import gradio as gr
import spaces
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
#Hack for ZeroGPU
torch.jit.script = lambda f: f
####
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
base_model="frankjoshua/albedobaseXL_v13"
snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2")
face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CPUExecutionProvider'])
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")
zoe_depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
pipeline = OmniZeroPipeline.from_pretrained(
base_model,
controlnet=[identitynet, zoedepthnet],
torch_dtype=dtype,
image_encoder=ip_adapter_plus_image_encoder,
).to("cuda")
config = pipeline.scheduler.config
config["timestep_spacing"] = "trailing"
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++", final_sigmas_type="zero")
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(image, target_image=None):
face_info = 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
@spaces.GPU()
def generate(
prompt="A person",
composition_image="https://github.com/okaris/omni-zero/assets/1448702/2ca63443-c7f3-4ba6-95c1-2a341414865f",
style_image="https://github.com/okaris/omni-zero/assets/1448702/64dc150b-f683-41b1-be23-b6a52c771584",
identity_image="https://github.com/okaris/omni-zero/assets/1448702/ba193a3a-f90e-4461-848a-560454531c58",
base_image="https://github.com/okaris/omni-zero/assets/1448702/2ca63443-c7f3-4ba6-95c1-2a341414865f",
seed=42,
negative_prompt="blurry, out of focus",
guidance_scale=3.0,
number_of_images=1,
number_of_steps=10,
base_image_strength=0.15,
composition_image_strength=1.0,
style_image_strength=1.0,
identity_image_strength=1.0,
depth_image=None,
depth_image_strength=0.5,
progress=gr.Progress(track_tqdm=True)
):
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 = 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 = 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 = get_largest_face_embedding_and_kps(base_image)
if face_embedding_base_image is not None:
target_kps = face_kps_base_image
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 = 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
#Move the components in the example fields outside so they are available when gr.Examples is instantiated
with gr.Blocks() as demo:
gr.Markdown("<h1 style='text-align: center'>Omni Zero</h1>")
gr.Markdown("<h4 style='text-align: center'>A diffusion pipeline for zero-shot stylized portrait creation [<a href='https://github.com/okaris/omni-zero' target='_blank'>GitHub</a>], [<a href='https://styleof.com/s/remix-yourself' target='_blank'>StyleOf Remix Yourself</a>]</h4>")
with gr.Row():
with gr.Column():
with gr.Row():
prompt = gr.Textbox(label="Prompt", value="A person")
with gr.Row():
negative_prompt = gr.Textbox(label="Negative Prompt", value="blurry, out of focus")
with gr.Row():
with gr.Column(min_width=140):
with gr.Row():
composition_image = gr.Image(label="Composition")
with gr.Row():
composition_image_strength = gr.Slider(label="Strength",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
#with gr.Row():
with gr.Column(min_width=140):
with gr.Row():
style_image = gr.Image(label="Style Image")
with gr.Row():
style_image_strength = gr.Slider(label="Strength",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
with gr.Column(min_width=140):
with gr.Row():
identity_image = gr.Image(label="Identity Image")
with gr.Row():
identity_image_strength = gr.Slider(label="Strenght",step=0.01, minimum=0.0, maximum=1.0, value=1.0)
with gr.Accordion("Advanced options", open=False):
with gr.Row():
with gr.Column(min_width=140):
with gr.Row():
base_image = gr.Image(label="Base Image")
with gr.Row():
base_image_strength = gr.Slider(label="Strength",step=0.01, minimum=0.0, maximum=1.0, value=0.15, min_width=120)
# with gr.Column(min_width=140):
# with gr.Row():
# depth_image = gr.Image(label="depth_image", value=None)
# with gr.Row():
# depth_image_strength = gr.Slider(label="depth_image_strength",step=0.01, minimum=0.0, maximum=1.0, value=0.5)
with gr.Row():
seed = gr.Slider(label="Seed",step=1, minimum=0, maximum=10000000, value=42)
number_of_images = gr.Slider(label="Number of Outputs",step=1, minimum=1, maximum=4, value=1)
with gr.Row():
guidance_scale = gr.Slider(label="Guidance Scale",step=0.1, minimum=0.0, maximum=14.0, value=3.0)
number_of_steps = gr.Slider(label="Number of Steps",step=1, minimum=1, maximum=50, value=10)
with gr.Column():
with gr.Row():
out = gr.Gallery(label="Output(s)")
with gr.Row():
# clear = gr.Button("Clear")
submit = gr.Button("Generate")
submit.click(generate, inputs=[
prompt,
composition_image,
style_image,
identity_image,
base_image,
seed,
negative_prompt,
guidance_scale,
number_of_images,
number_of_steps,
base_image_strength,
composition_image_strength,
style_image_strength,
identity_image_strength,
],
outputs=[out]
)
# clear.click(lambda: None, None, chatbot, queue=False)
gr.Examples(
examples=[["A person", "https://github.com/okaris/omni-zero/assets/1448702/2ca63443-c7f3-4ba6-95c1-2a341414865f", "https://github.com/okaris/omni-zero/assets/1448702/64dc150b-f683-41b1-be23-b6a52c771584", "https://github.com/okaris/omni-zero/assets/1448702/ba193a3a-f90e-4461-848a-560454531c58"]],
inputs=[prompt, composition_image, style_image, identity_image],
outputs=[out],
fn=generate,
cache_examples="lazy",
)
if __name__ == "__main__":
demo.launch()