feat: update workspace paths and enhance gitignore

- Updated stablediffusion crate path from "../stable-diffusion-burn" to "./crates/stable-diffusion-burn" for proper workspace resolution
- Enhanced .gitignore to include generated model files (.mpk, .pt, .bin, .safetensors, .ckpt) and user_data directory
- Added Cargo.lock to gitignore with appropriate comment
- Reorganized IDE files section in gitignore for better clarity
- Added newline at end of file for proper formatting

crates/stable-diffusion-burn/python/unet.py

@@ -0,0 +1,153 @@
+import pathlib
+import os
+import save
+from save import *
+
+from tinygrad.nn import Conv2d
+
+def save_res_block(res_block, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+    # We can't directly save activation functions, but as they are just attribute of the block,
+    # we don't need to save them separately, they will be recreated along with the block.
+    
+    # saving group normalization layer
+    save_group_norm(res_block.in_layers[0], os.path.join(path, 'norm_in'))
+    
+    # saving the convolutional layer
+    save_conv2d(res_block.in_layers[2], os.path.join(path, 'conv_in'))
+
+    # saving the linear layer
+    save_linear(res_block.emb_layers[1], os.path.join(path, 'lin_embed'))
+    
+    # saving group normalization in out_layers
+    save_group_norm(res_block.out_layers[0], os.path.join(path, 'norm_out'))
+
+    # saving the convolutional layer in out_layers
+    save_conv2d(res_block.out_layers[3], os.path.join(path, 'conv_out'))
+
+    # save skip_connection based on the object type
+    if isinstance(res_block.skip_connection, Conv2d):
+        save_conv2d(res_block.skip_connection, os.path.join(path, 'skip_connection'))
+
+def save_cross_attention(cross_attention, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save Linear layers
+    save_linear(cross_attention.to_q, os.path.join(path, 'query'))
+    save_linear(cross_attention.to_k, os.path.join(path, 'key'))
+    save_linear(cross_attention.to_v, os.path.join(path, 'value'))
+    save_linear(cross_attention.to_out[0], os.path.join(path, 'out'))
+
+    # Save parameters
+    save_scalar(cross_attention.num_heads, 'n_head', path)
+
+def save_geglu(geglu, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save Linear layers
+    save_linear(geglu.proj, os.path.join(path, 'proj'))
+
+def save_feed_forward(feed_forward, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save GEGLU module
+    save_geglu(feed_forward.net[0], os.path.join(path, 'geglu'))
+
+    # Save Linear layer
+    save_linear(feed_forward.net[2], os.path.join(path, 'lin'))
+
+
+def save_basic_transformer_block(basic_transformer_block, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save CrossAttention, FeedForward and LayerNorm instances
+    save_cross_attention(basic_transformer_block.attn1, os.path.join(path, 'attn1'))
+    save_feed_forward(basic_transformer_block.ff, os.path.join(path, 'mlp'))
+    save_cross_attention(basic_transformer_block.attn2, os.path.join(path, 'attn2'))
+
+    save_layer_norm(basic_transformer_block.norm1, os.path.join(path, 'norm1'))
+    save_layer_norm(basic_transformer_block.norm2, os.path.join(path, 'norm2'))  
+    save_layer_norm(basic_transformer_block.norm3, os.path.join(path, 'norm3'))  
+
+
+
+def save_spatial_transformer(spatial_transformer, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save GroupNorm, Conv2d, BasicTransformerBlock instances
+    save_group_norm(spatial_transformer.norm, os.path.join(path, 'norm'))
+    save_conv2d(spatial_transformer.proj_in, os.path.join(path, 'proj_in'))
+    save_basic_transformer_block(spatial_transformer.transformer_blocks[0], os.path.join(path, 'transformer'))
