Create app.py

app.py

@@ -0,0 +1,363 @@
+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 = "Juggernaut XL"
+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/RunDiffusion/Juggernaut-XL-v9/blob/main/Juggernaut-XL_v9_RunDiffusionPhoto_v2.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,
+        variant="fp16",
+    )
+
+    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 = 30,
+    sampler: str = "DPM++ 2M SDE Karras",
+    aspect_ratio_selector: str = "1024 x 1024",
+    use_upscaler: bool = False,
+    upscaler_strength: float = 0.55,
+    upscale_by: float = 1.5,
+    progress=gr.Progress(track_tqdm=True),
+) -> Image:
+    generator = utils.seed_everything(seed)
+
+    width, height = utils.aspect_ratio_handler(
+        aspect_ratio_selector,
+        custom_width,
+        custom_height,
+    )
+
+    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,
+    }
+
+    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
+    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 and IS_COLAB:
+            for image in images:
+                filepath = utils.save_image(image, metadata, OUTPUT_DIR)
+                logger.info(f"Image saved as {filepath} with metadata")
+
+        return images, 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
+
+with gr.Blocks(css="style.css") as demo:
+    title = gr.HTML(
+        f"""<h1><span>{DESCRIPTION}</span></h1>""",
+        elem_id="title",
+    )
+    gr.Markdown(
+        f"""Gradio demo for [Juggernaut XL](https://huggingface.co/RunDiffusion/Juggernaut-XL-v9)""",
+        elem_id="subtitle",
+    )
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=5,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button(
+                "Generate", 
+                variant="primary", 
+                scale=0
+            )
+        result = gr.Gallery(
+            label="Result", 
+            columns=1, 
+            preview=True, 
+            show_label=False
+        )
+    with gr.Accordion(label="Advanced Settings", open=False):
+        negative_prompt = gr.Text(
+            label="Negative Prompt",
+            max_lines=5,
+            placeholder="Enter a negative prompt",
+            value="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
+        )
+        aspect_ratio_selector = gr.Radio(
+            label="Aspect Ratio",
+            choices=config.aspect_ratios,
+            value="1024 x 1024",
+            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,
+                )
+        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,
+            )
+
+        sampler = gr.Dropdown(
+            label="Sampler",
+            choices=config.sampler_list,
+            interactive=True,
+            value="DPM++ 2M SDE Karras",
+        )
+        with gr.Row():
+            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.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,
+    )
+
+    inputs = [
+        prompt,
+        negative_prompt,
+        seed,
+        custom_width,
+        custom_height,
+        guidance_scale,
+        num_inference_steps,
+        sampler,
+        aspect_ratio_selector,
+        use_upscaler,
+        upscaler_strength,
+        upscale_by,
+    ]
+
+    prompt.submit(
+        fn=utils.randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name="run",
+    )
+    negative_prompt.submit(
+        fn=utils.randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+    run_button.click(
+        fn=utils.randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=[result, gr_metadata],
+        api_name=False,
+    )
+demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)