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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 transformers import pipeline
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
NSFW_THRESHOLD = 0.85
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)
nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
return model, ae, t5, clip, nsfw_classifier
class FluxGenerator:
def __init__(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.offload = True # Enable offloading for free tier
self.model_name = "flux-schnell" # Use flux-schnell
self.model, self.ae, self.t5, self.clip, self.nsfw_classifier = 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(
prompt,
id_image,
seed,
width=512, # Reduced for free tier
height=512, # Reduced for free tier
num_steps=4, # Optimized for schnell
id_weight=1.0,
):
flux_generator.t5.max_length = 128
seed = int(seed) if seed != -1 else torch.Generator(device="cpu").seed()
opts = SamplingOptions(
prompt=prompt,
width=width,
height=height,
num_steps=num_steps,
guidance=0.0, # No guidance for schnell
seed=seed,
)
print(f"Generating '{opts.prompt}' with seed {opts.seed}")
t0 = time.perf_counter()
# Process ID image if provided
if id_image is not None:
id_image = resize_numpy_image_long(id_image, 512) # Smaller size for memory
id_embeddings, uncond_id_embeddings = flux_generator.pulid_model.get_id_embedding(id_image, cal_uncond=False)
else:
id_embeddings = None
uncond_id_embeddings = None
# Prepare noise and schedule
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)
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
x = denoise(
flux_generator.model,
**inp,
timesteps=timesteps,
guidance=opts.guidance,
id=id_embeddings,
id_weight=id_weight,
start_step=0,
uncond_id=uncond_id_embeddings,
true_cfg=1.0, # No true CFG for schnell
)
if flux_generator.offload:
flux_generator.model.cpu()
torch.cuda.empty_cache()
flux_generator.ae.decoder.to(x.device)
# Decode
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.")
# Convert to PIL
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())
# NSFW check
nsfw_score = [x["score"] for x in flux_generator.nsfw_classifier(img) if x["label"] == "nsfw"][0]
if nsfw_score < NSFW_THRESHOLD:
return img, str(opts.seed)
else:
return None, f"Image may contain NSFW content (score: {nsfw_score})"
def create_demo():
with gr.Blocks() as demo:
gr.Markdown("# PuLID with FLUX.1 Schnell Demo")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", value="A person in a futuristic city")
id_image = gr.Image(label="Reference Image (ID)")
seed = gr.Textbox(label="Seed (-1 for random)", value="-1")
width = gr.Slider(256, 1024, 512, step=16, label="Width")
height = gr.Slider(256, 1024, 512, step=16, label="Height")
num_steps = gr.Slider(1, 4, 4, step=1, label="Number of Steps")
id_weight = gr.Slider(0.0, 2.0, 1.0, step=0.05, label="ID Weight")
generate_btn = gr.Button("Generate")
with gr.Column():
output_image = gr.Image(label="Generated Image")
seed_output = gr.Textbox(label="Used Seed")
generate_btn.click(
fn=generate_image,
inputs=[prompt, id_image, seed, width, height, num_steps, id_weight],
outputs=[output_image, seed_output]
)
return demo
if __name__ == "__main__":
import huggingface_hub
huggingface_hub.login(os.getenv("HF_TOKEN"))
demo = create_demo()
demo.launch() |