Update app.py

app.py

@@ -1,19 +1,19 @@
 import gradio as gr
-from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL
+import spaces
 import torch
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler, LCMScheduler, AutoencoderKL
 from huggingface_hub import hf_hub_download
 from safetensors.torch import load_file
-import spaces
 
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
 
 ### SDXL Turbo #### 
-pipe_turbo = StableDiffusionXLPipeline.from_pretrained("stabilityai/sdxl-turbo",
-                                                       vae=vae,
-                                                       torch_dtype=torch.float16,
-                                                       variant="fp16"
-                                                      )
-pipe_turbo.to("cuda")
+pipe_turbo = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/sdxl-turbo",
+    vae=vae,
+    torch_dtype=torch.float16,
+    variant="fp16"
+).to("cuda")
 
 ### SDXL Lightning ### 
 base = "stabilityai/stable-diffusion-xl-base-1.0"
@@ -22,16 +22,17 @@ ckpt = "sdxl_lightning_1step_unet_x0.safetensors"
 
 unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float16)
 unet.load_state_dict(load_file(hf_hub_download(repo, ckpt)))
-pipe_lightning = StableDiffusionXLPipeline.from_pretrained(base,
-                                                           unet=unet,
-                                                           vae=vae,
-                                                           text_encoder=pipe_turbo.text_encoder,
-                                                           text_encoder_2=pipe_turbo.text_encoder_2,
-                                                           tokenizer=pipe_turbo.tokenizer,
-                                                           tokenizer_2=pipe_turbo.tokenizer_2,
-                                                           torch_dtype=torch.float16,
-                                                           variant="fp16"
-                                                          )#.to("cuda")
+pipe_lightning = StableDiffusionXLPipeline.from_pretrained(
+    base,
+    unet=unet,
+    vae=vae,
+    text_encoder=pipe_turbo.text_encoder,
+    text_encoder_2=pipe_turbo.text_encoder_2,
+    tokenizer=pipe_turbo.tokenizer,
+    tokenizer_2=pipe_turbo.tokenizer_2,
+    torch_dtype=torch.float16,
+    variant="fp16"
+)#.to("cuda")
 del unet
 pipe_lightning.scheduler = EulerDiscreteScheduler.from_config(pipe_lightning.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
 pipe_lightning.to("cuda")
@@ -42,16 +43,17 @@ ckpt_name = "Hyper-SDXL-1step-Unet.safetensors"
 
 unet = UNet2DConditionModel.from_config(base, subfolder="unet").to(torch.float16)
 unet.load_state_dict(load_file(hf_hub_download(repo_name, ckpt_name)))
-pipe_hyper = StableDiffusionXLPipeline.from_pretrained(base,
-                                                       unet=unet,
-                                                       vae=vae,
-                                                       text_encoder=pipe_turbo.text_encoder,
-                                                       text_encoder_2=pipe_turbo.text_encoder_2,
-                                                       tokenizer=pipe_turbo.tokenizer,
-                                                       tokenizer_2=pipe_turbo.tokenizer_2,
-                                                       torch_dtype=torch.float16,
-                                                       variant="fp16"
-                                                      )#.to("cuda")
+pipe_hyper = StableDiffusionXLPipeline.from_pretrained(
+    base,
+    unet=unet,
+    vae=vae,
+    text_encoder=pipe_turbo.text_encoder,
+    text_encoder_2=pipe_turbo.text_encoder_2,
+    tokenizer=pipe_turbo.tokenizer,
+    tokenizer_2=pipe_turbo.tokenizer_2,
+    torch_dtype=torch.float16,
+    variant="fp16"
+    )#.to("cuda")
 pipe_hyper.scheduler = LCMScheduler.from_config(pipe_hyper.scheduler.config)
 pipe_hyper.to("cuda")
 del unet
@@ -65,13 +67,14 @@ def run_comparison(prompt, progress=gr.Progress(track_tqdm=True)):
     image_hyper=pipe_hyper(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[800]).images[0]
     yield image_turbo, image_lightning, image_hyper
 
-examples = ["A dignified beaver wearing glasses, a vest, and colorful neck tie.",
-"The spirit of a tamagotchi wandering in the city of Barcelona",
-"an ornate, high-backed mahogany chair with a red cushion",
-"a sketch of a camel next to a stream",
-"a delicate porcelain teacup sits on a saucer, its surface adorned with intricate blue patterns",
-"a baby swan grafitti",
-"A bald eagle made of chocolate powder, mango, and whipped cream"
+examples = [
+    "A dignified beaver wearing glasses, a vest, and colorful neck tie.",
+    "The spirit of a tamagotchi wandering in the city of Barcelona",
+    "an ornate, high-backed mahogany chair with a red cushion",
+    "a sketch of a camel next to a stream",
+    "a delicate porcelain teacup sits on a saucer, its surface adorned with intricate blue patterns",
+    "a baby swan grafitti",
+    "A bald eagle made of chocolate powder, mango, and whipped cream"
 ]
 
 with gr.Blocks() as demo:
@@ -104,4 +107,5 @@ with gr.Blocks() as demo:
         cache_examples=False,
         run_on_click=True
     )
+    
 demo.launch()