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--- |
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dataset_info: |
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features: |
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- name: Prompt |
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dtype: string |
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- name: Category |
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dtype: string |
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- name: Challenge |
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dtype: string |
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- name: Note |
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dtype: string |
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- name: images |
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dtype: image |
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- name: model_name |
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dtype: string |
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- name: seed |
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dtype: int64 |
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- name: upvotes |
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dtype: int64 |
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splits: |
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- name: train |
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num_bytes: 19633368.0 |
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num_examples: 219 |
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download_size: 19625614 |
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dataset_size: 19633368.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Wuerstchen |
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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. |
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The following script was used to generate the images: |
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```py |
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import torch |
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from datasets import Dataset, Features |
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from datasets import Image as ImageFeature |
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from datasets import Value, load_dataset |
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from diffusers import AutoPipelineForText2Image |
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import PIL |
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def main(): |
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print("Loading dataset...") |
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parti_prompts = load_dataset("nateraw/parti-prompts", split="train") |
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print("Loading pipeline...") |
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seed = 0 |
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device = "cuda" |
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generator = torch.Generator(device).manual_seed(seed) |
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dtype = torch.float16 |
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ckpt_id = "warp-diffusion/wuerstchen" |
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pipeline = AutoPipelineForText2Image.from_pretrained( |
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ckpt_id, torch_dtype=dtype |
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).to(device) |
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pipeline.prior_prior = torch.compile(pipeline.prior_prior, mode="reduce-overhead", fullgraph=True) |
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pipeline.decoder = torch.compile(pipeline.decoder, mode="reduce-overhead", fullgraph=True) |
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print("Running inference...") |
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main_dict = {} |
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for i in range(len(parti_prompts)): |
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sample = parti_prompts[i] |
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prompt = sample["Prompt"] |
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image = pipeline( |
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prompt=prompt, |
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height=1024, |
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width=1024, |
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prior_guidance_scale=4.0, |
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decoder_guidance_scale=0.0, |
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generator=generator, |
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).images[0] |
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image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS) |
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img_path = f"wuerstchen_{i}.png" |
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image.save(img_path) |
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main_dict.update( |
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{ |
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prompt: { |
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"img_path": img_path, |
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"Category": sample["Category"], |
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"Challenge": sample["Challenge"], |
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"Note": sample["Note"], |
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"model_name": ckpt_id, |
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"seed": seed, |
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} |
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} |
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) |
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def generation_fn(): |
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for prompt in main_dict: |
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prompt_entry = main_dict[prompt] |
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yield { |
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"Prompt": prompt, |
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"Category": prompt_entry["Category"], |
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"Challenge": prompt_entry["Challenge"], |
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"Note": prompt_entry["Note"], |
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"images": {"path": prompt_entry["img_path"]}, |
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"model_name": prompt_entry["model_name"], |
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"seed": prompt_entry["seed"], |
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} |
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print("Preparing HF dataset...") |
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ds = Dataset.from_generator( |
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generation_fn, |
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features=Features( |
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Prompt=Value("string"), |
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Category=Value("string"), |
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Challenge=Value("string"), |
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Note=Value("string"), |
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images=ImageFeature(), |
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model_name=Value("string"), |
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seed=Value("int64"), |
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), |
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) |
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ds_id = "diffusers-parti-prompts/wuerstchen" |
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ds.push_to_hub(ds_id) |
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if __name__ == "__main__": |
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main() |
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``` |
