Create app-backup.py

app-backup.py

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+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()
+ print(f"Generating '{opts.prompt}' with seed {opts.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
+
+ print(id_embeddings)
+
+ # prepare input
+ x = get_noise(
+ 1,
+ opts.height,
+ opts.width,
+ device=flux_generator.device,
+ dtype=torch.bfloat16,
+ seed=opts.seed,
+ )
+ print(x)
+ 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()
+
+ print(f"Done in {t1 - t0:.1f}s.")
+ # bring into PIL format
+ x = x.clamp(-1, 1)
+ # x = embed_watermark(x.float())
+ 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")
+ id_weight = gr.Slider(0.0, 3.0, 1, step=0.05, label="id weight")
+
+
+ width = gr.Slider(256, 1536, 896, step=16, label="Width")
+ height = gr.Slider(256, 1536, 1152, step=16, label="Height")
+ num_steps = gr.Slider(1, 20, 20, step=1, label="Number of steps")
+ start_step = gr.Slider(0, 10, 0, step=1, label="timestep to start inserting ID")
+ guidance = gr.Slider(1.0, 10.0, 4, step=0.1, label="Guidance")
+ seed = gr.Textbox(-1, label="Seed (-1 for random)")
+ max_sequence_length = gr.Slider(128, 512, 128, step=128,
+ label="max_sequence_length for prompt (T5), small will be faster")
+
+ with gr.Accordion("Advanced Options (True CFG, true_cfg_scale=1 means use fake CFG, >1 means use true CFG, if using true CFG, we recommend set the guidance scale to 1)", open=False): # noqa E501
+ neg_prompt = gr.Textbox(
+ label="Negative Prompt",
+ value="bad quality, worst quality, text, signature, watermark, extra limbs")
+ true_cfg = gr.Slider(1.0, 10.0, 1, step=0.1, label="true CFG scale")
+ timestep_to_start_cfg = gr.Slider(0, 20, 1, step=1, label="timestep to start cfg", visible=args.dev)
+
+ generate_btn = gr.Button("Generate")
+ 
+ with gr.Column():
+ output_image = gr.Image(label="Generated Image")
+ seed_output = gr.Textbox(label="Used Seed")
+ intermediate_output = gr.Gallery(label='Output', elem_id="gallery", visible=args.dev)
+ # _CITE_ 관련 부분 제거
+ 
+
+ with gr.Row(), gr.Column():
+ gr.Markdown("## Examples")
+ example_inps = [
+ [
+ 'a woman holding sign with glowing green text \"PuLID for FLUX\"',
+ 'example_inputs/liuyifei.png',
+ 4, 4, 2680261499100305976, 1
+ ],
+ [
+ 'portrait, side view',
+ 'example_inputs/liuyifei.png',
+ 4, 4, 1205240166692517553, 1
+ ],
+ [
+ 'white-haired woman with vr technology atmosphere, revolutionary exceptional magnum with remarkable details', # noqa E501
+ 'example_inputs/liuyifei.png',
+ 4, 4, 6349424134217931066, 1
+ ],
+ [
+ 'a young child is eating Icecream',
+ 'example_inputs/liuyifei.png',
+ 4, 4, 10606046113565776207, 1
+ ],
+ [
+ 'a man is holding a sign with text \"PuLID for FLUX\", winter, snowing, top of the mountain',
+ 'example_inputs/pengwei.jpg',
+ 4, 4, 2410129802683836089, 1
+ ],
+ [
+ 'portrait, candle light',
+ 'example_inputs/pengwei.jpg',
+ 4, 4, 17522759474323955700, 1
+ ],
+ [
+ 'profile shot dark photo of a 25-year-old male with smoke escaping from his mouth, the backlit smoke gives the image an ephemeral quality, natural face, natural eyebrows, natural skin texture, award winning photo, highly detailed face, atmospheric lighting, film grain, monochrome', # noqa E501
+ 'example_inputs/pengwei.jpg',
+ 4, 4, 17733156847328193625, 1
+ ],
+ [
+ 'American Comics, 1boy',
+ 'example_inputs/pengwei.jpg',
+ 1, 4, 13223174453874179686, 1
+ ],
+ [
+ 'portrait, pixar',
+ 'example_inputs/pengwei.jpg',
+ 1, 4, 9445036702517583939, 1
+ ],
+ ]
+ gr.Examples(examples=example_inps, inputs=[prompt, id_image, start_step, guidance, seed, true_cfg],
+ label='fake CFG')
+
+ example_inps = [
+ [
+ 'portrait, made of ice sculpture',
+ 'example_inputs/lecun.jpg',
+ 1, 1, 3811899118709451814, 5
+ ],
+ ]
+ gr.Examples(examples=example_inps, inputs=[prompt, id_image, start_step, guidance, seed, true_cfg],
+ label='true CFG')
+
+ generate_btn.click(
+ fn=generate_image,
+ inputs=[width, height, num_steps, start_step, guidance, seed, prompt, id_image, id_weight, neg_prompt,
+ true_cfg, timestep_to_start_cfg, max_sequence_length],
+ outputs=[output_image, seed_output, intermediate_output],
+ )
+
+ 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()