import os import gradio as gr import spaces os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" import torch #Hack for ZeroGPU torch.jit.script = lambda f: f #### import cv2 import numpy as np import PIL from controlnet_aux import ZoeDetector from diffusers import DPMSolverMultistepScheduler from diffusers.image_processor import IPAdapterMaskProcessor from diffusers.models import ControlNetModel from huggingface_hub import snapshot_download from insightface.app import FaceAnalysis from pipeline import OmniZeroPipeline from transformers import CLIPVisionModelWithProjection from utils import align_images, draw_kps, load_and_resize_image def patch_onnx_runtime( inter_op_num_threads: int = 16, intra_op_num_threads: int = 16, omp_num_threads: int = 16, ): import os import onnxruntime as ort os.environ["OMP_NUM_THREADS"] = str(omp_num_threads) _default_session_options = ort.capi._pybind_state.get_default_session_options() def get_default_session_options_new(): _default_session_options.inter_op_num_threads = inter_op_num_threads _default_session_options.intra_op_num_threads = intra_op_num_threads return _default_session_options ort.capi._pybind_state.get_default_session_options = get_default_session_options_new base_model = "frankjoshua/albedobaseXL_v13" patch_onnx_runtime() snapshot_download("okaris/antelopev2", local_dir="./models/antelopev2") face_analysis = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', '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") ip_adapter_mask_processor = IPAdapterMaskProcessor() pipeline = OmniZeroPipeline.from_pretrained( base_model, controlnet=[identitynet, 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", "okaris/ip-adapter-instantid", "h94/IP-Adapter"], subfolder=[None, None, "sdxl_models"], weight_name=["ip-adapter-instantid.bin", "ip-adapter-instantid.bin", "ip-adapter-plus_sdxl_vit-h.safetensors"]) @spaces.GPU() def generate( base_image="https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg", style_image="https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg", identity_image_1="https://cdn-prod.styleof.com/inferences/cm1hp4lea14oz14jeoghnex7g/dlgc5xwo0qzey7qaixy45i1o-medium.jpeg", identity_image_2="https://cdn-prod.styleof.com/inferences/cm1ho69ha14np14jesnusqiep/mp3aaktzqz20ujco5i3bi5s1-medium.jpeg", seed=42, prompt="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy", negative_prompt="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", guidance_scale=3.0, number_of_images=1, number_of_steps=10, base_image_strength=0.3, style_image_strength=1.0, identity_image_strength_1=1.0, identity_image_strength_2=1.0, depth_image=None, depth_image_strength=0.2, mask_guidance_start=0.0, mask_guidance_end=1.0, progress=gr.Progress(track_tqdm=True) ): resolution = 1024 if base_image is not None: base_image = load_and_resize_image(base_image, resolution, resolution) 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 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_1 is not None: identity_image_1 = load_and_resize_image(identity_image_1, resolution, resolution) else: raise ValueError("You must provide an identity image") if identity_image_2 is not None: identity_image_2 = load_and_resize_image(identity_image_2, resolution, resolution) else: raise ValueError("You must provide an identity image 2") height, width = base_image.size face_info_1 = face_analysis.get(cv2.cvtColor(np.array(identity_image_1), cv2.COLOR_RGB2BGR)) for i, face in enumerate(face_info_1): print(f"Face 1 -{i}: Age: {face['age']}, Gender: {face['gender']}") face_info_1 = sorted(face_info_1, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face face_emb_1 = torch.tensor(face_info_1['embedding']).to("cuda", dtype=dtype) face_info_2 = face_analysis.get(cv2.cvtColor(np.array(identity_image_2), cv2.COLOR_RGB2BGR)) for i, face in enumerate(face_info_2): print(f"Face 2 -{i}: Age: {face['age']}, Gender: {face['gender']}") face_info_2 = sorted(face_info_2, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face face_emb_2 = torch.tensor(face_info_2['embedding']).to("cuda", dtype=dtype) zero = np.zeros((width, height, 3), dtype=np.uint8) # face_kps_identity_image_1 = draw_kps(zero, face_info_1['kps']) # face_kps_identity_image_2 = draw_kps(zero, face_info_2['kps']) face_info_img2img = face_analysis.get(cv2.cvtColor(np.array(base_image), cv2.COLOR_RGB2BGR)) faces_info_img2img = sorted(face_info_img2img, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1]) face_info_a = faces_info_img2img[-1] face_info_b = faces_info_img2img[-2] # face_emb_a = torch.tensor(face_info_a['embedding']).to("cuda", dtype=dtype) # face_emb_b = torch.tensor(face_info_b['embedding']).to("cuda", dtype=dtype) face_kps_identity_image_a = draw_kps(zero, face_info_a['kps']) face_kps_identity_image_b = draw_kps(zero, face_info_b['kps']) general_mask = PIL.Image.fromarray(np.ones((width, height, 3), dtype=np.uint8)) control_mask_1 = zero.copy() x1, y1, x2, y2 = face_info_a["bbox"] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) control_mask_1[y1:y2, x1:x2] = 255 control_mask_1 = PIL.Image.fromarray(control_mask_1.astype(np.uint8)) control_mask_2 = zero.copy() x1, y1, x2, y2 = face_info_b["bbox"] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) control_mask_2[y1:y2, x1:x2] = 255 control_mask_2 = PIL.Image.fromarray(control_mask_2.astype(np.uint8)) controlnet_masks = [control_mask_1, control_mask_2, general_mask] ip_adapter_images = [face_emb_1, face_emb_2, style_image, ] masks = ip_adapter_mask_processor.preprocess([control_mask_1, control_mask_2, general_mask], height=height, width=width) ip_adapter_masks = [mask.unsqueeze(0) for mask in masks] inpaint_mask = torch.logical_or(torch.tensor(np.array(control_mask_1)), torch.tensor(np.array(control_mask_2))).float() inpaint_mask = PIL.Image.fromarray((inpaint_mask.numpy() * 255).astype(np.uint8)).convert("RGB") new_ip_adapter_masks = [] for ip_img, mask in zip(ip_adapter_images, controlnet_masks): if isinstance(ip_img, list): num_images = len(ip_img) mask = mask.repeat(1, num_images, 1, 1) new_ip_adapter_masks.append(mask) generator = torch.Generator(device="cpu").manual_seed(seed) pipeline.set_ip_adapter_scale([identity_image_strength_1, identity_image_strength_2, { "down": { "block_2": [0.0, 0.0] }, #Composition "up": { "block_0": [0.0, style_image_strength, 0.0] } #Style } ]) images = pipeline( prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=number_of_steps, num_images_per_prompt=number_of_images, ip_adapter_image=ip_adapter_images, cross_attention_kwargs={"ip_adapter_masks": ip_adapter_masks}, image=base_image, mask_image=inpaint_mask, i2i_mask_guidance_start=mask_guidance_start, i2i_mask_guidance_end=mask_guidance_end, control_image=[face_kps_identity_image_a, face_kps_identity_image_b, depth_image], control_mask=controlnet_masks, identity_control_indices=[(0,0), (1,1)], controlnet_conditioning_scale=[identity_image_strength_1, identity_image_strength_2, depth_image_strength], 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 buy_me_a_coffee_button = """ [![Buy me a coffee](https://img.buymeacoffee.com/button-api/?text=Buy%20me%20a%20coffee&emoji=&slug=vk654cf2pv8&button_colour=BD5FFF&font_colour=ffffff&font_family=Bree&outline_colour=000000&coffee_colour=FFDD00)](https://www.buymeacoffee.com/vk654cf2pv8) """ with gr.Blocks() as demo: gr.Markdown("

