backup.py

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

def load_predefined_images():
    predefined_images = [
        "assets/cm1.webp",
        "assets/cm2.webp",
        "assets/cm3.webp",
        "assets/cm4.webp",
        "assets/cm5.webp",
        "assets/cm6.webp",
    ]
    return predefined_images

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]",
    "Claude Monet's 1869 masterpiece, The Magpie, showcasing a snow-covered rural landscape at dawn. A single black magpie perches on a wooden gate, contrasting against the pristine white snow. The scene captures the subtle interplay of light and shadow on the snow's surface, with delicate blue-gray tones in the shadows and warm golden hints where sunlight touches the snow-laden branches. [trigger]",
    "Claude Monet's Impression, Sunrise (1872), depicting the port of Le Havre at dawn. The orange sun hangs low in a misty gray-blue sky, its reflection dancing across the rippling harbor waters. Small boats appear as dark silhouettes against the luminous morning light, while industrial chimneys in the background release wisps of smoke into the atmospheric scene. [trigger]",
    "Claude Monet's Rouen Cathedral series (1892-1894), focusing on the western facade at sunset. The gothic architecture is bathed in warm golden light, with deep purple shadows in the intricate stone carvings. The cathedral's spires reach toward a sky painted in soft pinks and lavenders, showcasing Monet's masterful handling of light and atmospheric effects. [trigger]",
    "Claude Monet's Japanese Bridge at Giverny (1899), featuring the iconic green curved bridge spanning his water garden. Clusters of purple and white wisteria cascade from above, their reflections merging with the lily pads below in the tranquil pond. Weeping willows frame the scene in characteristic Monet brushstrokes, creating a dreamy, impressionist atmosphere. [trigger]",
    "Claude Monet's Haystacks at Sunset (1890), showing golden wheat stacks in a field at dusk. The massive forms of the haystacks stand silhouetted against a dramatic sky painted in bold strokes of orange, pink, and deep purple. The surrounding field catches the last rays of sunlight, creating a patchwork of warm earth tones and long blue-violet shadows. [trigger]"
]

css = """
footer {
    visibility: hidden;
}
"""

with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css
) as demo:
    gr.HTML('<div class="title"> Claude Monet STUDIO </div>')
    gr.HTML('<div class="title">😄Image to Video Explore: <a href="https://huggingface.co/spaces/ginigen/theater" target="_blank">https://huggingface.co/spaces/ginigen/theater</a></div>')

    
    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,
            inputs=[prompt],
            outputs=[result, seed],
        )

    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],
    )
    
    # Add gallery section at the bottom
    gr.Markdown("### Claude Monet Style Examples")
    predefined_gallery = gr.Gallery(
        label="Sample Images", 
        columns=3, 
        rows=2,
        show_label=False, 
        value=load_predefined_images()
    )

demo.queue()
demo.launch()