From af081211ee93622473ee575de30fed2fd8263c09 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 11 Jul 2023 21:16:43 +0300
Subject: getting SD2.1 to run on SDXL repo

---
 modules/sd_models_xl.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 modules/sd_models_xl.py

(limited to 'modules/sd_models_xl.py')

diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
new file mode 100644
index 00000000..d43b8868
--- /dev/null
+++ b/modules/sd_models_xl.py
@@ -0,0 +1,40 @@
+from __future__ import annotations
+
+import torch
+
+import sgm.models.diffusion
+import sgm.modules.diffusionmodules.denoiser_scaling
+import sgm.modules.diffusionmodules.discretizer
+from modules import devices
+
+
+def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: list[str]):
+    for embedder in self.conditioner.embedders:
+        embedder.ucg_rate = 0.0
+
+    c = self.conditioner({'txt': batch})
+
+    return c
+
+
+def apply_model(self: sgm.models.diffusion.DiffusionEngine, x, t, cond):
+    return self.model(x, t, cond)
+
+
+def extend_sdxl(model):
+    dtype = next(model.model.diffusion_model.parameters()).dtype
+    model.model.diffusion_model.dtype = dtype
+    model.model.conditioning_key = 'crossattn'
+
+    model.cond_stage_model = [x for x in model.conditioner.embedders if type(x).__name__ == 'FrozenOpenCLIPEmbedder'][0]
+    model.cond_stage_key = model.cond_stage_model.input_key
+
+    model.parameterization = "v" if isinstance(model.denoiser.scaling, sgm.modules.diffusionmodules.denoiser_scaling.VScaling) else "eps"
+
+    discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
+    model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
+
+
+sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
+sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
+
-- 
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