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## write a diffuers function to generate images
import torch
from diffusers import StableDiffusion3Pipeline
import random
class ImageGenerator:
def __init__(self, repo: str):
self.repo = repo
self.pipeline = StableDiffusion3Pipeline.from_pretrained(self.repo, torch_dtype=torch.float16, local_files_only = True)
self.pipeline.enable_model_cpu_offload()
def generate_image(self, prompt: str, width: int = 1024, height: int = 1024, scale_factor: float = 4.5, steps: int = 28, seed: int = None):
seed = seed if seed is not None else random.randint(0, 2**32 - 1)
print(f"using {seed} to generate image...")
generator = torch.Generator("cuda").manual_seed(seed)
image = self.pipeline(
prompt,
negative_prompt="",
width=width,
height=height,
num_inference_steps=steps,
guidance_scale=scale_factor,
max_sequence_length=512,
generator=generator,
).images[0]
return image
Sd_repo = "/tmp/pretrainmodel/stable-diffusion-3.5-medium-ungated"
sd_model = ImageGenerator(Sd_repo)
prompt = "A close-up portrait of an Asian girl with blunt bangs and big eyes, side profile, holding a red apple on top of her head, in a winter beach setting. She looks very happy, with snowflakes gently falling on her hair. The scene is captured with a high-quality DSLR camera, showcasing natural light and bokeh effects, with a fresh, crisp light and shadow play, reminiscent of a snowy film scene."
image = sd_model.generate_image(prompt)
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