Back to StabilityXL untill 3.5 Included in Diffusers

app.py

@@ -3,44 +3,20 @@ import random
 import spaces
 import gradio as gr
 from PIL import Image
-from models_transformer_sd3 import SD3Transformer2DModel
-from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
-import gc
-import os
-from huggingface_hub import login
-
-TOKEN = os.getenv('TOKEN')
-login(TOKEN)
-
-model_path = 'stabilityai/stable-diffusion-3.5-large'
-ip_adapter_path = './ip-adapter.bin'
-image_encoder_path = "google/siglip-so400m-patch14-384"
+from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-
-transformer = SD3Transformer2DModel.from_pretrained(
-    model_path, subfolder="transformer", torch_dtype=torch.bfloat16
-)
-
-pipe = StableDiffusion3Pipeline.from_pretrained(
-    model_path, transformer=transformer, torch_dtype=torch.bfloat16
-) ## For ZeroGPU no .to("cuda")
-
-pipe.init_ipadapter(
-    ip_adapter_path=ip_adapter_path, 
-    image_encoder_path=image_encoder_path, 
-    nb_token=64, 
-)
-
+pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=dtype)
+pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
 pipe.to(device)
-
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, 2000)
     return seed
-
-@spaces.GPU() ## For ZeroGPU
+    
+@spaces.GPU()
 def create_image(image_pil,
                  prompt,
                  n_prompt,
@@ -52,9 +28,6 @@ def create_image(image_pil,
                  target="Load only style blocks",
                  ):
     
-    if image_pil is None:
-        return None
-    
     if target !="Load original IP-Adapter":
         if target=="Load only style blocks":
             scale = {
@@ -70,25 +43,19 @@ def create_image(image_pil,
                 "up": {"block_0": [0.0, control_scale, 0.0]},
             }
         pipe.set_ip_adapter_scale(scale)
-    
-    style_image = Image.open(image_pil).convert('RGB')
+
+    style_image = load_image(image_pil)
+    generator = torch.Generator().manual_seed(randomize_seed_fn(seed, True))
     
     
     image = pipe(
-            width=1024,
-            height=1024,
-            prompt=prompt,
-            negative_prompt="lowres, low quality, worst quality",
-            num_inference_steps=24, 
-            guidance_scale=guidance_scale,
-            generator=torch.Generator("cuda").manual_seed(randomize_seed_fn(seed, True)),  ## For ZeroGPU no device="cpu"
-            clip_image=style_image,
-            ipadapter_scale=scale,
-        ).images[0]
-    
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-        gc.collect()
+        prompt=prompt,
+        ip_adapter_image=style_image,
+        negative_prompt=n_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    ).images[0]
     
     return image