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NOTEBOOK_MODIFICATIONS.md

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+# Text2PointCloud Notebook Modifications
+
+## Overview
+The `text2pointcloud_test.ipynb` notebook has been modified to generate 100 variations of point clouds with different prompts and model parameters.
+
+## What Changed
+
+### Original Behavior
+- Generated single point clouds one at a time
+- Manual parameter adjustment required for variations
+- No systematic tracking of parameters
+- Random filenames without organization
+
+### New Behavior
+- Automatically generates 100 variations in a loop
+- Systematically varies prompts and model parameters
+- Saves all point clouds with sequential IDs (pointcloud_001.ply to pointcloud_100.ply)
+- Creates a CSV file tracking all parameters for each variation
+
+## Notebook Structure
+
+### Cells 0-4: Setup (unchanged)
+- Cell 0: Clone repository
+- Cell 1: Change to point-e directory
+- Cell 2: Install dependencies
+- Cell 3: Import libraries
+- Cell 4: Load models and checkpoints
+
+### Cells 5-8: New Variation Generation
+- **Cell 5**: Configuration
+  - Defines 15 different text prompts
+  - Sets up parameter ranges:
+    - `guidance_scale`: 2.0 to 5.0 (7 values)
+    - `karras_steps`: 64, 96, 128 (3 values)
+    - `s_churn`: 0.5 to 5.0 (5 values)
+  - Creates output directory `pointcloud_variations/`
+
+- **Cell 6**: Generation Loop
+  - Generates 100 variations
+  - Cycles through prompts and parameters
+  - Saves each point cloud as `pointcloud_XXX.ply`
+  - Records all parameters to tracking data
+  - Shows progress for each variation
+
+- **Cell 7**: Save Tracking Data
+  - Creates `pointcloud_parameters.csv`
+  - Displays first and last 10 variations
+  - Shows total count
+
+- **Cell 8**: Optional Visualization
+  - Allows viewing parameters for specific variations
+  - Change `variation_to_view` to inspect different results
+
+## Output Files
+
+### Point Cloud Files
+- Location: `pointcloud_variations/`
+- Format: `pointcloud_001.ply` through `pointcloud_100.ply`
+- Each file contains 4096 points with RGB color
+
+### Parameter Tracking CSV
+- Filename: `pointcloud_parameters.csv`
+- Columns:
+  - `id`: Variation number (1-100)
+  - `prompt`: Text description used
+  - `guidance_scale_base`: Guidance scale for base model
+  - `guidance_scale_upsample`: Guidance scale for upsampler (always 0.0)
+  - `karras_steps_base`: Number of diffusion steps for base model
+  - `karras_steps_upsample`: Number of diffusion steps for upsampler
+  - `s_churn_base`: Stochasticity parameter for base model
+  - `s_churn_upsample`: Stochasticity parameter for upsampler
+  - `num_points`: Total points in cloud (always 4096)
+  - `filename`: PLY filename
+  - `timestamp`: When the variation was generated
+
+## Parameter Variation Strategy
+
+The notebook systematically varies parameters to create diverse outputs:
+
+1. **Prompts** (15 variations): Cycles through different furniture descriptions
+2. **Guidance Scale** (7 values): Controls how closely the model follows the prompt
+   - Lower values (2.0-3.0): More creative/organic results
+   - Higher values (4.0-5.0): More precise adherence to prompt
+3. **Karras Steps** (3 values): Number of diffusion steps
+   - 64: Faster, less refined
+   - 96: Balanced
+   - 128: Slower, more refined
+4. **S-Churn** (5 values): Controls randomness/noise
+   - Lower (0.5-1.0): Cleaner, sharper boundaries
+   - Higher (3.0-5.0): More organic, varied appearance
+
+## Usage
+
+1. Run cells 0-4 to set up the environment (one-time setup)
+2. Run cell 5 to configure parameters
+3. Run cell 6 to generate all 100 variations (this will take time!)
+4. Run cell 7 to save and view the tracking data
+5. Run cell 8 to inspect parameters of specific variations
+
+## Finding Successful Models
+
+After generation, you can:
+1. Open `pointcloud_parameters.csv` in a spreadsheet application
+2. Review the point cloud files in `pointcloud_variations/`
+3. Identify successful variations by their ID number
+4. Look up the exact parameters in the CSV file
+5. Use those parameters to generate similar models
+
+## Example Workflow
+
+```python
+# After running all cells, to find parameters for variation 42:
+params = df[df['id'] == 42].iloc[0]
+print(f"Prompt: {params['prompt']}")
+print(f"Guidance: {params['guidance_scale_base']}")
+print(f"Steps: {params['karras_steps_base']}")
+print(f"Churn: {params['s_churn_base']}")
+```
+
+## Customization
+
+To modify the variations:
+- **Change prompts**: Edit the `prompts` list in cell 5
+- **Adjust parameter ranges**: Modify `guidance_scales`, `karras_steps_options`, or `s_churn_options`
+- **Generate more/fewer variations**: Change `num_variations` in cell 6
+- **Change output directory**: Modify `output_dir` in cell 5