Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,889 Bytes
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import gradio as gr
import torch
import spaces
from huggingface_hub import hf_hub_download
from diffusers import FluxControlPipeline, FluxTransformer2DModel
import numpy as np
####################################
# Load the model(s) on GPU #
####################################
path = "sayakpaul/FLUX.1-dev-edit-v0"
edit_transformer = FluxTransformer2DModel.from_pretrained(path, torch_dtype=torch.bfloat16)
pipeline = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", transformer=edit_transformer, torch_dtype=torch.bfloat16
).to("cuda")
pipeline.load_lora_weights(
hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
)
pipeline.set_adapters(["hyper-sd"], adapter_weights=[0.125])
MAX_SEED = np.iinfo(np.int32).max
def get_seed(randomize_seed: bool, seed: int) -> int:
"""
Get the random seed.
"""
return
#####################################
# The function for our Gradio app #
#####################################
@spaces.GPU(duration=120)
def generate(prompt, input_image, seed):
"""
Runs the Flux Control pipeline for editing the given `input_image`
with the specified `prompt`. The pipeline is on GPU by default.
"""
generator = torch.Generator(device="cuda").manual_seed(seed) # Maintain reproducibility
output_image = pipeline(
control_image=input_image,
prompt=prompt,
guidance_scale=30.,
num_inference_steps=8,
max_sequence_length=512,
height=input_image.height,
width=input_image.width,
generator=generator # Pass the seeded generator
).images[0]
return output_image
def launch_app():
css = '''
.gradio-container{max-width: 1100px !important}
'''
with gr.Blocks(css=css) as demo:
gr.Markdown(
"""
# Flux Control Editing 🖌️
Edit any image with the [FLUX.1-dev](https://huggingface.co./black-forest-labs/FLUX.1-dev)
[Flux Control edit framework](https://github.com/sayakpaul/flux-image-editing) by [Sayak Paul](https://huggingface.co./sayakpaul).
"""
)
with gr.Row():
with gr.Column():
with gr.Group():
input_image = gr.Image(
label="Image you would like to edit",
type="pil",
)
prompt = gr.Textbox(
label="Your edit prompt",
placeholder="e.g. 'Turn the color of the mushroom to blue'"
)
seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
generate_button = gr.Button("Generate")
output_image = gr.Image(label="Edited Image")
# Connect button to function
generate_button.click(
fn=generate,
inputs=[prompt, input_image, seed],
outputs=[output_image],
)
gr.Examples(
examples=[
["Turn the color of the mushroom to gray", "mushroom.jpg", 42],
["Make the mushroom polka-dotted", "mushroom.jpg", 100],
],
inputs=[prompt, input_image, seed],
outputs=[output_image],
fn=generate,
cache_examples="lazy"
)
gr.Markdown(
"""
**Acknowledgements**:
- [Sayak Paul](https://huggingface.co./sayakpaul) for open-sourcing FLUX.1-dev-edit-v0
- [black-forest-labs](https://huggingface.co./black-forest-labs) for FLUX.1-dev
- [ByteDance/Hyper-SD](https://huggingface.co./ByteDance/Hyper-SD) for the Turbo LoRA which we use to speed up inference
"""
)
return demo
if __name__ == "__main__":
demo = launch_app()
demo.launch()
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