| 12 | |
| 13 | |
| 14 | class SDImagePipeline(BasePipeline): |
| 15 | |
| 16 | def __init__(self, device="cuda", torch_dtype=torch.float16): |
| 17 | super().__init__(device=device, torch_dtype=torch_dtype) |
| 18 | self.scheduler = EnhancedDDIMScheduler() |
| 19 | self.prompter = SDPrompter() |
| 20 | # models |
| 21 | self.text_encoder: SDTextEncoder = None |
| 22 | self.unet: SDUNet = None |
| 23 | self.vae_decoder: SDVAEDecoder = None |
| 24 | self.vae_encoder: SDVAEEncoder = None |
| 25 | self.controlnet: MultiControlNetManager = None |
| 26 | self.ipadapter_image_encoder: IpAdapterCLIPImageEmbedder = None |
| 27 | self.ipadapter: SDIpAdapter = None |
| 28 | self.model_names = ['text_encoder', 'unet', 'vae_decoder', 'vae_encoder', 'controlnet', 'ipadapter_image_encoder', 'ipadapter'] |
| 29 | |
| 30 | |
| 31 | def denoising_model(self): |
| 32 | return self.unet |
| 33 | |
| 34 | |
| 35 | def fetch_models(self, model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[]): |
| 36 | # Main models |
| 37 | self.text_encoder = model_manager.fetch_model("sd_text_encoder") |
| 38 | self.unet = model_manager.fetch_model("sd_unet") |
| 39 | self.vae_decoder = model_manager.fetch_model("sd_vae_decoder") |
| 40 | self.vae_encoder = model_manager.fetch_model("sd_vae_encoder") |
| 41 | self.prompter.fetch_models(self.text_encoder) |
| 42 | self.prompter.load_prompt_refiners(model_manager, prompt_refiner_classes) |
| 43 | |
| 44 | # ControlNets |
| 45 | controlnet_units = [] |
| 46 | for config in controlnet_config_units: |
| 47 | controlnet_unit = ControlNetUnit( |
| 48 | Annotator(config.processor_id, device=self.device), |
| 49 | model_manager.fetch_model("sd_controlnet", config.model_path), |
| 50 | config.scale |
| 51 | ) |
| 52 | controlnet_units.append(controlnet_unit) |
| 53 | self.controlnet = MultiControlNetManager(controlnet_units) |
| 54 | |
| 55 | # IP-Adapters |
| 56 | self.ipadapter = model_manager.fetch_model("sd_ipadapter") |
| 57 | self.ipadapter_image_encoder = model_manager.fetch_model("sd_ipadapter_clip_image_encoder") |
| 58 | |
| 59 | |
| 60 | @staticmethod |
| 61 | def from_model_manager(model_manager: ModelManager, controlnet_config_units: List[ControlNetConfigUnit]=[], prompt_refiner_classes=[], device=None): |
| 62 | pipe = SDImagePipeline( |
| 63 | device=model_manager.device if device is None else device, |
| 64 | torch_dtype=model_manager.torch_dtype, |
| 65 | ) |
| 66 | pipe.fetch_models(model_manager, controlnet_config_units, prompt_refiner_classes=[]) |
| 67 | return pipe |
| 68 | |
| 69 | |
| 70 | def encode_image(self, image, tiled=False, tile_size=64, tile_stride=32): |
| 71 | latents = self.vae_encoder(image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) |