Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import spaces
 import numpy as np
 import random
-from diffusers import DiffusionPipeline, AutoencoderTiny, StableDiffusion3Pipeline, SD3Transformer2DModel, FlashFlowMatchEulerDiscreteScheduler
+from diffusers import DiffusionPipeline
 import torch
 from PIL import Image
 
@@ -10,25 +10,13 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo"
 
 torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
-# Load Tiny Autoencoder
-taesd3 = AutoencoderTiny.from_pretrained("madebyollin/taesd3", torch_dtype=torch.float16).to(device)
-taesd3.decoder.layers = torch.compile(
-    taesd3.decoder.layers,
-    fullgraph=True,
-    dynamic=False,
-    mode="max-autotune-no-cudagraphs",
-)
-
-# Load main Stable Diffusion pipeline
-pipe = StableDiffusion3Pipeline.from_pretrained(
-    model_path,
-    transformer=SD3Transformer2DModel.from_pretrained(model_path, torch_dtype=torch.float16),
-    torch_dtype=torch_dtype,
-    vae=taesd3,
-).to(device)
-
-pipe.scheduler = FlashFlowMatchEulerDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler")
-pipe.set_progress_bar_config(disable=True)
+
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = pipe.to(device)
+
+pipe.load_lora_weights("prithivMLmods/SD3.5-Large-Turbo-HyperRealistic-LoRA", weight_name="SD3.5-4Step-Large-Turbo-HyperRealistic-LoRA.safetensors")
+trigger_word = "hyper realistic"  # Specify trigger word for LoRA
+pipe.fuse_lora(lora_scale=1.0)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -218,12 +206,27 @@ with gr.Blocks(css=css, theme="prithivMLmods/Minecraft-Theme") as demo:
                     value=4,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=False)
+        gr.Examples(examples=examples, 
+                    inputs=[prompt], 
+                    outputs=[result, seed], 
+                    fn=infer, 
+                    cache_examples=False)
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, style_selection, grid_size_selection],
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            style_selection,
+            grid_size_selection,
+        ],
         outputs=[result, seed],
     )