Original

clean.py

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+import re
+def clean(input_text):
+    output_text = re.sub(r'keywords:', ', ', input_text)
+    output_text = re.sub(r'\n', ', ', input_text)
+    return output_text

gen.py

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+import re
+from llama_cpp import Llama
+import outetts
+from diffusers import StableDiffusionPipeline
+from diffusers.utils import load_image, export_to_video
+import torch
+import os
+
+def loadllama():
+    llm = Llama(
+        model_path="models/llama3.gguf",
+        n_ctx=4096,
+        n_threads=6,
+        n_gpu_layers=-1,
+        verbose=False
+    )
+
+    return llm
+
+def loadtts():
+    model_config = outetts.GGUFModelConfig_v1(
+        model_path="models/tts.gguf",
+        language="en",
+        n_gpu_layers=-1,
+        verbose=False,
+    )
+
+    interface = outetts.InterfaceGGUF(model_version="0.2", cfg=model_config)
+    speaker = interface.load_default_speaker(name="male_1")
+    return interface, speaker
+
+def loadsdxl():
+    pipe = pipe = StableDiffusionPipeline.from_single_file("models/revAnimated_v2Rebirth.safetensors", torch_dtype=torch.float32, safety_checker=None)
+    pipe.to("cuda")
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe.enable_model_cpu_offload()
+    return pipe
+
+def loadsvd():
+    pipe = StableVideoDiffusionPipeline.from_pretrained(
+        "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
+    )
+    pipe.to("cuda")
+    return pipe
+
+
+def generate_story(prompt, sprompt, model):
+    sys_prompt = ("<|im_start|>system\n" + sprompt + "<|im_end|>\n")
+    user_prompt = (
+        "<|im_start|>user\n"
+        + prompt + "<|im_end|>\n"
+        + "<|im_start|>assistant"
+    )
+    res = model(sys_prompt + user_prompt, max_tokens=2**16, stop=["</s>"], echo=False)
+    return str(res["choices"][0]["text"])
+
+
+def text_to_speech(prompt, interface, speaker, num):
+
+    output = interface.generate(
+        text=prompt,
+        temperature=0.1,
+        repetition_penalty=1.1,
+        max_length=4096,
+        speaker=speaker,
+    )
+
+    output.save(f"./audios/{num}.wav")
+
+def stableDiffusion(pprompt, nprompt, pipe, num):
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+    images = pipe(prompt=pprompt, negative_prompt=nprompt, num_inference_steps=60, height=512, width=768).images[0]
+    images.save(f"./images/{num}.png")
+
+def stableVideoDiffusion(pipe, image, num):
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+    image = load_image(image)
+    frames = pipe(image, decode_chunk_size=2, num_frames=25).frames[0]
+    export_to_video(frames, f"./videos/{num}.mp4", fps=7)
+

