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Model: FredZhang7/distilgpt2-stable-diffusion
Source: Original Platform

.gitattributes

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README.md

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+---
+license: creativeml-openrail-m
+tags:
+- stable-diffusion
+- prompt-generator
+- distilgpt2
+datasets:
+- FredZhang7/krea-ai-prompts
+- Gustavosta/Stable-Diffusion-Prompts
+- bartman081523/stable-diffusion-discord-prompts
+widget:
+- text: "amazing"
+- text: "a photo of"
+- text: "a sci-fi"
+- text: "a portrait of"
+- text: "a person standing"
+- text: "a boy watching"
+---
+# DistilGPT2 Stable Diffusion Model Card
+
+
+<a href="https://huggingface.co/FredZhang7/distilgpt2-stable-diffusion-v2"> <font size="4"> <bold> Version 2 is here! </bold> </font> </a>
+
+
+DistilGPT2 Stable Diffusion is a text generation model used to generate creative and coherent prompts for text-to-image models, given any text.
+This model was finetuned on 2.03 million descriptive stable diffusion prompts from [Stable Diffusion discord](https://huggingface.co/datasets/bartman081523/stable-diffusion-discord-prompts), [Lexica.art](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts), and (my hand-picked) [Krea.ai](https://huggingface.co/datasets/FredZhang7/krea-ai-prompts). I filtered the hand-picked prompts based on the output results from Stable Diffusion v1.4.
+
+Compared to other prompt generation models using GPT2, this one runs with 50% faster forwardpropagation and 40% less disk space & RAM.
+
+
+### PyTorch
+
+```bash
+pip install --upgrade transformers
+```
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# load the pretrained tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained('distilgpt2')
+tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+tokenizer.max_len = 512
+
+# load the fine-tuned model
+model = GPT2LMHeadModel.from_pretrained('FredZhang7/distilgpt2-stable-diffusion')
+
+# generate text using fine-tuned model
+from transformers import pipeline
+nlp = pipeline('text-generation', model=model, tokenizer=tokenizer)
+ins = "a beautiful city"
+
+# generate 10 samples
+outs = nlp(ins, max_length=80, num_return_sequences=10)
+
+# print the 10 samples
+for i in range(len(outs)):
+    outs[i] = str(outs[i]['generated_text']).replace('  ', '')
+print('\033[96m' + ins + '\033[0m')
+print('\033[93m' + '\n\n'.join(outs) + '\033[0m')
+```
+
+Example Output:
+![Example Output](./prompt-examples.png)

config.json

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+{
+  "_name_or_path": "distilgpt2",
+  "_num_labels": 1,
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 6,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.21.2",
+  "use_cache": true,
+  "vocab_size": 50257
+}

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