Update app.py

app.py

@@ -1,3 +1,5 @@
+
+
 import os
 import random
 import uuid
@@ -7,23 +9,19 @@ import numpy as np
 from PIL import Image
 import spaces
 import torch
-from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler, AutoencoderKL
+from diffusers import StableDiffusion3Pipeline, DPMSolverSinglestepScheduler, AutoencoderKL
 from huggingface_hub import snapshot_download
 
-# Çevre değişkeninden token'ı al
 huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-"""
-# Model ve VAE dosyalarını indir
-model_path = snapshot_download(
+
+snapshot_download(
     repo_id="stabilityai/stable-diffusion-3-medium", 
     repo_type="model", 
     ignore_patterns=["*.md", "*..gitattributes"],
     local_dir="stable-diffusion-3-medium",
-    token=huggingface_token,
-)
-print(model_path)
+    token=huggingface_token, # yeni bir token-id yazın.
+    )
 
-"""
 DESCRIPTION = """# Stable Diffusion 3"""
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
@@ -35,24 +33,23 @@ USE_TORCH_COMPILE = False
 ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 
-pipe = StableDiffusionPipeline.from_single_file(
-    "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/tachyon.safetensors", 
-    torch_dtype=torch.float16,
-    use_auth_token=huggingface_token
-)
-pipe.to(device)
+pipe = StableDiffusion3Pipeline.from_pretrained("stable-diffusion-3-medium", torch_dtype=torch.float16)
+    
 
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
     return unique_name
 
+
 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,
@@ -68,9 +65,12 @@ def generate(
     use_resolution_binning: bool = True,
     progress=gr.Progress(track_tqdm=True),
 ):
+    pipe.to(device)
     seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
-
+    #pipe.scheduler = DPMSolverSinglestepScheduler(use_karras_sigmas=True).from_config(pipe.scheduler.config)
+    #pipe.scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++").from_config(pipe.scheduler.config)
+    
     if not use_negative_prompt:
         negative_prompt = None  # type: ignore
     
@@ -88,6 +88,7 @@ def generate(
 
     return output
 
+
 examples = [
     "neon holography crystal cat",
     "a cat eating a piece of cheese",
@@ -101,7 +102,6 @@ css = '''
 .gradio-container{max-width: 1000px !important}
 h1{text-align:center}
 '''
-
 with gr.Blocks(css=css) as demo:
     with gr.Row():
         with gr.Column():
@@ -137,7 +137,7 @@ with gr.Blocks(css=css) as demo:
             negative_prompt = gr.Text(
                 label="Negative prompt",
                 max_lines=1,
-                value="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
+                value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
                 visible=True,
             )
         seed = gr.Slider(
@@ -226,4 +226,4 @@ with gr.Blocks(css=css) as demo:
     )
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue().launch()