+    save_conv2d(spatial_transformer.proj_out, os.path.join(path, 'proj_out')) 
+
+
+def save_downsample(downsample, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save Conv2d instance
+    save_conv2d(downsample.op, path)
+
+def save_upsample(upsample, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+
+    # Save Conv2d instance
+    save_conv2d(upsample.conv, os.path.join(path, 'conv')) 
+
+
+def save_res_transformer_res(block, path):
+    save_res_block(block[0], pathlib.Path(path, 'res1'))
+    save_spatial_transformer(block[1], pathlib.Path(path, 'transformer'))
+    save_res_block(block[2], pathlib.Path(path, 'res2'))
+
+def save_res_upsample(block, path):
+    save_res_block(block[0], pathlib.Path(path, 'res'))
+    save_upsample(block[1], pathlib.Path(path, 'upsample'))
+
+def save_res_transformer(block, path):
+    save_res_block(block[0], pathlib.Path(path, 'res'))
+    save_spatial_transformer(block[1], pathlib.Path(path, 'transformer'))
+
+def save_res_transformer_upsample(block, path):
+    save_res_block(block[0], pathlib.Path(path, 'res'))
+    save_spatial_transformer(block[1], pathlib.Path(path, 'transformer'))
+    save_upsample(block[2], pathlib.Path(path, 'upsample'))
+
+
+def save_unet_input_blocks(input_blocks, path):
+    save_conv2d(input_blocks[0][0], pathlib.Path(path, 'conv'))
+    save_res_transformer(input_blocks[1], pathlib.Path(path, 'rt1'))
+    save_res_transformer(input_blocks[2], pathlib.Path(path, 'rt2'))
+    save_downsample(input_blocks[3][0], pathlib.Path(path, 'd1'))
+    save_res_transformer(input_blocks[4], pathlib.Path(path, 'rt3'))
+    save_res_transformer(input_blocks[5], pathlib.Path(path, 'rt4'))
+    save_downsample(input_blocks[6][0], pathlib.Path(path, 'd2'))
+    save_res_transformer(input_blocks[7], pathlib.Path(path, 'rt5'))
+    save_res_transformer(input_blocks[8], pathlib.Path(path, 'rt6'))
+    save_downsample(input_blocks[9][0], pathlib.Path(path, 'd3'))
+    save_res_block(input_blocks[10][0], pathlib.Path(path, 'r1'))
+    save_res_block(input_blocks[11][0], pathlib.Path(path, 'r2'))
+
+def save_unet_output_blocks(output_blocks, path):
+    save_res_block(output_blocks[0][0], pathlib.Path(path, 'r1'))
+    save_res_block(output_blocks[1][0], pathlib.Path(path, 'r2'))
+    save_res_upsample(output_blocks[2], pathlib.Path(path, 'ru'))
+    save_res_transformer(output_blocks[3], pathlib.Path(path, 'rt1'))
+    save_res_transformer(output_blocks[4], pathlib.Path(path, 'rt2'))
+    save_res_transformer_upsample(output_blocks[5], pathlib.Path(path, 'rtu1'))
+    save_res_transformer(output_blocks[6], pathlib.Path(path, 'rt3'))
+    save_res_transformer(output_blocks[7], pathlib.Path(path, 'rt4'))
+    save_res_transformer_upsample(output_blocks[8], pathlib.Path(path, 'rtu2'))
+    save_res_transformer(output_blocks[9], pathlib.Path(path, 'rt5'))
+    save_res_transformer(output_blocks[10], pathlib.Path(path, 'rt6'))
+    save_res_transformer(output_blocks[11], pathlib.Path(path, 'rt7'))
+
+def save_unet_model(model, path):
+    pathlib.Path(path).mkdir(parents=True, exist_ok=True)
+    save_linear(model.time_embed[0], pathlib.Path(path, 'lin1_time_embed'))
+    save_linear(model.time_embed[2], pathlib.Path(path, 'lin2_time_embed'))
+    save_unet_input_blocks(model.input_blocks, pathlib.Path(path, 'input_blocks'))
+    save_res_transformer_res(model.middle_block, pathlib.Path(path, 'middle_block'))
+    save_unet_output_blocks(model.output_blocks, pathlib.Path(path, 'output_blocks'))
+    save_group_norm(model.out[0], pathlib.Path(path, 'norm_out'))
+    save_conv2d(model.out[2], pathlib.Path(path, 'conv_out'))
+