Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,200 Bytes
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import gradio as gr
import spaces
from PIL import Image
import numpy as np
import torch
import torch.nn.functional as F
from utils.pipeline_utils import load_pipeline
from utils import get_args
from main import run
pipeline = load_pipeline(fp16=False, cache_dir=None)
def process_masks(masks):
# masks: list of file paths
processed_masks = []
mask_composit = torch.zeros((512, 512), dtype=torch.float32, device='cuda')
for mask_path in masks:
mask = Image.open(mask_path).convert("L").resize((512, 512))
mask = torch.tensor(np.array(mask), dtype=torch.float32, device='cuda')
mask[mask > 0] = 255.0
mask = mask / 255.0
processed_masks.append(mask)
mask_composit += mask
mask_composit = torch.clamp(mask_composit, 0, 1)
mask_composit = F.interpolate(mask_composit[None, None, :, :], size=(64, 64), mode="nearest")[0, 0]
if mask_composit.sum() == 0:
mask_composit = None
return mask_composit
@spaces.GPU
def main_pipeline(
input_image: str,
src_prompt: str,
tgt_prompt: str,
alpha: float,
beta: float,
w1: float,
seed: int,
dift_correction: bool = True,
):
args = get_args()
args.alpha = alpha
args.beta = beta
args.w1 = w1
args.seed = seed
args.structural_alignment = True
args.support_new_object = True
args.apply_dift_correction = dift_correction
torch.cuda.empty_cache()
res_image = run(input_image['background'], src_prompt, tgt_prompt, masks=process_masks(input_image['layers']),
pipeline=pipeline, args=args)[2]
return res_image
DESCRIPTION = """# Cora 🖼️🐱🦅
## Fast & Controllable Image Editing
### 🛠️ Quick start
1. **Upload** or drag-and-drop the image you’d like to edit.
2. **Source prompt** – describe what’s in the original image.
3. **Target prompt** – describe the result you want.
4. Adjust the parameters as needed.
5. *(Optional)* Paint a mask to specify the area to edit.
6. Click **Edit** and wait a few seconds for the output.
### ⚙️ Parameter cheat-sheet
| Parameter | What it does | `0` (minimum) | `1` (maximum) |
|-----------|--------------|---------------|---------------|
| **alpha** | Appearance transfer control | preserve source appearance | target prompt affects appearance |
| **beta** | Structural change control | preserve original structure | full layout change |
| **w** | Prompt strength | subtle tweaks | strong changes |
| **Seed** | Fixes randomness for reproducibility | – | – |
| **Apply correspondence correction** | Uses correspondence-aware latent fix | – | – |
### 📜 Tips
- To replicate **TurboEdit**, set **alpha = 1**, **beta = 1**, and turn **off** *Apply correspondence correction*.
- To test reconstruction quality of the inversion, use identical source & target prompts with **alpha = 1**, **beta = 1**, and **w = 1**.
#### 🙏 Acknowledgements
The demo template is largely adapted from **[TurboEdit on Hugging Face Spaces](https://huggingface.co/spaces/turboedit/turbo_edit)**.
"""
with gr.Blocks(css="app/style.css") as demo:
gr.HTML(
"""<a href="https://huggingface.co/spaces/armikaeili/cora?duplicate=true">
<img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to run privately without waiting in queue"""
)
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
input_image = gr.ImageMask(
label="Input image", type="filepath", height=512, width=512, brush=gr.Brush(color_mode='defaults')
)
result = gr.Image(label="Result", type="pil", height=512, width=512)
with gr.Column():
src_prompt = gr.Text(
label="Source Prompt",
max_lines=1,
placeholder="Source Prompt",
)
tgt_prompt = gr.Text(
label="Target Prompt",
max_lines=1,
placeholder="Target Prompt",
)
with gr.Accordion("Advanced Options", open=False):
seed = gr.Slider(
label="seed", minimum=0, maximum=16 * 1024, value=200, step=1
)
w1 = gr.Slider(
label="w", minimum=1.0, maximum=3.0, value=1.9, step=0.05
)
alpha = gr.Slider(
label="alpha", minimum=0, maximum=1, value=0, step=0.01
)
beta = gr.Slider(
label="beta", minimum=0, maximum=1, value=0.04, step=0.01
)
with gr.Row():
dift_correction = gr.Checkbox(
label="Apply correspondence correction",
value=True)
run_button = gr.Button("Edit")
examples = [
[
"assets/white_cat.png", # input_image
"a cat", # src_prompt
"a cat wearing a suit", # tgt_prompt
0.1, # alpha
0.1, # beta
1.9, # w1
7, # seed
True # dift_correction
],
[
"assets/bear.png", # input_image
"a sitting brown bear", # src_prompt
"a roaring blue bear", # tgt_prompt
0.7, # alpha
0.1, # beta
1.9, # w1
7, # seed
True # dift_correction
],
[
"assets/cat.jpg", # input_image
"a cat", # src_prompt
"an eagle", # tgt_prompt
0.7, # alpha
0.3, # beta
1.9, # w1
7, # seed
True # dift_correction
],
[
"assets/dog.png", # input_image
"a photo of a dog", # src_prompt
"a photo of a dog lying", # tgt_prompt
0.0, # alpha
1, # beta
1.9, # w1
7, # seed
True # dift_correction
],
]
inputs = [
input_image,
src_prompt,
tgt_prompt,
alpha,
beta,
w1,
seed,
dift_correction
]
outputs = [result]
#
gr.Examples(
examples=examples,
inputs=inputs,
outputs=outputs,
fn=main_pipeline,
cache_examples=False,
)
run_button.click(fn=main_pipeline, inputs=inputs, outputs=outputs)
demo.queue(max_size=50).launch(share=False) |