Update app.py

app.py

@@ -1,227 +0,0 @@
-import os
-import random
-import uuid
-
-import gradio as gr
-import numpy as np
-from PIL import Image
-import spaces
-import torch
-from diffusers import StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL
-from huggingface_hub import snapshot_download
-
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-
-model_path = snapshot_download(
-    repo_id="stabilityai/stable-diffusion-3-medium", 
-    revision="refs/pr/26",
-    repo_type="model", 
-    ignore_patterns=["*.md", "*..gitattributes"],
-    local_dir="stable-diffusion-3-medium",
-    token=huggingface_token, # yeni bir token-id yazın.
-    )
-
-DESCRIPTION = """# Stable Diffusion 3"""
-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
-CACHE_EXAMPLES = False
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
-USE_TORCH_COMPILE = False
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-pipe = StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
-    
-
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-@spaces.GPU(duration=30,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 = 7,
-    randomize_seed: bool = False,
-    num_inference_steps=30,
-    NUM_IMAGES_PER_PROMPT=1,
-    use_resolution_binning: bool = True,
-    progress=gr.Progress(track_tqdm=True),
-):
-    pipe.to(device)
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)
-    
-    #pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-    
-    if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-    
-    output = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
-        output_type="pil",
-    ).images
-
-    return output
-
-
-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"
-]
-
-css = '''
-.gradio-container{max-width: 1000px !important}
-h1{text-align:center}
-'''
-with gr.Blocks(css=css) as demo:
-    with gr.Row():
-        with gr.Column():
-            gr.HTML(
-            """
-            <h1 style='text-align: center'>
-            Stable Diffusion 3 
-            </h1>
-            """
-        )
-            gr.HTML(
-                """
-                <h3 style='text-align: center'>
-                Follow me for more! 
-                <a href='https://twitter.com/sot_data' target='_blank'>Twitter</a> | <a href='https://github.com/sourceoftruthdata' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/danielwcovarrubias/' target='_blank'>Linkedin</a>
-                </h3>
-                """
-        )
-    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", elem_id="gallery", show_label=False)
-    with gr.Accordion("Advanced options", open=False):
-        with gr.Row():
-            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
-                visible=True,
-            )
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-        )
-
-        steps = gr.Slider(
-            label="Steps",
-            minimum=0,
-            maximum=60,
-            step=1,
-            value=25,
-        )
-        number_image = gr.Slider(
-            label="Number of Image",
-            minimum=1,
-            maximum=4,
-            step=1,
-            value=1,
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Row(visible=True):
-            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=1024,
-            )
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                minimum=0.1,
-                maximum=10,
-                step=0.1,
-                value=7.0,
-            )
-
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result],
-        fn=generate,
-        cache_examples=CACHE_EXAMPLES,
-    )
-
-    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,
-            steps,
-            number_image,
-        ],
-        outputs=[result],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue().launch()