Update app.py

app.py

@@ -3,7 +3,7 @@ import torch
 import numpy as np
 import modin.pandas as pd
 from PIL import Image
-from diffusers import DiffusionPipeline #, StableDiffusion3Pipeline
+from diffusers import DiffusionPipeline, StableDiffusion3Pipeline
 from huggingface_hub import hf_hub_download
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -52,9 +52,24 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed):
         image = pipe(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=.99).images[0]
         torch.cuda.empty_cache()
         return image
+    if Model == "SD3.5":
+        torch.cuda.max_memory_allocated(device=device)
+        torch.cuda.empty_cache()
+        pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium", torch_dtype=torch.float16).to(device)
+        pipe.enable_xformers_memory_efficient_attention()
+        torch.cuda.empty_cache()
+        image = pipe(
+        prompt=Prompt,
+        height=height,
+        width=width,
+        negative_prompt=negative_prompt,
+        guidance_scale=scale,
+        num_images_per_prompt=1,
+        num_inference_steps=steps).images[0]
+        torch.cuda.empty_cache()
     return image
     
-gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 4', "FXL"], value='PhotoReal', label='Choose Model'),
+gr.Interface(fn=genie, inputs=[gr.Radio(['PhotoReal', 'Animagine XL 4', "FXL", "SD3.5"], value='PhotoReal', label='Choose Model'),
                                gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
                                gr.Textbox(label='What you Do Not want the AI to generate. 77 Token Limit'),
                                gr.Slider(512, 1024, 768, step=128, label='Height'),