1, "max": 24.0, "step": 0.1, "round": False}),
"height": ("INT", {"default": 768, "min": 256, "max": 4096, "step": 64}),
"width": ("INT", {"default": 768, "min": 256, "max": 4096, "step": 64}),
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = "pops_decoder"
CATEGORY = "Pops"
def pops_decoder(self,vae,positive_emb,negative_emb,seed, steps,guidance_scale,height,width,):
images = vae(image_embeds=positive_emb, negative_image_embeds=negative_emb,
num_inference_steps=steps, height=height,
width=width, guidance_scale=guidance_scale,
generator=torch.Generator(device=device).manual_seed(seed)).images
images=phi2narry(images[0])
return (images,)
NODE_CLASS_MAPPINGS = {
"Pops_Repo_Loader": Pops_Repo_Loader,
"Pops_Sampler":Pops_Sampler,
"Pops_Decoder":Pops_Decoder,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"Pops_Repo_Loader": "Pops_Repo_Loader",
"Pops_Sampler":"Pops_Sampler",
"Pops_Decoder":"Pops_Decode",
}