Update app.py

app.py

@@ -10,7 +10,6 @@ import json
 import os
 
 from diffusers import DiffusionPipeline
-from huggingface_hub import snapshot_download
 from transformers import pipeline
 from peft import PeftModel
 from PIL import Image
@@ -45,14 +44,14 @@ repo_nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_im
 
 repo_default = DiffusionPipeline.from_pretrained("fluently/Fluently-XL-Final", device=DEVICE, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False)
 
-# repo_large = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", device=DEVICE, torch_dtype=torch.bfloat16, add_watermarker=False, revision="refs/pr/1")
+# repo_large = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, add_watermarker=False, revision="refs/pr/1")
 # repo_large.load_lora_weights("ehristoforu/dalle-3-xl-v2", adapter_name="base")
 # repo_large.set_adapters(["base"], adapter_weights=[0.7])
 
 repo_customs = {
     "Default": repo_default,
-    "Realistic": DiffusionPipeline.from_pretrained("ehristoforu/Visionix-alpha", device=DEVICE, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False),
-    "Anime": DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.1", device=DEVICE, torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False),
+    "Realistic": DiffusionPipeline.from_pretrained("ehristoforu/Visionix-alpha", torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False),
+    "Anime": DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.1", torch_dtype=torch.float16, use_safetensors=True, add_watermarker=False),
     "Pixel": repo_default,
     # "Large": repo_neo,
 }
@@ -73,7 +72,7 @@ def get_seed(seed):
 @spaces.GPU(duration=60)
 def generate(input=DEFAULT_INPUT, filter_input="", negative_input=DEFAULT_NEGATIVE_INPUT, model=DEFAULT_MODEL, height=DEFAULT_HEIGHT, width=DEFAULT_WIDTH, steps=1, guidance=0, number=1, seed=None):
 
-    repo = repo_customs[model or "Default"]
+    repo = repo_customs[model or "Default"].to(DEVICE)
     filter_input = filter_input or ""
     negative_input = negative_input or DEFAULT_NEGATIVE_INPUT
     steps_set = steps
@@ -111,8 +110,6 @@ def generate(input=DEFAULT_INPUT, filter_input="", negative_input=DEFAULT_NEGATI
         guidance = guidance_set
     
     print(steps, guidance)
-
-    repo.to(DEVICE)
     
     parameters = {
         "prompt": input,