Update `app.py`

app.py

@@ -1,51 +1,63 @@
+import random
+
 import gradio as gr
 import numpy as np
-import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import spaces
 import torch
+from diffusers import DiffusionPipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
+repo_id = "black-forest-labs/FLUX.1-dev"
+adapter_id = "alvarobartt/ghibli-characters-flux-lora"
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
+pipeline.load_lora_weights(adapter_id)
+pipeline = pipeline.to(device)
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
+@spaces.GPU(duration=120)
+def inference(
+    prompt: str,
+    seed: int,
+    randomize_seed: bool,
+    width: int,
+    height: int,
+    guidance_scale: float,
+    num_inference_steps: int,
+    lora_scale: float,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),
+):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+
     generator = torch.Generator().manual_seed(seed)
-    
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
-    
+
+    image = pipeline(
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+        lora_scale=lora_scale,
+    ).images[0]
+
     return image, seed
 
+
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    (
+        "Ghibli style futuristic stormtrooper with glossy white armor and a sleek helmet,"
+        " standing heroically on a lush alien planet, vibrant flowers blooming around, soft"
+        " sunlight illuminating the scene, a gentle breeze rustling the leaves"
+    )
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
@@ -53,14 +65,10 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
+        gr.Markdown("# FLUX.1 Ghibli Studio LoRA")
+
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
@@ -68,75 +76,79 @@ with gr.Blocks(css=css) as demo:
                 placeholder="Enter your prompt",
                 container=False,
             )
-            
+
             run_button = gr.Button("Run", scale=0)
-        
+
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,
             )
-            
+
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=1024,
                 )
-                
+
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024, #Replace with defaults that work for your model
+                    value=768,
                 )
-            
+
             with gr.Row():
-                
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
+                    value=3.5,
                 )
-                
+
+                lora_scale = gr.Slider(
+                    label="LoRA scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    value=1.0,
+                )
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2, #Replace with defaults that work for your model
+                    value=30,
                 )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
+
+        gr.Examples(examples=examples, inputs=[prompt])
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        fn=inference,
+        inputs=[
+            prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
     )
 
-demo.queue().launch()
+demo.queue().launch()