Delete generate_kandinsky.py

generate_kandinsky.py

@@ -1,100 +0,0 @@
-import PIL
-import torch
-from datasets import Dataset, Features
-from datasets import Image as ImageFeature
-from datasets import Value, load_dataset
-
-from diffusers import DiffusionPipeline
-
-
-def main():
-    print("Loading dataset...")
-    parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
-
-    print("Loading pipeline...")
-    pipe_prior = DiffusionPipeline.from_pretrained(
-        "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-    )
-    pipe_prior.to("cuda")
-    pipe_prior.set_progress_bar_config(disable=True)
-
-    t2i_pipe = DiffusionPipeline.from_pretrained(
-        "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
-    )
-    t2i_pipe.to("cuda")
-    t2i_pipe.set_progress_bar_config(disable=True)
-
-    seed = 0
-    generator = torch.Generator("cuda").manual_seed(seed)
-    ckpt_id = (
-        "kandinsky-community/" + "kandinsky-2-2-prior" + "_" + "kandinsky-2-2-decoder"
-    )
-
-    print("Running inference...")
-    main_dict = {}
-    for i in range(len(parti_prompts)):
-        sample = parti_prompts[i]
-        prompt = sample["Prompt"]
-
-        image_embeds, negative_image_embeds = pipe_prior(
-            prompt,
-            generator=generator,
-            num_inference_steps=100,
-            guidance_scale=7.5,
-        ).to_tuple()
-        image = t2i_pipe(
-            image_embeds=image_embeds,
-            negative_image_embeds=negative_image_embeds,
-            generator=generator,
-            num_inference_steps=100,
-            guidance_scale=7.5,
-        ).images[0]
-
-        image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS)
-        img_path = f"kandinsky_22_{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/kandinsky-2-2"
-    ds.push_to_hub(ds_id)
-
-
-if __name__ == "__main__":
-    main()