feat: add test for inference and remove the picture.

stable_diffusion_inference.py

@@ -25,7 +25,7 @@ if torch.cuda.is_available():
 else:
     torch_dtype = torch.float32
 pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch_dtype,
-                                               # low_cpu_mem_usage=True,
+                                               low_cpu_mem_usage=True,
                                                # requires_safety_checker=False
                                                )
 pipe = pipe.to(device)
@@ -35,10 +35,10 @@ pipe = accelerator.prepare(pipe)
 # pipe.enable_xformers_memory_efficient_attention()
 
 # Enable attention slicing
-# pipe.enable_attention_slicing()
+pipe.enable_attention_slicing()
 
 # Enable VAE slicing
-# pipe.enable_vae_slicing()
+pipe.enable_vae_slicing()
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -46,13 +46,13 @@ MAX_IMAGE_SIZE = 1024
 
 @spaces.GPU(duration=65)
 def inference(prompt,
-              negative_prompt="",
-              seed=42,
+              # negative_prompt="",
+              seed=0,
               randomize_seed=False,
               width=MAX_IMAGE_SIZE,
               height=MAX_IMAGE_SIZE,
               guidance_scale=7.5,
-              num_inference_steps=100,
+              num_inference_steps=10,
               progress=gr.Progress(track_tqdm=True), ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -62,8 +62,9 @@ def inference(prompt,
     logger.info('Generating image ...')
     image = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
+        # negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
+        eta=0.0,
         num_inference_steps=num_inference_steps,
         width=width,
         height=height,