Spaces:
Sleeping
Sleeping
| import spaces | |
| import random | |
| import torch | |
| import cv2 | |
| import insightface | |
| import gradio as gr | |
| import numpy as np | |
| import os | |
| from huggingface_hub import snapshot_download | |
| from transformers import CLIPVisionModelWithProjection,CLIPImageProcessor | |
| from SAK.pipelines.pipeline_stable_diffusion_xl_chatglm_256_ipadapter_FaceID import StableDiffusionXLPipeline | |
| from SAK.models.modeling_chatglm import ChatGLMModel | |
| from SAK.models.tokenization_chatglm import ChatGLMTokenizer | |
| from diffusers import AutoencoderKL | |
| from SAK.models.unet_2d_condition import UNet2DConditionModel | |
| from diffusers import EulerDiscreteScheduler | |
| from PIL import Image | |
| from insightface.app import FaceAnalysis | |
| from insightface.data import get_image as ins_get_image | |
| MARKDOWN = """ | |
| This demo utilizes <a href="https://huggingface.co/docs/diffusers/en/api/pipelines/stable_diffusion/stable_diffusion_xl">Stable Diffusion XL Pipeline</a> | |
| Try out with different prompts using your image and do provide your feedback. | |
| **Demo by [Sunder Ali Khowaja](https://sander-ali.github.io) - [X](https://x.com/SunderAKhowaja) -[Github](https://github.com/sander-ali) -[Hugging Face](https://huggingface.co/SunderAli17)** | |
| """ | |
| device = "cuda" | |
| ckpt_dir = snapshot_download(repo_id="SunderAli17/SAK") | |
| ckpt_dir_faceid = snapshot_download(repo_id="SunderAli17/SAK-IP-Adapter-FaceTransform-Plus") | |
| text_encoder = ChatGLMModel.from_pretrained(f'{ckpt_dir}/text_encoder', torch_dtype=torch.float16).half().to(device) | |
| tokenizer = ChatGLMTokenizer.from_pretrained(f'{ckpt_dir}/text_encoder') | |
| vae = AutoencoderKL.from_pretrained(f"{ckpt_dir}/vae", revision=None).half().to(device) | |
| scheduler = EulerDiscreteScheduler.from_pretrained(f"{ckpt_dir}/scheduler") | |
| unet = UNet2DConditionModel.from_pretrained(f"{ckpt_dir}/unet", revision=None).half().to(device) | |
| clip_image_encoder = CLIPVisionModelWithProjection.from_pretrained(f'{ckpt_dir_faceid}/clip-vit-large-patch14-336', ignore_mismatched_sizes=True) | |
| clip_image_encoder.to(device) | |
| clip_image_processor = CLIPImageProcessor(size = 336, crop_size = 336) | |
| pipe = StableDiffusionXLPipeline( | |
| vae = vae, | |
| text_encoder = text_encoder, | |
| tokenizer = tokenizer, | |
| unet = unet, | |
| scheduler = scheduler, | |
| face_clip_encoder = clip_image_encoder, | |
| face_clip_processor = clip_image_processor, | |
| force_zeros_for_empty_prompt = False, | |
| ) | |
| class FaceInfoGenerator(): | |
| def __init__(self, root_dir = "./.insightface/"): | |
| self.app = FaceAnalysis(name = 'antelopev2', root = root_dir, providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) | |
| self.app.prepare(ctx_id = 0, det_size = (640, 640)) | |
| def get_faceinfo_one_img(self, face_image): | |
| face_info = self.app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)) | |
| if len(face_info) == 0: | |
| face_info = None | |
| else: | |
| 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 | |
| return face_info | |
| def face_bbox_to_square(bbox): | |
| ## l, t, r, b to square l, t, r, b | |
| l,t,r,b = bbox | |
| cent_x = (l + r) / 2 | |
| cent_y = (t + b) / 2 | |
| w, h = r - l, b - t | |
| r = max(w, h) / 2 | |
| l0 = cent_x - r | |
| r0 = cent_x + r | |
| t0 = cent_y - r | |
| b0 = cent_y + r | |
| return [l0, t0, r0, b0] | |
| MAX_SEED = np.iinfo(np.int32).max | |
| MAX_IMAGE_SIZE = 1024 | |
| face_info_generator = FaceInfoGenerator() | |
| def infer(prompt, | |
| image = None, | |
