94 lines
4.0 KiB
Python
94 lines
4.0 KiB
Python
from diffusers import DDPMScheduler, EulerDiscreteScheduler, StableDiffusionPipeline, StableDiffusionXLPipeline
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from peft import PeftModel
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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, sys
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sys.path.append('.')
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sys.path.append('..')
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from trainer.models import load_models, pretrained_models
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from trainer.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.utils.utils import seed_everything, pick_best_gpu_id
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from trainer.inference import encode_prompt_advanced
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from trainer.checkpoint import load_checkpoint
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if __name__ == "__main__":
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pretrained_model = pretrained_models['sdxl']
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lora_path = 'lora_models/xander_one_img--23_15-25-00-sdxl_face_lora_512_1.0_gpt4-v/checkpoints/checkpoint-360'
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lora_scales = np.linspace(0.6, 0.9, 4)
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token_scale = None # None means it well get automatically set using lora_scale
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render_size = (1024, 1024) # H,W
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n_imgs = 14
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n_loops = 2
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n_steps = 35
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guidance_scale = 7.5
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seed = 12
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use_lightning = 0
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#####################################################################################
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output_dir = f'rendered_images/{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 = load_checkpoint(
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pretrained_model_version="sdxl",
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pretrained_model_path=pretrained_models["sdxl"]["path"],
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checkpoint_folder=lora_path,
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is_lora=True,
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device="cuda:0"
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)
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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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if training_args["concept_mode"] == "style":
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validation_prompts_raw = random.choices(val_prompts['style'], k=n_imgs)
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elif training_args["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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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 jj in range(n_loops):
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for i in range(len(validation_prompts_raw)):
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for lora_scale in lora_scales:
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seed += 1
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#pipe = load_model(pretrained_model)
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#pipe.unet = PeftModel.from_pretrained(model = pipe.unet, model_id = lora_path, adapter_name = 'eden_lora')
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pipe = patch_pipe_with_lora(pipe, lora_path, lora_scale=lora_scale)
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generator = torch.Generator(device='cuda').manual_seed(seed)
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c, uc, pc, puc = encode_prompt_advanced(pipe, lora_path, validation_prompts_raw[i], negative_prompt, lora_scale, guidance_scale, concept_mode = training_args["concept_mode"], token_scale = token_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)
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seed += 1 |