#!/usr/bin/env python3 from diffusers import DiffusionPipeline, DDPMScheduler import torch import time import os from pathlib import Path from huggingface_hub import HfApi import random import numpy as np from deepfloyd_if.modules import IFStageI, IFStageII, IFStageIII, T5Embedder import sys api = HfApi() start_time = time.time() seed = 0 use_diffusers = bool(int(sys.argv[1])) t5_pos_embeds = torch.load("/home/patrick/tensors/embeds_orig.pt").to("cuda") t5_neg_embeds = torch.load("/home/patrick/tensors/neg_embeds.pt").to("cuda") def seed_everything(seed=None): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = True return seed if use_diffusers: pipe = DiffusionPipeline.from_pretrained("/home/patrick/if-diff-ckpts/IF-I-IF-v1.0", torch_dtype=torch.float32, use_safetensors=True, text_encoder=None, safety_checker=None) config = dict(pipe.scheduler.config) config["timestep_spacing"] = "even_border" pipe.scheduler = DDPMScheduler.from_config(config) pipe.to("cuda") with torch.no_grad(): # text_embeddings = t5.get_text_embeddings([prompt]) seed_everything(0) out_image = pipe(prompt_embeds=t5_pos_embeds, negative_prompt_embeds=t5_neg_embeds, num_inference_steps=5).images[0] out_image.save("/home/patrick/images/if_diff.png") else: if_I = IFStageI(device="cuda", dir_or_name="/home/patrick/IF-I-IF-v1.0/", model_kwargs={"precision": "fp32"}) if_I_kwargs = {} if_I_kwargs['negative_t5_embs'] = t5_neg_embeds if_I_kwargs['seed'] = seed if_I_kwargs['t5_embs'] = t5_pos_embeds if_I_kwargs['aspect_ratio'] = "1:1" if_I_kwargs['progress'] = True if_I_kwargs['sample_timestep_respacing'] = '5' seed_everything(0) stageI_generations, _ = if_I.embeddings_to_image(**if_I_kwargs) if_I.to_images(stageI_generations)[0].save("/home/patrick/images/if_ref.png")