Omni Zero Couples

") gr.Markdown("

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

") gr.Markdown(buy_me_a_coffee_button) with gr.Row(): with gr.Column(): with gr.Row(): prompt = gr.Textbox(label="Prompt", value="Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy") with gr.Row(): negative_prompt = gr.Textbox(label="Negative Prompt", value="anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured") 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=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.Row(): 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.Column(min_width=140): with gr.Row(): identity_image_2 = gr.Image(label="Identity Image 2") with gr.Row(): identity_image_strength_2 = 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(): 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.Row(): mask_guidance_start = gr.Slider(label="Mask Guidance Start",step=0.01, minimum=0.0, maximum=1.0, value=0.0) mask_guidance_end = gr.Slider(label="Mask Guidance End",step=0.01, minimum=0.0, maximum=1.0, value=1.0) 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, base_image, style_image, identity_image, identity_image_2, seed, negative_prompt, guidance_scale, number_of_images, number_of_steps, base_image_strength, style_image_strength, identity_image_strength, identity_image_strength_2, mask_guidance_start, mask_guidance_end, ], outputs=[out] ) # clear.click(lambda: None, None, chatbot, queue=False) gr.Examples( examples=[ [ "Cinematic still photo of a couple. emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy", "https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg", "https://cdn-prod.styleof.com/inferences/cm1ho5cjl14nh14jec6phg2h8/i6k59e7gpsr45ufc7l8kun0g-medium.jpeg", "https://cdn-prod.styleof.com/inferences/cm1hp4lea14oz14jeoghnex7g/dlgc5xwo0qzey7qaixy45i1o-medium.jpeg", "https://cdn-prod.styleof.com/inferences/cm1ho69ha14np14jesnusqiep/mp3aaktzqz20ujco5i3bi5s1-medium.jpeg" ] ], inputs=[prompt, base_image, style_image, identity_image, identity_image_2], outputs=[out], fn=generate, cache_examples="lazy", ) if __name__ == "__main__": demo.launch()