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import os
import random
import torch
import gradio as gr
from diffusers import StableDiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from transformers import CLIPFeatureExtractor
from huggingface_hub import hf_hub_download
# Configuration
BASE_MODEL = "stabilityai/stable-diffusion-2-1-base"
LORA_REPO = "Norod78/Flux-LoRA"
LORA_FILENAME = "flux_lora.safetensors"
MODEL_CACHE = "model_cache"
os.makedirs(MODEL_CACHE, exist_ok=True)
def get_pipeline():
# Load safety checker + feature extractor
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
)
feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
# Load base pipeline
pipe = StableDiffusionPipeline.from_pretrained(
BASE_MODEL,
torch_dtype=torch.float32,
cache_dir=MODEL_CACHE,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
use_safetensors=True
)
# Load and apply LoRA weights
lora_path = hf_hub_download(
repo_id=LORA_REPO,
filename=LORA_FILENAME,
cache_dir=MODEL_CACHE
)
pipe.load_lora_weights(lora_path)
pipe.to("cpu")
pipe.enable_attention_slicing()
return pipe
# Load model once
pipeline = get_pipeline()
def generate_image(prompt, negative_prompt="", width=768, height=768, seed=-1, guidance_scale=7.5, num_inference_steps=25):
if seed == -1:
seed = random.randint(0, 2**31 - 1)
generator = torch.Generator(device="cpu").manual_seed(seed)
with torch.no_grad():
output = pipeline(
prompt=f"flux style, {prompt}", # Stylized prompt
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator
)
image = output.images[0]
return image, seed
# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🌀 Flux-LoRA Anime Image Generator (CPU only)")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", lines=3)
negative_prompt = gr.Textbox(label="Negative Prompt", value="blurry, lowres, bad anatomy")
generate_btn = gr.Button("Generate", variant="primary")
with gr.Accordion("Advanced", open=False):
width = gr.Slider(512, 1024, value=768, step=64, label="Width")
height = gr.Slider(512, 1024, value=768, step=64, label="Height")
guidance = gr.Slider(1.0, 15.0, value=7.5, step=0.5, label="Guidance")
steps = gr.Slider(15, 50, value=25, step=1, label="Steps")
seed = gr.Number(label="Seed", value=-1)
with gr.Column():
output_image = gr.Image(label="Result", type="pil")
used_seed = gr.Textbox(label="Used Seed")
generate_btn.click(
generate_image,
inputs=[prompt, negative_prompt, width, height, seed, guidance, steps],
outputs=[output_image, used_seed]
)
if __name__ == "__main__":
demo.launch()
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