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.ipynb_checkpoints/README-checkpoint.md

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+# LTX-Video 13B Generation Pipeline (Escoffier Style)
+
+This repository contains a complete workflow for generating high-quality animated videos featuring anime-style characters in the distinctive **Escoffier art style** using the **Lightricks LTX-Video 13B model** with LoRA adaptation. The pipeline is optimized for consistent **832x480 resolution output**, supporting multiple scene configurations while maintaining character aesthetics.
+
+---
+
+## 🎨 Character Description
+
+The Escoffier-style character features:
+
+- **Blonde hair with striking blue eyes**
+- **Long flowing hair with a signature curled strand on top**
+- **Elegant white and purple dress with gold accents**
+- **Large magenta waist bow**
+- **White thigh-high stockings with intricate floral designs**
+- **White frilled hat adorned with pink ribbon**
+- **Graceful posture in magical, ethereal settings**
+
+---
+
+## 🧰 Installation
+
+### System Dependencies
+```bash
+sudo apt-get update && sudo apt-get install ffmpeg git-lfs cbm
+```
+
+### Python Dependencies
+```bash
+pip install -U diffusers transformers torch sentencepiece peft moviepy protobuf
+pip install git+https://github.com/Lightricks/LTX-Video.git
+pip install git+https://github.com/huggingface/diffusers.git
+```
+
+---
+
+## 🚀 Generation Pipeline
+
+### Model Initialization with Escoffier LoRA
+```python
+import torch
+from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
+from diffusers.utils import export_to_video
+
+# Load base model and LoRA weights
+pipe = LTXConditionPipeline.from_pretrained(
+    "Lightricks/LTX-Video-0.9.7-dev",
+    torch_dtype=torch.bfloat16
+)
+pipe.load_lora_weights("LTXV_13B_097_DEV_escoffier_im_lora/lora_weights_step_19000.safetensors")
+
+# Load latent upscaler
+pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained(
+    "Lightricks/ltxv-spatial-upscaler-0.9.7",
+    vae=pipe.vae,
+    torch_dtype=torch.bfloat16
+)
+
+# Memory optimization
+pipe.enable_sequential_cpu_offload()
+pipe_upsample.enable_sequential_cpu_offload()
+```
+
+---
+
+## 📦 Complete Generation Function
+
+```python
+def generate_escoffier_video(prompt, output_name):
+    """Complete generation pipeline for 832x480 videos"""
+    
+    # Fixed resolution parameters
+    expected_width, expected_height = 832, 480
+    downscale_factor = 2/3
+    num_frames = 121  # ~5 seconds at 24fps
+    negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
+    
+    # Resolution rounding helper
+    def round_resolution(h, w):
+        ratio = pipe.vae_spatial_compression_ratio
+        return h - (h % ratio), w - (w % ratio)
+    
+    low_res_h, low_res_w = round_resolution(
+        int(expected_height * downscale_factor),
+        int(expected_width * downscale_factor)
+    )
+    
+    # 1. Initial generation at low resolution
+    latents = pipe(
+        conditions=None,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=low_res_w,
+        height=low_res_h,
+        num_frames=num_frames,
+        num_inference_steps=30,
+        generator=torch.Generator().manual_seed(0),
+        output_type="latent",
+    ).frames
+
+    # 2. Latent upscaling (2x)
+    upscaled_latents = pipe_upsample(
+        latents=latents,
+        output_type="latent"
+    ).frames
+
+    # 3. Quality refinement pass
+    video = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=low_res_w*2,  # 2x upscaled
+        height=low_res_h*2,
+        num_frames=num_frames,
+        denoise_strength=0.4,  # 4/10 steps
+        num_inference_steps=10,
+        latents=upscaled_latents,
+        decode_timestep=0.05,
+        image_cond_noise_scale=0.025,
+        generator=torch.Generator().manual_seed(0),
+        output_type="pil",
+    ).frames[0]
+
+    # 4. Final resize to target resolution
+    video = [frame.resize((expected_width, expected_height)) for frame in video]
+    export_to_video(video, f"{output_name}.mp4", fps=24)
+```
+
+---
+
