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import os |
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" |
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import sys |
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sys.path.insert(0, './diffusers/src') |
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import torch |
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import torch.nn as nn |
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torch.jit.script = lambda f: f |
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from huggingface_hub import snapshot_download |
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from diffusers import DPMSolverMultistepScheduler |
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from diffusers.models import ControlNetModel |
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from transformers import CLIPVisionModelWithProjection |
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from pipeline import OmniZeroPipeline |
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from insightface.app import FaceAnalysis |
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from controlnet_aux import ZoeDetector |
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from utils import draw_kps, load_and_resize_image, align_images |
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import cv2 |
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import numpy as np |
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class OmniZeroSingle(): |
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def __init__(self, |
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base_model="stabilityai/stable-diffusion-xl-base-1.0", |
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): |
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snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2") |
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self.face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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self.face_analysis.prepare(ctx_id=0, det_size=(640, 640)) |
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dtype = torch.float16 |
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ip_adapter_plus_image_encoder = CLIPVisionModelWithProjection.from_pretrained( |
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"h94/IP-Adapter", |
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subfolder="models/image_encoder", |
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torch_dtype=dtype, |
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).to("cuda") |
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zoedepthnet_path = "okaris/zoe-depth-controlnet-xl" |
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zoedepthnet = ControlNetModel.from_pretrained(zoedepthnet_path,torch_dtype=dtype).to("cuda") |
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identitiynet_path = "okaris/face-controlnet-xl" |
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identitynet = ControlNetModel.from_pretrained(identitiynet_path, torch_dtype=dtype).to("cuda") |
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self.zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda") |
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self.pipeline = OmniZeroPipeline.from_pretrained( |
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base_model, |
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controlnet=[identitynet, zoedepthnet], |
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torch_dtype=dtype, |
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image_encoder=ip_adapter_plus_image_encoder, |
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).to("cuda") |
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config = self.pipeline.scheduler.config |
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config["timestep_spacing"] = "trailing" |
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self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero") |
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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"]) |
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def get_largest_face_embedding_and_kps(self, image, target_image=None): |
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face_info = self.face_analysis.get(cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)) |
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if len(face_info) == 0: |
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return None, None |
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largest_face = sorted(face_info, key=lambda x: x['bbox'][2] * x['bbox'][3], reverse=True)[0] |
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face_embedding = torch.tensor(largest_face['embedding']).to("cuda") |
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if target_image is None: |
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target_image = image |
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zeros = np.zeros((target_image.size[1], target_image.size[0], 3), dtype=np.uint8) |
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face_kps_image = draw_kps(zeros, largest_face['kps']) |
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return face_embedding, face_kps_image |
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def generate(self, |
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seed=42, |
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prompt="A person", |
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negative_prompt="blurry, out of focus", |
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guidance_scale=3.0, |
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number_of_images=1, |
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number_of_steps=10, |
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base_image=None, |
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base_image_strength=0.15, |
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composition_image=None, |
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composition_image_strength=1.0, |
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style_image=None, |
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style_image_strength=1.0, |
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identity_image=None, |
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identity_image_strength=1.0, |
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depth_image=None, |
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depth_image_strength=0.5, |
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): |
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resolution = 1024 |
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if base_image is not None: |
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base_image = load_and_resize_image(base_image, resolution, resolution) |
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else: |
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if composition_image is not None: |
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base_image = load_and_resize_image(composition_image, resolution, resolution) |
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else: |
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raise ValueError("You must provide a base image or a composition image") |
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if depth_image is None: |
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depth_image = self.zoe_depth_detector(base_image, detect_resolution=resolution, image_resolution=resolution) |
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else: |
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depth_image = load_and_resize_image(depth_image, resolution, resolution) |
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base_image, depth_image = align_images(base_image, depth_image) |
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if composition_image is not None: |
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composition_image = load_and_resize_image(composition_image, resolution, resolution) |
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else: |
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composition_image = base_image |
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if style_image is not None: |
