Delete app.py

app.py

@@ -1,243 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import random
-import uuid
-import base64
-import gradio as gr
-import numpy as np
-from PIL import Image
-import spaces
-import torch
-
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-
-DESCRIPTION = """# DALL•E 3 XL v2 High Fi"""
-
-def create_download_link(filename):
-    with open(filename, "rb") as file:
-        encoded_string = base64.b64encode(file.read()).decode('utf-8')
-        download_link = f'<a href="data:image/png;base64,{encoded_string}" download="{filename}">Download Image</a>'
-        return download_link
-        
-def save_image(img, prompt):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-
-    # save with promp to save prompt as image file name
-    filename = f"{prompt}.png"
-    img.save(filename)
-    return filename
-    
-    return unique_name
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-MAX_SEED = np.iinfo(np.int32).max
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
-
-MAX_SEED = np.iinfo(np.int32).max
-
-USE_TORCH_COMPILE = 0
-ENABLE_CPU_OFFLOAD = 0
-
-
-if torch.cuda.is_available():
-    pipe = StableDiffusionXLPipeline.from_pretrained(
-        "fluently/Fluently-XL-v4",
-        torch_dtype=torch.float16,
-        use_safetensors=True,
-    )
-    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-    
-    
-    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
-    pipe.set_adapters("dalle")
-
-    pipe.to("cuda")
-    
-    
-
-@spaces.GPU(enable_queue=True)
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    use_negative_prompt: bool = False,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    randomize_seed: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-):
-
-    
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-
-    if not use_negative_prompt:
-        negative_prompt = ""  # type: ignore
-
-    images = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=25,
-        num_images_per_prompt=1,
-        cross_attention_kwargs={"scale": 0.65},
-        output_type="pil",
-    ).images
-    image_paths = [save_image(img, prompt) for img in images]
-
-    #image_paths = [save_image(img) for img in images]
-    download_links = [create_download_link(path) for path in image_paths]
-
-    print(image_paths)
-    #return image_paths, seed
-    return image_paths, seed, download_links
-
-
-#examples = [
-#    "neon holography crystal cat",
-#    "a cat eating a piece of cheese",
-#    "an astronaut riding a horse in space",
-#    "a cartoon of a boy playing with a tiger",
-#    "a cute robot artist painting on an easel, concept art",
-#    "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
-#]
-
-examples = [
-"a modern hospital room with advanced medical equipment and a patient resting comfortably",
-"a team of surgeons performing a delicate operation using state-of-the-art surgical robots",
-"a elderly woman smiling while a nurse checks her vital signs using a holographic display",
-"a child receiving a painless vaccination from a friendly robot nurse in a colorful pediatric clinic",
-"a group of researchers working in a high-tech laboratory, developing new treatments for rare diseases",
-"a telemedicine consultation between a doctor and a patient, using virtual reality technology for a immersive experience"
-]
-
-
-css = '''
-.gradio-container{max-width: 1024px !important}
-h1{text-align:center}
-footer {
-visibility: hidden
-}
-'''
-
-
-#css = '''
-#.gradio-container{max-width: 560px !important}
-#h1{text-align:center}
-#footer {
-#    visibility: hidden
-#}
-#'''
-
-
-with gr.Blocks(css=css, theme="pseudolab/huggingface-korea-theme") as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=False,
-    )
-
-    with gr.Group():
-        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.Gallery(label="Result", columns=1, preview=True, show_label=False)
-    with gr.Accordion("Advanced options", open=False):
-        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-        negative_prompt = gr.Text(
-            label="Negative prompt",
-            lines=4,
-            max_lines=6,
-            value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
-            placeholder="Enter a negative prompt",
-            visible=True,
-        )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-            visible=True
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row(visible=True):
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=2048,
-                step=8,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=2048,
-                step=8,
-                value=1024,
-            )
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                minimum=0.1,
-                maximum=20.0,
-                step=0.1,
-                value=6,
-            )
-
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result, seed],
-        fn=generate,
-        cache_examples=False,
-    )
-
-    use_negative_prompt.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt,
-        outputs=negative_prompt,
-        api_name=False,
-    )
-    
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            negative_prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            use_negative_prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-        ],
-        outputs=[result, seed],
-        api_name="run",
-    )
-    
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch(show_api=False, debug=False)