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Running
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Zero
File size: 6,417 Bytes
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import gradio as gr
import numpy as np
import random
import torch
import spaces
from PIL import Image
import os
from huggingface_hub import hf_hub_download
import torch
import diffusers import DiffusionPipeline
# Constants
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev",
custom_pipeline="pipeline_flux_rf_inversion",
torch_dtype=torch.bfloat16)
pipe.to(DEVICE)
def reset_do_inversion():
return True
def resize_img(image, max_size=1024):
width, height = image.size
scaling_factor = min(max_size / width, max_size / height)
new_width = int(width * scaling_factor)
new_height = int(height * scaling_factor)
return image.resize((new_width, new_height), Image.LANCZOS)
def invert_and_edit(image,
prompt,
eta,
gamma,
start_timestep,
stop_timestep,
num_inversion_steps,
width,
height,
inverted_latents,
image_latents,
latent_image_ids,
do_inversion,
seed,
randomize_seed,
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
if do_inversion:
inverted_latents_tensor, image_latents_tensor, latent_image_ids_tensor = pipe.invert(image, num_inversion_steps=num_inversion_steps, gamma=gamma)
inverted_latents = gr.State(value=inverted_latents_tensor)
image_latents = gr.State(value=image_latents_tensor)
latent_image_ids = gr.State(value=latent_image_ids_tensor)
do_inversion = False
else:
output = pipe(prompt,
inverted_latents=inverted_latents.value,
image_latents=image_latents.value,
latent_image_ids=latent_image_ids.value,
start_timestep=start_timestep,
stop_timestep=stop_timestep,
num_inference_steps=num_inversion_steps,
eta=eta,
).images[0]
return output, inverted_latents, image_latents, latent_image_ids, do_inversion, seed
# UI CSS
css = """
#col-container {
margin: 0 auto;
max-width: 960px;
}
"""
# Create the Gradio interface
with gr.Blocks(css=css) as demo:
inverted_latents = gr.State()
image_latents = gr.State()
latent_image_ids = gr.State()
do_inversion = gr.State(False)
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# RF inversion with FLUX.1 [dev] 🖌️🏞️
Edit real images with Flux, based on the algorithm proposed in [*Semantic Image Inversion and Editing using
Stochastic Rectified Differential Equations*](https://rf-inversion.github.io/data/rf-inversion.pdf)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[project page](https://rf-inversion.github.io/] [[arxiv](https://arxiv.org/pdf/2410.10792)]
""")
with gr.Row():
with gr.Column():
input_image = gr.Image(
label="Input Image",
type="pil"
)
eta = gr.Slider(
label="eta",
info = "lower eta to ehnace the edits",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.9,
)
prompt = gr.Text(
label="Edit Prompt",
max_lines=1,
placeholder="describe the edited output",
)
run_button = gr.Button("Edit", variant="primary")
with gr.Column():
result = gr.Image(label="Result")
with gr.Accordion("Advanced Settings", open=False):
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():
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():
gamma = gr.Slider(
label="gamma",
info = "lower gamma to ehnace the edits",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.9,
)
start_timestep = gr.Slider(
label="start timestep",
info = "lower gamma to ehnace the edits",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.9,
)
stop_timestep = gr.Slider(
label="stop timestep",
info = "lower gamma to ehnace the edits",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.9,
)
run_button.click(
fn=invert_and_edit,
inputs=[
input_image,
prompt,
eta,
gamma,
start_timestep,
stop_timestep,
num_inversion_steps,
width,
height,
inverted_latents,
image_latents,
latent_image_ids,
do_inversion,
seed,
randomize_seed
],
outputs=[result, inverted_latents, image_latents, latent_image_ids, do_inversion, seed],
)
input_image.change(
fn=reset_do_inversion,
outputs=[do_inversion]
)
if __name__ == "__main__":
demo.launch() |