add sd2 template

backend/diffusion_engine/sd20.py

@@ -0,0 +1,81 @@
+import torch
+
+from huggingface_guess import model_list
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.vae import VAE
+from backend.patcher.unet import UnetPatcher
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.args import dynamic_args
+from backend import memory_management
+
+
+class StableDiffusion2(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SD20]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={
+                'clip_h': huggingface_components['text_encoder']
+            },
+            tokenizer_dict={
+                'clip_h': huggingface_components['tokenizer']
+            }
+        )
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['unet'],
+            diffusers_scheduler=huggingface_components['scheduler']
+        )
+
+        self.text_processing_engine = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_h,
+            tokenizer=clip.tokenizer.clip_h,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_h',
+            embedding_expected_shape=1024,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=False,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=False,
+            final_layer_norm=True,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sd2 = True
+        self.first_stage_model = vae.first_stage_model
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        cond = self.text_processing_engine(prompt)
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine.process_texts([prompt])
+        return token_count, self.text_processing_engine.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)

backend/loader.py

@@ -13,10 +13,11 @@ from backend.nn.clip import IntegratedCLIP, CLIPTextConfig
 from backend.nn.unet import IntegratedUNet2DConditionModel
 
 from backend.diffusion_engine.sd15 import StableDiffusion
+from backend.diffusion_engine.sd20 import StableDiffusion2
 from backend.diffusion_engine.sdxl import StableDiffusionXL
 
 
-possible_models = [StableDiffusion, StableDiffusionXL]
+possible_models = [StableDiffusion, StableDiffusion2, StableDiffusionXL]
 
 
 logging.getLogger("diffusers").setLevel(logging.ERROR)