Datasets:
Tasks:
Image Classification
Modalities:
Image
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K<n<10K
License:
| import os | |
| from PIL.Image import Image | |
| from diffusers import StableDiffusionPipeline as SDP | |
| from diffusers import AutoPipelineForText2Image as AP | |
| from diffusers import DiffusionPipeline as DP | |
| import torch | |
| import json | |
| import random | |
| DEVICE = "mps" if torch.backends.mps.is_available() else \ | |
| "cuda" if torch.cuda.is_available() else "cpu" | |
| # Model 1 | |
| pipe = SDP.from_pretrained("nitrosocke/Ghibli-Diffusion", | |
| torch_dtype=torch.float16).to(DEVICE) | |
| ID_PREFIX = "nitrosocke" | |
| # Model 2 | |
| # pipe = AP.from_pretrained("black-forest-labs/FLUX.1-dev", | |
| # torch_dtype=torch.bfloat16).to(DEVICE) | |
| # pipe.load_lora_weights('openfree/flux-chatgpt-ghibli-lora', | |
| # weight_name='flux-chatgpt-ghibli-lora.safetensors') | |
| # ID_PREFIX = "openfree" | |
| # Model 3 | |
| # pipe = DP.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", | |
| # torch_dtype=torch.float16, variant="fp16", | |
| # use_safetensors=True,).to(DEVICE) | |
| # pipe.load_lora_weights("KappaNeuro/studio-ghibli-style") | |
| # pipe.to(DEVICE) | |
| # pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| # pipe.enable_model_cpu_offload() | |
| # ID_PREFIX = "KappaNeuro" | |
| NUM_IMAGES = 3 | |
| out_dir = f"data/{ID_PREFIX}" | |
| os.makedirs(out_dir, exist_ok=True) | |
| with open("metadata.jsonl", "r", encoding="utf-8") as fin, \ | |
| open("ai_entries.jsonl", "w", encoding="utf-8") as fout, \ | |
| open("pairs.jsonl", "a", encoding="utf-8") as pairs: | |
| for i, line in enumerate(fin): | |
| sample = json.loads(line) | |
| if sample["label"] != "real": | |
| continue | |
| description = sample["description"] | |
| real_id: str = sample["id"] | |
| aigen_id = real_id.replace("real", ID_PREFIX) | |
| prompt = f"ghibli style, {description}" | |
| seeds = [random.randrange(2**32) for _ in range(NUM_IMAGES)] | |
| gens = [torch.Generator(device=DEVICE).manual_seed(s) for s in seeds] | |
| images: list[Image] = pipe(prompt, num_images_per_prompt=NUM_IMAGES, generator=gens).images | |
| src_path = sample["image"] | |
| src_file: str = os.path.basename(src_path) | |
| file_noext = src_file.split(".")[0] | |
| for j, image in enumerate(images): | |
| img_id = f"{aigen_id}-{j}" | |
| dst_path = os.path.join(out_dir, f"{file_noext}_{j}.jpg") | |
| fout.write(json.dumps({ | |
| "id": img_id, | |
| "image": dst_path, | |
| "label": ID_PREFIX, | |
| "description": description, | |
| }) + "\n") | |
| pairs.write(json.dumps({ | |
| "real_image": src_path, | |
| "ai_image": dst_path, | |
| "description": description, | |
| "seed": seeds[j] | |
| }) + "\n") | |
| image.save(dst_path) | |