~~

app.py

@@ -1,7 +1,66 @@
+import os
+import random
+from typing import Callable, Dict, Optional, Tuple
+
 import gradio as gr
+import numpy as np
+import PIL.Image
+import spaces
+import torch
+
+from transformers import CLIPTextModel
+from diffusers import AutoencoderKL, StableDiffusionXLPipeline, DDIMScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler
+
+MODEL = "eienmojiki/Starry-XL-v5.2"
+HF_TOKEN = os.getenv("HF_TOKEN")
+MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
+MAX_SEED = np.iinfo(np.int32).max
+
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        return seed
+
+def seed_everything(seed: int) -> torch.Generator:
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    generator = torch.Generator()
+    generator.manual_seed(seed)
+    return generator
+
+def get_scheduler(scheduler_config: Dict, name: str) -> Optional[Callable]:
+    scheduler_factory_map = {
+        "DPM++ 2M Karras": lambda: DPMSolverMultistepScheduler.from_config(
+            scheduler_config, use_karras_sigmas=True
+            ),
+        "DPM++ SDE Karras": lambda: DPMSolverSinglestepScheduler.from_config(
+            scheduler_config, use_karras_sigmas=True
+            ),
+        "DPM++ 2M SDE Karras": lambda: DPMSolverMultistepScheduler.from_config(
+            scheduler_config, use_karras_sigmas=True, algorithm_type="sde-dpmsolver++"
+            ),
+        "Euler": lambda: EulerDiscreteScheduler.from_config(scheduler_config),
+        "Euler a": lambda: EulerAncestralDiscreteScheduler.from_config(scheduler_config),
+        "DDIM": lambda: DDIMScheduler.from_config(scheduler_config),
+    }
+    return scheduler_factory_map.get(name, lambda: None)()
 
-def greet(name):
-    return "Hello " + name + "!!"
+def load_pipeline(model_name):
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        model_name,
+        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
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()