| import torch |
| import os |
|
|
| import numpy as np |
| from hashlib import md5 |
| def hash_img(img): |
| return md5(np.array(img).tobytes()).hexdigest() |
| def hash_any(obj): |
| return md5(str(obj).encode()).hexdigest() |
|
|
| def refine_lr_with_sd(pil_image_list, concept_img_list, control_image_list, prompt_list, pipe=None, strength=0.35, neg_prompt_list="", output_size=(512, 512), controlnet_conditioning_scale=1.): |
| with torch.no_grad(): |
| images = pipe( |
| image=pil_image_list, |
| ip_adapter_image=concept_img_list, |
| prompt=prompt_list, |
| neg_prompt=neg_prompt_list, |
| num_inference_steps=50, |
| strength=strength, |
| height=output_size[0], |
| width=output_size[1], |
| control_image=control_image_list, |
| guidance_scale=5.0, |
| controlnet_conditioning_scale=controlnet_conditioning_scale, |
| generator=torch.manual_seed(233), |
| ).images |
| return images |
|
|
| SR_cache = None |
|
|
| def run_sr_fast(source_pils, scale=4): |
| from PIL import Image |
| from scripts.upsampler import RealESRGANer |
| import numpy as np |
| global SR_cache |
| if SR_cache is not None: |
| upsampler = SR_cache |
| else: |
| upsampler = RealESRGANer( |
| scale=4, |
| onnx_path="ckpt/realesrgan-x4.onnx", |
| tile=0, |
| tile_pad=10, |
| pre_pad=0, |
| half=True, |
| gpu_id=0, |
| ) |
| ret_pils = [] |
| for idx, img_pils in enumerate(source_pils): |
| np_in = isinstance(img_pils, np.ndarray) |
| assert isinstance(img_pils, (Image.Image, np.ndarray)) |
| img = np.array(img_pils) |
| output, _ = upsampler.enhance(img, outscale=scale) |
| if np_in: |
| ret_pils.append(output) |
| else: |
| ret_pils.append(Image.fromarray(output)) |
| if SR_cache is None: |
| SR_cache = upsampler |
| return ret_pils |
|
|
