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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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splits: |
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- name: train |
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num_bytes: 163668480.032 |
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num_examples: 1632 |
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download_size: 163766653 |
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dataset_size: 163668480.032 |
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--- |
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# Dataset Card for "kandinsky-2-2" |
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The dataset was generated using the code below: |
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```python |
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import PIL |
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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 DiffusionPipeline |
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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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pipe_prior = DiffusionPipeline.from_pretrained( |
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"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16 |
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) |
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pipe_prior.to("cuda") |
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pipe_prior.set_progress_bar_config(disable=True) |
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t2i_pipe = DiffusionPipeline.from_pretrained( |
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"kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16 |
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) |
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t2i_pipe.to("cuda") |
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t2i_pipe.set_progress_bar_config(disable=True) |
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seed = 0 |
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generator = torch.Generator("cuda").manual_seed(seed) |
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ckpt_id = ( |
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"kandinsky-community/" + "kandinsky-2-2-prior" + "_" + "kandinsky-2-2-decoder" |
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) |
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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_embeds, negative_image_embeds = pipe_prior( |
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prompt, |
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generator=generator, |
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num_inference_steps=100, |
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guidance_scale=7.5, |
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).to_tuple() |
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image = t2i_pipe( |
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image_embeds=image_embeds, |
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negative_image_embeds=negative_image_embeds, |
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generator=generator, |
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num_inference_steps=100, |
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guidance_scale=7.5, |
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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"kandinsky_22_{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/kandinsky-2-2" |
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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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``` |
