Update app.py

app.py

@@ -53,7 +53,7 @@ torch_dtype = torch.bfloat16
 
 checkpoint = "microsoft/Phi-3.5-mini-instruct"
 #vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16)
+#vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16)
 
 pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16", torch_dtype=torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium", token=hftoken, torch_dtype=torch.float32, device_map='balanced')
@@ -139,15 +139,16 @@ def infer(
     print('-- filtered prompt --')
     print(enhanced_prompt)
     print('-- generating image --')
-    sd_image = pipe(
-        prompt=enhanced_prompt,  # This conversion is fine
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator
-    ).images[0]
+      with torch.no_grad():
+        sd_image = pipe(
+            prompt=enhanced_prompt,  # This conversion is fine
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator
+        ).images[0]
     print('-- got image --')
     image_path = f"sd35m_{seed}.png"
     sd_image.save(image_path,optimize=False,compress_level=0)