Spaces:
Build error
Build error
File size: 2,189 Bytes
45ce449 6853258 9eb98e1 6853258 e68ce70 e26163a 6853258 365daee 6853258 dd47fae 10fe40c 6853258 365daee 45ce449 6853258 10fe40c 6853258 e26163a 6853258 10fe40c 6853258 4a092d4 6853258 4a092d4 6853258 10fe40c 331df7b 48de697 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
from PIL import Image
import torch
from transformers.agents.tools import Tool
from transformers.utils import (
is_accelerate_available,
is_vision_available,
)
from diffusers import DiffusionPipeline
if is_accelerate_available():
from accelerate import PartialState
IMAGE_TRANSFORMATION_DESCRIPTION = (
"This is a tool that transforms an image according to a prompt and returns the "
"modified image."
)
class ImageTransformationTool(Tool):
name = "image_transformation"
default_stable_diffusion_checkpoint = "timbrooks/instruct-pix2pix"
description = IMAGE_TRANSFORMATION_DESCRIPTION
inputs = {
'image': {"type": Image.Image, "description": "the image to transform"},
'prompt': {"type": str, "description": "the prompt to use to change the image"}
}
output_type = Image.Image
def __init__(self, device=None, controlnet=None, stable_diffusion=None, **hub_kwargs) -> None:
if not is_accelerate_available():
raise ImportError("Accelerate should be installed in order to use tools.")
if not is_vision_available():
raise ImportError("Pillow should be installed in order to use the StableDiffusionTool.")
super().__init__()
self.stable_diffusion = self.default_stable_diffusion_checkpoint
self.device = device
self.hub_kwargs = hub_kwargs
def setup(self):
if self.device is None:
self.device = PartialState().default_device
self.pipeline = DiffusionPipeline.from_pretrained(self.stable_diffusion)
self.pipeline.to(self.device)
if self.device.type == "cuda":
self.pipeline.to(torch_dtype=torch.float16)
self.is_initialized = True
def __call__(self, image, 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,
image,
negative_prompt=negative_prompt,
num_inference_steps=50,
).images[0] |