From da464a3fb39ecc6ea7b22fe87271194480d8501c Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Wed, 12 Jul 2023 23:52:43 +0300
Subject: SDXL support

---
 modules/sd_models_xl.py | 27 +++++++++++++++++++++++----
 1 file changed, 23 insertions(+), 4 deletions(-)

(limited to 'modules/sd_models_xl.py')

diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index d43b8868..e8e270c3 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -1,18 +1,30 @@
 from __future__ import annotations
 
+import sys
+
 import torch
 
 import sgm.models.diffusion
 import sgm.modules.diffusionmodules.denoiser_scaling
 import sgm.modules.diffusionmodules.discretizer
-from modules import devices
+from modules import devices, shared, prompt_parser
 
 
-def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: list[str]):
+def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch: prompt_parser.SdConditioning | list[str]):
     for embedder in self.conditioner.embedders:
         embedder.ucg_rate = 0.0
 
-    c = self.conditioner({'txt': batch})
+    width = getattr(self, 'target_width', 1024)
+    height = getattr(self, 'target_height', 1024)
+
+    sdxl_conds = {
+        "txt": batch,
+        "original_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
+        "crop_coords_top_left": torch.tensor([shared.opts.sdxl_crop_top, shared.opts.sdxl_crop_left]).repeat(len(batch), 1).to(devices.device, devices.dtype),
+        "target_size_as_tuple": torch.tensor([height, width]).repeat(len(batch), 1).to(devices.device, devices.dtype),
+    }
+
+    c = self.conditioner(sdxl_conds)
 
     return c
 
@@ -26,7 +38,7 @@ def extend_sdxl(model):
     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_model = [x for x in model.conditioner.embedders if 'CLIPEmbedder' in type(x).__name__][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"
@@ -34,7 +46,14 @@ def extend_sdxl(model):
     discretization = sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization()
     model.alphas_cumprod = torch.asarray(discretization.alphas_cumprod, device=devices.device, dtype=dtype)
 
+    model.is_xl = True
+
 
 sgm.models.diffusion.DiffusionEngine.get_learned_conditioning = get_learned_conditioning
 sgm.models.diffusion.DiffusionEngine.apply_model = apply_model
 
+sgm.modules.attention.print = lambda *args: None
+sgm.modules.diffusionmodules.model.print = lambda *args: None
+sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None
+sgm.modules.encoders.modules.print = lambda *args: None
+
-- 
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