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style_image = load_and_resize_image(style_image, resolution, resolution) |
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else: |
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raise ValueError("You must provide a style image") |
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if identity_image is not None: |
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identity_image = load_and_resize_image(identity_image, resolution, resolution) |
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else: |
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raise ValueError("You must provide an identity image") |
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face_embedding_identity_image, target_kps = self.get_largest_face_embedding_and_kps(identity_image, base_image) |
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if face_embedding_identity_image is None: |
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raise ValueError("No face found in the identity image, the image might be cropped too tightly or the face is too small") |
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face_embedding_base_image, face_kps_base_image = self.get_largest_face_embedding_and_kps(base_image) |
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if face_embedding_base_image is not None: |
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target_kps = face_kps_base_image |
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self.pipeline.set_ip_adapter_scale([identity_image_strength, |
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{ |
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"down": { "block_2": [0.0, 0.0] }, |
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"up": { "block_0": [0.0, style_image_strength, 0.0] } |
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}, |
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{ |
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"down": { "block_2": [0.0, composition_image_strength] }, |
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"up": { "block_0": [0.0, 0.0, 0.0] } |
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} |
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]) |
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generator = torch.Generator(device="cpu").manual_seed(seed) |
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images = self.pipeline( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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guidance_scale=guidance_scale, |
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ip_adapter_image=[face_embedding_identity_image, style_image, composition_image], |
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image=base_image, |
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control_image=[target_kps, depth_image], |
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controlnet_conditioning_scale=[identity_image_strength, depth_image_strength], |
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identity_control_indices=[(0,0)], |
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num_inference_steps=number_of_steps, |
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num_images_per_prompt=number_of_images, |
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strength=(1-base_image_strength), |
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generator=generator, |
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seed=seed, |
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).images |
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return images |
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class OmniZeroCouple(): |
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def __init__(self, |
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base_model="stabilityai/stable-diffusion-xl-base-1.0", |
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): |
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snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2") |
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self.face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) |
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self.face_analysis.prepare(ctx_id=0, det_size=(640, 640)) |
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dtype = torch.float16 |
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ip_adapter_plus_image_encoder = CLIPVisionModelWithProjection.from_pretrained( |
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"h94/IP-Adapter", |
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subfolder="models/image_encoder", |
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torch_dtype=dtype, |
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).to("cuda") |
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zoedepthnet_path = "okaris/zoe-depth-controlnet-xl" |
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zoedepthnet = ControlNetModel.from_pretrained(zoedepthnet_path,torch_dtype=dtype).to("cuda") |
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identitiynet_path = "okaris/face-controlnet-xl" |
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identitynet = ControlNetModel.from_pretrained(identitiynet_path, torch_dtype=dtype).to("cuda") |
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self.zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda") |
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self.pipeline = OmniZeroPipeline.from_pretrained( |
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base_model, |
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controlnet=[identitynet, zoedepthnet], |
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torch_dtype=dtype, |
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image_encoder=ip_adapter_plus_image_encoder, |
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).to("cuda") |
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config = self.pipeline.scheduler.config |
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config["timestep_spacing"] = "trailing" |
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self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero") |
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self.pipeline.load_ip_adapter(["okaris/ip-adapter-instantid", "okaris/ip-adapter-instantid", "h94/IP-Adapter", "h94/IP-Adapter"], subfolder=[None, None, "sdxl_models", "sdxl_models"], weight_name=["ip-adapter-instantid.bin", "ip-adapter-instantid.bin", "ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus_sdxl_vit-h.safetensors"]) |
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def generate(self, |
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seed=42, |
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prompt="A person", |
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negative_prompt="blurry, out of focus", |
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guidance_scale=3.0, |
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number_of_images=1, |
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number_of_steps=10, |
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base_image=None, |
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base_image_strength=0.15, |
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composition_image=None, |
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composition_image_strength=1.0, |
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style_image=None, |
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style_image_strength=1.0, |
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style_image_2=None, |
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style_image_strength_2=1.0, |
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identity_image=None, |
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identity_image_strength=1.0, |
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identity_image_2=None, |
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identity_image_strength_2=1.0, |
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depth_image=None, |
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depth_image_strength=0.5, |
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): |
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print("Not implemented yet") |
