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README.md

@@ -1,111 +1,32 @@
-```py
-from PIL import Image  
-import torch
-from muse import PipelineMuse, MaskGiTUViT
-from datasets import Dataset, Features
-from datasets import Image as ImageFeature
-from datasets import Value, load_dataset
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = PipelineMuse.from_pretrained(
-    transformer_path="valhalla/research-run",
-    text_encoder_path="openMUSE/clip-vit-large-patch14-text-enc",
-    vae_path="openMUSE/vqgan-f16-8192-laion",
-).to(device)
-
-pipe.transformer = MaskGiTUViT.from_pretrained("valhalla/research-run-finetuned-journeydb", revision="06bcd6ab6580a2ed3275ddfc17f463b8574457da", subfolder="ema_model").to(device)
-pipe.tokenizer.pad_token_id = 49407
-
-if device == "cuda":
-    pipe.transformer.enable_xformers_memory_efficient_attention()
-    pipe.text_encoder.to(torch.float16)
-    pipe.transformer.to(torch.float16)
-
-
-import PIL
-
-
-def main():
-    print("Loading dataset...")
-    parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
-
-    print("Loading pipeline...")
-    seed = 0
-
-    device = "cuda"
-    torch.manual_seed(0)
-
-    ckpt_id = "openMUSE/muse-512"
-
-    scale = 10
-
-    print("Running inference...")
-    main_dict = {}
-    for i in range(len(parti_prompts)):
-        sample = parti_prompts[i]
-        prompt = sample["Prompt"]
-
-        image = pipe(
-            prompt,
-            timesteps=16,
-            negative_text=None,
-            guidance_scale=scale,
-            temperature=(2, 0),
-            orig_size=(512, 512),
-            crop_coords=(0, 0),
-            aesthetic_score=6,
-            use_fp16=device == "cuda",
-            transformer_seq_len=1024,
-            use_tqdm=False,
-        )[0]
-
-        image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
-        img_path = f"/home/patrick/muse_images/muse_512_{i}.png"
-        image.save(img_path)
-        main_dict.update(
-            {
-                prompt: {
-                    "img_path": img_path,
-                    "Category": sample["Category"],
-                    "Challenge": sample["Challenge"],
-                    "Note": sample["Note"],
-                    "model_name": ckpt_id,
-                    "seed": seed,
-                }
-            }
-        )
-
-    def generation_fn():
-        for prompt in main_dict:
-            prompt_entry = main_dict[prompt]
-            yield {
-                "Prompt": prompt,
-                "Category": prompt_entry["Category"],
-                "Challenge": prompt_entry["Challenge"],
-                "Note": prompt_entry["Note"],
-                "images": {"path": prompt_entry["img_path"]},
-                "model_name": prompt_entry["model_name"],
-                "seed": prompt_entry["seed"],
-            }
-
-    print("Preparing HF dataset...")
-    ds = Dataset.from_generator(
-        generation_fn,
-        features=Features(
-            Prompt=Value("string"),
-            Category=Value("string"),
-            Challenge=Value("string"),
-            Note=Value("string"),
-            images=ImageFeature(),
-            model_name=Value("string"),
-            seed=Value("int64"),
-        ),
-    )
-    ds_id = "diffusers-parti-prompts/muse512"
-    ds.push_to_hub(ds_id)
-
-
-if __name__ == "__main__":
-    main()
-```
+---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: Prompt
+    dtype: string
+  - name: Category
+    dtype: string
+  - name: Challenge
+    dtype: string
+  - name: Note
+    dtype: string
+  - name: images
+    dtype: image
+  - name: model_name
+    dtype: string
+  - name: seed
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 128701081.568
+    num_examples: 1632
+  download_size: 127769152
+  dataset_size: 128701081.568
+---
+# Dataset Card for "muse_512"
+
+[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)