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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from huggingface_hub import hf_hub_download
import os
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Initialize the pipeline globally
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
@spaces.GPU(duration=300)
def infer(prompt, lora_model, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
global pipe
# Load LoRA if specified
if lora_model:
try:
pipe.load_lora_weights(lora_model)
except Exception as e:
return None, seed, f"Failed to load LoRA model: {str(e)}"
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
try:
image = pipe(
prompt=prompt,
width=width,
height=height,
num_inference_steps=num_inference_steps,
generator=generator,
guidance_scale=guidance_scale
).images[0]
# Unload LoRA weights after generation
if lora_model:
pipe.unload_lora_weights()
return image, seed, "Image generated successfully."
except Exception as e:
return None, seed, f"Error during image generation: {str(e)}"
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", ""],
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 [dev] with LoRA Support
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
[[non-commercial license](https://huggingface.co./black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co./black-forest-labs/FLUX.1-dev)]
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
lora_model = gr.Text(
label="LoRA Model ID (optional)",
placeholder="Enter Hugging Face LoRA model ID",
)
result = gr.Image(label="Result", show_label=False)
output_message = gr.Textbox(label="Output Message")
with gr.Accordion("Advanced Settings", open=False):
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():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
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,
)
gr.Examples(
examples=examples,
fn=infer,
inputs=[prompt, lora_model],
outputs=[result, seed, output_message],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[prompt, lora_model, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed, output_message]
)
demo.launch() |