Create text_to_image.py

text_to_image.py

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+from transformers.tools.base import Tool, get_default_device
+from transformers.utils import is_accelerate_available
+import torch
+
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+
+
+TEXT_TO_IMAGE_DESCRIPTION = (
+    "This is a tool that creates an image according to a prompt, which is a text description. It takes an input named `prompt` which "
+    "contains the image description and outputs an image."
+)
+
+
+class TextToImageTool(Tool):
+    default_checkpoint = "runwayml/stable-diffusion-v1-5"
+    description = TEXT_TO_IMAGE_DESCRIPTION
+    inputs = ['text']
+    outputs = ['image']
+
+    def __init__(self, device=None, **hub_kwargs) -> None:
+        if not is_accelerate_available():
+            raise ImportError("Accelerate should be installed in order to use tools.")
+
+        super().__init__()
+
+        self.device = device
+        self.pipeline = None
+        self.hub_kwargs = hub_kwargs
+
+    def setup(self):
+        if self.device is None:
+            self.device = get_default_device()
+
+        self.pipeline = DiffusionPipeline.from_pretrained(self.default_checkpoint)
+        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config)
+        self.pipeline.to(self.device)
+
+        if self.device.type == "cuda":
+            self.pipeline.to(torch_dtype=torch.float16)
+
+        self.is_initialized = True
+
+    def __call__(self, prompt):
+        if not self.is_initialized:
+            self.setup()
+
+        negative_prompt = "low quality, bad quality, deformed, low resolution"
+        added_prompt = " , highest quality, highly realistic, very high resolution"
+
+        return self.pipeline(prompt + added_prompt, negative_prompt=negative_prompt, num_inference_steps=25).images[0]
+