|
import random |
|
|
|
import gradio as gr |
|
import numpy as np |
|
import spaces |
|
import torch |
|
from diffusers import DiffusionPipeline |
|
from PIL import Image |
|
|
|
device = "cuda" if torch.cuda.is_available() else "cpu" |
|
repo_id = "black-forest-labs/FLUX.1-dev" |
|
adapter_id = "openfree/claude-monet" |
|
|
|
pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16) |
|
pipeline.load_lora_weights(adapter_id) |
|
pipeline = pipeline.to(device) |
|
|
|
|
|
MAX_SEED = np.iinfo(np.int32).max |
|
MAX_IMAGE_SIZE = 1024 |
|
|
|
|
|
@spaces.GPU(duration=120) |
|
def inference( |
|
prompt: str, |
|
seed: int, |
|
randomize_seed: bool, |
|
width: int, |
|
height: int, |
|
guidance_scale: float, |
|
num_inference_steps: int, |
|
lora_scale: float, |
|
progress: gr.Progress = gr.Progress(track_tqdm=True), |
|
): |
|
if randomize_seed: |
|
seed = random.randint(0, MAX_SEED) |
|
generator = torch.Generator(device=device).manual_seed(seed) |
|
|
|
image = pipeline( |
|
prompt=prompt, |
|
guidance_scale=guidance_scale, |
|
num_inference_steps=num_inference_steps, |
|
width=width, |
|
height=height, |
|
generator=generator, |
|
joint_attention_kwargs={"scale": lora_scale}, |
|
).images[0] |
|
|
|
return image, seed |
|
|
|
|
|
examples = [ |
|
( |
|
"Claude Monet's 1916 painting, Water Lilies, which is currently on display at the Metropolitan Museum of Art. The painting depicts a tranquil pond with water lilies floating on the surface, surrounded by lush green foliage and a variety of colorful flowers. The colors of the flowers range from bright pinks and purples to deep blues and greens, creating a peaceful and calming atmosphere. [trigger]" |
|
), |
|
] |
|
|
|
css = """ |
|
footer { |
|
visibility: hidden; |
|
} |
|
""" |
|
|
|
with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css |
|
) as demo: |
|
with gr.Column(elem_id="col-container"): |
|
with gr.Row(): |
|
prompt = gr.Text( |
|
label="Prompt", |
|
show_label=False, |
|
max_lines=1, |
|
placeholder="Enter your prompt", |
|
container=False, |
|
) |
|
|
|
run_button = gr.Button("Run", scale=0) |
|
|
|
result = gr.Image(label="Result", show_label=False) |
|
|
|
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(): |
|
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=768, |
|
) |
|
|
|
with gr.Row(): |
|
guidance_scale = gr.Slider( |
|
label="Guidance scale", |
|
minimum=0.0, |
|
maximum=10.0, |
|
step=0.1, |
|
value=3.5, |
|
) |
|
|
|
num_inference_steps = gr.Slider( |
|
label="Number of inference steps", |
|
minimum=1, |
|
maximum=50, |
|
step=1, |
|
value=30, |
|
) |
|
|
|
lora_scale = gr.Slider( |
|
label="LoRA scale", |
|
minimum=0.0, |
|
maximum=1.0, |
|
step=0.1, |
|
value=1.0, |
|
) |
|
|
|
gr.Examples( |
|
examples=examples, |
|
fn=lambda x: (Image.open("./example.jpg"), 42), |
|
inputs=[prompt], |
|
outputs=[result, seed], |
|
run_on_click=True, |
|
) |
|
|
|
|
|
gr.on( |
|
triggers=[run_button.click, prompt.submit], |
|
fn=inference, |
|
inputs=[ |
|
prompt, |
|
seed, |
|
randomize_seed, |
|
width, |
|
height, |
|
guidance_scale, |
|
num_inference_steps, |
|
lora_scale, |
|
], |
|
outputs=[result, seed], |
|
) |
|
|
|
demo.queue() |
|
demo.launch() |
