Update README.md

README.md

@@ -21,27 +21,27 @@ Here is a simple example:
 import torch
 from diffusers import  StableDiffusionPipeline, TCDScheduler
 
-device = "cuda"
-base_model_id = "stabilityai/stable-diffusion-2-1-base"
-tcd_lora_id = "h1t/TCD-SD21-base-LoRA"
+device = "cuda"
+base_model_id = "stabilityai/stable-diffusion-2-1-base"
+tcd_lora_id = "h1t/TCD-SD21-base-LoRA"
 
-pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device)
-pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device)
+pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
 
 pipe.load_lora_weights(tcd_lora_id)
 pipe.fuse_lora()
 
-prompt = "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor."
+prompt = "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor."
 
-image = pipe(
-    prompt=prompt,
-    num_inference_steps=4,
-    guidance_scale=0,
-    # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step.
+image = pipe(
+    prompt=prompt,
+    num_inference_steps=4,
+    guidance_scale=0,
+    # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step.
     # A value of 0.3 often yields good results.
     # We recommend using a higher eta when increasing the number of inference steps.
-    eta=0.3, 
-    generator=torch.Generator(device=device).manual_seed(0),
+    eta=0.3, 
+    generator=torch.Generator(device=device).manual_seed(0),
 ).images[0]
 ```