+## 🎥 Example Generations (All 832x480)
+
+### 🌌 Cosmic Fantasy Scene
+```python
+generate_escoffier_video(
+    prompt="In the style of Escoffier, This is a digital anime-style illustration of a blonde, blue-eyed female character with long, flowing hair and a large, curled strand on top. She wears a white and purple dress with gold accents, a large magenta bow on the waist, and white thigh-high stockings with intricate designs. The background features glowing, crystal-like structures and a dark blue, starry sky. Her expression is gentle, and she holds up the hem of her skirt with her right hand. The overall style is vibrant and dynamic, with a focus on her detailed, fantasy-inspired outfit and the magical, ethereal setting.",
+    output_name="escoffier_cosmic_scene"
+)
+```
+
+### 🌸 Mystical Garden Scene
+```python
+generate_escoffier_video(
+    prompt="In the style of Escoffier, This is a digital anime-style illustration of a blonde, blue-eyed female character with long, flowing hair and a large, curled strand on top. She wears a white and purple dress with gold accents, a large magenta bow on the waist, and white thigh-high stockings with intricate floral designs. She stands gracefully in a mystical garden filled with floating crystal butterflies and glowing lilies, reaching out to touch a shimmering orb.",
+    output_name="escoffier_garden_scene"
+)
+```
+
+---
+
+## 📄 License
+
+This pipeline is provided under the same license as the base **LTX-Video** model. Please refer to the original Lightricks repository for licensing details.
+
+---
+
+## 🤝 Acknowledgments
+
+- **Lightricks** – For developing and open-sourcing the LTX-Video model
+- **Hugging Face** – For hosting and community support
+- **svjack** – For adapting and fine-tuning the Escoffier LoRA weights
+
+---
+
+## 📬 Support
+
+For issues or feature requests, please [open an issue](https://github.com/svjack/LTX-Video-Escoffier/issues) on GitHub.
+
+--- 
+
+✅ You now have a fully functional **Escoffier-style anime video generation pipeline** using **LTX-Video 13B**!

README.md

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+# LTX-Video 13B Generation Pipeline (Escoffier Style)
+
+This repository contains a complete workflow for generating high-quality animated videos featuring anime-style characters in the distinctive **Escoffier art style** using the **Lightricks LTX-Video 13B model** with LoRA adaptation. The pipeline is optimized for consistent **832x480 resolution output**, supporting multiple scene configurations while maintaining character aesthetics.
+
+---
+
+## 🎨 Character Description
+
+The Escoffier-style character features:
+
+- **Blonde hair with striking blue eyes**
+- **Long flowing hair with a signature curled strand on top**
+- **Elegant white and purple dress with gold accents**
+- **Large magenta waist bow**
+- **White thigh-high stockings with intricate floral designs**
+- **White frilled hat adorned with pink ribbon**
+- **Graceful posture in magical, ethereal settings**
+
+---
+
+## 🧰 Installation
+
+### System Dependencies
+```bash
+sudo apt-get update && sudo apt-get install ffmpeg git-lfs cbm
+```
+
+### Python Dependencies
+```bash
+pip install -U diffusers transformers torch sentencepiece peft moviepy protobuf
+pip install git+https://github.com/Lightricks/LTX-Video.git
+pip install git+https://github.com/huggingface/diffusers.git
+```
+
+---
+
+## 🚀 Generation Pipeline
+
+### Model Initialization with Escoffier LoRA
+```python
+import torch
+from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
+from diffusers.utils import export_to_video
+
+# Load base model and LoRA weights
+pipe = LTXConditionPipeline.from_pretrained(
+    "Lightricks/LTX-Video-0.9.7-dev",
+    torch_dtype=torch.bfloat16
+)
+pipe.load_lora_weights("LTXV_13B_097_DEV_escoffier_im_lora/lora_weights_step_19000.safetensors")
+
+# Load latent upscaler
+pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained(
+    "Lightricks/ltxv-spatial-upscaler-0.9.7",
+    vae=pipe.vae,
+    torch_dtype=torch.bfloat16
+)
+
+# Memory optimization
