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import os
import io
import random
import requests
import gradio as gr
import numpy as np
from PIL import Image
import replicate
MAX_SEED = np.iinfo(np.int32).max
def predict(replicate_api, prompt, lora_id, lora_scale=0.95, aspect_ratio="1:1", seed=-1, randomize_seed=True, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
# Validate API key and prompt
if not replicate_api or not prompt:
return "Error: Missing necessary inputs.", -1, None
# Set the seed if randomize_seed is True
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Set the Replicate API token in the environment variable
os.environ["REPLICATE_API_TOKEN"] = replicate_api
# Construct the input for the replicate model
input_params = {
"prompt": prompt,
"output_format": "jpg",
"aspect_ratio": aspect_ratio,
"num_inference_steps": num_inference_steps,
"guidance_scale": guidance_scale,
"seed": seed,
"disable_safety_checker": True
}
# If lora_id is provided, include it in the input
if lora_id and lora_id.strip()!="":
input_params["hf_lora"] = lora_id.strip()
input_params["lora_scale"] = lora_scale
try:
# Run the model using the user's API token from the environment variable
output = replicate.run(
"lucataco/flux-dev-lora:a22c463f11808638ad5e2ebd582e07a469031f48dd567366fb4c6fdab91d614d",
input=input_params
)
print("\nGeneration completed!:"+ prompt + lora_id)
return output[0], seed, seed # Return the generated image and seed
except Exception as e:
# Catch any exceptions, such as invalid API token or lack of credits
return f"Error: {str(e)}", -1, None
finally:
# Always remove the API key from the environment
if "REPLICATE_API_TOKEN" in os.environ:
del os.environ["REPLICATE_API_TOKEN"]
demo = gr.Interface(fn=predict, inputs="text", outputs="image")
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("# FLUX Dev with Replicate API")
replicate_api = gr.Text(label="Replicate API Key", type='password', show_label=True, max_lines=1, placeholder="Enter your Replicate API token", container=True)
prompt = gr.Text(label="Prompt", show_label=True, lines = 2, max_lines=4, show_copy_button = True, placeholder="Enter your prompt", container=True)
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
lora_scale = gr.Slider(
label="LoRA Scale",
minimum=0,
maximum=1,
step=0.01,
value=0.95,
)
aspect_ratio = gr.Radio(label="Aspect ratio", value="1:1", choices=["1:1", "4:5", "2:3", "3:4","9:16", "4:3", "16:9"])
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
submit = gr.Button("Generate Image", variant="primary",scale=1)
output = gr.Image(label="Output Image", show_label=True)
seed_used = gr.Textbox(label="Seed Used", show_copy_button = True)
gr.Examples(
examples=examples,
fn=predict,
inputs=[prompt]
)
gr.on(
triggers=[submit.click, prompt.submit],
fn=predict,
inputs=[replicate_api, prompt, custom_lora, lora_scale, aspect_ratio, seed, randomize_seed, guidance_scale, num_inference_steps],
outputs = [output, seed, seed_used]
)
demo.launch() |