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modules/default_pipeline.py

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+import os
+import torch
+import modules.core as core
+
+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')
+
+xl_base = core.load_model(xl_base_filename)
+
+
+@torch.no_grad()
+def process(positive_prompt, negative_prompt, width=1024, height=1024, batch_size=1):
+    positive_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=positive_prompt)
+    negative_conditions = core.encode_prompt_condition(clip=xl_base.clip, prompt=negative_prompt)
+
+    empty_latent = core.generate_empty_latent(width=width, height=height, batch_size=batch_size)
+
+    sampled_latent = core.ksample(
+        unet=xl_base.unet,
+        positive_condition=positive_conditions,
+        negative_condition=negative_conditions,
+        latent_image=empty_latent
+    )
+
+    decoded_latent = core.decode_vae(vae=xl_base.vae, latent_image=sampled_latent)
+
+    images = core.image_to_numpy(decoded_latent)
+    return images