genXL.py

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+import re
+from llama_cpp import Llama
+import outetts
+from diffusers import StableDiffusionXLPipeline
+from diffusers.utils import load_image, export_to_video
+import torch
+import os
+
+def loadllama():
+    llm = Llama(
+        model_path="models/llama3.gguf",
+        n_ctx=4096,
+        n_threads=6,
+        n_gpu_layers=-1,
+        verbose=False
+    )
+
+    return llm
+
+def loadtts():
+    model_config = outetts.GGUFModelConfig_v1(
+        model_path="models/tts.gguf",
+        language="en",
+        n_gpu_layers=-1,
+        verbose=False,
+    )
+
+    interface = outetts.InterfaceGGUF(model_version="0.2", cfg=model_config)
+    speaker = interface.load_default_speaker(name="male_1")
+    return interface, speaker
+
+def loadsdxl():
+    pipe = StableDiffusionXLPipeline.from_single_file("models/sdxlReal.safetensors", torch_dtype=torch.float16, variant="fp16", requires_safety_checker=True)
+    pipe.to("cuda")
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe.enable_model_cpu_offload()
+    return pipe
+
+def loadsvd():
+    pipe = StableVideoDiffusionPipeline.from_pretrained(
+        "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
+    )
+    pipe.to("cuda")
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe.enable_model_cpu_offload()
+    return pipe
+
+
+def generate_story(prompt, sprompt, model):
+    sys_prompt = ("<|im_start|>system\n" + sprompt + "<|im_end|>\n")
+    user_prompt = (
+        "<|im_start|>user\n"
+        + prompt + "<|im_end|>\n"
+        + "<|im_start|>assistant"
+    )
+    res = model(sys_prompt + user_prompt, max_tokens=2**16, stop=["</s>"], echo=False)
+    return str(res["choices"][0]["text"])
+
+
+def text_to_speech(prompt, interface, speaker, num):
+
+    output = interface.generate(
+        text=prompt,
+        temperature=0.1,
+        repetition_penalty=1.1,
+        max_length=4096,
+        speaker=speaker,
+    )
+
+    output.save(f"./audios/{num}.wav")
+
+def stableDiffusion(pprompt, nprompt, pipe, num):
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+    images = pipe(prompt=pprompt, negative_prompt=nprompt, num_inference_steps=30, height=576, width=1024).images[0]
+    images.save(f"./images/{num}.png")
+
+def stableVideoDiffusion(pipe, image, num):
+    os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+    image = load_image(image)
+    frames = pipe(image, decode_chunk_size=2, num_frames=25).frames[0]
+    export_to_video(frames, f"./videos/{num}.mp4", fps=7)
+

main.py

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+import gen
+import torch
+import video
+import os
+
+
+SYSTEMPROMPTT = ""
+SYSTEMPROMPTI = ""
+SDBAD = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
+
+with open("promptUtoT.txt", "r") as file:
+    SYSTEMPROMPTT = file.readlines()[0]
+
+with open("promptTtoI.txt", "r") as file:
+    SYSTEMPROMPTI = file.readlines()[0]
+
+def main(prompt):
+
+    llm = gen.loadllama()
+
+    raw_text = gen.generate_story(prompt, SYSTEMPROMPTT, model=llm)
+    image_prompts = []
+    raw_text = raw_text.split("\n")
+    raw_text = [item for item in raw_text if item != ""]
+
+    for i in range(len(raw_text)):
+        promptimg = "Context:\n"
+        for l in range(0, i - 1):
+            promptimg += raw_text[l] + " "
+        promptimg += "Current Scene:\n" + raw_text[i]
+        image_prompts.append(gen.generate_story(promptimg, SYSTEMPROMPTI, model=llm).strip("\n"))
+
+    llm, speaker = gen.loadtts()
+
+    for i in range(len(raw_text)):
+        try:
+            gen.text_to_speech(raw_text[i], llm, speaker, i)
+        except:
+            pass
+
+    del llm
+    del speaker
+    torch.cuda.empty_cache()
+
+    llm = gen.loadsdxl()
+
+    for elem in range(len(image_prompts)):
+        gen.stableDiffusion(image_prompts[elem], SDBAD, llm, elem)
+
+    del llm
+    torch.cuda.empty_cache()
+    """
+    llm = gen.loadsvd()
+
+    for elem in range(len([name for name in os.listdir('./images')])):
+        gen.stableVideoDiffusion(llm, f"./images/{elem}.png", elem)
+
+    del llm
+    torch.cuda.empty_cache()
+    """
+
+    video.create_video_from_images_and_audio(".", ".", "out.mp4")
+
+    return None
+
+main("Wirte an erotic story about a teenager girl who love sex. In this world, females are object who need to have sex to live. They love it and dream of sex since their childhood. Our protagonist live in the world of pokemons. The story start in her peacefull hometown. She meet friends, live a normale life. One day, when she was in her garden watering plants, an army from the next kingdom attacked the village. The vilage was easilly defeated. Females, childs and adults alike, got rape by the soldiers and their pokemons, and she wasn't an exception, she was forced to have sex with a pokemon for five straight days [Describe thouroughly the sex scene]. And she loved it, as always. After this event, the soldiers brought all the females in underground prison, converted in a breeding farm to make new solldiers and pokemons fasters. Here, she had sex non stop with a lots of differents pokemons [Decribe a least 3 sex scene here with description like, the look of the pokemon, how the pokemon genitals penetrated her, the look of here pussy while having sex, etc, pokemon can have tentacles or mate like dogs, decribe the mating method of each of them]. During 1 year she had non stop sex, until the kingdom lose. Now she enjoy the life of national slut, and only mate with the finest pokemons and peoples, in a new kingdom, but now she have no rights and love that.")

