Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,777 Bytes
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import torch
import spaces
import gradio as gr
from diffusers import FluxFillPipeline
pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda")
# reference https://huggingface.co/spaces/black-forest-labs/FLUX.1-Fill-dev/blob/main/app.py
def calculate_optimal_dimensions(image):
# Extract the original dimensions
original_width, original_height = image.size
# Set constants
MIN_ASPECT_RATIO = 9 / 16
MAX_ASPECT_RATIO = 16 / 9
FIXED_DIMENSION = 1024
# Calculate the aspect ratio of the original image
original_aspect_ratio = original_width / original_height
# Determine which dimension to fix
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)
# Ensure dimensions are multiples of 8
width = (width // 8) * 8
height = (height // 8) * 8
# Enforce aspect ratio limits
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
# Ensure width and height remain above the minimum dimensions
width = max(width, 576) if width == FIXED_DIMENSION else width
height = max(height, 576) if height == FIXED_DIMENSION else height
return width, height
@spaces.GPU(duration=120)
def inpaint(
image,
mask,
prompt="",
num_inference_steps=28,
guidance_scale=50,
):
image = image.convert("RGB")
mask = mask.convert("L")
width, height = calculate_optimal_dimensions(image)
result = pipe(
prompt=prompt,
height= height,
width= width,
image= image,
mask_image=mask,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
).images[0]
result = result.convert("RGBA")
return result
demo = gr.Interface(
fn=inpaint,
inputs=[
gr.Image(label="image", type="pil"),
gr.Image(label="mask", type="pil"),
gr.Text(label="prompt"),
gr.Number(value=40, label="num_inference_steps"),
gr.Number(value=28, label="guidance_scale"),
],
outputs=["image"],
api_name="inpaint",
examples=[["./assets/rocket.png", "./assets/Inpainting mask.png"]],
cache_examples=False,
description="it is recommended that you use https://github.com/la-voliere/react-mask-editor when creating an image mask in JS and then inverse it before sending it to this space",
)
demo.launch() |