(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage)
| 95 | |
| 96 | |
| 97 | def convert_super_res_pipeline(tokenizer, text_encoder, feature_extractor, safety_checker, args, stage): |
| 98 | if stage == 2: |
| 99 | unet_checkpoint_path = args.unet_checkpoint_path_stage_2 |
| 100 | sample_size = None |
| 101 | dump_path = args.dump_path_stage_2 |
| 102 | elif stage == 3: |
| 103 | unet_checkpoint_path = args.unet_checkpoint_path_stage_3 |
| 104 | sample_size = 1024 |
| 105 | dump_path = args.dump_path_stage_3 |
| 106 | else: |
| 107 | assert False |
| 108 | |
| 109 | unet = get_super_res_unet(unet_checkpoint_path, verify_param_count=False, sample_size=sample_size) |
| 110 | |
| 111 | image_noising_scheduler = DDPMScheduler( |
| 112 | beta_schedule="squaredcos_cap_v2", |
| 113 | ) |
| 114 | |
| 115 | scheduler = DDPMScheduler( |
| 116 | variance_type="learned_range", |
| 117 | beta_schedule="squaredcos_cap_v2", |
| 118 | prediction_type="epsilon", |
| 119 | thresholding=True, |
| 120 | dynamic_thresholding_ratio=0.95, |
| 121 | sample_max_value=1.0, |
| 122 | ) |
| 123 | |
| 124 | pipe = IFSuperResolutionPipeline( |
| 125 | tokenizer=tokenizer, |
| 126 | text_encoder=text_encoder, |
| 127 | unet=unet, |
| 128 | scheduler=scheduler, |
| 129 | image_noising_scheduler=image_noising_scheduler, |
| 130 | safety_checker=safety_checker, |
| 131 | feature_extractor=feature_extractor, |
| 132 | requires_safety_checker=True, |
| 133 | ) |
| 134 | |
| 135 | pipe.save_pretrained(dump_path) |
| 136 | |
| 137 | |
| 138 | def get_stage_1_unet(unet_config, unet_checkpoint_path): |
no test coverage detected