mirror of
https://github.com/nod-ai/SHARK-Studio.git
synced 2026-01-14 16:28:01 -05:00
128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
import sys
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import torch
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import time
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from PIL import Image
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import transformers
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from apps.stable_diffusion.src import (
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args,
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Image2ImagePipeline,
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StencilPipeline,
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resize_stencil,
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get_schedulers,
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set_init_device_flags,
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utils,
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clear_all,
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save_output_img,
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)
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from apps.stable_diffusion.src.utils import get_generation_text_info
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def main():
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if args.clear_all:
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clear_all()
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if args.img_path is None:
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print("Flag --img_path is required.")
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exit()
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image = Image.open(args.img_path).convert("RGB")
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# When the models get uploaded, it should be default to False.
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args.import_mlir = True
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use_stencil = args.use_stencil
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if use_stencil:
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args.scheduler = "DDIM"
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args.hf_model_id = "runwayml/stable-diffusion-v1-5"
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image, args.width, args.height = resize_stencil(image)
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elif "Shark" in args.scheduler:
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print(
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f"Shark schedulers are not supported. Switching to EulerDiscrete scheduler"
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)
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args.scheduler = "EulerDiscrete"
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cpu_scheduling = not args.scheduler.startswith("Shark")
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dtype = torch.float32 if args.precision == "fp32" else torch.half
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set_init_device_flags()
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schedulers = get_schedulers(args.hf_model_id)
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scheduler_obj = schedulers[args.scheduler]
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seed = utils.sanitize_seed(args.seed)
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# Adjust for height and width based on model
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if use_stencil:
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img2img_obj = StencilPipeline.from_pretrained(
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scheduler_obj,
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args.import_mlir,
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args.hf_model_id,
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args.ckpt_loc,
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args.custom_vae,
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args.precision,
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args.max_length,
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args.batch_size,
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args.height,
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args.width,
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args.use_base_vae,
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args.use_tuned,
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low_cpu_mem_usage=args.low_cpu_mem_usage,
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use_stencil=use_stencil,
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debug=args.import_debug if args.import_mlir else False,
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use_lora=args.use_lora,
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ondemand=args.ondemand,
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)
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else:
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img2img_obj = Image2ImagePipeline.from_pretrained(
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scheduler_obj,
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args.import_mlir,
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args.hf_model_id,
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args.ckpt_loc,
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args.custom_vae,
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args.precision,
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args.max_length,
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args.batch_size,
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args.height,
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args.width,
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args.use_base_vae,
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args.use_tuned,
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low_cpu_mem_usage=args.low_cpu_mem_usage,
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debug=args.import_debug if args.import_mlir else False,
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use_lora=args.use_lora,
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ondemand=args.ondemand,
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)
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start_time = time.time()
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generated_imgs = img2img_obj.generate_images(
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args.prompts,
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args.negative_prompts,
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image,
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args.batch_size,
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args.height,
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args.width,
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args.steps,
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args.strength,
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args.guidance_scale,
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seed,
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args.max_length,
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dtype,
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args.use_base_vae,
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cpu_scheduling,
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args.max_embeddings_multiples,
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use_stencil=use_stencil,
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)
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total_time = time.time() - start_time
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text_output = f"prompt={args.prompts}"
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text_output += f"\nnegative prompt={args.negative_prompts}"
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text_output += f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
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text_output += f"\nscheduler={args.scheduler}, device={args.device}"
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text_output += f"\nsteps={args.steps}, strength={args.strength}, guidance_scale={args.guidance_scale}, seed={seed}, size={args.height}x{args.width}"
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text_output += (
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f", batch size={args.batch_size}, max_length={args.max_length}"
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)
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text_output += img2img_obj.log
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text_output += f"\nTotal image generation time: {total_time:.4f}sec"
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extra_info = {"STRENGTH": args.strength}
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save_output_img(generated_imgs[0], seed, extra_info)
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print(text_output)
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if __name__ == "__main__":
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main()
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