import gradio as gr import torch torch.jit.script = lambda f: f import timm import time from huggingface_hub import hf_hub_download from safetensors.torch import load_file from share_btn import community_icon_html, loading_icon_html, share_js from cog_sdxl_dataset_and_utils import TokenEmbeddingsHandler import lora import copy import json import gc import random from urllib.parse import quote import gdown import os import diffusers from diffusers.utils import load_image from diffusers.models import ControlNetModel from diffusers import AutoencoderKL, DPMSolverMultistepScheduler import cv2 import torch import numpy as np from PIL import Image from insightface.app import FaceAnalysis from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInstantIDImg2ImgPipeline, draw_kps from controlnet_aux import ZoeDetector from compel import Compel, ReturnedEmbeddingsType import spaces #from gradio_imageslider import ImageSlider with open("sdxl_loras.json", "r") as file: data = json.load(file) sdxl_loras_raw = [ { "image": item["image"], "title": item["title"], "repo": item["repo"], "trigger_word": item["trigger_word"], "weights": item["weights"], "is_compatible": item["is_compatible"], "is_pivotal": item.get("is_pivotal", False), "text_embedding_weights": item.get("text_embedding_weights", None), "likes": item.get("likes", 0), "downloads": item.get("downloads", 0), "is_nc": item.get("is_nc", False), "new": item.get("new", False), } for item in data ] with open("defaults_data.json", "r") as file: lora_defaults = json.load(file) device = "cuda" state_dicts = {} for item in sdxl_loras_raw: saved_name = hf_hub_download(item["repo"], item["weights"]) if not saved_name.endswith('.safetensors'): state_dict = torch.load(saved_name) else: state_dict = load_file(saved_name) state_dicts[item["repo"]] = { "saved_name": saved_name, "state_dict": state_dict } sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True] # download models hf_hub_download( repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="/data/checkpoints", ) hf_hub_download( repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="/data/checkpoints", ) hf_hub_download( repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="/data/checkpoints" ) hf_hub_download( repo_id="latent-consistency/lcm-lora-sdxl", filename="pytorch_lora_weights.safetensors", local_dir="/data/checkpoints", ) # download antelopev2 if not os.path.exists("/data/antelopev2.zip"): gdown.download(url="https://drive.google.com/file/d/18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8/view?usp=sharing", output="/data/", quiet=False, fuzzy=True) os.system("unzip /data/antelopev2.zip -d /data/models/") app = FaceAnalysis(name='antelopev2', root='/data', providers=['CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) # prepare models under ./checkpoints face_adapter = f'/data/checkpoints/ip-adapter.bin' controlnet_path = f'/data/checkpoints/ControlNetModel' # load IdentityNet identitynet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) zoedepthnet = ControlNetModel.from_pretrained("diffusers/controlnet-zoe-depth-sdxl-1.0",torch_dtype=torch.float16) vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) pipe = StableDiffusionXLInstantIDImg2ImgPipeline.from_pretrained("rubbrband/albedobaseXL_v21", vae=vae, controlnet=[identitynet, zoedepthnet], torch_dtype=torch.float16) compel = Compel(tokenizer=[pipe.tokenizer, pipe.tokenizer_2] , text_encoder=[pipe.text_encoder, pipe.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, True]) pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, use_karras_sigmas=True) pipe.load_ip_adapter_instantid(face_adapter) pipe.set_ip_adapter_scale(0.8) zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators") zoe.to(device) pipe.to(device) last_lora = "" last_merged = False last_fused = False js = ''' var button = document.getElementById('button'); // Add a click event listener to the button button.addEventListener('click', function() { element.classList.add('selected'); }); ''' def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative, is_new=False): lora_repo = sdxl_loras[selected_state.index]["repo"] new_placeholder = "Type a prompt to use your selected LoRA" weight_name = sdxl_loras[selected_state.index]["weights"] updated_text = f"### Selected: [{lora_repo}](https://huggingface.co./{lora_repo}) ✨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }" for lora_list in lora_defaults: if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]: face_strength = lora_list.get("face_strength", 0.85) image_strength = lora_list.get("image_strength", 0.15) weight = lora_list.get("weight", 0.9) depth_control_scale = lora_list.get("depth_control_scale", 0.8) negative = lora_list.get("negative", "") if(is_new): if(selected_state.index == 