| negative_prompt = "nsfw，Face shadows，Low resolution，JPEG artifacts、Vague、bad，Neon lights", | |
| seed = 66, | |
| randomize_seed = False, | |
| guidance_scale = 5.0, | |
| num_inference_steps = 50 | |
| ): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| generator = torch.Generator().manual_seed(seed) | |
| global pipe | |
| pipe = pipe.to(device) | |
| pipe.load_ip_adapter_faceid_plus(f'{ckpt_dir_faceid}/ipa-faceid-plus.bin', device = device) | |
| scale = 0.8 | |
| pipe.set_face_fidelity_scale(scale) | |
| face_info = face_info_generator.get_faceinfo_one_img(image) | |
| face_bbox_square = face_bbox_to_square(face_info["bbox"]) | |
| crop_image = image.crop(face_bbox_square) | |
| crop_image = crop_image.resize((336, 336)) | |
| crop_image = [crop_image] | |
| face_embeds = torch.from_numpy(np.array([face_info["embedding"]])) | |
| face_embeds = face_embeds.to(device, dtype = torch.float16) | |
| image = pipe( | |
| prompt = prompt, | |
| negative_prompt = negative_prompt, | |
| height = 1024, | |
| width = 1024, | |
| num_inference_steps= num_inference_steps, | |
| guidance_scale = guidance_scale, | |
| num_images_per_prompt = 1, | |
| generator = generator, | |
| face_crop_image = crop_image, | |
| face_insightface_embeds = face_embeds | |
| ).images[0] | |
| return image, seed | |
| examples = [ | |
| ["wearing a full suit sitting in a restaurant with candle lights", "image/SunderAli_Khowaja.png"], | |
| ["Wild cowboy hat with western town and horses in the background", "image/test2.png"] | |
| ] | |
| css=""" | |
| #col-left { | |
| margin: 0 auto; | |
| max-width: 600px; | |
| } | |
| #col-right { | |
| margin: 0 auto; | |
| max-width: 750px; | |
| } | |
| #button { | |
| color: blue; | |
| } | |
| """ | |
| def load_description(fp): | |
| with open(fp, 'r', encoding='utf-8') as f: | |
| content = f.read() | |
| return content | |
| theme = gr.themes.Soft( | |
| font=[gr.themes.GoogleFont('Source Code Pro'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'], | |
| ) | |
| js_func = """ | |
| function refresh() { | |
| const url = new URL(window.location); | |
| if (url.searchParams.get('__theme') !== 'dark') { | |
| url.searchParams.set('__theme', 'dark'); | |
| window.location.href = url.href; | |
| } | |
| } | |
| """ | |
| with gr.Blocks(js = js_func, theme = theme) as SAK: | |
| gr.Markdown(MARKDOWN) | |
| with gr.Row(): | |
| with gr.Column(elem_id="col-left"): | |
| with gr.Row(): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| placeholder="Enter your prompt", | |
| lines=2 | |
| ) | |
| with gr.Row(): | |
| image = gr.Image(label="Image", type="pil") | |
| with gr.Accordion("Advanced Settings", open=False): | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| placeholder="Enter a negative prompt", | |
| visible=True, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.0, | |
| maximum=10.0, | |
| step=0.1, | |
| value=5.0, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=10, | |
| maximum=50, | |
| step=1, | |
| value=25, | |
| ) | |
| with gr.Row(): | |
| button = gr.Button("Run", elem_id="button") | |
| with gr.Column(elem_id="col-right"): | |
| result = gr.Image(label="Result", show_label=False) | |
| seed_used = gr.Number(label="Seed Used") | |
| with gr.Row(): | |
| gr.Examples( | |
| fn = infer, | |
| examples = examples, | |
| inputs = [prompt, image], | |
| outputs = [result, seed_used], | |
| ) | |
| button.click( | |
| fn = infer, | |
| inputs = [prompt, image, negative_prompt, seed, randomize_seed, guidance_scale, num_inference_steps], | |
| outputs = [result, seed_used] | |
| ) | |
| SAK.queue().launch(debug=True, share=True) | |