Update README.md

README.md

@@ -1,5 +1,8 @@
 ---
 license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- prompt-generator
 widget:
 - text: "amazing"
 - text: "a photo of"
@@ -7,14 +10,92 @@ widget:
 - text: "a portrait of"
 - text: "a person standing"
 - text: "a boy watching"
+datasets:
+- poloclub/diffusiondb
+- Gustavosta/Stable-Diffusion-Prompts
+- bartman081523/stable-diffusion-discord-prompts
+- FredZhang7/krea-ai-prompts
 ---
+# DistilGPT2 Stable Diffusion V2 Model Card
+DistilGPT2 Stable Diffusion V2 is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text.
+This model was trained on 2.47 million descriptive stable diffusion prompts on the [FredZhang7/distilgpt2-stable-diffusion](https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion) checkpoint for 4.27 million steps.
 
-**Under Development**
+Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.
 
-Major improvements from v1 will include:
+Major improvements from v1 are:
 - 25% more variations
-- more capable of generating story-like prompts, using contrastive search instead of greedy search
+- more capable of generating story-like prompts
 - cleaned training data
-  * removed prompts that generate images with nsfw scores >= 0.5
-  * removed duplicates (including prompts that differ by capitalization and punctuations)
-  * removed punctuations in random places
+  * removed prompts that generate images with nsfw scores > 0.5
+  * removed duplicates, including prompts that differ by capitalization and punctuations
+  * removed punctuations at random places
+  * removed prompts shorter than 15 characters
+
+
+### PyTorch
+
+```bash
+pip install --upgrade transformers
+```
+
+Faster but less fluent generation:
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2', pad_token_id=tokenizer.eos_token_id)
+
+prompt = r'a cat sitting'
+
+# generate text using fine-tuned model
+from transformers import pipeline
+nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
+
+# generate 5 samples
+outs = nlp(prompt, max_length=80, num_return_sequences=5)
+
+print('\nInput:\n' + 100 * '-')
+print('\033[96m' + prompt + '\033[0m')
+print('\nOutput:\n' + 100 * '-')
+for i in range(len(outs)):
+    outs[i] = str(outs[i]['generated_text']).replace('  ', '')
+print('\033[92m' + '\n\n'.join(outs) + '\033[0m\n')
+```
+
+Example output:
+![greedy search](./greedy_search.png)
+
+<br>
+
+Slower but more fluent generation:
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion-v2', pad_token_id=tokenizer.eos_token_id)
+model.eval()
+
+prompt = r'a cat sitting'     # the beginning of the prompt
+temperature = 0.9             # a higher temperature will produce more diverse results, but with a higher risk of less coherent text.
+top_k = 8                     # the number of tokens to sample from at each step
+max_length = 80               # the maximum number of tokens for the output of the model
+repitition_penalty = 1.2      # the penalty value for each repetition of a token
+num_beams=10
+num_return_sequences=5        # the number of results with the highest probabilities out of num_beams
+
+# generate the result with contrastive search.
+input_ids = tokenizer(prompt, return_tensors='pt').input_ids
+output = model.generate(input_ids, do_sample=True, temperature=temperature, top_k=top_k, max_length=max_length, num_return_sequences=num_return_sequences, num_beams=num_beams, repetition_penalty=repitition_penalty, penalty_alpha=0.6, no_repeat_ngram_size=1, early_stopping=True)
+
+# print results
+print('\nInput:\n' + 100 * '-')
+print('\033[96m' + prompt + '\033[0m')
+print('\nOutput:\n' + 100 * '-')
+for i in range(len(output)):
+    print('\033[92m' + tokenizer.decode(output[i], skip_special_tokens=True) + '\033[0m\n')
+```
+
+Example output:
+![constrastive search](./constrastive_search.png)