(controlnet, args, accelerator, weight_dtype, step, is_final_validation=False)
| 76 | |
| 77 | |
| 78 | def log_validation(controlnet, args, accelerator, weight_dtype, step, is_final_validation=False): |
| 79 | logger.info("Running validation... ") |
| 80 | |
| 81 | if not is_final_validation: |
| 82 | controlnet = accelerator.unwrap_model(controlnet) |
| 83 | else: |
| 84 | controlnet = SD3ControlNetModel.from_pretrained(args.output_dir, torch_dtype=weight_dtype) |
| 85 | |
| 86 | pipeline = StableDiffusion3ControlNetPipeline.from_pretrained( |
| 87 | args.pretrained_model_name_or_path, |
| 88 | controlnet=controlnet, |
| 89 | safety_checker=None, |
| 90 | revision=args.revision, |
| 91 | variant=args.variant, |
| 92 | torch_dtype=weight_dtype, |
| 93 | ) |
| 94 | pipeline = pipeline.to(torch.device(accelerator.device)) |
| 95 | pipeline.set_progress_bar_config(disable=True) |
| 96 | |
| 97 | if args.seed is None: |
| 98 | generator = None |
| 99 | else: |
| 100 | generator = torch.manual_seed(args.seed) |
| 101 | |
| 102 | if len(args.validation_image) == len(args.validation_prompt): |
| 103 | validation_images = args.validation_image |
| 104 | validation_prompts = args.validation_prompt |
| 105 | elif len(args.validation_image) == 1: |
| 106 | validation_images = args.validation_image * len(args.validation_prompt) |
| 107 | validation_prompts = args.validation_prompt |
| 108 | elif len(args.validation_prompt) == 1: |
| 109 | validation_images = args.validation_image |
| 110 | validation_prompts = args.validation_prompt * len(args.validation_image) |
| 111 | else: |
| 112 | raise ValueError( |
| 113 | "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`" |
| 114 | ) |
| 115 | |
| 116 | image_logs = [] |
| 117 | inference_ctx = contextlib.nullcontext() if is_final_validation else torch.autocast(accelerator.device.type) |
| 118 | |
| 119 | for validation_prompt, validation_image in zip(validation_prompts, validation_images): |
| 120 | validation_image = Image.open(validation_image).convert("RGB") |
| 121 | |
| 122 | images = [] |
| 123 | |
| 124 | for _ in range(args.num_validation_images): |
| 125 | with inference_ctx: |
| 126 | image = pipeline( |
| 127 | validation_prompt, control_image=validation_image, num_inference_steps=20, generator=generator |
| 128 | ).images[0] |
| 129 | |
| 130 | images.append(image) |
| 131 | |
| 132 | image_logs.append( |
| 133 | {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt} |
| 134 | ) |
| 135 |
no test coverage detected