add exhaustive tests

run_local_xl.py

@@ -22,46 +22,45 @@ 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)
+    print(time.time() - start_time)
     pipe.to("cuda")
 
     if use_refiner:
+        start_time = time.time()
         refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+        print(time.time() - start_time)
         refiner.to("cuda")
         # refiner.enable_sequential_cpu_offload()
 else:
+    start_time = time.time()
     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)
+    print(time.time() - start_time)
     pipe.to("cuda")
 
     if use_refiner:
+        start_time = time.time()
         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)
+        print(time.time() - start_time)
         refiner.to("cuda")
 
 
 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:
-            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]
+steps = 30
+seed = 0
+seed_everything(seed)
+image = pipe(prompt=prompt, num_inference_steps=steps, output_type="latent" if use_refiner else "pil").images[0]
 
-# image = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=40, output_type="latent").images
+if use_refiner:
+    image = refiner(prompt=prompt, num_inference_steps=steps - 10, image=image[None, :]).images[0]
 
-        file_name = f"{steps}_{denoising_end_t2i}"
-        path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.png")
-        image.save(path)
+file_name = f"aaa"
+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")
+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")

run_xl_ediffi.py

@@ -0,0 +1,48 @@
+#!/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
+
+from torch.nn.functional import fractional_max_pool2d_with_indices
+
+api = HfApi()
+start_time = time.time()
+
+pipe_high_noise = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, local_files_only=True)
+pipe_high_noise.to("cuda")
+
+pipe_low_noise = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", text_encoder_2=pipe_high_noise.text_encoder_2, vae=pipe_high_noise.vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+pipe_low_noise.to("cuda")
+
+prompt = "A majestic lion jumping from a big stone at night"
+
+num_inference_steps = 40
+high_noise_frac = 0.7
+
+seed = 0
+seed_everything(seed)
+
+image = pipe_high_noise(prompt=prompt, num_inference_steps=num_inference_steps, denoising_end=high_noise_frac, output_type="latent").images
+image = pipe_low_noise(prompt=prompt, num_inference_steps=num_inference_steps, denoising_start=high_noise_frac, image=image).images[0]
+
+file_name = f"aaa"
+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/{file_name}.png")