Delete woman2.webp

Delete woman1.webp

Delete person1.webp

Delete diffusers.zip

Delete cat_window.webp

Delete bird.webp

Upload 8 files

Distilled (4 steps)

zf.write(image_path, arcname=os.path.basename(image_path))

Base (50 steps)

from pathlib import Path

def save_on_path(img: Image, filename: str, format_: str = None) -> Path:

def export_images_to_zip(gallery) -> str:

change()

A cat

return infer(prompt, None, mode_choice, seed, randomize_seed, width, height, num_inference_steps, guidance_scale, prompt_upsampling)

return infer(prompt, [input_image], mode_choice, seed, randomize_seed, width, height, num_inference_steps, guidance_scale, prompt_upsampling)

input_image = gr.Image(label="Upload the image for editing", type="pil")

def infer_example()

Prompt

Adapt

examples=examples_images,

git+https://github.com/huggingface/diffusers.git

Upload 10 files

Upload 8 files

js="window.scrollTo(0, 0)"

js="document.getElementById('output_id').scrollIntoView()"

js="document.getElementById('output_id')?.scrollIntoView({behavior: 'smooth', block: 'start', inline: 'nearest'})"

Upload 9 files

zf.write(image_path, arcname=os.path.basename(image_path))

zf.write(image_path, arcname=os.path.basename(filepath))

image_filename = re.sub(r"^.*[\/\\]([^\/\\]+)$", r"\1", image_path)

import zipfile

print(str(len(gallery)) + " images zipped")

result_gallery = gr.Gallery(label = 'Downloadable results', show_label = True, interactive = False, elem_id = "gallery1")

zip_path = export_compiled_transformers_to_zip(gallery)

Zip

Upload 8 files

use_precompiled = False

payload["constants_map"] = constants

Black video

Black video

use_precompiled = False

use_precompiled = True

def drain_module_parameters(module: torch.nn.Module):

Format

Format

use_precompiled = True

class ZeroGPUCompiledModel:

def to_serializable_dict(self) -> dict[str, Any]: