i

webui.py

@@ -1,19 +1,12 @@
 import os
 import random
+import torch
 
 from comfy.sd import load_checkpoint_guess_config
-from comfy.model_management import unload_model
-
-from nodes import (
-    VAEDecode,
-    KSamplerAdvanced,
-    EmptyLatentImage,
-    SaveImage,
-    CLIPTextEncode,
-)
-
+from nodes import VAEDecode, KSamplerAdvanced, EmptyLatentImage, SaveImage, CLIPTextEncode
 from modules.path import modelfile_path
 
+
 xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0.safetensors')
 xl_refiner_filename = os.path.join(modelfile_path, 'sd_xl_refiner_1.0.safetensors')
 
@@ -25,28 +18,28 @@ opEmptyLatentImage = EmptyLatentImage()
 opKSamplerAdvanced = KSamplerAdvanced()
 opVAEDecode = VAEDecode()
 
-positive_conditions = opCLIPTextEncode.encode(clip=xl_base_clip, text='a handsome man in forest')[0]
-negative_conditions = opCLIPTextEncode.encode(clip=xl_base_clip, text='bad, ugly')[0]
-
-initial_latent_image = opEmptyLatentImage.generate(width=1024, height=1024, batch_size=1)[0]
-
-samples = opKSamplerAdvanced.sample(
-    add_noise="enable",
-    noise_seed=random.randint(1, 2 ** 64),
-    steps=25,
-    cfg=9,
-    sampler_name="euler",
-    scheduler="normal",
-    start_at_step=0,
-    end_at_step=25,
-    return_with_leftover_noise="enable",
-    model=xl_base,
-    positive=positive_conditions,
-    negative=negative_conditions,
-    latent_image=initial_latent_image,
-)[0]
-unload_model()
-
-vae_decoded = opVAEDecode.decode(samples=samples, vae=xl_base_vae)[0]
-
-a = 0
+with torch.no_grad():
+    positive_conditions = opCLIPTextEncode.encode(clip=xl_base_clip, text='a handsome man in forest')[0]
+    negative_conditions = opCLIPTextEncode.encode(clip=xl_base_clip, text='bad, ugly')[0]
+
+    initial_latent_image = opEmptyLatentImage.generate(width=1024, height=1024, batch_size=1)[0]
+
+    samples = opKSamplerAdvanced.sample(
+        add_noise="enable",
+        noise_seed=random.randint(1, 2 ** 64),
+        steps=25,
+        cfg=9,
+        sampler_name="euler",
+        scheduler="normal",
+        start_at_step=0,
+        end_at_step=25,
+        return_with_leftover_noise="enable",
+        model=xl_base,
+        positive=positive_conditions,
+        negative=negative_conditions,
+        latent_image=initial_latent_image,
+    )[0]
+
+    vae_decoded = opVAEDecode.decode(samples=samples, vae=xl_base_vae)[0]
+
+    a = 0