sd3.5 integration (naive)

backend/diffusion_engine/sd35.py

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+import torch
+
+from huggingface_guess import model_list
+# from huggingface_guess.latent import SD3
+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.text_processing.t5_engine import T5TextProcessingEngine
+from backend.args import dynamic_args
+from backend import memory_management
+from backend.modules.k_prediction import PredictionDiscreteFlow
+
+class StableDiffusion3(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SD35]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={
+                'clip_l': huggingface_components['text_encoder'],
+                'clip_g': huggingface_components['text_encoder_2'],
+                't5xxl': huggingface_components['text_encoder_3']
+            },
+            tokenizer_dict={
+                'clip_l': huggingface_components['tokenizer'],
+                'clip_g': huggingface_components['tokenizer_2'],
+                't5xxl': huggingface_components['tokenizer_3']
+            }
+        )
+
+        k_predictor = PredictionDiscreteFlow( shift=3.0)
+
+        vae = VAE(model=huggingface_components['vae'])
+
+        unet = UnetPatcher.from_model(
+            model=huggingface_components['transformer'],
+            diffusers_scheduler= None,
+            k_predictor=k_predictor,
+            config=estimated_config
+        )
+
+        self.text_processing_engine_l = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_l',
+            embedding_expected_shape=768,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=True,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.text_processing_engine_g = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_g,
+            tokenizer=clip.tokenizer.clip_g,
+            embedding_dir=dynamic_args['embedding_dir'],
+            embedding_key='clip_g',
+            embedding_expected_shape=1280,
+            emphasis_name=dynamic_args['emphasis_name'],
+            text_projection=True,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.text_processing_engine_t5 = T5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.t5xxl,
+            tokenizer=clip.tokenizer.t5xxl,
+            emphasis_name=dynamic_args['emphasis_name'],
+        )
+
+
+        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_sd3 = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_l.clip_skip = clip_skip
+        self.text_processing_engine_g.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_g, g_pooled = self.text_processing_engine_g(prompt)
+        cond_l, l_pooled = self.text_processing_engine_l(prompt)
+        # if enabled?
+        cond_t5 = self.text_processing_engine_t5(prompt)
+
+        is_negative_prompt = getattr(prompt, 'is_negative_prompt', False)
+
+        force_zero_negative_prompt = is_negative_prompt and all(x == '' for x in prompt)
+
+        if force_zero_negative_prompt:
+            l_pooled = torch.zeros_like(l_pooled)
+            g_pooled = torch.zeros_like(g_pooled)
+            cond_l = torch.zeros_like(cond_l)
+            cond_g = torch.zeros_like(cond_g)
+            cond_t5 = torch.zeros_like(cond_t5)
+
+        cond_lg = torch.cat([cond_l, cond_g], dim=-1)
+        cond_lg = torch.nn.functional.pad(cond_lg, (0, 4096 - cond_lg.shape[-1]))
+
+        cond = dict(
+            crossattn=torch.cat([cond_lg, cond_t5], dim=-2),
+            vector=torch.cat([l_pooled, g_pooled], dim=-1),
+        )
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(255, 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/huggingface/stabilityai/stable-diffusion-3.5-large/model_index.json

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+{
+  "_class_name": "StableDiffusion3Pipeline",
+  "_diffusers_version": "0.30.3.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "text_encoder_3": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_3": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "SD3Transformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

backend/huggingface/stabilityai/stable-diffusion-3.5-large/scheduler/scheduler_config.json

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+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.29.0.dev0",
+  "num_train_timesteps": 1000,
+  "shift": 3.0
+}

backend/huggingface/stabilityai/stable-diffusion-3.5-large/text_encoder/config.json

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+{
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "vocab_size": 49408
+}

backend/huggingface/stabilityai/stable-diffusion-3.5-large/text_encoder_2/config.json

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+{
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "pad_token_id": 1,
+  "projection_dim": 1280,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "vocab_size": 49408
+}

backend/huggingface/stabilityai/stable-diffusion-3.5-large/text_encoder_3/config.json

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+{
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 32128
+}