import spaces import time import os import gradio as gr import torch from einops import rearrange from PIL import Image from flux.cli import SamplingOptions from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack from flux.util import load_ae, load_clip, load_flow_model, load_t5 from pulid.pipeline_flux import PuLIDPipeline from pulid.utils import resize_numpy_image_long def get_models(name: str, device: torch.device, offload: bool): t5 = load_t5(device, max_length=128) clip = load_clip(device) model = load_flow_model(name, device="cpu" if offload else device) model.eval() ae = load_ae(name, device="cpu" if offload else device) return model, ae, t5, clip class FluxGenerator: def __init__(self): self.device = torch.device('cuda') self.offload = False self.model_name = 'flux-dev' self.model, self.ae, self.t5, self.clip = get_models( self.model_name, device=self.device, offload=self.offload, ) self.pulid_model = PuLIDPipeline(self.model, 'cuda', weight_dtype=torch.bfloat16) self.pulid_model.load_pretrain() flux_generator = FluxGenerator() @spaces.GPU @torch.inference_mode() def generate_image( width, height, num_steps, start_step, guidance, seed, prompt, id_image=None, id_weight=1.0, neg_prompt="", true_cfg=1.0, timestep_to_start_cfg=1, max_sequence_length=128, ): flux_generator.t5.max_length = max_sequence_length seed = int(seed) if seed == -1: seed = None opts = SamplingOptions( prompt=prompt, width=width, height=height, num_steps=num_steps, guidance=guidance, seed=seed, ) if opts.seed is None: opts.seed = torch.Generator(device="cpu").seed() t0 = time.perf_counter() use_true_cfg = abs(true_cfg - 1.0) > 1e-2 if id_image is not None: id_image = resize_numpy_image_long(id_image, 1024) id_embeddings, uncond_id_embeddings = flux_generator.pulid_model.get_id_embedding(id_image, cal_uncond=use_true_cfg) else: id_embeddings = None uncond_id_embeddings = None # prepare input x = get_noise( 1, opts.height, opts.width, device=flux_generator.device, dtype=torch.bfloat16, seed=opts.seed, ) timesteps = get_schedule( opts.num_steps, x.shape[-1] * x.shape[-2] // 4, shift=True, ) if flux_generator.offload: flux_generator.t5, flux_generator.clip = flux_generator.t5.to(flux_generator.device), flux_generator.clip.to(flux_generator.device) inp = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=opts.prompt) inp_neg = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=neg_prompt) if use_true_cfg else None # offload TEs to CPU, load model to gpu if flux_generator.offload: flux_generator.t5, flux_generator.clip = flux_generator.t5.cpu(), flux_generator.clip.cpu() torch.cuda.empty_cache() flux_generator.model = flux_generator.model.to(flux_generator.device) # denoise initial noise x = denoise( flux_generator.model, **inp, timesteps=timesteps, guidance=opts.guidance, id=id_embeddings, id_weight=id_weight, start_step=start_step, uncond_id=uncond_id_embeddings, true_cfg=true_cfg, timestep_to_start_cfg=timestep_to_start_cfg, neg_txt=inp_neg["txt"] if use_true_cfg else None, neg_txt_ids=inp_neg["txt_ids"] if use_true_cfg else None, neg_vec=inp_neg["vec"] if use_true_cfg else None, ) # offload model, load autoencoder to gpu if flux_generator.offload: flux_generator.model.cpu() torch.cuda.empty_cache() flux_generator.ae.decoder.to(x.device) # decode latents to pixel space x = unpack(x.float(), opts.height, opts.width) with torch.autocast(device_type=flux_generator.device.type, dtype=torch.bfloat16): x = flux_generator.ae.decode(x) if flux_generator.offload: flux_generator.ae.decoder.cpu() torch.cuda.empty_cache() t1 = time.perf_counter() # bring into PIL format x = x.clamp(-1, 1) x = rearrange(x[0], "c h w -> h w c") img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy()) return img, str(opts.seed), flux_generator.pulid_model.debug_img_list css = """ footer { visibility: hidden; } """ def create_demo(args, model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu", offload: bool = False): with gr.Blocks(theme="Nymbo/Nymbo_Theme", css=css) as demo: with gr.Row(): with gr.Column(): prompt = gr.Textbox(label="Prompt", value="portrait, color, cinematic") id_image = gr.Image(label="ID Image") generate_btn = gr.Button("Generate") with gr.Column(): output_image = gr.Image(label="Generated Image") with gr.Row(): with gr.Column(): gr.Markdown("## Examples") all_examples = [ ['a woman holding sign with glowing green text \"PuLID for FLUX\"', 'example_inputs/liuyifei.png'], ['portrait, side view', 'example_inputs/liuyifei.png'], ['white-haired woman with vr technology atmosphere', 'example_inputs/liuyifei.png'], ['a young child is eating Icecream', 'example_inputs/liuyifei.png'], ['a man is holding a sign with text \"PuLID for FLUX\", winter, snowing', 'example_inputs/pengwei.jpg'], ['portrait, candle light', 'example_inputs/pengwei.jpg'], ['profile shot dark photo of a 25-year-old male with smoke', 'example_inputs/pengwei.jpg'], ['American Comics, 1boy', 'example_inputs/pengwei.jpg'], ['portrait, pixar', 'example_inputs/pengwei.jpg'], ['portrait, made of ice sculpture', 'example_inputs/lecun.jpg'], ] example_images = [example[1] for example in all_examples] example_captions = [example[0] for example in all_examples] gallery = gr.Gallery( value=list(zip(example_images, example_captions)), label="Example Gallery", show_label=False, elem_id="gallery", columns=5, rows=2, object_fit="contain", height="auto" ) def fill_example(evt: gr.SelectData): return [all_examples[evt.index][i] for i in [0, 1]] gallery.select( fill_example, None, [prompt, id_image], ) generate_btn.click( fn=generate_image, inputs=[ gr.Slider(256, 1536, 896, step=16, visible=False), # width gr.Slider(256, 1536, 1152, step=16, visible=False), # height gr.Slider(1, 20, 20, step=1, visible=False), # num_steps gr.Slider(0, 10, 0, step=1, visible=False), # start_step gr.Slider(1.0, 10.0, 4, step=0.1, visible=False), # guidance gr.Textbox(-1, visible=False), # seed prompt, id_image, gr.Slider(0.0, 3.0, 1, step=0.05, visible=False), # id_weight gr.Textbox("bad quality, worst quality, text, signature, watermark, extra limbs", visible=False), # neg_prompt gr.Slider(1.0, 10.0, 1, step=0.1, visible=False), # true_cfg gr.Slider(0, 20, 1, step=1, visible=False), # timestep_to_start_cfg gr.Slider(128, 512, 128, step=128, visible=False), # max_sequence_length ], outputs=[output_image], ) return demo if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="PuLID for FLUX.1-dev") parser.add_argument("--name", type=str, default="flux-dev", choices=list('flux-dev'), help="currently only support flux-dev") parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", help="Device to use") parser.add_argument("--offload", action="store_true", help="Offload model to CPU when not in use") parser.add_argument("--port", type=int, default=8080, help="Port to use") parser.add_argument("--dev", action='store_true', help="Development mode") parser.add_argument("--pretrained_model", type=str, help='for development') args = parser.parse_args() import huggingface_hub huggingface_hub.login(os.getenv('HF_TOKEN')) demo = create_demo(args, args.name, args.device, args.offload) demo.launch()