upload

run_local.py

@@ -4,6 +4,7 @@ from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepSchedu
 import time
 import os
 from huggingface_hub import HfApi
+from compel import Compel
 import torch
 import sys
 from pathlib import Path
@@ -16,21 +17,31 @@ start_time = time.time()
 #pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
 pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+
+compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)
+
+
 pipe = pipe.to("cuda")
 
 prompt = "a highly realistic photo of green turtle"
 
-generator = torch.Generator(device="cuda").manual_seed(0)
-image = pipe(prompt, generator=generator, num_inference_steps=15).images[0]
+prompts = ["a cat playing with a ball++ in the forest", "a cat playing with a ball++ in the forest", "a cat playing with a ball-- in the forest"]
+
+prompt_embeds = torch.cat([compel.build_conditioning_tensor(prompt) for prompt in prompts])
+
+generator = [torch.Generator(device="cuda").manual_seed(0) for _ in range(prompt_embeds.shape[0])]
+images = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=15).images
+
 print("Time", time.time() - start_time)
 
-path = os.path.join(Path.home(), "images", "aa.png")
-image.save(path)
+for i, image in enumerate(image):
+    path = os.path.join(Path.home(), "images", f"aa_{i}.png")
+    image.save(path)
 
-api.upload_file(
-    path_or_fileobj=path,
-    path_in_repo=path.split("/")[-1],
-    repo_id="patrickvonplaten/images",
-    repo_type="dataset",
-)
-print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png")
+    api.upload_file(
+        path_or_fileobj=path,
+        path_in_repo=path.split("/")[-1],
+        repo_id="patrickvonplaten/images",
+        repo_type="dataset",
+    )
+    print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa_{i}.png")