Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,793 Bytes
5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b d4cae39 5b8270b |
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 |
import gradio as gr
import numpy as np
import spaces
import torch
import random
from PIL import Image
from pipeline_flux_kontext import FluxKontextPipeline
from diffusers import FluxTransformer2DModel
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download
kontext_path = hf_hub_download(repo_id="diffusers/kontext-v2", filename="dev-opt-2-a-3.safetensors")
MAX_SEED = np.iinfo(np.int32).max
transformer = FluxTransformer2DModel.from_single_file(kontext_path, torch_dtype=torch.bfloat16)
pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16).to("cuda")
@spaces.GPU
def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
input_image = input_image.convert("RGB")
# original_width, original_height = input_image.size
# if original_width >= original_height:
# new_width = 1024
# new_height = int(original_height * (new_width / original_width))
# new_height = round(new_height / 64) * 64
# else:
# new_height = 1024
# new_width = int(original_width * (new_height / original_height))
# new_width = round(new_width / 64) * 64
#input_image_resized = input_image.resize((new_width, new_height), Image.LANCZOS)
image = pipe(
image=input_image,
prompt=prompt,
guidance_scale=guidance_scale,
# width=new_width,
# height=new_height,
generator=torch.Generator().manual_seed(seed),
).images[0]
return image, seed, gr.update(visible=True)
css="""
#col-container {
margin: 0 auto;
max-width: 960px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 Kontext [dev]
""")
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Upload the image for editing", type="pil")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
container=False,
)
run_button = gr.Button("Run", scale=0)
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)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=10,
step=0.1,
value=2.5,
)
with gr.Column():
result = gr.Image(label="Result", show_label=False, interactive=False)
reuse_button = gr.Button("Reuse this image", visible=False)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [input_image, prompt, seed, randomize_seed, guidance_scale],
outputs = [result, seed, reuse_button]
)
reuse_button.click(
fn = lambda image: image,
inputs = [result],
outputs = [input_image]
)
demo.launch() |