0): selected_state.index = -9999 else: selected_state.index *= -1 return ( updated_text, gr.update(placeholder=new_placeholder), face_strength, image_strength, weight, depth_control_scale, negative, selected_state ) def center_crop_image_as_square(img): square_size = min(img.size) left = (img.width - square_size) / 2 top = (img.height - square_size) / 2 right = (img.width + square_size) / 2 bottom = (img.height + square_size) / 2 img_cropped = img.crop((left, top, right, bottom)) return img_cropped def check_selected(selected_state): if not selected_state: raise gr.Error("You must select a LoRA") def merge_incompatible_lora(full_path_lora, lora_scale): for weights_file in [full_path_lora]: if ";" in weights_file: weights_file, multiplier = weights_file.split(";") multiplier = float(multiplier) else: multiplier = lora_scale lora_model, weights_sd = lora.create_network_from_weights( multiplier, full_path_lora, pipe.vae, pipe.text_encoder, pipe.unet, for_inference=True, ) lora_model.merge_to( pipe.text_encoder, pipe.unet, weights_sd, torch.float16, "cuda" ) del weights_sd del lora_model @spaces.GPU def generate_image(prompt, negative, face_emb, face_image, image_strength, images, guidance_scale, face_strength, depth_control_scale, last_lora, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index): global last_fused if last_lora != repo_name: if(last_fused): st = time.time() pipe.unfuse_lora() pipe.unload_lora_weights() et = time.time() elapsed_time = et - st print('Unfuse and unload LoRA took: ', elapsed_time, 'seconds') st = time.time() pipe.load_lora_weights(loaded_state_dict) pipe.fuse_lora(lora_scale) et = time.time() elapsed_time = et - st print('Fuse and load LoRA took: ', elapsed_time, 'seconds') last_fused = True is_pivotal = sdxl_loras[selected_state_index]["is_pivotal"] if(is_pivotal): #Add the textual inversion embeddings from pivotal tuning models text_embedding_name = sdxl_loras[selected_state_index]["text_embedding_weights"] embedding_path = hf_hub_download(repo_id=repo_name, filename=text_embedding_name, repo_type="model") state_dict_embedding = load_file(embedding_path) try: pipe.unload_textual_inversion() pipe.load_textual_inversion(state_dict_embedding["clip_l"], token=["", ""], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer) pipe.load_textual_inversion(state_dict_embedding["clip_g"], token=["", ""], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2) except: pipe.unload_textual_inversion() pipe.load_textual_inversion(state_dict_embedding["text_encoders_0"], token=["", ""], text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer) pipe.load_textual_inversion(state_dict_embedding["text_encoders_1"], token=["", ""], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2) print("Processing prompt...") conditioning, pooled = compel(prompt) if(negative): negative_conditioning, negative_pooled = compel(negative) else: negative_conditioning, negative_pooled = None, None print("Processing image...") image = pipe( prompt_embeds=conditioning, pooled_prompt_embeds=pooled, negative_prompt_embeds=negative_conditioning, negative_pooled_prompt_embeds=negative_pooled, width=1024, height=1024, image_embeds=face_emb, image=face_image, strength=1-image_strength, control_image=images, num_inference_steps=20, guidance_scale = guidance_scale, controlnet_conditioning_scale=[face_strength, depth_control_scale], ).images[0] return image def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)): global last_lora, last_merged, last_fused, pipe selected_state_index = selected_state.index face_image = center_crop_image_as_square(face_image) st = time.time() try: face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)) face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face face_emb = face_info['embedding'] face_kps = draw_kps(face_image, face_info['kps']) except: raise gr.Error("No face found in your image. Only face images work here. Try again") et = time.time() elapsed_time = et - st print('Calculating face embeds took: ', elapsed_time, 'seconds') for lora_list in lora_defaults: if lora_list["model"] == sdxl_loras[selected_state_index]["repo"]: prompt_full = lora_list.get("prompt", None) if(prompt_full): prompt = prompt_full.replace("", prompt) print("Prompt:", prompt) if(prompt == ""): prompt = "a person" #prepare face zoe st = time.time() with torch.no_grad(): image_zoe = zoe(face_image) et = time.time() elapsed_time = et - st print('Zoe Depth calculations took: ', elapsed_time, 'seconds') width, height = face_kps.size images = [face_kps, image_zoe.resize((height, width))] #if(selected_state.index < 0): # if(selected_state.index == -9999): # selected_state.index = 0 # else: # selected_state.index *= -1 #sdxl_loras = sdxl_loras_new print("Selected State: ", selected_state_index) print(sdxl_loras[selected_state_index]["repo"]) if negative == "": negative = None if not selected_state: raise gr.Error("You must select a LoRA") repo_name = sdxl_loras[selected_state_index]["repo"] weight_name = sdxl_loras[selected_state_index]["weights"] full_path_lora = state_dicts[repo_name]["saved_name"] loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"]) cross_attention_kwargs = None print("Last LoRA: ", last_lora) print("Current LoRA: ", repo_name) print("Last fused: ", last_fused) image = generate_image(prompt, negative, face_emb, face_image, image_strength, images, guidance_scale, face_strength, depth_control_scale, last_lora, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index) last_lora = repo_name return image, gr.update(visible=True) def shuffle_gallery(sdxl_loras): random.shuffle(sdxl_loras) return [(item["image"], item["title"]) for item in sdxl_loras], sdxl_loras def classify_gallery(sdxl_loras): sorted_gallery = sorted(sdxl_loras, key=lambda x: x.get("likes", 0), reverse=True) return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery def swap_gallery(order, sdxl_loras): if(order == "random"): return shuffle_gallery(sdxl_loras) else: return classify_gallery(sdxl_loras) def deselect(): return gr.Gallery(selected_index=None) with gr.Blocks(css="custom.css") as demo: gr_sdxl_loras = gr.State(value=sdxl_loras_raw) title = gr.HTML( """

Face to All

""", elem_id="title", ) selected_state = gr.State() with gr.Row(elem_id="main_app"): with gr.Column(scale=4): with gr.Group(elem_id="gallery_box"): photo = gr.Image(label="Upload a picture of yourself", interactive=True, type="pil", height=300) selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", ) #order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio") #new_gallery = gr.Gallery( # label="New LoRAs", # elem_id="gallery_new", # columns=3, # value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False) gallery = gr.Gallery( #value=[(item["image"], item["title"]) for item in sdxl_loras], label="Style gallery", allow_preview=False, columns=4, elem_id="gallery", show_share_button=False, height=550 ) custom_model = gr.Textbox(label="Enter a custom Hugging Face or CivitAI SDXL LoRA", interactive=False, info="Coming soon...") with gr.Column(scale=5): with gr.Row(): prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, info="Describe your subject (optional)", value="A person", elem_id="prompt") button = gr.Button("Run", elem_id="run_button") with gr.Group(elem_id="share-btn-container", visible=False) as share_group: community_icon = gr.HTML(community_icon_html) loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button("Share to community", elem_id="share-btn") result = gr.Image( interactive=False, label="Generated Image", elem_id="result-image" ) with gr.Accordion("Advanced options", open=False): negative = gr.Textbox(label="Negative Prompt") weight = gr.Slider(0, 10, value=0.9, step=0.1, label="LoRA weight") face_strength = gr.Slider(0, 1, value=0.85, step=0.01, label="Face strength", info="Higher values increase the face likeness but reduce the creative liberty of the models") image_strength = gr.Slider(0, 1, value=0.15, step=0.01, label="Image strength", info="Higher values increase the similarity with the structure/colors of the original photo") guidance_scale = gr.Slider(0, 50, value=7, step=0.1, label="Guidance Scale") depth_control_scale = gr.Slider(0, 1, value=0.8, step=0.01, label="Zoe Depth ControlNet strenght") prompt_title = gr.Markdown( value="### Click on a LoRA in the gallery to select it", visible=True, elem_id="selected_lora", ) #order_gallery.change( # fn=swap_gallery, # inputs=[order_gallery, gr_sdxl_loras], # outputs=[gallery, gr_sdxl_loras], # queue=False #) gallery.select( fn=update_selection, inputs=[gr_sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative], outputs=[prompt_title, prompt, face_strength, image_strength, weight, depth_control_scale, negative, selected_state], queue=False, show_progress=False ) #new_gallery.select( # fn=update_selection, # inputs=[gr_sdxl_loras_new, gr.State(True)], # outputs=[prompt_title, prompt, prompt, selected_state, gallery], # queue=False, # show_progress=False #) prompt.submit( fn=check_selected, inputs=[selected_state], queue=False, show_progress=False ).success( fn=run_lora, inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras], outputs=[result, share_group], ) button.click( fn=check_selected, inputs=[selected_state], queue=False, show_progress=False ).success( fn=run_lora, inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras], outputs=[result, share_group], ) share_button.click(None, [], [], js=share_js) demo.load(fn=classify_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False, js=js) demo.queue(max_size=20) demo.launch(share=True)