236 lines
8.3 KiB
Python
236 lines
8.3 KiB
Python
import os
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import cv2
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import sys
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import torch
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import argparse
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import numpy as np
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project_root = os.path.abspath(os.path.dirname(__file__))
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if project_root not in sys.path:
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sys.path.append(project_root)
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from PIL import Image
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from diffusers import (
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StableDiffusionControlNetPipeline,
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StableDiffusionAdapterPipeline,
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StableDiffusionPipeline,
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ControlNetModel,
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UNet2DConditionModel,
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T2IAdapter,
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)
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from annotator.hed import SOFT_HEDdetector
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from annotator.lineart import LineartDetector
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from huggingface_hub import snapshot_download
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from ip_adapter import StyleShot, StyleContentStableDiffusionControlNetPipeline
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model_dir = os.path.join(project_root, "prtrained_models")
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device = "cuda"
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class PipelineLoader:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"mode": (["text_driven", "image_driven", "controlnet", "t2i-adapter"]),
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"base_model_path": (
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"STRING",
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{"default": "runwayml/stable-diffusion-v1-5"},
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),
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"transformer_block_path": (
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"STRING",
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{"default": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"},
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),
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"styleshot_model_path": ("STRING", {"default": "Gaojunyao/StyleShot"}),
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"controlnet_model_path": (
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"STRING",
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{"default": "lllyasviel/control_v11f1p_sd15_depth"},
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),
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"adapter_model_path": (
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"STRING",
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{"default": "TencentARC/t2iadapter_depth_sd15v2"},
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),
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"preprocessor": (["Lineart", "Contour"]),
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}
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}
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RETURN_TYPES = ("PIPELINE",)
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FUNCTION = "load_pipeline"
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OUTPUT_NODE = True
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CATEGORY = "PIPELINE"
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def load_pipeline(
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self,
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mode,
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base_model_path,
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transformer_block_path,
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styleshot_model_path,
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controlnet_model_path,
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adapter_model_path,
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preprocessor,
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):
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if preprocessor == "Lineart":
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# detector = LineartDetector()
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styleshot_model_path = "Gaojunyao/StyleShot_lineart"
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elif preprocessor == "Contour":
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# detector = SOFT_HEDdetector()
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styleshot_model_path = "Gaojunyao/StyleShot"
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else:
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raise ValueError("Invalid preprocessor")
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if not os.path.isdir(base_model_path):
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base_model_path = snapshot_download(
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base_model_path,
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allow_patterns=["*fp16.safetensors", "*.json", "*yaml", "*.txt"],
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local_dir=os.path.join(model_dir, base_model_path.split("/")[-1]),
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)
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print(f"Downloaded model to {base_model_path}")
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if not os.path.isdir(transformer_block_path):
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transformer_block_path = snapshot_download(
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transformer_block_path,
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# allow_patterns=["*.safetensors","*.json"],
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ignore_patterns=["open_clip*", "*.bin"],
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local_dir=os.path.join(
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model_dir, transformer_block_path.split("/")[-1]
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),
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)
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print(f"Downloaded model to {transformer_block_path}")
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if not os.path.isdir(styleshot_model_path):
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styleshot_model_path = snapshot_download(
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styleshot_model_path,
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local_dir=os.path.join(model_dir, styleshot_model_path.split("/")[-1]),
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)
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print(f"Downloaded model to {styleshot_model_path}")
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ip_ckpt = os.path.join(styleshot_model_path, "pretrained_weight/ip.bin")
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style_aware_encoder_path = os.path.join(
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styleshot_model_path, "pretrained_weight/style_aware_encoder.bin"
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)
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if mode == "text_driven":
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pipe = StableDiffusionPipeline.from_pretrained(
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base_model_path, variant="fp16"
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)
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styleshot = StyleShot(
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device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path
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)
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if mode == "image_driven":
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unet = UNet2DConditionModel.from_pretrained(
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base_model_path, subfolder="unet", variant="fp16"
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)
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content_fusion_encoder = ControlNetModel.from_unet(unet)
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pipe = StyleContentStableDiffusionControlNetPipeline.from_pretrained(
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base_model_path, variant="fp16", controlnet=content_fusion_encoder
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)
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styleshot = StyleShot(
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device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path
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)
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if mode == "t2i-adapter":
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if not os.path.isdir(adapter_model_path):
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adapter_model_path = snapshot_download(
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adapter_model_path,
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ignore_patterns=["*.png"],
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local_dir=os.path.join(
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model_dir, adapter_model_path.split("/")[-1]
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),
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)
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print(f"Downloaded model to {adapter_model_path}")
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adapter = T2IAdapter.from_pretrained(
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adapter_model_path, torch_dtype=torch.float16
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)
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pipe = StableDiffusionAdapterPipeline.from_pretrained(
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base_model_path, adapter=adapter, variant="fp16"
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)
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styleshot = StyleShot(
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device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path
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)
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if mode == "controlnet":
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if not os.path.isdir(controlnet_model_path):
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controlnet_model_path = snapshot_download(
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controlnet_model_path,
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allow_patterns=["*.json", "*.fp16.safetensors"],
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local_dir=os.path.join(
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model_dir, controlnet_model_path.split("/")[-1]
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),
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)
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print(f"Downloaded model to {controlnet_model_path}")
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controlnet = ControlNetModel.from_pretrained(
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controlnet_model_path, torch_dtype=torch.float16
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)
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pipe = StableDiffusionControlNetPipeline.from_pretrained(
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base_model_path, controlnet=controlnet, variant="fp16"
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)
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styleshot = StyleShot(
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device, pipe, ip_ckpt, style_aware_encoder_path, transformer_block_path
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)
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return styleshot
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class StyleShotApply:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"pipeline": ("PIPELINE",),
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"mode": (["text_driven", "image_driven", "controlnet", "t2i-adapter"],),
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"style_image": ("IMAGE",),
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},
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"optional": {
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"condition_image": ("IMAGE",),
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"prompt": ("STRING",),
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"preprocessor": (["Contour", "Lineart"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "generate"
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OUTPUT_NODE = True
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CATEGORY = "StyleShot"
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def generate(
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self, pipeline, mode, style_image, condition_image, prompt, preprocessor
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):
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if preprocessor == "Lineart":
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detector = LineartDetector()
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elif preprocessor == "Contour":
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detector = SOFT_HEDdetector()
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else:
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raise ValueError("Invalid preprocessor")
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if mode == "text_driven":
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generation = pipeline.generate(style_image=style_image, prompt=[[prompt]])
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elif mode == "image_driven":
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content_image = cv2.imread(condition_image)
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content_image = cv2.cvtColor(content_image, cv2.COLOR_BGR2RGB)
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content_image = detector(content_image)
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content_image = Image.fromarray(content_image)
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generation = pipeline.generate(
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style_image=style_image, prompt=[[prompt]], content_image=content_image
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)
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elif mode == "controlnet":
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generation = pipeline.generate(
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style_image=style_image, prompt=[[prompt]], image=condition_image
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)
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elif mode == "t2i-adapter":
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generation = pipeline.generate(
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style_image=style_image, prompt=[[prompt]], image=[condition_image]
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)
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else:
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raise ValueError("Invalid mode")
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result = torch.from_numpy(np.array(generation[0][0]) / 255.0).unsqueeze(0)
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return result
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