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generate.py

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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'![Generated Image](data:image/png;base64,{image_data})'[:200]}...")
+    readme_content = f"![Generated Image](data:image/png;base64,{image_data})"
+
+    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()