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import numpy as np
import spaces
import torch
import random
from diffusers import FluxFillPipeline
from PIL import Image
import os
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Debugging CUDA environment and decorator
print("CUDA_VISIBLE_DEVICES:", os.environ.get("CUDA_VISIBLE_DEVICES", "Not set"))
print("CUDA Available:", torch.cuda.is_available())
print("GPU Count:", torch.cuda.device_count())
print(
"Current Device:",
torch.cuda.current_device() if torch.cuda.is_available() else "None",
)
print("Spaces GPU Decorator Active: True")
def calculate_optimal_dimensions(image: Image.Image):
original_width, original_height = image.size
MIN_ASPECT_RATIO = 9 / 16
MAX_ASPECT_RATIO = 16 / 9
FIXED_DIMENSION = 1024
original_aspect_ratio = original_width / original_height
if original_aspect_ratio > 1: # Wider than tall
width = FIXED_DIMENSION
height = round(FIXED_DIMENSION / original_aspect_ratio)
else: # Taller than wide
height = FIXED_DIMENSION
width = round(FIXED_DIMENSION * original_aspect_ratio)
width = (width // 8) * 8
height = (height // 8) * 8
calculated_aspect_ratio = width / height
if calculated_aspect_ratio > MAX_ASPECT_RATIO:
width = (height * MAX_ASPECT_RATIO // 8) * 8
elif calculated_aspect_ratio < MIN_ASPECT_RATIO:
height = (width / MIN_ASPECT_RATIO // 8) * 8
width = max(width, 576) if width == FIXED_DIMENSION else width
height = max(height, 576) if height == FIXED_DIMENSION else height
return width, height
def create_full_mask(image):
"""Generate a fully white mask for the entire image."""
return Image.fromarray(
np.ones((image.height, image.width), dtype=np.uint8) * 255
).convert("L")
@spaces.GPU # Removed duration parameter
def infer(
image,
prompt,
seed=42,
randomize_seed=False,
width=1024,
height=1024,
guidance_scale=50,
num_inference_steps=28,
):
if not image:
raise gr.Error("Please upload an image.")
mask = create_full_mask(image) # Auto-generate full white mask
width, height = calculate_optimal_dimensions(image)
if randomize_seed:
seed = random.randint(0, MAX_SEED)
pipe = FluxFillPipeline.from_pretrained(
"black-forest-labs/FLUX.1-FILL-dev", torch_dtype=torch.bfloat16
).to("cuda")
generator = torch.Generator("cuda").manual_seed(seed)
try:
image = pipe(
prompt=prompt,
image=image,
mask_image=mask,
height=height,
width=width,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
max_sequence_length=512,
generator=generator,
).images[0]
return image, seed
except Exception as e:
raise gr.Error(f"Error during inpainting: {str(e)}")
examples = [
"Add a glowing crescent moon on the forehead, vivid red eyes, and a shadowy, dark, misty background to the subject, while preserving the exact structure and details of the original human face, animal, or object",
"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: 1000px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
f"""# FLUX.1 Fill [dev]
12B param rectified flow transformer structural conditioning tuned, guidance-distilled from [FLUX.1 [pro]](https://huggingface.co/black-forest-labs/FLUX.1-pro)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev/blob/main/LICENSE.txt)] [[blog](https://bfl.ai/blog/2024/08/01/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev)]
"""
)
with gr.Row():
with gr.Column():
image_input = gr.Image(
label="Upload image for editing",
type="pil",
sources=["upload"],
image_mode="RGB",
height=600,
)
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
gr.Examples(
examples=examples,
inputs=[prompt],
label="Example Prompts",
)
run_button = gr.Button("Run")
result = gr.Image(label="Result", show_label=False)
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,
visible=False,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
visible=False,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=50,
step=0.5,
value=50,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[
image_input,
prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
],
outputs=[result, seed],
)
demo.launch()
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