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import os
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
import sys
sys.path.insert(0, './diffusers/src')
import torch
import torch.nn as nn
from huggingface_hub import snapshot_download
from diffusers import DPMSolverMultistepScheduler
from diffusers.models import ControlNetModel
from transformers import CLIPVisionModelWithProjection
from pipeline import OmniZeroPipeline
from insightface.app import FaceAnalysis
from controlnet_aux import ZoeDetector
from utils import draw_kps, load_and_resize_image, align_images
import cv2
import numpy as np
class OmniZeroSingle():
def __init__(self,
base_model="stabilityai/stable-diffusion-xl-base-1.0",
):
snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2")
self.face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
dtype = torch.float16
ip_adapter_plus_image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=dtype,
).to("cuda")
zoedepthnet_path = "okaris/zoe-depth-controlnet-xl"
zoedepthnet = ControlNetModel.from_pretrained(zoedepthnet_path,torch_dtype=dtype).to("cuda")
identitiynet_path = "okaris/face-controlnet-xl"
identitynet = ControlNetModel.from_pretrained(identitiynet_path, torch_dtype=dtype).to("cuda")
self.zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
self.pipeline = OmniZeroPipeline.from_pretrained(
base_model,
controlnet=[identitynet, zoedepthnet],
torch_dtype=dtype,
image_encoder=ip_adapter_plus_image_encoder,
).to("cuda")
config = self.pipeline.scheduler.config
config["timestep_spacing"] = "trailing"
self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero")
self.pipeline.load_ip_adapter(["okaris/ip-adapter-instantid", "h94/IP-Adapter", "h94/IP-Adapter"], subfolder=[None, "sdxl_models", "sdxl_models"], weight_name=["ip-adapter-instantid.bin", "ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus_sdxl_vit-h.safetensors"])
def get_largest_face_embedding_and_kps(self, image, target_image=None):
face_info = self.face_analysis.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR))
if len(face_info) == 0:
return None, None
largest_face = sorted(face_info, key=lambda x: x['bbox'][2] * x['bbox'][3], reverse=True)[0]
face_embedding = torch.tensor(largest_face['embedding']).to("cuda")
if target_image is None:
target_image = image
zeros = np.zeros((target_image.size[1], target_image.size[0], 3), dtype=np.uint8)
face_kps_image = draw_kps(zeros, largest_face['kps'])
return face_embedding, face_kps_image
def generate(self,
seed=42,
prompt="A person",
negative_prompt="blurry, out of focus",
guidance_scale=3.0,
number_of_images=1,
number_of_steps=10,
base_image=None,
base_image_strength=0.15,
composition_image=None,
composition_image_strength=1.0,
style_image=None,
style_image_strength=1.0,
identity_image=None,
identity_image_strength=1.0,
depth_image=None,
depth_image_strength=0.5,
):
resolution = 1024
if base_image is not None:
base_image = load_and_resize_image(base_image, resolution, resolution)
else:
if composition_image is not None:
base_image = load_and_resize_image(composition_image, resolution, resolution)
else:
raise ValueError("You must provide a base image or a composition image")
if depth_image is None:
depth_image = self.zoe_depth_detector(base_image, detect_resolution=resolution, image_resolution=resolution)
else:
depth_image = load_and_resize_image(depth_image, resolution, resolution)
base_image, depth_image = align_images(base_image, depth_image)
if composition_image is not None:
composition_image = load_and_resize_image(composition_image, resolution, resolution)
else:
composition_image = base_image
if style_image is not None:
style_image = load_and_resize_image(style_image, resolution, resolution)
else:
raise ValueError("You must provide a style image")
if identity_image is not None:
identity_image = load_and_resize_image(identity_image, resolution, resolution)
else:
raise ValueError("You must provide an identity image")
face_embedding_identity_image, target_kps = self.get_largest_face_embedding_and_kps(identity_image, base_image)
if face_embedding_identity_image is None:
raise ValueError("No face found in the identity image, the image might be cropped too tightly or the face is too small")
face_embedding_base_image, face_kps_base_image = self.get_largest_face_embedding_and_kps(base_image)
if face_embedding_base_image is not None:
target_kps = face_kps_base_image
self.pipeline.set_ip_adapter_scale([identity_image_strength,
{
"down": { "block_2": [0.0, 0.0] },
"up": { "block_0": [0.0, style_image_strength, 0.0] }
},
{
"down": { "block_2": [0.0, composition_image_strength] },
"up": { "block_0": [0.0, 0.0, 0.0] }
}
])
generator = torch.Generator(device="cpu").manual_seed(seed)
images = self.pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
ip_adapter_image=[face_embedding_identity_image, style_image, composition_image],
image=base_image,
control_image=[target_kps, depth_image],
controlnet_conditioning_scale=[identity_image_strength, depth_image_strength],
identity_control_indices=[(0,0)],
num_inference_steps=number_of_steps,
num_images_per_prompt=number_of_images,
strength=(1-base_image_strength),
generator=generator,
seed=seed,
).images
return images |