Merge pull request #42 from chavinlo/inference-option
Add options and local inference
This commit is contained in:
Anthony Mercurio
GitHub
commit
c8eeaaf353
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@ -86,6 +86,8 @@ parser.add_argument('--clip_penultimate', type=bool_t, default='False', help='Us
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parser.add_argument('--output_bucket_info', type=bool_t, default='False', help='Outputs bucket information and exits')
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parser.add_argument('--resize', type=bool_t, default='False', help="Resizes dataset's images to the appropriate bucket dimensions.")
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parser.add_argument('--use_xformers', type=bool_t, default='False', help='Use memory efficient attention')
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parser.add_argument('--wandb', dest='enablewandb', type=bool_t, default='True', help='Enable WeightsAndBiases Reporting')
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parser.add_argument('--inference', dest='enableinference', type=bool_t, default='True', help='Enable Inference during training (Consumes 2GB of VRAM)')
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parser.add_argument('--extended_validation', type=bool_t, default='False', help='Perform extended validation of images to catch truncated or corrupt images.')
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parser.add_argument('--no_migration', type=bool_t, default='False', help='Do not perform migration of dataset while the `--resize` flag is active. Migration creates an adjacent folder to the dataset with <dataset_dirname>_cropped.')
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parser.add_argument('--skip_validation', type=bool_t, default='False', help='Skip validation of images, useful for speeding up loading of very large datasets that have already been validated.')
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@ -621,7 +623,12 @@ def main():
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if rank == 0:
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os.makedirs(args.output_path, exist_ok=True)
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run = wandb.init(project=args.project_id, name=args.run_name, config=vars(args), dir=args.output_path+'/wandb')
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mode = 'enabled'
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if args.enablewandb:
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mode = 'disabled'
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run = wandb.init(project=args.project_id, name=args.run_name, config=vars(args), dir=args.output_path+'/wandb', mode=mode)
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# Inform the user of host, and various versions -- useful for debugging issues.
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print("RUN_NAME:", args.run_name)
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@ -634,9 +641,16 @@ def main():
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print("FP16:", args.fp16)
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print("RESOLUTION:", args.resolution)
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if args.hf_token is None:
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args.hf_token = os.environ['HF_API_TOKEN']
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if args.hf_token is not None:
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print('It is recommended to set the HF_API_TOKEN environment variable instead of passing it as a command line argument since WandB will automatically log it.')
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else:
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try:
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args.hf_token = os.environ['HF_API_TOKEN']
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print("HF Token set via enviroment variable")
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except Exception:
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print("No HF Token detected in arguments or enviroment variable, setting it to none (as in string)")
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args.hf_token = "none"
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device = torch.device('cuda')
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@ -853,49 +867,68 @@ def main():
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if global_step % args.save_steps == 0:
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save_checkpoint(global_step)
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if global_step % args.image_log_steps == 0:
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if rank == 0:
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# get prompt from random batch
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prompt = tokenizer.decode(batch['input_ids'][random.randint(0, len(batch['input_ids'])-1)].tolist())
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if args.enableinference:
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if global_step % args.image_log_steps == 0:
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if rank == 0:
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# get prompt from random batch
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prompt = tokenizer.decode(batch['input_ids'][random.randint(0, len(batch['input_ids'])-1)].tolist())
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if args.image_log_scheduler == 'DDIMScheduler':
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print('using DDIMScheduler scheduler')
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scheduler = DDIMScheduler(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
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)
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else:
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print('using PNDMScheduler scheduler')
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scheduler=PNDMScheduler(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
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)
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if args.image_log_scheduler == 'DDIMScheduler':
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print('using DDIMScheduler scheduler')
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scheduler = DDIMScheduler(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
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)
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else:
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print('using PNDMScheduler scheduler')
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scheduler=PNDMScheduler(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", skip_prk_steps=True
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)
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pipeline = StableDiffusionPipeline(
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text_encoder=text_encoder,
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vae=vae,
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unet=unet,
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tokenizer=tokenizer,
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scheduler=scheduler,
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safety_checker=None, # disable safety checker to save memory
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feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
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).to(device)
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# inference
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images = []
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with torch.no_grad():
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with torch.autocast('cuda', enabled=args.fp16):
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for _ in range(args.image_log_amount):
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images.append(
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wandb.Image(pipeline(
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prompt, num_inference_steps=args.image_log_inference_steps
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).images[0],
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caption=prompt)
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)
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# log images under single caption
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run.log({'images': images}, step=global_step)
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pipeline = StableDiffusionPipeline(
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text_encoder=text_encoder,
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vae=vae,
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unet=unet,
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tokenizer=tokenizer,
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scheduler=scheduler,
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safety_checker=None, # disable safety checker to save memory
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feature_extractor=CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32"),
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).to(device)
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# inference
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if args.enablewandb:
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images = []
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else:
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saveInferencePath = args.output_path + "/inference"
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os.makedirs(saveInferencePath, exist_ok=True)
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with torch.no_grad():
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with torch.autocast('cuda', enabled=args.fp16):
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for _ in range(args.image_log_amount):
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if args.enablewandb:
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images.append(
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wandb.Image(pipeline(
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prompt, num_inference_steps=args.image_log_inference_steps
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).images[0],
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caption=prompt)
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)
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else:
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from datetime import datetime
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images = pipeline(prompt, num_inference_steps=args.image_log_inference_steps).images[0]
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filenameImg = str(time.time_ns()) + ".png"
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filenameTxt = str(time.time_ns()) + ".txt"
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images.save(saveInferencePath + "/" + filenameImg)
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with open(saveInferencePath + "/" + filenameTxt, 'a') as f:
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f.write('Used prompt: ' + prompt + '\n')
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f.write('Generated Image Filename: ' + filenameImg + '\n')
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f.write('Generated at: ' + str(global_step) + ' steps' + '\n')
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f.write('Generated at: ' + str(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))+ '\n')
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# cleanup so we don't run out of memory
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del pipeline
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gc.collect()
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torch.distributed.barrier()
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# log images under single caption
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if args.enablewandb:
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run.log({'images': images}, step=global_step)
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# cleanup so we don't run out of memory
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del pipeline
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gc.collect()
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torch.distributed.barrier()
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except Exception as e:
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print(f'Exception caught on rank {rank} at step {global_step}, saving checkpoint...\n{e}\n{traceback.format_exc()}')
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pass
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