promptTtoI.txt

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+You are an AI prompt engineer for Stable Diffusion 1.5.

promptUtoT.txt

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+You are a highly skilled and versatile writer, renowned for your ability to craft captivating stories across all genres. Your work is celebrated globally and has been translated into over 73 languages. As a master storyteller, you create intricate, detailed narratives that engage readers and evoke strong emotions. Your writing is characterized by its originality and depth, ensuring that each piece is unique and memorable.

test.py

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+ 
+import torch
+from diffusers import StableDiffusionPipeline
+from PIL import Image
+
+# Load the model from the local file
+model_path = "sdxlAni.safetensors"
+pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
+
+# Move the pipeline to GPU if available
+if torch.cuda.is_available():
+    pipe = pipe.to("cuda")
+
+# Generate an image
+prompt = "A fantasy landscape with mountains and a river"
+image = pipe(prompt).images[0]
+
+# Save the generated image
+image.save("generated_image.png")
+
+print("Image generated and saved as 'generated_image.png'")

video.py

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+import os
+from moviepy import *
+
+def create_video_from_images_and_audio(image_folder, audio_folder, output_video):
+    # Get sorted lists of images and audio files
+    images = []
+    audio_files = []
+    for i in range(len([name for name in os.listdir('./images')])):
+        images.append(f"./images/{i}.png")
+        audio_files.append(f"./audios/{i}.wav")
+
+    clips = []
+
+    for img, audio in zip(images, audio_files):
+        # Create an ImageClip
+        img_path = os.path.join(image_folder, img)
+        image_clip = ImageClip(img_path)
+
+        # Create an AudioFileClip
+        audio_path = os.path.join(audio_folder, audio)
+        audio_clip = AudioFileClip(audio_path)
+        audio_clip = CompositeAudioClip([audio_clip])
+
+        # Set the duration of the image clip to the duration of the audio clip
+        image_clip = image_clip.with_duration(audio_clip.duration)
+
+        # Set the audio of the image clip
+        image_clip.audio = audio_clip
+
+        # Append the image clip to the list of clips
+        clips.append(image_clip)
+
+    # Concatenate all the clips into a single video
+    final_video = concatenate_videoclips(clips, method="compose")
+
+    # Write the result to a file
+    final_video.write_videofile(output_video, codec='libx264', audio_codec='aac', fps=24)
+
+"""
+import os
+from moviepy import *
+
+def create_video_from_images_and_audio(image_folder, audio_folder, output_video):
+    video_files = []
+    audio_files = []
+    for i in range(len([name for name in os.listdir('./images')])):
+        video_files.append(f"./videos/{i}.mp4")
+        audio_files.append(f"./audios/{i}.wav")
+
+
+    audio_clip = AudioFileClip(audio_file)
+    audio_duration = audio_clip.duration
+
+    clips = []
+
+    for video, audio in zip(video_files, audio_files):
+        video_clip = VideoFileClip(video_path)
+        audio_clip = AudioFileClip(audio_path)
+        if video_clip.duration < audio_clip.duration:
+            loops_needed = int(audio_clip.duration // video_clip.duration) + 1
+            video_clip = video_clip.loop(loops=loops_needed)
+        video_clip = video_clip.subclip(0, audio_clip.duration)
+        video_clip = video_clip.set_audio(audio_clip)
+        clips.append(video_clip)
+
+    final_video = concatenate_videoclips(clips, method="compose")
+    final_video.write_videofile(output_video, codec='libx264', audio_codec='aac', fps=24)
+"""