Update app.py

app.py

@@ -1,318 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import random
-
-import gradio as gr
-import numpy as np
-import PIL.Image
-import spaces
-import torch
-from diffusers import AutoencoderKL, DiffusionPipeline
-
-DESCRIPTION = """
-# TempestV0.1
-
-**Demo by [ameer azam) - [Twitter](https://twitter.com/Ameerazam18) - [GitHub](https://github.com/AMEERAZAM08)) - [Hugging Face](https://huggingface.co/ameerazam08)**
-
-This is a demo of <a href="https://huggingface.co/dataautogpt3/TempestV0.1">TempestV0.1</a> by @dataautogpt3.
-
-**The code for this demo is based on [@hysts's SD-XL demo](https://huggingface.co/spaces/hysts/SD-XL) running on a A10G GPU.**
-"""
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<h1>Running on CPU 🥶 This demo does not work on CPU.</a> instead</h1>"
-
-MAX_SEED = np.iinfo(np.int32).max
-CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
-MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2024"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
-ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0")#
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-if torch.cuda.is_available():
-    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-    pipe = DiffusionPipeline.from_pretrained(
-        "dataautogpt3/TempestV0.1",
-        vae=vae,
-        torch_dtype=torch.float16,
-        # variant="fp16",
-    )
-    if ENABLE_REFINER:
-        refiner = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-refiner-1.0",
-            vae=vae,
-            torch_dtype=torch.float16,
-            # variant="fp16",
-        )
-
-    if ENABLE_CPU_OFFLOAD:
-        pipe.enable_model_cpu_offload()
-        if ENABLE_REFINER:
-            refiner.enable_model_cpu_offload()
-    else:
-        pipe.to(device)
-        if ENABLE_REFINER:
-            refiner.to(device)
-
-    if USE_TORCH_COMPILE:
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-        if ENABLE_REFINER:
-            refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-@spaces.GPU(enable_queue=True)
-def generate(
-    prompt: str,
-    negative_prompt: str = "",
-    prompt_2: str = "",
-    negative_prompt_2: str = "",
-    use_negative_prompt: bool = False,
-    use_prompt_2: bool = False,
-    use_negative_prompt_2: bool = False,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale_base: float = 5.0,
-    guidance_scale_refiner: float = 7.0,
-    num_inference_steps_base: int = 60,
-    num_inference_steps_refiner: int = 35,
-    apply_refiner: bool = False,
-    progress=gr.Progress(track_tqdm=True),
-) -> PIL.Image.Image:
-    print(f"** Generating image for: \"{prompt}\" **")
-    generator = torch.Generator().manual_seed(seed)
-
-    if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-    if not use_prompt_2:
-        prompt_2 = None  # type: ignore
-    if not use_negative_prompt_2:
-        negative_prompt_2 = None  # type: ignore
-
-    if not apply_refiner:
-        return pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-            output_type="pil",
-        ).images[0]
-    else:
-        latents = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale_base,
-            num_inference_steps=num_inference_steps_base,
-            generator=generator,
-            output_type="latent",
-        ).images
-        image = refiner(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            prompt_2=prompt_2,
-            negative_prompt_2=negative_prompt_2,
-            guidance_scale=guidance_scale_refiner,
-            num_inference_steps=num_inference_steps_refiner,
-            image=latents,
-            generator=generator,
-        ).images[0]
-        return image
-
-
-examples = [
-    "A realistic photograph of an astronaut in a jungle, cold color palette, detailed, 8k",
-    "An astronaut riding a green horse",
-]
-
-theme = gr.themes.Base(
-    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif'],
-)
-with gr.Blocks(css="footer{display:none !important}", theme=theme) as demo:
-    gr.Markdown(DESCRIPTION)
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-    with gr.Group():
-        prompt = gr.Text(
-            label="Prompt",
-            show_label=False,
-            max_lines=1,
-            container=False,
-            placeholder="Enter your prompt",
-        )
-        run_button = gr.Button("Generate")
-    result = gr.Image(label="Result", show_label=False)
-    with gr.Accordion("Advanced options", open=False):
-        with gr.Row():
-            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
-            use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
-            use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
-        negative_prompt = gr.Text(
-            label="Negative prompt",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-            visible=False,
-        )
-        prompt_2 = gr.Text(
-            label="Prompt 2",
-            max_lines=1,
-            placeholder="Enter your prompt",
-            visible=False,
-        )
-        negative_prompt_2 = gr.Text(
-            label="Negative prompt 2",
-            max_lines=1,
-            placeholder="Enter a negative prompt",
-            visible=False,
-        )
-
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-        )
-        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=1024,
-            )
-        apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
-        with gr.Row():
-            guidance_scale_base = gr.Slider(
-                label="Guidance scale for base",
-                minimum=1,
-                maximum=20,
-                step=0.1,
-                value=7.5,
-            )
-            num_inference_steps_base = gr.Slider(
-                label="Number of inference steps for base",
-                minimum=10,
-                maximum=100,
-                step=1,
-                value=60,
-            )
-        with gr.Row(visible=False) as refiner_params:
-            guidance_scale_refiner = gr.Slider(
-                label="Guidance scale for refiner",
-                minimum=1,
-                maximum=20,
-                step=0.1,
-                value=7.5,
-            )
-            num_inference_steps_refiner = gr.Slider(
-                label="Number of inference steps for refiner",
-                minimum=10,
-                maximum=100,
-                step=1,
-                value=30,
-            )
-
-    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,
-        queue=False,
-        api_name=False,
-    )
-    use_prompt_2.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_prompt_2,
-        outputs=prompt_2,
-        queue=False,
-        api_name=False,
-    )
-    use_negative_prompt_2.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=use_negative_prompt_2,
-        outputs=negative_prompt_2,
-        queue=False,
-        api_name=False,
-    )
-    apply_refiner.change(
-        fn=lambda x: gr.update(visible=x),
-        inputs=apply_refiner,
-        outputs=refiner_params,
-        queue=False,
-        api_name=False,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            negative_prompt.submit,
-            prompt_2.submit,
-            negative_prompt_2.submit,
-            run_button.click,
-        ],
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=generate,
-        inputs=[
-            prompt,
-            negative_prompt,
-            prompt_2,
-            negative_prompt_2,
-            use_negative_prompt,
-            use_prompt_2,
-            use_negative_prompt_2,
-            seed,
-            width,
-            height,
-            guidance_scale_base,
-            guidance_scale_refiner,
-            num_inference_steps_base,
-            num_inference_steps_refiner,
-            apply_refiner,
-        ],
-        outputs=result,
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20, api_open=False).launch(show_api=False)