+pipe.enable_sequential_cpu_offload()
+pipe_upsample.enable_sequential_cpu_offload()
+```
+
+---
+
+## 📦 Complete Generation Function
+
+```python
+def generate_escoffier_video(prompt, output_name):
+    """Complete generation pipeline for 832x480 videos"""
+    
+    # Fixed resolution parameters
+    expected_width, expected_height = 832, 480
+    downscale_factor = 2/3
+    num_frames = 121  # ~5 seconds at 24fps
+    negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
+    
+    # Resolution rounding helper
+    def round_resolution(h, w):
+        ratio = pipe.vae_spatial_compression_ratio
+        return h - (h % ratio), w - (w % ratio)
+    
+    low_res_h, low_res_w = round_resolution(
+        int(expected_height * downscale_factor),
+        int(expected_width * downscale_factor)
+    )
+    
+    # 1. Initial generation at low resolution
+    latents = pipe(
+        conditions=None,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=low_res_w,
+        height=low_res_h,
+        num_frames=num_frames,
+        num_inference_steps=30,
+        generator=torch.Generator().manual_seed(0),
+        output_type="latent",
+    ).frames
+
+    # 2. Latent upscaling (2x)
+    upscaled_latents = pipe_upsample(
+        latents=latents,
+        output_type="latent"
+    ).frames
+
+    # 3. Quality refinement pass
+    video = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=low_res_w*2,  # 2x upscaled
+        height=low_res_h*2,
+        num_frames=num_frames,
+        denoise_strength=0.4,  # 4/10 steps
+        num_inference_steps=10,
+        latents=upscaled_latents,
+        decode_timestep=0.05,
+        image_cond_noise_scale=0.025,
+        generator=torch.Generator().manual_seed(0),
+        output_type="pil",
+    ).frames[0]
+
+    # 4. Final resize to target resolution
+    video = [frame.resize((expected_width, expected_height)) for frame in video]
+    export_to_video(video, f"{output_name}.mp4", fps=24)
+```
+
+---
+
+## 🎥 Example Generations (All 832x480)
+
+### 🌌 Cosmic Fantasy Scene
+```python
+generate_escoffier_video(
+    prompt="In the style of Escoffier, This is a digital anime-style illustration of a blonde, blue-eyed female character with long, flowing hair and a large, curled strand on top. She wears a white and purple dress with gold accents, a large magenta bow on the waist, and white thigh-high stockings with intricate designs. The background features glowing, crystal-like structures and a dark blue, starry sky. Her expression is gentle, and she holds up the hem of her skirt with her right hand. The overall style is vibrant and dynamic, with a focus on her detailed, fantasy-inspired outfit and the magical, ethereal setting.",
+    output_name="escoffier_cosmic_scene"
+)
+```
+
+### 🌸 Mystical Garden Scene
+```python
+generate_escoffier_video(
+    prompt="In the style of Escoffier, This is a digital anime-style illustration of a blonde, blue-eyed female character with long, flowing hair and a large, curled strand on top. She wears a white and purple dress with gold accents, a large magenta bow on the waist, and white thigh-high stockings with intricate floral designs. She stands gracefully in a mystical garden filled with floating crystal butterflies and glowing lilies, reaching out to touch a shimmering orb.",
+    output_name="escoffier_garden_scene"
+)
+```
+
+---
+
+## 📄 License
+
+This pipeline is provided under the same license as the base **LTX-Video** model. Please refer to the original Lightricks repository for licensing details.
+
+---
+
+## 🤝 Acknowledgments
+
+- **Lightricks** – For developing and open-sourcing the LTX-Video model
+- **Hugging Face** – For hosting and community support
+- **svjack** – For adapting and fine-tuning the Escoffier LoRA weights
+
+---
+
+## 📬 Support
+
+For issues or feature requests, please [open an issue](https://github.com/svjack/LTX-Video-Escoffier/issues) on GitHub.
+
+--- 
+
+✅ You now have a fully functional **Escoffier-style anime video generation pipeline** using **LTX-Video 13B**!

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