Upload generate_kandinsky.py with huggingface_hub

generate_kandinsky.py

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+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()