Delete app.py

app.py

@@ -1,395 +0,0 @@
-import os
-import gc
-import gradio as gr
-import numpy as np
-import torch
-import json
-import spaces
-import config
-import utils
-import logging
-from PIL import Image, PngImagePlugin
-from datetime import datetime
-from diffusers.models import AutoencoderKL
-from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-DESCRIPTION = "Animagine XL 3.1"
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
-IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
-HF_TOKEN = os.getenv("HF_TOKEN")
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
-MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
-OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")
-
-MODEL = os.getenv(
-    "MODEL",
-    "https://huggingface.co/cagliostrolab/animagine-xl-3.1/blob/main/animagine-xl-3.1.safetensors",
-)
-
-torch.backends.cudnn.deterministic = True
-torch.backends.cudnn.benchmark = False
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-def load_pipeline(model_name):
-    vae = AutoencoderKL.from_pretrained(
-        "madebyollin/sdxl-vae-fp16-fix",
-        torch_dtype=torch.float16,
-    )
-    pipeline = (
-        StableDiffusionXLPipeline.from_single_file
-        if MODEL.endswith(".safetensors")
-        else StableDiffusionXLPipeline.from_pretrained
-    )
-
-    pipe = pipeline(
-        model_name,
-        vae=vae,
-        torch_dtype=torch.float16,
-        custom_pipeline="lpw_stable_diffusion_xl",
-        use_safetensors=True,
-        add_watermarker=False,
-        use_auth_token=HF_TOKEN,
-    )
-
-    pipe.to(device)
-    return pipe
-
-
-@spaces.GPU
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    seed: int = 0,
-    custom_width: int = 1024,
-    custom_height: int = 1024,
-    guidance_scale: float = 7.0,
-    num_inference_steps: int = 28,
-    sampler: str = "Euler a",
-    aspect_ratio_selector: str = "896 x 1152",
-    style_selector: str = "(None)",
-    quality_selector: str = "Standard v3.1",
-    use_upscaler: bool = False,
-    upscaler_strength: float = 0.55,
-    upscale_by: float = 1.5,
-    add_quality_tags: bool = True,
-    progress=gr.Progress(track_tqdm=True),
-):
-    generator = utils.seed_everything(seed)
-
-    width, height = utils.aspect_ratio_handler(
-        aspect_ratio_selector,
-        custom_width,
-        custom_height,
-    )
-
-    prompt = utils.add_wildcard(prompt, wildcard_files)
-
-    prompt, negative_prompt = utils.preprocess_prompt(
-        quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags
-    )
-    prompt, negative_prompt = utils.preprocess_prompt(
-        styles, style_selector, prompt, negative_prompt
-    )
-
-    width, height = utils.preprocess_image_dimensions(width, height)
-
-    backup_scheduler = pipe.scheduler
-    pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
-
-    if use_upscaler:
-        upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
-    metadata = {
-        "prompt": prompt,
-        "negative_prompt": negative_prompt,
-        "resolution": f"{width} x {height}",
-        "guidance_scale": guidance_scale,
-        "num_inference_steps": num_inference_steps,
-        "seed": seed,
-        "sampler": sampler,
-        "sdxl_style": style_selector,
-        "add_quality_tags": add_quality_tags,
-        "quality_tags": quality_selector,
-    }
-
-    if use_upscaler:
-        new_width = int(width * upscale_by)
-        new_height = int(height * upscale_by)
-        metadata["use_upscaler"] = {
-            "upscale_method": "nearest-exact",
-            "upscaler_strength": upscaler_strength,
-            "upscale_by": upscale_by,
-            "new_resolution": f"{new_width} x {new_height}",
-        }
-    else:
-        metadata["use_upscaler"] = None
-        metadata["Model"] = {
-            "Model": DESCRIPTION,
-            "Model hash": "e3c47aedb0",
-        }
-    
-    logger.info(json.dumps(metadata, indent=4))
-
-    try:
-        if use_upscaler:
-            latents = pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                width=width,
-                height=height,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                output_type="latent",
-            ).images
-            upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
-            images = upscaler_pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                image=upscaled_latents,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                strength=upscaler_strength,
-                generator=generator,
-                output_type="pil",
-            ).images
-        else:
-            images = pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                width=width,
-                height=height,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                output_type="pil",
-            ).images
-
-        if images:
-            image_paths = [
-                utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
-                for image in images
-            ]
-
-            for image_path in image_paths:
-                logger.info(f"Image saved as {image_path} with metadata")
-
-        return image_paths, metadata
-    except Exception as e:
-        logger.exception(f"An error occurred: {e}")
-        raise
-    finally:
-        if use_upscaler:
-            del upscaler_pipe
-        pipe.scheduler = backup_scheduler
-        utils.free_memory()
-
-
-if torch.cuda.is_available():
-    pipe = load_pipeline(MODEL)
-    logger.info("Loaded on Device!")
