Update script.py

script.py

@@ -3,6 +3,7 @@ import subprocess
 from safetensors.torch import load_file
 from diffusers import AutoPipelineForText2Image
 from datasets import load_dataset
+from huggingface_hub.repocard import RepoCard
 import torch
 import re
 import argparse
@@ -22,46 +23,52 @@ def do_train(script_args):
     subprocess.run(['python', 'trainer.py'] + script_args)
 
 def do_inference(dataset_name, output_dir, num_tokens):
-    print("Starting inference to generate example images...")
-    dataset = load_dataset(dataset_name)
-    pipe = AutoPipelineForText2Image.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
-    )
-    pipe = pipe.to("cuda")
-    pipe.load_lora_weights(f'{output_dir}/pytorch_lora_weights.safetensors')
-    
-    prompts = dataset["train"]["prompt"]
-    card_string = ''
-    if(num_tokens > 0):
-        tokens_sequence = ''.join(f'<s{i}>' for i in range(num_tokens))
-        tokens_list = [f'<s{i}>' for i in range(num_tokens)]
-    
-        state_dict = load_file(f"{output_dir}/embeddings.safetensors")
-        pipe.load_textual_inversion(state_dict["clip_l"], token=tokens_list, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
-        pipe.load_textual_inversion(state_dict["clip_g"], token=tokens_list, text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
+    try:
+        print("Starting inference to generate example images...")
+        dataset = load_dataset(dataset_name)
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+        )
+        pipe = pipe.to("cuda")
+        pipe.load_lora_weights(f'{output_dir}/pytorch_lora_weights.safetensors')
         
-        prompts = [prompt.replace("TOK", tokens_sequence) for prompt in prompts]    
-    
-    for i, prompt in enumerate(prompts):
-        image = pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0]
-        filename = f"image-{i}.png"
-        image.save(f"{output_dir}/filename")
-        card_string += f"""
-- text: '{prompt}'
-    output:
-        url:
-            '{filename}'"""
-    with open(f'{output_dir}/README.md', 'r') as file:
-        readme_content = file.read()
+        prompts = dataset["train"]["prompt"]
+        widget_content = []
+        if(num_tokens > 0):
+            tokens_sequence = ''.join(f'<s{i}>' for i in range(num_tokens))
+            tokens_list = [f'<s{i}>' for i in range(num_tokens)]
+        
+            state_dict = load_file(f"{output_dir}/embeddings.safetensors")
+            pipe.load_textual_inversion(state_dict["clip_l"], token=tokens_list, text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)
+            pipe.load_textual_inversion(state_dict["clip_g"], token=tokens_list, text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
+            
+            prompts = [prompt.replace("TOK", tokens_sequence) for prompt in prompts]    
 
-    updated_readme_content = re.sub(r'(widget:\n)(.*?)(?=\n\S+:)', f'\\1{card_string}', readme_content, flags=re.DOTALL)
-    print("Images generated!")
-    with open('README.md', 'w') as file:
-        file.write(updated_readme_content)
+        for i, prompt in enumerate(prompts):
+            image = pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0]
+            filename = f"image-{i}.png"
+            image.save(f"{output_dir}/{filename}")
+            card_dict = {
+                "text": prompt,
+                "output": {
+                    "url": filename
+                }
+            }
+            widget_content.append(card_dict)
+        with open(f'{output_dir}/README.md', 'r') as file:
+            readme_content = file.read()
+    
+        card = RepoCard(readme_content)
+        card.data["widget"] = widget_content
+        card.save(f'{output_dir}/README.md')
+    except Exception as e:
+        print("Something went wrong with generating images, specifically: ", e)
+    
     from huggingface_hub import HfApi
     api = HfApi()
     username = api.whoami()["name"]
     print("Starting upload...")
+    api.create_repo(f"{username}/{output_dir}", exist_ok=True)
     api.upload_folder(
         folder_path=output_dir,
         repo_id=f"{username}/{output_dir}",