merge conflict

1

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
+from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
+import time
+from pytorch_lightning import seed_everything
+import os
+from huggingface_hub import HfApi
+# from compel import Compel
+import torch
+import sys
+from pathlib import Path
+import requests
+from PIL import Image
+from io import BytesIO
+
+api = HfApi()
+start_time = time.time()
+
+use_refiner = bool(int(sys.argv[1]))
+use_diffusers = True
+
+if use_diffusers:
+    start_time = time.time()
+    pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, local_files_only=True)
+    pipe.to("cuda")
+
+    if use_refiner:
+        refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+        refiner.to("cuda")
+        # refiner.enable_sequential_cpu_offload()
+else:
+    pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/sd_xl_base_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True)
+    pipe.to("cuda")
+
+    if use_refiner:
+        refiner = StableDiffusionXLImg2ImgPipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9/blob/main/sd_xl_refiner_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True)
+        refiner.to("cuda")
+
+
+prompt = "An astronaut riding a green horse on Mars"
+for steps in [24, 27, 31]:
+    for denoising_end in [0.63, 0.66, 0.67, 0.71]:
+        seed = 0
+        seed_everything(seed)
+        image = pipe(prompt=prompt, num_inference_steps=40, denoising_end=0.675, output_type="latent" if use_refiner else "pil").images[0]
+# image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
+
+        if use_refiner:
+            image = refiner(prompt=prompt, num_inference_steps=40, denoising_start=0.675, image=image[None, :]).images[0]
+
+# pipe.unet.to(memory_format=torch.channels_last)
+# pipe(prompt=prompt, num_inference_steps=2).images[0]
+
+# image = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=40, output_type="latent").images
+
+        file_name = f"aaa_{seed}"
+        path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.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(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/ediffi_sdxl/{file_name}.png")

run_local_xl.py

@@ -37,27 +37,31 @@ else:
         refiner.to("cuda")
 
 
-prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
-seed_everything(0)
-image = pipe(prompt=prompt, num_inference_steps=2, output_type="latent" if use_refiner else "pil").images[0]
+prompt = "An astronaut riding a green horse on Mars"
+for steps in [24, 27, 31]:
+    for denoising_end_t2i in [0.63, 0.66, 0.67, 0.71]:
+        seed = 0
+        seed_everything(seed)
+        image = pipe(prompt=prompt, num_inference_steps=steps, denoising_end=denoising_end_t2i, output_type="latent" if use_refiner else "pil").images[0]
 # image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]
 
-if use_refiner:
-    image = refiner(prompt=prompt, num_inference_steps=5, image=image[None, :]).images[0]
+        if use_refiner:
+            denoising_start = denoising_end_t2i  # denoising_start is denoising_end_t2i
+            image = refiner(prompt=prompt, num_inference_steps=steps, denoising_start=denoising_start, image=image[None, :]).images[0]
 
 # pipe.unet.to(memory_format=torch.channels_last)
 # pipe(prompt=prompt, num_inference_steps=2).images[0]
 
 # image = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=40, output_type="latent").images
 
-file_name = f"aaa"
-path = os.path.join(Path.home(), "images", f"{file_name}.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(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")
+        file_name = f"{steps}_{denoising_end_t2i}"
+        path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.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(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/ediffi_sdxl/{file_name}.png")