Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,322 Bytes
64fd258 fd85691 3d490be 64fd258 091c199 3d490be 64fd258 bff13e4 64fd258 615017c 64fd258 4ab9cd8 64fd258 1507f22 64fd258 3d490be 64fd258 a67e235 3d490be 64fd258 1507f22 bff13e4 1507f22 64fd258 bff13e4 1507f22 95e25dd 1507f22 64fd258 1507f22 64fd258 bff13e4 64fd258 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
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
from diffusers import DiffusionPipeline
from huggingface_hub import hf_hub_download
# 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.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), lora_scale=0.125)
pipe.fuse_lora(lora_scale=0.125)
pipe.to(DEVICE)
examples = [[Image.open("cat.jpg"), "a tiger"]]
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)
@spaces.GPU
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
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.value, image_latents.value, latent_image_ids.value, 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 🖌️🏞️
### Edit real images with FLUX.1 [dev]
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"
)
prompt = gr.Text(
label="Edit Prompt",
max_lines=1,
placeholder="describe the edited output",
)
with gr.Row():
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,
)
eta = gr.Slider(
label="eta",
info = "lower eta to ehnace the edits",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.9,
)
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():
gamma = gr.Slider(
label="gamma",
info = "lower gamma to ehnace the edits",
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.9,
)
num_inversion_steps = gr.Slider(
label="num inversion steps",
minimum=1,
maximum=50,
step=1,
value=8,
)
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,
)
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],
)
gr.Examples(
examples=examples,
inputs=[input_image, prompt,],
outputs=[result, inverted_latents, image_latents, latent_image_ids, do_inversion, seed],
fn=infer,
)
input_image.change(
fn=reset_do_inversion,
outputs=[do_inversion]
)
if __name__ == "__main__":
demo.launch() |