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Running
on
Zero
File size: 3,832 Bytes
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import gradio as gr
import torch
import numpy as np
from diffusers import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from functools import lru_cache
from PIL import Image
from torchvision import transforms
# Cache pipeline loading to improve performance
@lru_cache(maxsize=1)
def load_pipeline():
# Load base model
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(
base_model,
torch_dtype=torch.bfloat16
)
# Load LoRA weights
lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
pipe.load_lora_weights(lora_repo)
# Load safety checker
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
)
feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
# Optimizations
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to("cuda")
return pipe, safety_checker, feature_extractor
pipe, safety_checker, feature_extractor = load_pipeline()
def generate_image(
prompt,
seed=42,
width=1024,
height=1024,
guidance_scale=6,
steps=28,
progress=gr.Progress()
):
try:
progress(0, desc="Initializing...")
generator = torch.Generator(device="cuda").manual_seed(seed)
# Auto-add trigger words
if "super realism" not in prompt.lower():
prompt = f"Super Realism, {prompt}"
# Create callback for progress updates
def update_progress(step, _, __):
progress((step + 1) / steps, desc="Generating image...")
# Generate image
with torch.inference_mode():
image = pipe(
prompt=prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=steps,
generator=generator,
callback=update_progress
).images[0]
# Safety check
progress(1, desc="Safety checking...")
safety_input = feature_extractor(image, return_tensors="pt")
np_image = np.array(image)
safety_result = safety_checker(
images=[np_image],
clip_input=safety_input.pixel_values
)
if safety_result.nsfw[0]:
return Image.new("RGB", (512, 512)), "NSFW content detected"
return image, "Generation successful"
except Exception as e:
return Image.new("RGB", (512, 512)), f"Error: {str(e)}"
# Create Gradio interface with rate limiting
with gr.Blocks() as app:
gr.Markdown("# Flux Super Realism Generator")
with gr.Row():
with gr.Column():
prompt = gr.Textbox(label="Prompt", value="A portrait of a person")
seed = gr.Slider(0, 1000, value=42, label="Seed")
width = gr.Slider(512, 2048, value=1024, label="Width")
height = gr.Slider(512, 2048, value=1024, label="Height")
guidance = gr.Slider(1, 20, value=6, label="Guidance Scale")
steps = gr.Slider(10, 100, value=28, label="Steps")
submit = gr.Button("Generate")
with gr.Column():
output_image = gr.Image(label="Result", type="pil")
status = gr.Textbox(label="Status")
submit.click(
generate_image,
inputs=[prompt, seed, width, height, guidance, steps],
outputs=[output_image, status]
)
# Rate limiting example (1 request every 30 seconds)
app.queue(concurrency_count=1, max_size=3).launch()
# For multiple GPU support (advanced)
# pipe.enable_model_cpu_offload()
# pipe.enable_sequential_cpu_offload() |