mirror of
https://github.com/nod-ai/SHARK-Studio.git
synced 2026-01-09 13:57:54 -05:00
91ab594744
* - fix overflowing text (a janky fix) - add DEISMultistep scheduler as an option - set default scheduler to DEISMultistep - set default CFG to 3.5 - set default steps to 16 - add `xzuyn/PhotoMerge` as a model option - add 3 new example prompts (which work nicely with PhotoMerge) - formatting * Set DEISMultistep in the cpu_only list instead * formatting * formatting * modify prompts * resize window to 81% & 85% monitor resolution instead of (WxH / 1.0625). * increase steps to 32 after some testing. somewhere in between 16 and 32 is best compromise on speed/quality for DEIS, so 32 steps to play it safe. * black line length 79 * revert settings DEIS as default scheduler. * add more schedulers & revert accidental DDIM change - add DPMSolverSingleStep, KDPM2AncestralDiscrete, & HeunDiscrete. - did not add `DPMSolverMultistepInverse` or `DDIMInverse` as they only output latent noise, there are a few I did not try adding yet. - accidentally set `upscaler_ui.py` to EulerDiscrete by default last commit while reverting DEIS changes. - add `xzuyn/PhotoMerge-inpainting` as an in or out painting model. * black line length 79 * add help section stuff and some other changes - list the rest of the schedulers in argument help section. - replace mutable default arguments. - increased default window height to 91% to remove any scrolling for the main txt2img page (tested on a 1920x1080 monitor). width is the same as its just enough to have the image output on the side instead of the bottom. - cleanup
93 lines
2.6 KiB
Python
93 lines
2.6 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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UpscalerPipeline,
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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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if __name__ == "__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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# When the models get uploaded, it should be defaulted to False.
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args.import_mlir = True
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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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image = (
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Image.open(args.img_path)
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.convert("RGB")
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.resize((args.height, args.width))
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)
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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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upscaler_obj = UpscalerPipeline.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_lora=args.use_lora,
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ddpm_scheduler=schedulers["DDPM"],
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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 = upscaler_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.noise_level,
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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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)
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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}, noise_level={args.noise_level}, 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 += upscaler_obj.log
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text_output += f"\nTotal image generation time: {total_time:.4f}sec"
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extra_info = {"NOISE LEVEL": args.noise_level}
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save_output_img(generated_imgs[0], seed, extra_info)
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print(text_output)
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