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handler.py

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+from typing import Dict, List, Any
+# import transformers
+# from transformers import AutoTokenizer
+# import torch
+from datetime import datetime
+
+import torch
+
+# torch.backends.cuda.matmul.allow_tf32 = True
+
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+
+class EndpointHandler():
+    
+    def __init__(self, path=""):
+
+        # Use the DPMSolverMultistepScheduler (DPM-Solver++) scheduler here instead
+        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        self.pipe = self.pipe.to("cuda")
+
+        # self.pipe.enable_attention_slicing()
+        self.pipe.enable_xformers_memory_efficient_attention()
+
+        # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        # self.model.eval()
+        # self.model.to(device=device, dtype=self.torch_dtype)
+        
+        # self.generate_kwargs = {
+        #     'max_new_tokens': 512,
+        #     'temperature': 0.0001,
+        #     'top_p': 1.0,
+        #     'top_k': 0,
+        #     'use_cache': True,
+        #     'do_sample': True,
+        #     'eos_token_id': self.tokenizer.eos_token_id,
+        #     'pad_token_id': self.tokenizer.pad_token_id,
+        #     "repetition_penalty": 1.1
+        # }
+    
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+
+        # streamer = TextIteratorStreamer(
+        # self.tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
+        # )
+        
+        ## Model Parameters
+        # self.generate_kwargs['max_new_tokens'] = data['max_new_tokens'] if 'max_new_tokens' in data else self.generate_kwargs['max_new_tokens']
+        # self.generate_kwargs['temperature'] = data['temperature'] if 'temperature' in data else self.generate_kwargs['temperature']
+        # self.generate_kwargs['top_p'] = data['top_p'] if 'top_p' in data else self.generate_kwargs['top_p']
+        # self.generate_kwargs['top_k'] = data['top_k'] if 'top_k' in data else self.generate_kwargs['top_k']
+        # self.generate_kwargs['do_sample'] = data['do_sample'] if 'do_sample' in data else self.generate_kwargs['do_sample']
+        # self.generate_kwargs['repetition_penalty'] = data['repetition_penalty'] if 'repetition_penalty' in data else self.generate_kwargs['repetition_penalty']
+        
+        
+        ## Prepare the inputs
+        # inputs = data.pop("inputs",data)
+        # input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
+        # input_ids = input_ids.to(self.model.device)
+
+
+        # pip install accelerate
+
+        batch_size = data.pop("batch_size",data)
+        
+        now = datetime.now()
+
+        with torch.inference_mode(): 
+            prompt = "a photo of an astronaut riding a horse on mars"
+            image = pipe([prompt]*batch_size, num_inference_steps=20)
+                
+        # image.save("astronaut_rides_horse.png")
+
+        current = datetime.now()
+
+        # encoded_inp = self.tokenizer(inputs, return_tensors='pt', padding=True)
+        # for key, value in encoded_inp.items():
+        #     encoded_inp[key] = value.to('cuda:0')
+
+        ## Invoke the model     
+        # with torch.no_grad():
+        #     gen_tokens =  self.model.generate(
+        #         input_ids=encoded_inp['input_ids'],
+        #         attention_mask=encoded_inp['attention_mask'],
+        #         **generate_kwargs,
+        #     )
+
+        # ## Decode using tokenizer
+        # decoded_gen = self.tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)        
+
+        # with torch.no_grad():
+        #     output_ids = self.model.generate(input_ids, **self.generate_kwargs)
+        # # Slice the output_ids tensor to get only new tokens
+        # new_tokens = output_ids[0, len(input_ids[0]) :]
+        # output_text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
+        
+        return [{"batch_size":batch_size, "time_elapsed": str(current-now)}]

requirements.txt

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+accelerate
+torch
+transformers
+diffusers
+scipy
+safetensors
+xformers