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import random |
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import io |
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import zipfile |
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import numpy as np |
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from PIL.PngImagePlugin import PngInfo |
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from PIL import Image |
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from curl_cffi import requests |
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from tqdm import tqdm |
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jwt_token = "" |
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random_seed = random.randint(0, 2**32 - 1) |
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url = "https://image.novelai.net/ai/generate-image" |
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headers = { |
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"Authorization": f"Bearer {jwt_token}", |
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"Accept": "application/json, text/plain, */*", |
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"Content-Type": "application/json", |
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"Origin": "https://novelai.net", |
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"Referer": "https://novelai.net/" |
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} |
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QUALITY_TAGS = "best quality, amazing quality, very aesthetic, absurdres" |
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def generate(prompt="1girl, best quality, amazing quality, very aesthetic, absurdres"): |
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payload = { |
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"action": "generate", |
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"input": f'{prompt}, best quality, amazing quality, very aesthetic, absurdres', |
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"model": "nai-diffusion-3", |
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"parameters": { |
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"width": 832, |
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"height": 1216, |
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"scale": 5, |
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"sampler": "k_euler_ancestral", |
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"steps": 28, |
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"n_samples": 1, |
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"ucPreset": 0, |
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"qualityToggle": True, |
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"add_original_image": False, |
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"cfg_rescale": 0, |
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"controlnet_strength": 1, |
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"dynamic_thresholding": False, |
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"legacy": False, |
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"noise_schedule": "karras", |
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"seed": 8888, |
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"sm": False, |
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"sm_dyn": False, |
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"uncond_scale": 1, |
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"negative_prompt":"nsfw, lowres, bad, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, watermark, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], lowres, bad, error, fewer, extra, missing, worst quality, jpeg artifacts, bad quality, unfinished, displeasing, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract], chibi,doll, +_+", |
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"legacy_v3_extend": False, |
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} |
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} |
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response = requests.post(url, impersonate="safari15_5", json=payload, headers=headers, timeout=120) |
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zipfile_in_memory = io.BytesIO(response.content) |
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with zipfile.ZipFile(zipfile_in_memory, 'r') as zip_ref: |
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file_names = zip_ref.namelist() |
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if file_names: |
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with zip_ref.open(file_names[0]) as file: |
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return Image.open(io.BytesIO(file.read())), payload |
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def process_image_and_save(image, path): |
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metadata = PngInfo() |
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image = image.convert('RGBA') |
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image = Image.fromarray(np.array(image)[:,:,:3]) |
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image.save(path, pnginfo=metadata, quality=95, format="WEBP") |
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print(path) |
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with open("prompts.csv") as f: |
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prompts = f.readlines() |
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generate("abcd") |
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for i, prompt in tqdm(enumerate(prompts), total=len(prompts)): |
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try: |
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image, payload = generate(prompt.strip()) |
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image = image.convert('RGBA') |
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image = Image.fromarray(np.array(image)[:,:,:3]) |
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fn = f"naiv3/{i+1}.webp" |
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image.save(fn, quality=95, format="WEBP") |
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except Exception as e: |
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print(e) |
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continue |