mirror of
https://github.com/nod-ai/SHARK-Studio.git
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a2a436eb0c
* Generates the seeds for all batch_count batches being run up front rather than generating the seed for a batch just before it is run. * Adds a --repeatable_seeds argument defaulting to False * When repeatable_seeds=True, the first seed for a set of batches will also be used as the rng seed for the subsequent batch seeds in the run. The rng seed is then reset. * When repeatable_seeds=False, batch seeding works as currently. * Update scripts under apps/scripts that support the batch_count argument to also support the repeatable_seeds argument. * UI/Web: Adds a checkbox element on each SD tab after batch count/size for toggling repeatable seeds, and update _inf functions to take this into account. * UI/Web: Moves the Stop buttons out of the Advanced sections and next to Generate to make things not fit quite so badly with the extra UI elements. * UI/Web: Fixes logging to the upscaler output text box not working correctly when running multiple batches.
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
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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InpaintPipeline,
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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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if args.mask_path is None:
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print("Flag --mask_path is required.")
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exit()
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dtype = torch.float32 if args.precision == "fp32" else torch.half
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cpu_scheduling = not args.scheduler.startswith("Shark")
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set_init_device_flags()
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model_id = (
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args.hf_model_id
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if "inpaint" in args.hf_model_id
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else "stabilityai/stable-diffusion-2-inpainting"
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)
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schedulers = get_schedulers(model_id)
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scheduler_obj = schedulers[args.scheduler]
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seed = args.seed
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image = Image.open(args.img_path)
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mask_image = Image.open(args.mask_path)
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inpaint_obj = InpaintPipeline.from_pretrained(
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scheduler=scheduler_obj,
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import_mlir=args.import_mlir,
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model_id=args.hf_model_id,
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ckpt_loc=args.ckpt_loc,
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custom_vae=args.custom_vae,
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precision=args.precision,
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max_length=args.max_length,
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batch_size=args.batch_size,
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height=args.height,
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width=args.width,
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use_base_vae=args.use_base_vae,
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use_tuned=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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seeds = utils.batch_seeds(seed, args.batch_count, args.repeatable_seeds)
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for current_batch in range(args.batch_count):
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start_time = time.time()
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generated_imgs = inpaint_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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mask_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.inpaint_full_res,
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args.inpaint_full_res_padding,
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args.steps,
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args.guidance_scale,
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seeds[current_batch],
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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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)
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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 += (
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f"\nmodel_id={args.hf_model_id}, ckpt_loc={args.ckpt_loc}"
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)
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text_output += f"\nscheduler={args.scheduler}, device={args.device}"
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text_output += (
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f"\nsteps={args.steps}, guidance_scale={args.guidance_scale},"
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)
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text_output += f"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 += inpaint_obj.log
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text_output += f"\nTotal image generation time: {total_time:.4f}sec"
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save_output_img(generated_imgs[0], seed)
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print(text_output)
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if __name__ == "__main__":
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main()
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