84 lines
3.4 KiB
Python
84 lines
3.4 KiB
Python
from trainer.models import load_models, pretrained_models
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from trainer.utils.lora import patch_pipe_with_lora
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from trainer.utils.val_prompts import val_prompts
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from trainer.utils.io import make_validation_img_grid
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from trainer.dataset_and_utils import pick_best_gpu_id
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from trainer.utils.seed import seed_everything
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from diffusers import EulerDiscreteScheduler
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from trainer.utils.inference import encode_prompt_advanced
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import numpy as np
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import torch
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from huggingface_hub import hf_hub_download
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import os, json, random, time
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if __name__ == "__main__":
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pretrained_model = pretrained_models['sdxl']
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lora_path = 'lora_models/plantoid_best--05_21-39-46-sdxl_object_dora/checkpoints/checkpoint-400'
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lora_scale = 0.5
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render_size = (1024+1024, 1024) # H,W
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n_imgs = 30
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n_steps = 25
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guidance_scale = 8
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seed = 1
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use_lightning = False
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#####################################################################################
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output_dir = f'test_images4/{lora_path.split("/")[-1]}'
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os.makedirs(output_dir, exist_ok=True)
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seed_everything(seed)
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pick_best_gpu_id()
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(pipe,
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tokenizer_one,
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tokenizer_two,
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noise_scheduler,
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text_encoder_one,
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text_encoder_two,
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vae,
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unet) = load_models(pretrained_model, 'cuda', torch.float16)
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if use_lightning:
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repo = "ByteDance/SDXL-Lightning"
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ckpt = "sdxl_lightning_8step_lora.safetensors" # Use the correct ckpt for your step setting!
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pipe.load_lora_weights(hf_hub_download(repo, ckpt))
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pipe.fuse_lora()
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n_steps = 8
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guidance_scale=1.5
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with open(os.path.join(lora_path, "training_args.json"), "r") as f:
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training_args = json.load(f)
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concept_mode = training_args["concept_mode"]
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if concept_mode == "style":
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validation_prompts_raw = random.choices(val_prompts['style'], k=n_imgs)
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elif concept_mode == "face":
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validation_prompts_raw = random.choices(val_prompts['face'], k=n_imgs)
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else:
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validation_prompts_raw = random.choices(val_prompts['object'], k=n_imgs)
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pipe = patch_pipe_with_lora(pipe, lora_path, lora_scale=lora_scale)
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) #, timestep_spacing="trailing")
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generator = torch.Generator(device='cuda').manual_seed(seed)
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negative_prompt = "nude, naked, poorly drawn face, ugly, tiling, out of frame, extra limbs, disfigured, deformed body, blurry, blurred, watermark, text, grainy, signature, cut off, draft"
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pipeline_args = {
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"num_inference_steps": n_steps,
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"guidance_scale": guidance_scale,
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"height": render_size[0],
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"width": render_size[1],
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}
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for i in range(len(validation_prompts_raw)):
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c, uc, pc, puc = encode_prompt_advanced(pipe, lora_path, validation_prompts_raw[i], negative_prompt, lora_scale, guidance_scale)
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pipeline_args['prompt_embeds'] = c
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pipeline_args['negative_prompt_embeds'] = uc
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if pretrained_model['version'] == 'sdxl':
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pipeline_args['pooled_prompt_embeds'] = pc
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pipeline_args['negative_pooled_prompt_embeds'] = puc
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image = pipe(**pipeline_args, generator=generator).images[0]
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image.save(os.path.join(output_dir, f"{validation_prompts_raw[i][:40]}_seed_{seed}_{i}_lora_scale_{lora_scale:.2f}_{int(time.time())}.jpg"), format="JPEG", quality=95) |