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https://github.com/nod-ai/AMD-SHARK-Studio.git
synced 2026-04-03 03:00:17 -04:00
[SD] Add upscalar pipeline
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit is contained in:
Gaurav Shukla
260
apps/stable_diffusion/scripts/upscaler.py
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260
apps/stable_diffusion/scripts/upscaler.py
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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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from dataclasses import dataclass
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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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@dataclass
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class Config:
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model_id: str
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ckpt_loc: str
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precision: str
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batch_size: int
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max_length: int
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height: int
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width: int
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device: str
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upscaler_obj = None
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config_obj = None
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schedulers = None
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# set initial values of iree_vulkan_target_triple, use_tuned and import_mlir.
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init_iree_vulkan_target_triple = args.iree_vulkan_target_triple
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init_use_tuned = args.use_tuned
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init_import_mlir = args.import_mlir
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# Exposed to UI.
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def upscaler_inf(
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prompt: str,
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negative_prompt: str,
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init_image: Image,
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height: int,
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width: int,
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steps: int,
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noise_level: int,
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guidance_scale: float,
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seed: int,
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batch_count: int,
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batch_size: int,
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scheduler: str,
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custom_model: str,
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hf_model_id: str,
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precision: str,
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device: str,
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max_length: int,
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save_metadata_to_json: bool,
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save_metadata_to_png: bool,
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):
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global upscaler_obj
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global config_obj
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global schedulers
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args.prompts = [prompt]
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args.negative_prompts = [negative_prompt]
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args.guidance_scale = guidance_scale
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args.seed = seed
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args.steps = steps
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args.scheduler = scheduler
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if init_image is None:
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return None, "An Initial Image is required"
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image = init_image.convert("RGB").resize((args.height, args.width))
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# set ckpt_loc and hf_model_id.
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# set ckpt_loc and hf_model_id.
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types = (
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".ckpt",
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".safetensors",
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) # the tuple of file types
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args.ckpt_loc = ""
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args.hf_model_id = ""
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if custom_model == "None":
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if not hf_model_id:
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return (
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None,
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"Please provide either custom model or huggingface model ID, both must not be empty",
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)
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args.hf_model_id = hf_model_id
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elif ".ckpt" in custom_model or ".safetensors" in custom_model:
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args.ckpt_loc = custom_model
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else:
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args.hf_model_id = custom_model
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args.save_metadata_to_json = save_metadata_to_json
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args.write_metadata_to_png = save_metadata_to_png
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dtype = torch.float32 if precision == "fp32" else torch.half
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cpu_scheduling = not scheduler.startswith("Shark")
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new_config_obj = Config(
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args.hf_model_id,
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args.ckpt_loc,
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precision,
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batch_size,
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max_length,
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height,
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width,
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device,
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)
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if not upscaler_obj or config_obj != new_config_obj:
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config_obj = new_config_obj
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args.batch_size = batch_size
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args.max_length = max_length
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args.height = height
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args.width = width
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args.device = device.split("=>", 1)[1].strip()
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args.iree_vulkan_target_triple = init_iree_vulkan_target_triple
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args.use_tuned = init_use_tuned
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args.import_mlir = init_import_mlir
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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 args.hf_model_id
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else "stabilityai/stable-diffusion-2-1-base"
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)
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schedulers = get_schedulers(model_id)
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scheduler_obj = schedulers[scheduler]
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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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)
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upscaler_obj.scheduler = schedulers[scheduler]
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upscaler_obj.low_res_scheduler = schedulers["DDPM"]
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start_time = time.time()
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upscaler_obj.log = ""
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generated_imgs = []
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seeds = []
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img_seed = utils.sanitize_seed(seed)
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extra_info = {"NOISE LEVEL": noise_level}
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for current_batch in range(batch_count):
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if current_batch > 0:
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img_seed = utils.sanitize_seed(-1)
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out_imgs = upscaler_obj.generate_images(
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prompt,
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negative_prompt,
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image,
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batch_size,
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height,
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width,
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steps,
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noise_level,
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guidance_scale,
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img_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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)
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save_output_img(out_imgs[0], img_seed, extra_info)
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generated_imgs.extend(out_imgs)
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seeds.append(img_seed)
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upscaler_obj.log += "\n"
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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={device}"
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text_output += f"\nsteps={steps}, noise_level={noise_level}, guidance_scale={guidance_scale}, seed={seeds}"
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text_output += f"\nsize={height}x{width}, batch_count={batch_count}, batch_size={batch_size}, max_length={args.max_length}"
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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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return generated_imgs, text_output
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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 default 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 = Image.open(args.img_path).convert("RGB").resize((args.height, args.width))
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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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ddpm_scheduler=schedulers["DDPM"],
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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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)
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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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