Update README.md

README.md

@@ -29,6 +29,103 @@ configs:
   - split: train
     path: data/train-*
 ---
-# Dataset Card for "wuerst"
+# Wuerstchen
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+All images included in this dataset were voted as "Not solved" by the community in https://huggingface.co/spaces/OpenGenAI/open-parti-prompts. This means that according to the community the model did not generate an image that corresponds sufficiently enough to the prompt.
+
+The following script was used to generate the images:
+
+```py
+import torch
+from datasets import Dataset, Features
+from datasets import Image as ImageFeature
+from datasets import Value, load_dataset
+from diffusers import AutoPipelineForText2Image
+
+import PIL
+
+
+def main():
+    print("Loading dataset...")
+    parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
+
+    print("Loading pipeline...")
+    seed = 0
+
+    device = "cuda"
+    generator = torch.Generator(device).manual_seed(seed)
+    dtype = torch.float16
+
+    ckpt_id = "warp-diffusion/wuerstchen"
+
+    pipeline =  AutoPipelineForText2Image.from_pretrained(
+        ckpt_id, torch_dtype=dtype
+    ).to(device)
+
+    pipeline.prior_prior = torch.compile(pipeline.prior_prior, mode="reduce-overhead", fullgraph=True)
+    pipeline.decoder = torch.compile(pipeline.decoder, mode="reduce-overhead", fullgraph=True)
+
+    print("Running inference...")
+    main_dict = {}
+    for i in range(len(parti_prompts)):
+        sample = parti_prompts[i]
+        prompt = sample["Prompt"]
+
+        image = pipeline(
+            prompt=prompt,
+            height=1024,
+            width=1024,
+            prior_guidance_scale=4.0,
+            decoder_guidance_scale=0.0,
+            generator=generator,
+        ).images[0]
+
+        image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
+        img_path = f"wuerstchen_{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/wuerstchen"
+    ds.push_to_hub(ds_id)
+
+
+if __name__ == "__main__":
+    main()
+```