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| from __future__ import annotations | |
| import gc | |
| import pathlib | |
| import sys | |
| import gradio as gr | |
| import PIL.Image | |
| import numpy as np | |
| import torch | |
| from diffusers import StableDiffusionPipeline | |
| sys.path.insert(0, 'custom-diffusion') | |
| from sys import diffuser_training | |
| # def load_model(text_encoder, tokenizer, unet, save_path, modifier_token, freeze_model='crossattn_kv'): | |
| # st = torch.load(save_path) | |
| # if 'text_encoder' in st: | |
| # text_encoder.load_state_dict(st['text_encoder']) | |
| # if modifier_token in st: | |
| # _ = tokenizer.add_tokens(modifier_token) | |
| # modifier_token_id = tokenizer.convert_tokens_to_ids(modifier_token) | |
| # # Resize the token embeddings as we are adding new special tokens to the tokenizer | |
| # text_encoder.resize_token_embeddings(len(tokenizer)) | |
| # token_embeds = text_encoder.get_input_embeddings().weight.data | |
| # token_embeds[modifier_token_id] = st[modifier_token] | |
| # print(st.keys()) | |
| # for name, params in unet.named_parameters(): | |
| # if freeze_model == 'crossattn': | |
| # if 'attn2' in name: | |
| # params.data.copy_(st['unet'][f'{name}']) | |
| # else: | |
| # if 'attn2.to_k' in name or 'attn2.to_v' in name: | |
| # params.data.copy_(st['unet'][f'{name}']) | |
| class InferencePipeline: | |
| def __init__(self): | |
| self.pipe = None | |
| self.device = torch.device( | |
| 'cuda:0' if torch.cuda.is_available() else 'cpu') | |
| self.weight_path = None | |
| def clear(self) -> None: | |
| self.weight_path = None | |
| del self.pipe | |
| self.pipe = None | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| def get_weight_path(name: str) -> pathlib.Path: | |
| curr_dir = pathlib.Path(__file__).parent | |
| return curr_dir / name | |
| def load_pipe(self, model_id: str, filename: str) -> None: | |
| weight_path = self.get_weight_path(filename) | |
| if weight_path == self.weight_path: | |
| return | |
| self.weight_path = weight_path | |
| weight = torch.load(self.weight_path, map_location=self.device) | |
| if self.device.type == 'cpu': | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id) | |
| else: | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| model_id, torch_dtype=torch.float16) | |
| pipe = pipe.to(self.device) | |
| diffuser_training.load_model(pipe.text_encoder, pipe.tokenizer, pipe.unet, weight_path, '<new1>') | |
| self.pipe = pipe | |
| def run( | |
| self, | |
| base_model: str, | |
| weight_name: str, | |
| prompt: str, | |
| seed: int, | |
| n_steps: int, | |
| guidance_scale: float, | |
| eta: float, | |
| batch_size: int, | |
| ) -> PIL.Image.Image: | |
| if not torch.cuda.is_available(): | |
| raise gr.Error('CUDA is not available.') | |
| self.load_pipe(base_model, weight_name) | |
| generator = torch.Generator(device=self.device).manual_seed(seed) | |
| out = self.pipe([prompt]*batch_size, | |
| num_inference_steps=n_steps, | |
| guidance_scale=guidance_scale, | |
| eta = eta, | |
| generator=generator) # type: ignore | |
| out = out.images | |
| out = PIL.Image.fromarray(np.hstack([np.array(x) for x in out])) | |
| return out | |