cristiano.hoshikawa/image_lora_training
1
0
Fork 0
mirror of https://github.com/hoshikawa2/image_lora_training.git synced 2026-03-11 17:14:57 +00:00
Code Issues Packages Projects Releases Wiki Activity

optimized

Browse Source
This commit is contained in:
hoshikawa2
2026-03-08 18:14:58 -03:00
parent 51564871d3
commit 5be51ee951
1 changed files with 32 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

47
generate_image3.py
View File

@@ -1,21 +1,33 @@
import hashlib import hashlib
from pathlib import Path from pathlib import Path
import torch import torch
from diffusers import DiffusionPipeline
from diffusers import StableDiffusionXLPipeline from diffusers import StableDiffusionXLPipeline
from diffusers import DPMSolverMultistepScheduler
from peft import PeftModel from peft import PeftModel
from project.generate_image import negative_prompt
CACHE_DIR = Path("images/cache") CACHE_DIR = Path("images/cache")
CACHE_DIR.mkdir(parents=True, exist_ok=True) CACHE_DIR.mkdir(parents=True, exist_ok=True)
DEVICE = "cuda"
pipe = StableDiffusionXLPipeline.from_pretrained( pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", "stabilityai/stable-diffusion-xl-base-1.0",
dtype=torch.float32 torch_dtype=torch.float32,
).to("cuda") use_safetensors=True
).to(DEVICE)
# scheduler mais rápido
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
# otimizações de memória
pipe.enable_attention_slicing()
pipe.enable_vae_slicing()
# VAE precisa FP32
pipe.vae.to(torch.float32) pipe.vae.to(torch.float32)
# carregar LoRA
pipe.unet = PeftModel.from_pretrained(pipe.unet, "sdxl_lora") pipe.unet = PeftModel.from_pretrained(pipe.unet, "sdxl_lora")
pipe.unet = pipe.unet.merge_and_unload() pipe.unet = pipe.unet.merge_and_unload()
pipe.unet.eval() pipe.unet.eval()
@@ -44,26 +56,31 @@ def generate_slide_image(prompt: str) -> str:
print("Generating image...") print("Generating image...")
image = pipe( with torch.no_grad():
prompt,
negative_prompt=NEGATIVE, image = pipe(
num_inference_steps=30, prompt,
guidance_scale=7, negative_prompt=NEGATIVE,
height=1024, num_inference_steps=25,
width=1024 guidance_scale=7,
).images[0] height=1024,
width=1024
).images[0]
image.save(image_path) image.save(image_path)
return str(image_path) return str(image_path)
if __name__ == "__main__": if __name__ == "__main__":
prompt = """ prompt = """
clean enterprise illustration, clean enterprise illustration,
corporate presentation slide, corporate presentation slide,
minimal design, minimal design,
white background, white background,
blue gradient accents, blue gradient accents,
expose oracle integration connected to autonomous database and exposing through api gateway oracle integration connected to autonomous database through api gateway
""" """
generate_slide_image(prompt) generate_slide_image(prompt)