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import gradio as gr
import logging
import random
import warnings
import os
import numpy as np
import spaces
import torch
from diffusers import FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline
from gradio_imageslider import ImageSlider
from PIL import Image
from huggingface_hub import snapshot_download
from huggingface_hub import login
import subprocess
css = """
#col-container {
margin: 0 auto;
max-width: 512px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
device = "cuda"
else:
power_device = "CPU"
device = "cpu"
huggingface_token = os.getenv("token1704")
def login_to_huggingface():
if huggingface_token:
login(token=huggingface_token)
model_path = snapshot_download(
repo_id="black-forest-labs/FLUX.1-dev",
repo_type="model",
ignore_patterns=["*.md", "*..gitattributes"],
local_dir="FLUX.1-dev",
token=huggingface_token, # type a new token-id.
)
controlnet = FluxControlNetModel.from_pretrained(
"jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
).to(device)
pipe = FluxControlNetPipeline.from_pretrained(
model_path, controlnet=controlnet, torch_dtype=torch.bfloat16
)
pipe.to(device)
MAX_SEED = 1000000
MAX_PIXEL_BUDGET = 1024 * 1024
def process_input(input_image, upscale_factor, **kwargs):
w, h = input_image.size
w_original, h_original = w, h
aspect_ratio = w / h
was_resized = False
if w * h * upscale_factor**2 > MAX_PIXEL_BUDGET:
warnings.warn(
f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels."
)
gr.Info(
f"Requested output image is too large ({w * upscale_factor}x{h * upscale_factor}). Resizing input to ({int(aspect_ratio * MAX_PIXEL_BUDGET ** 0.5 // upscale_factor), int(MAX_PIXEL_BUDGET ** 0.5 // aspect_ratio // upscale_factor)}) pixels budget."
)
input_image = input_image.resize(
(
int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor),
int(MAX_PIXEL_BUDGET**0.5 // aspect_ratio // upscale_factor),
)
)
was_resized = True
# resize to multiple of 8
w, h = input_image.size
w = w - w % 8
h = h - h % 8
return input_image.resize((w, h)), w_original, h_original, was_resized
@spaces.GPU#(duration=42)
def infer(
seed,
randomize_seed,
input_image,
num_inference_steps,
upscale_factor,
controlnet_conditioning_scale,
progress=gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
true_input_image = input_image
input_image, w_original, h_original, was_resized = process_input(
input_image, upscale_factor
)
# rescale with upscale factor
w, h = input_image.size
control_image = input_image.resize((w * upscale_factor, h * upscale_factor))
generator = torch.Generator().manual_seed(seed)
gr.Info("Upscaling image...")
image = pipe(
prompt="",
control_image=control_image,
controlnet_conditioning_scale=controlnet_conditioning_scale,
num_inference_steps=num_inference_steps,
guidance_scale=3.5,
height=control_image.size[1],
width=control_image.size[0],
generator=generator,
).images[0]
if was_resized:
gr.Info(
f"Resizing output image to targeted {w_original * upscale_factor}x{h_original * upscale_factor} size."
)
# resize to target desired size
image = image.resize((w_original * upscale_factor, h_original * upscale_factor))
image.save("output.jpg")
# convert to numpy
return [true_input_image, image, seed]
with gr.Blocks() as demo:
with gr.Row():
button = gr.LoginButton("Sign in")
gr.load("models/black-forest-labs/FLUX.1-dev", accept_token=button, provider="hf-inference")
# with gr.Row():
# run_button = gr.Button(value="Run")
# with gr.Row():
# with gr.Column(scale=4):
# input_im = gr.Image(label="Input Image", type="pil")
# with gr.Column(scale=1):
# num_inference_steps = gr.Slider(
# label="Number of Inference Steps",
# minimum=8,
# maximum=50,
# step=1,
# value=28,
# )
# upscale_factor = gr.Slider(
# label="Upscale Factor",
# minimum=1,
# maximum=4,
# step=1,
# value=4,
# )
# controlnet_conditioning_scale = gr.Slider(
# label="Controlnet Conditioning Scale",
# minimum=0.1,
# maximum=1.5,
# step=0.1,
# value=0.6,
# )
# seed = gr.Slider(
# label="Seed",
# minimum=0,
# maximum=MAX_SEED,
# step=1,
# value=42,
# )
# randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
# with gr.Row():
# result = ImageSlider(label="Input / Output", type="pil", interactive=True)
# examples = gr.Examples(
# examples=[
# [42, False, "z1.webp", 28, 4, 0.6],
# [42, False, "z2.webp", 28, 4, 0.6],
# ],
# inputs=[
# seed,
# randomize_seed,
# input_im,
# num_inference_steps,
# upscale_factor,
# controlnet_conditioning_scale,
# ],
# fn=infer,
# outputs=result,
# cache_examples="lazy",
# )
# gr.on(
# [run_button.click],
# fn=infer,
# inputs=[
# seed,
# randomize_seed,
# input_im,
# num_inference_steps,
# upscale_factor,
# controlnet_conditioning_scale,
# ],
# outputs=result,
# show_api=False,
# # show_progress="minimal",
# )
login_to_huggingface()
demo.queue().launch(share=True, show_api=True)
@app.get("/")
def read_root():
return {"message": "Hello, World!"}
@app.get("/items/{item_id}")
def read_item(item_id: int, q: str = None):
return {"item_id": item_id, "q": q}
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