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__init__.py
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__init__.py
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import os
now_dir = os.path.dirname(os.path.abspath(__file__))
import torch
import numpy as np
from PIL import Image
import cuda_malloc
from huggingface_hub import snapshot_download
from diffusers import StableDiffusionPipeline,UNet2DConditionModel, ControlNetModel,StableDiffusionAdapterPipeline, T2IAdapter
import cv2
from .styleshot.annotator.hed import SOFT_HEDdetector
from .styleshot.annotator.lineart import LineartDetector
from .styleshot.ip_adapter.ip_adapter import StyleShot,StyleContentStableDiffusionControlNetPipeline,StableDiffusionControlNetPipeline
prtrained_dir = os.path.join(now_dir,"prtrained_models")
device = "cuda" if cuda_malloc.cuda_malloc_supported() else "cpu"
def load_weights(use_case="text_driven",preprocessor = "Contour",seed=42):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
base_model_path = "runwayml/stable-diffusion-v1-5"
transformer_block_path = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
if preprocessor == "Lineart":
# detector = LineartDetector()
styleshot_model_path = "Gaojunyao/StyleShot_lineart"
elif preprocessor == "Contour":
# detector = SOFT_HEDdetector()
styleshot_model_path = "Gaojunyao/StyleShot"
else:
raise ValueError("Invalid preprocessor")
if not os.path.isdir(base_model_path):
base_model_path = snapshot_download(base_model_path,
allow_patterns=["*fp16.safetensors","*.json","*yaml","*.txt"],
local_dir=os.path.join(prtrained_dir,base_model_path.split("/")[-1]))
print(f"Downloaded model to {base_model_path}")
if not os.path.isdir(transformer_block_path):
transformer_block_path = snapshot_download(transformer_block_path,
# allow_patterns=["*.safetensors","*.json"],
ignore_patterns=["open_clip*","*.bin"],
local_dir=os.path.join(prtrained_dir,transformer_block_path.split("/")[-1]))
print(f"Downloaded model to {transformer_block_path}")
if not os.path.isdir(styleshot_model_path):
styleshot_model_path = snapshot_download(styleshot_model_path,
local_dir=os.path.join(prtrained_dir,styleshot_model_path.split("/")[-1]))
print(f"Downloaded model to {styleshot_model_path}")
ip_ckpt = os.path.join(styleshot_model_path, "pretrained_weight/ip.bin")
style_aware_encoder_path = os.path.join(styleshot_model_path, "pretrained_weight/style_aware_encoder.bin")
if use_case == "text_driven":
pipe = StableDiffusionPipeline.from_pretrained(base_model_path,variant="fp16")
styleshot = StyleShot(device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path)
if use_case == "image_driven":
unet = UNet2DConditionModel.from_pretrained(base_model_path, subfolder="unet",variant="fp16")
content_fusion_encoder = ControlNetModel.from_unet(unet)
pipe = StyleContentStableDiffusionControlNetPipeline.from_pretrained(base_model_path,variant="fp16",controlnet=content_fusion_encoder)
styleshot = StyleShot(device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path)
if use_case == "t2i-adapter":
adapter_model_path = "TencentARC/t2iadapter_depth_sd15v2"
if not os.path.isdir(adapter_model_path):
adapter_model_path = snapshot_download(adapter_model_path,
ignore_patterns=["*.png"],
local_dir=os.path.join(prtrained_dir,adapter_model_path.split("/")[-1]))
print(f"Downloaded model to {adapter_model_path}")
adapter = T2IAdapter.from_pretrained(adapter_model_path, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(base_model_path, adapter=adapter,variant="fp16")
styleshot = StyleShot(device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path)
if use_case == "controlnet":
controlnet_model_path = "lllyasviel/control_v11f1p_sd15_depth"
if not os.path.isdir(controlnet_model_path):
controlnet_model_path = snapshot_download(controlnet_model_path,
allow_patterns=["*.json","*.fp16.safetensors"],
local_dir=os.path.join(prtrained_dir,controlnet_model_path.split("/")[-1]))
print(f"Downloaded model to {controlnet_model_path}")
controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(base_model_path, controlnet=controlnet,variant="fp16")
styleshot = StyleShot(device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path)
return styleshot
class TextNode:
@classmethod
def INPUT_TYPES(s):
return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True})}}
RETURN_TYPES = ("TEXT",)
FUNCTION = "encode"
CATEGORY = "AIFSH_StyleShot"
def encode(self, text):
return (text, )
class StyleShotNode:
def __init__(self) -> None:
self.use_case = None
@classmethod
def INPUT_TYPES(s):
return {
"required":{
"style":("IMAGE",),
"prompt":("TEXT",),
"use_case":(["text_driven","image_driven","controlnet","t2i-adapter"],),
"seed":("INT",{
"default":42
})
},
"optional":{
"content":("IMAGE",),
"preprocessor":(["Contour", "Lineart"],),
"condition":("IMAGE",)
}
}
RETURN_TYPES = ("IMAGE",)
#RETURN_NAMES = ("image_output_name",)
FUNCTION = "generate"
#OUTPUT_NODE = False
CATEGORY = "AIFSH_StyleShot"
def comfyimage2Image(self,comfyimage):
comfyimage = comfyimage.numpy()[0] * 255
image_np = comfyimage.astype(np.uint8)
image = Image.fromarray(image_np)
return image
def generate(self,style,prompt,use_case,seed,
content=None,preprocessor="Contour",condition=None):
if self.use_case != use_case:
self.use_case = use_case
self.styleshot = load_weights(self.use_case,preprocessor,seed)
torch.cuda.empty_cache()
style_image = self.comfyimage2Image(style)
if self.use_case == "image_driven":
annotator_ckpts_path = os.path.join(prtrained_dir,"Annotators")
snapshot_download("lllyasviel/Annotators",
allow_patterns=["sk_model*","ControlNetHED*"],
local_dir=annotator_ckpts_path)
if preprocessor == "Lineart":
detector = LineartDetector(annotator_ckpts_path)
else:
detector = SOFT_HEDdetector(annotator_ckpts_path)
content_image = self.comfyimage2Image(content)
content_image = cv2.cvtColor(np.asarray(content_image), cv2.COLOR_BGR2RGB)
content_image = detector(content_image)
content_image = Image.fromarray(content_image)
else:
content_image = None
negative_prompt = "nsfw,bare,naked"
if use_case in ["t2i-adapter","controlnet"]:
condition_image = self.comfyimage2Image(condition)
generation = self.styleshot.generate(style_image=style_image, prompt=[[prompt]],negative_prompt=negative_prompt, image=[condition_image])
else:
generation = self.styleshot.generate(style_image=style_image, prompt=[[prompt]],negative_prompt=negative_prompt,content_image=content_image)
# generation[0][0].save("test.png")
out_image = torch.from_numpy(np.array(generation[0][0]) / 255.0).unsqueeze(0)
return (out_image,)
NODE_CLASS_MAPPINGS = {
"TextNode":TextNode,
"StyleShotNode": StyleShotNode
}