Update app.py

app.py

@@ -1,49 +1,66 @@
+import json
+import random
+
 import gradio as gr
 import numpy as np
-import random
-from diffusers import DiffusionPipeline
+import spaces
 import torch
+from diffusers import DiffusionPipeline, LCMScheduler
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+
+pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.load_lora_weights("JacobLinCool/sdxl-lora-gdsc-1")
+pipe.to(device=DEVICE, dtype=torch.float16)
 
-if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
-    pipe.load_lora_weights("JacobLinCool/sdxl-lora-gdsc-1")
-    # pipe.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", use_safetensors=True)
-    pipe.load_lora_weights("JacobLinCool/sdxl-lora-gdsc-1")
-    pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
+@spaces.GPU
+def infer(
+    pre_prompt,
+    prompt,
+    seed,
+    randomize_seed,
+    num_inference_steps,
+    negative_prompt,
+    guidance_scale,
+    progress=gr.Progress(track_tqdm=True),
+):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-        
+
     generator = torch.Generator().manual_seed(seed)
-    
+
+    if pre_prompt != "":
+        prompt = f"{pre_prompt} {prompt}"
+
     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] 
-    
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    ).images[0]
+
     return image
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
+
+css = """
+
+h1 {
+    text-align: center;
+    display:block;
 }
+
+p {
+    text-align: justify;
+    display:block;
+}
+
 """
 
 if torch.cuda.is_available():
@@ -52,87 +69,84 @@ else:
     power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                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,
-                value="",
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+    with gr.Row():
+        with gr.Column():
             with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
+                prompt = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False,
+                    scale=5,
                 )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
+
+                run_button = gr.Button("Run", scale=1)
+
+            result = gr.Image(label="Result", show_label=False)
+
+            with gr.Accordion("Advanced Settings", open=False):
+
+                pre_prompt = gr.Text(
+                    label="Pre-Prompt",
+                    show_label=True,
+                    max_lines=1,
+                    placeholder="Pre Prompt from the LoRA config",
+                    container=True,
+                    scale=5,
                 )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
+
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
                     step=1,
-                    value=2,
+                    value=0,
+                )
+
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                with gr.Row():
+
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=4,
+                        maximum=8,
+                        step=1,
+                        value=4,
+                    )
+
+                with gr.Row():
+
+                    guidance_scale = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=1,
+                        maximum=6,
+                        step=0.5,
+                        value=1,
+                    )
+
+                negative_prompt = gr.Text(
+                    label="Negative Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter a negative Prompt",
+                    container=False,
                 )
 
     run_button.click(
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result]
+        fn=infer,
+        inputs=[
+            pre_prompt,
+            prompt,
+            seed,
+            randomize_seed,
+            num_inference_steps,
+            negative_prompt,
+            guidance_scale,
+        ],
+        outputs=[result],
     )
 
+
 demo.queue().launch()