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Running
on
Zero
| import torch | |
| from diffusers.pipelines.flux.pipeline_flux_fill_unmasked_image_condition_version import FluxFillPipeline_token12_depth as FluxFillPipeline | |
| from diffusers.utils import load_image | |
| import os, glob | |
| import numpy as np | |
| import cv2 | |
| from PIL import Image | |
| image_path = ["example_data/I-210618_I01001_W01_I-210618_I01001_W01_F0153_img.jpg"] | |
| pipe = FluxFillPipeline.from_pretrained("black-forest-labs/FLUX.1-Fill-dev", torch_dtype=torch.bfloat16).to("cuda") | |
| pipe.load_lora_weights("stage2/checkpoint-20000") | |
| for image_ep in image_path: | |
| image = Image.open(image_ep) | |
| mask = Image.new("L", image.size, 0) # place_hold | |
| depth_path = image_ep.replace("_img.jpg", "_depth_img.png") | |
| depth_image = Image.open(depth_path) | |
| depth = Image.open(depth_path.replace("_img", "_img_fill_in")) | |
| image_name = os.path.basename(image_ep) | |
| orig_w, orig_h = image.size | |
| w, h = image.size | |
| MAX_SIZE = 1024 | |
| if max(w, h) > MAX_SIZE: | |
| factor = MAX_SIZE / max(w, h) | |
| w = int(factor * w) | |
| h = int(factor * h) | |
| width, height = map(lambda x: x - x % 64, (w, h)) | |
| # # Resize to 1024 × 1024 | |
| target_size = (width, height) | |
| # target_size = (1024, 1024) | |
| # image_resized = image.resize(target_size, Image.BICUBIC) | |
| # mask_resized = mask.resize(target_size, Image.NEAREST) | |
| # depth_resized = depth.resize(target_size, Image.BICUBIC) | |
| # depth_image_resized = depth_image.resize(target_size, Image.BICUBIC) | |
| image = pipe( | |
| prompt="A beautiful scene", | |
| image=image, | |
| mask_image=mask, | |
| width=target_size[0], | |
| height=target_size[1], | |
| guidance_scale=30, | |
| num_inference_steps=50, | |
| max_sequence_length=512, | |
| generator=torch.Generator("cpu").manual_seed(0), | |
| depth=depth, | |
| depth_image=depth_image, | |
| ).images[0] | |
| image_final = image.resize((orig_w * 3, orig_h), Image.BICUBIC) | |
| output_dir = "./test_images/" | |
| os.makedirs(output_dir, exist_ok=True) | |
| image_final.save(os.path.join(output_dir,image_name)) | |