File size: 1,331 Bytes
9ab34a3 | 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 | import base64
import io
import torch
from diffusers.pipelines.glm_image import GlmImagePipeline
class EndpointHandler:
def __init__(self, path=""):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.dtype = torch.bfloat16
self.pipe = GlmImagePipeline.from_pretrained(
"zai-org/GLM-Image",
torch_dtype=self.dtype,
device_map="cuda",
enable_model_cpu_offload=True,
)
def __call__(self, data):
prompt = data.pop("inputs", "")
params = data.pop("parameters", {})
width = params.get("width", 1024)
height = params.get("height", 1024)
num_inference_steps = params.get("num_inference_steps", 50)
guidance_scale = params.get("guidance_scale", 1.5)
# GLM-Image requires dimensions divisible by 32
width = (width // 32) * 32
height = (height // 32) * 32
image = self.pipe(
prompt=prompt,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
).images[0]
buf = io.BytesIO()
image.save(buf, format="PNG")
img_b64 = base64.b64encode(buf.getvalue()).decode("utf-8")
return {"image": img_b64, "format": "png"}
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