Update README.md

README.md

@@ -12,7 +12,12 @@ For all purposes and definitions the autoencoder is the **world first** publishe
 ## Model Details
 The autoencoder (tokenizer) is a relative small model using 50M parameters and the transformer model uses 184M parameters and the core is based on GPT2-small.
 Due to hardware contraints it's trained using a codebook/vocabablity size of 2048.<br/>
-Devoloped by: Me (with credits for MeshGPT codebase to [Phil Wang](https://github.com/lucidrains))
+Devoloped & trained by: Me with credits for MeshGPT codebase to [Phil Wang](https://github.com/lucidrains)
+
+## Preformance:
+CPU 10 triangles/s<br/>
+3060 GPU: 40 triangles/s<br/>
+4090 GPU: 110 triangles/s<br/>
 
 ### Warning: 
 This model has been created without any sponsors or renting any GPU hardware, so it has a very limited capability in terms what it can generate. 
@@ -36,15 +41,20 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 transformer = MeshTransformer.from_pretrained("MarcusLoren/MeshGPT_tiny_alpha").to(device)
 
 output = []  
-for text in [ 'bed' , "chair"]: 
-    face_coords, face_mask = transformer.generate(texts = [text],  temperature = 0.0)
-    # (batch, num faces, vertices (3), coordinates (3)), (batch, num faces)
-    output.append(face_coords)       
+output.append((transformer.generate(texts = ['sofa','bed', 'computer screen', 'bench', 'chair', 'table' ] , temperature = 0.0) ))   
+output.append((transformer.generate(texts = ['milk carton', 'door', 'shovel', 'heart', 'trash can', 'ladder'], temperature = 0.0) )) 
+output.append((transformer.generate(texts = ['hammer', 'pedestal', 'pickaxe', 'wooden cross', 'coffee bean', 'crowbar'],  temperature = 0.0) )) 
+output.append((transformer.generate(texts = ['key', 'minecraft character', 'dragon head', 'open book', 'minecraft turtle', 'wooden table'], temperature = 0.0) )) 
+output.append((transformer.generate(texts = ['gun', 'ice cream cone', 'axe', 'helicopter', 'shotgun', 'plastic bottle'], temperature = 0.0) )) 
 
-mesh_render.combind_mesh(f'./render.obj', output)
+mesh_render.save_rendering(f'./render.obj', output)
 
 ```
- 
+## Expected output:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e233acec775bfe0d5cbc6/K04Qj_xgwmNT_MldTA1l8.png)
+
+
 Random samples generated by text only:
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e233acec775bfe0d5cbc6/UH1r5s9Lfj4sUSgClqhrf.png)
   
@@ -69,5 +79,4 @@ The tokens generated by the transformer can then be converted into 3D mesh using
 The idea for MeshGPT came from the paper ( https://arxiv.org/abs/2311.15475 ) but the creators didn't release any code or model.
 Phil Wang (https://github.com/lucidrains) drew inspiration from the paper and did a ton of improvements over the papers implementation and created the repo : https://github.com/lucidrains/meshgpt-pytorch
 My goal has been to figure out how to train and implement MeshGPT into reality. <br/>
-See my github repo for a notebook on how to get started training your own MeshGPT! [MarcusLoppe/meshgpt-pytorch](https://github.com/MarcusLoppe/meshgpt-pytorch/)
-
+See my github repo for a notebook on how to get started training your own MeshGPT! [MarcusLoppe/meshgpt-pytorch](https://github.com/MarcusLoppe/meshgpt-pytorch/)