Create README.md

README.md

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+```python
+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.transformer = MaskGiTUViT.from_pretrained("valhalla/muse-research-run", 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-256"
+
+    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=(256, 256),
+            crop_coords=(0, 0),
+            aesthetic_score=6,
+            use_fp16=device == "cuda",
+            transformer_seq_len=256,
+            use_tqdm=False,
+        )[0]
+
+        image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
+        img_path = f"/home/patrick/muse_images/muse_256_{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/muse256"
+    ds.push_to_hub(ds_id)
+
+
+if __name__ == "__main__":
+    main()
+```