-else:
-    pipe = None
-
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list}
-quality_prompt = {
-    k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list
-}
-
-wildcard_files = utils.load_wildcard_files("wildcard")
-
-with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo:
-    title = gr.HTML(
-        f"""<h1><span>{DESCRIPTION}</span></h1>""",
-        elem_id="title",
-    )
-    gr.Markdown(
-        f"""Gradio demo for [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""",
-        elem_id="subtitle",
-    )
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Row():
-        with gr.Column(scale=2):
-            with gr.Tab("Txt2img"):
-                with gr.Group():
-                    prompt = gr.Text(
-                        label="Prompt",
-                        max_lines=5,
-                        placeholder="Enter your prompt",
-                    )
-                    negative_prompt = gr.Text(
-                        label="Negative Prompt",
-                        max_lines=5,
-                        placeholder="Enter a negative prompt",
-                    )
-                    with gr.Accordion(label="Quality Tags", open=True):
-                        add_quality_tags = gr.Checkbox(
-                            label="Add Quality Tags", value=True
-                        )
-                        quality_selector = gr.Dropdown(
-                            label="Quality Tags Presets",
-                            interactive=True,
-                            choices=list(quality_prompt.keys()),
-                            value="Standard v3.1",
-                        )
-            with gr.Tab("Advanced Settings"):
-                with gr.Group():
-                    style_selector = gr.Radio(
-                        label="Style Preset",
-                        container=True,
-                        interactive=True,
-                        choices=list(styles.keys()),
-                        value="(None)",
-                    )
-                with gr.Group():
-                    aspect_ratio_selector = gr.Radio(
-                        label="Aspect Ratio",
-                        choices=config.aspect_ratios,
-                        value="896 x 1152",
-                        container=True,
-                    )
-                with gr.Group(visible=False) as custom_resolution:
-                    with gr.Row():
-                        custom_width = gr.Slider(
-                            label="Width",
-                            minimum=MIN_IMAGE_SIZE,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=8,
-                            value=1024,
-                        )
-                        custom_height = gr.Slider(
-                            label="Height",
-                            minimum=MIN_IMAGE_SIZE,
-                            maximum=MAX_IMAGE_SIZE,
-                            step=8,
-                            value=1024,
-                        )
-                with gr.Group():
-                    use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
-                    with gr.Row() as upscaler_row:
-                        upscaler_strength = gr.Slider(
-                            label="Strength",
-                            minimum=0,
-                            maximum=1,
-                            step=0.05,
-                            value=0.55,
-                            visible=False,
-                        )
-                        upscale_by = gr.Slider(
-                            label="Upscale by",
-                            minimum=1,
-                            maximum=1.5,
-                            step=0.1,
-                            value=1.5,
-                            visible=False,
-                        )
-                with gr.Group():
-                    sampler = gr.Dropdown(
-                        label="Sampler",
-                        choices=config.sampler_list,
-                        interactive=True,
-                        value="Euler a",
-                    )
-                with gr.Group():
-                    seed = gr.Slider(
-                        label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0
-                    )
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                with gr.Group():
-                    with gr.Row():
-                        guidance_scale = gr.Slider(
-                            label="Guidance scale",
-                            minimum=1,
-                            maximum=12,
-                            step=0.1,
-                            value=7.0,
-                        )
-                        num_inference_steps = gr.Slider(
-                            label="Number of inference steps",
-                            minimum=1,
-                            maximum=50,
-                            step=1,
-                            value=28,
-                        )
-        with gr.Column(scale=3):
-            with gr.Blocks():
-                run_button = gr.Button("Generate", variant="primary")
-            result = gr.Gallery(
-                label="Result", 
-                columns=1, 
-                height='100%', 
-                preview=True, 
-                show_label=False
-            )
-            with gr.Accordion(label="Generation Parameters", open=False):
-                gr_metadata = gr.JSON(label="metadata", show_label=False)
-            gr.Examples(
-                examples=config.examples,
-                inputs=prompt,
-                outputs=[result, gr_metadata],
-                fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
-                cache_examples=CACHE_EXAMPLES,
-            )
-    use_upscaler.change(
-        fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
-        inputs=use_upscaler,
-        outputs=[upscaler_strength, upscale_by],
-        queue=False,
-        api_name=False,
-    )
-    aspect_ratio_selector.change(
-        fn=lambda x: gr.update(visible=x == "Custom"),
-        inputs=aspect_ratio_selector,
-        outputs=custom_resolution,
-        queue=False,
-        api_name=False,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            negative_prompt.submit,
-            run_button.click,
-        ],
-        fn=utils.randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            custom_width,
-            custom_height,
-            guidance_scale,
-            num_inference_steps,
-            sampler,
-            aspect_ratio_selector,
-            style_selector,
-            quality_selector,
-            use_upscaler,
-            upscaler_strength,
-            upscale_by,
-            add_quality_tags,
-        ],
-        outputs=[result, gr_metadata],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)