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="stablediffusionapi/clarity-xl" 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 = 1280 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("

Omni Zero

") gr.Markdown("

A diffusion pipeline for zero-shot stylized portrait creation [GitHub], [StyleOf Remix Yourself]

") 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=28) with gr.Column(): with gr.Row(): out = gr.Gallery(label="Output(s)",format="png") 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) if __name__ == "__main__": demo.launch()