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import safetensors.torch as sf |
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import torch |
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from huggingface_guess import model_list |
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from backend import memory_management, utils |
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from backend.args import dynamic_args |
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from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects |
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from backend.patcher.clip import CLIP |
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from backend.patcher.unet import UnetPatcher |
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from backend.patcher.vae import VAE |
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from backend.text_processing.classic_engine import ClassicTextProcessingEngine |
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class StableDiffusion(ForgeDiffusionEngine): |
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matched_guesses = [model_list.SD15] |
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def __init__(self, estimated_config, huggingface_components): |
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super().__init__(estimated_config, huggingface_components) |
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clip = CLIP(model_dict={"clip_l": huggingface_components["text_encoder"]}, tokenizer_dict={"clip_l": huggingface_components["tokenizer"]}) |
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vae = VAE(model=huggingface_components["vae"]) |
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unet = UnetPatcher.from_model(model=huggingface_components["unet"], diffusers_scheduler=huggingface_components["scheduler"], config=estimated_config) |
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self.text_processing_engine = ClassicTextProcessingEngine( |
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text_encoder=clip.cond_stage_model.clip_l, |
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tokenizer=clip.tokenizer.clip_l, |
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embedding_dir=dynamic_args["embedding_dir"], |
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embedding_key="clip_l", |
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embedding_expected_shape=768, |
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text_projection=False, |
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minimal_clip_skip=1, |
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clip_skip=1, |
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return_pooled=False, |
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final_layer_norm=True, |
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) |
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self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None) |
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self.forge_objects_original = self.forge_objects.shallow_copy() |
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self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy() |
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self.is_sd1 = True |
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def set_clip_skip(self, clip_skip): |
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self.text_processing_engine.clip_skip = clip_skip |
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@torch.inference_mode() |
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def get_learned_conditioning(self, prompt: list[str]): |
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memory_management.load_model_gpu(self.forge_objects.clip.patcher) |
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cond = self.text_processing_engine(prompt) |
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return cond |
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@torch.inference_mode() |
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def get_prompt_lengths_on_ui(self, prompt): |
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_, token_count = self.text_processing_engine.process_texts([prompt]) |
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return token_count, self.text_processing_engine.get_target_prompt_token_count(token_count) |
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@torch.inference_mode() |
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def encode_first_stage(self, x): |
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sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5) |
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sample = self.forge_objects.vae.first_stage_model.process_in(sample) |
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return sample.to(x) |
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@torch.inference_mode() |
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def decode_first_stage(self, x): |
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sample = self.forge_objects.vae.first_stage_model.process_out(x) |
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sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0 |
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return sample.to(x) |
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def save_checkpoint(self, filename): |
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sd = {} |
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sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model.")) |
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sd.update(model_list.SD15.process_clip_state_dict_for_saving(self, utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix=""))) |
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sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="first_stage_model.")) |
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sf.save_file(sd, filename) |
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return filename |
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