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handler.py

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+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"}

requirements.txt

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+git+https://github.com/huggingface/diffusers.git
+transformers>=4.51.0
+accelerate>=0.34.0
+torch>=2.1.0
+sentencepiece
+protobuf