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Zero
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from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="InstantX/InstantIR", filename="models/adapter.pt", local_dir=".")
hf_hub_download(repo_id="InstantX/InstantIR", filename="models/aggregator.pt", local_dir=".")
hf_hub_download(repo_id="InstantX/InstantIR", filename="models/previewer_lora_weights.bin", local_dir=".")
import torch
from PIL import Image
from diffusers import DDPMScheduler
from schedulers.lcm_single_step_scheduler import LCMSingleStepScheduler
from module.ip_adapter.utils import load_adapter_to_pipe
from pipelines.sdxl_instantir import InstantIRPipeline
def resize_img(input_image, max_side=1280, min_side=1024, size=None,
pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64):
w, h = input_image.size
if size is not None:
w_resize_new, h_resize_new = size
else:
# ratio = min_side / min(h, w)
# w, h = round(ratio*w), round(ratio*h)
ratio = max_side / max(h, w)
input_image = input_image.resize([round(ratio*w), round(ratio*h)], mode)
w_resize_new = (round(ratio * w) // base_pixel_number) * base_pixel_number
h_resize_new = (round(ratio * h) // base_pixel_number) * base_pixel_number
input_image = input_image.resize([w_resize_new, h_resize_new], mode)
if pad_to_max_side:
res = np.ones([max_side, max_side, 3], dtype=np.uint8) * 255
offset_x = (max_side - w_resize_new) // 2
offset_y = (max_side - h_resize_new) // 2
res[offset_y:offset_y+h_resize_new, offset_x:offset_x+w_resize_new] = np.array(input_image)
input_image = Image.fromarray(res)
return input_image
# prepare models under ./models
instantir_path = f'./models'
# load pretrained models
pipe = InstantIRPipeline.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16)
# load adapter
load_adapter_to_pipe(
pipe,
f"{instantir_path}/adapter.pt",
image_encoder_or_path = 'facebook/dinov2-large',
)
# load previewer lora
pipe.prepare_previewers(instantir_path)
pipe.scheduler = DDPMScheduler.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', subfolder="scheduler")
lcm_scheduler = LCMSingleStepScheduler.from_config(pipe.scheduler.config)
# load aggregator weights
pretrained_state_dict = torch.load(f"{instantir_path}/aggregator.pt")
pipe.aggregator.load_state_dict(pretrained_state_dict)
# send to GPU and fp16
pipe.to(device='cuda', dtype=torch.float16)
pipe.aggregator.to(device='cuda', dtype=torch.float16)
PROMPT = "Photorealistic, highly detailed, hyper detailed photo - realistic maximum detail, 32k, \
ultra HD, extreme meticulous detailing, skin pore detailing, \
hyper sharpness, perfect without deformations, \
taken using a Canon EOS R camera, Cinematic, High Contrast, Color Grading. "
NEG_PROMPT = "blurry, out of focus, unclear, depth of field, over-smooth, \
sketch, oil painting, cartoon, CG Style, 3D render, unreal engine, \
dirty, messy, worst quality, low quality, frames, painting, illustration, drawing, art, \
watermark, signature, jpeg artifacts, deformed, lowres"
def infer(prompt, input_image, steps=30, cfg_scale=7.0, guidance_end=1.0,
creative_restoration=False, seed=3407, height=1024, width=1024):
# load a broken image
low_quality_image = Image.open(input_image).convert("RGB")
lq = [resize_img(low_quality_image, size=(width, height))]
generator = torch.Generator(device=device).manual_seed(seed)
timesteps = [
i * (1000//steps) + pipe.scheduler.config.steps_offset for i in range(0, steps)
]
timesteps = timesteps[::-1]
prompt = PROMPT if len(prompt)==0 else prompt
neg_prompt = NEG_PROMPT
# InstantIR restoration
image = pipe(
prompt=[prompt]*len(lq),
image=lq,
num_inference_steps=steps,
generator=generator,
timesteps=timesteps,
negative_prompt=[neg_prompt]*len(lq),
guidance_scale=cfg_scale,
previewer_scheduler=lcm_scheduler,
).images[0]
return image
import gradio as gr
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
with gr.Column():
lq_img = gr.Image(label="Low-quality image", type="filepath")
with gr.Group():
prompt = gr.Textbox(label="Prompt", value="")
submit_btn = gr.Button("InstantIR magic!")
output_img = gr.Image(label="InstantIR restored")
submit_btn.click(
fn=infer,
inputs=[prompt, lq_img],
outputs=[output_img]
)
demo.launch(show_error=True) |