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# === Standard Library ===
import requests
import os
from datetime import datetime
import random
import time
import base64
# === Third-Party Libraries ===
import torch
from PIL import Image, PngImagePlugin
from diffusers import StableDiffusionPipeline
# === Configuration ===
model_id = "runwayml/stable-diffusion-v1-5"
output_dir = "generated_images"
os.makedirs(output_dir, exist_ok=True)
ROTATIONS = 32
base_prompt = "antiwar"
negative_prompt = (
"(nsfw:1.5), (easynegative:1.3) (bad_prompt:1.3) badhandv4 bad-hands-5 (negative_hand-neg) "
"(bad-picture-chill-75v), (worst quality:1.3), (low quality:1.3), (bad quality:1.3), "
"(a shadow on skin:1.3), (a shaded skin:1.3), (a dark skin:1.3), (blush:1.3), "
"(signature, watermark, username, letter, copyright name, copyright, chinese text, artist name, name tag, "
"company name, name tag, text, error:1.5), (bad anatomy:1.5), (low quality hand:1.5), (worst quality hand:1.5)"
)
generation_config = {
"vae": "vae-ft-mse-840000",
"sampler": "Euler a",
"steps": 25,
"guidance_scale": 7.0
}
GIST_LOG_FILE = "gist_log.md"
# === Initialize Model ===
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to(device)
# === Functions ===
def add_metadata_and_save(image: Image.Image, filepath: str, prompt: str, negative_prompt: str, seed: int):
"""Embed generation metadata into a PNG and save it."""
meta = PngImagePlugin.PngInfo()
meta.add_text("Prompt", prompt)
meta.add_text("NegativePrompt", negative_prompt)
meta.add_text("Model", model_id)
meta.add_text("VAE", generation_config["vae"])
meta.add_text("Sampler", generation_config["sampler"])
meta.add_text("Steps", str(generation_config["steps"]))
meta.add_text("Seed", str(seed))
meta.add_text("Date", datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
image.save(filepath, "PNG", pnginfo=meta)
def upload_to_gist(image_path, prompt, negative_prompt, seed, model_id):
"""
Uploads an image and metadata to GitHub Gist using Base64 encoding.
Returns Gist URL if successful.
"""
# HF_SECRET INSERT HERE
USERNAME = "ajsbsd"
headers = {
"Authorization": f"token {GITHUB_TOKEN}",
"Accept": "application/vnd.github+json"
}
try:
with open(image_path, "rb") as img_file:
image_bytes = img_file.read()
image_data = base64.b64encode(image_bytes).decode("utf-8")
print(f"✅ Image encoded. Length: {len(image_data)} characters")
except Exception as e:
print(f"❌ Failed to read image: {e}")
return None
# Build metadata
metadata = (
f"Prompt: {prompt}\n"
f"Negative Prompt: {negative_prompt}\n"
f"Seed: {seed}\n"
f"Model: {model_id}\n"
f"VAE: {generation_config['vae']}\n"
f"Sampler: {generation_config['sampler']}\n"
f"Steps: {generation_config['steps']}\n"
f"Guidance Scale: {generation_config['guidance_scale']}\n"
f"Date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
)
print(f"README.md content preview: {f''[:200]}...")
readme_content = f""
print("README.md content length:", len(readme_content)) # Optional debug
print("README.md sample:", readme_content[:200]) # Optional debug
payload = {
"description": "Stable Diffusion Generated Image",
"public": True,
"files": {
os.path.basename(image_path): {
"content": image_data,
"encoding": "base64"
},
"metadata.txt": {
"content": metadata
},
"README.md": {
"content": readme_content
}
}
}
response = requests.post("https://api.github.com/gists", headers=headers, json=payload)
if response.status_code == 201:
gist_url = response.json()["html_url"]
print(f"✅ Uploaded to GitHub Gist: {gist_url}")
return gist_url
else:
print(f"❌ Failed to create Gist: {response.status_code} - {response.text[:200]}")
return None
def generate_and_process_images(num_images: int = 1):
"""Generate images with metadata and upload to GitHub Gist."""
for i in range(num_images):
variation = ", vibrant colors, neon lights" if i % 2 == 0 else ", soft pastel tones, morning light"
prompt = base_prompt + variation
seed = random.randint(10000000, 99999999)
generator = torch.Generator(device=device).manual_seed(seed)
print(f"Generating image {i + 1} with seed {seed}...")
result = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=generation_config["steps"],
guidance_scale=generation_config["guidance_scale"],
generator=generator,
)
image = result.images[0]
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S_%f")
filename = f"{output_dir}/image_{timestamp}_{i}.png"
add_metadata_and_save(image, filename, prompt, negative_prompt, seed)
print(f"Saved: {filename}")
# Upload to GitHub Gist
#gist_url = upload_to_gist(filename, prompt, negative_prompt, seed, model_id)
#if gist_url:
# with open(GIST_LOG_FILE, "a") as f:
# f.write(f"- [{prompt}]({gist_url})\n")
# print(f"📌 Gist created: {gist_url}")
# === Execution ===
if __name__ == "__main__":
generate_and_process_images(num_images=ROTATIONS)
del pipe
torch.cuda.empty_cache()
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