feat: add test for inference and remove the picture.

stable_diffusion_inference.py

@@ -17,7 +17,7 @@ from loguru import logger
 
 model_path = 'Ngene787/Faice_text2face'
 
-accelerator = Accelerator(mixed_precision="fp16", gradient_accumulation_steps=1)
+# accelerator = Accelerator(mixed_precision="fp16", gradient_accumulation_steps=1)
 logger.info("Loading model ...")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 if torch.cuda.is_available():
@@ -30,15 +30,15 @@ pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch_dty
                                                )
 pipe = pipe.to(device)
 
-pipe = accelerator.prepare(pipe)
+# pipe = accelerator.prepare(pipe)
 # Enable memory-efficient attention
 # 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
@@ -46,7 +46,7 @@ MAX_IMAGE_SIZE = 1024
 
 @spaces.GPU(duration=65)
 def inference(prompt,
-              # negative_prompt="",
+              negative_prompt="",
               seed=0,
               randomize_seed=False,
               width=MAX_IMAGE_SIZE,
@@ -62,7 +62,7 @@ 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,