Update app.py

app.py

@@ -1,68 +1,21 @@
-#!/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 = "# SDXL"
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
 ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
 
-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(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        vae=vae,
-        torch_dtype=torch.float16,
-        use_safetensors=True,
-        variant="fp16",
-    )
-    if ENABLE_REFINER:
-        refiner = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-refiner-1.0",
-            vae=vae,
-            torch_dtype=torch.float16,
-            use_safetensors=True,
-            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
 def generate(
     prompt: str,
     negative_prompt: str = "",
@@ -79,53 +32,9 @@ def generate(
     num_inference_steps_base: int = 25,
     num_inference_steps_refiner: int = 25,
     apply_refiner: bool = False,
-) -> PIL.Image.Image:
-    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
+)
+    print("hello")
+    #return image
 
 
 examples = [