up

run_xl_ediffi.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler, HeunDiscreteScheduler, DEISMultistepScheduler
+from diffusers import DPMSolverMultistepScheduler, DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler, HeunDiscreteScheduler, DEISMultistepScheduler
 from diffusers import DiffusionPipeline
 import time
 from pytorch_lightning import seed_everything
@@ -18,16 +18,35 @@ from torch.nn.functional import fractional_max_pool2d_with_indices
 api = HfApi()
 start_time = time.time()
 
+random_generator = torch.Generator()
+random_generator.manual_seed(12345)
+
+scheduler = DPMSolverMultistepScheduler(
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    prediction_type="epsilon",
+    num_train_timesteps=1000,
+    trained_betas=None,
+    thresholding=False,
+    algorithm_type="dpmsolver++",
+    solver_type="midpoint",
+    lower_order_final=True,
+    use_karras_sigmas=True,
+)
+
 model_id = "stabilityai/stable-diffusion-xl-base-0.9"
 pipe_high_noise = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16", use_safetensors=True, local_files_only=True)
-pipe_high_noise.scheduler = DEISMultistepScheduler.from_config(pipe_high_noise.scheduler.config)
+# pipe_high_noise.scheduler = EulerDiscreteScheduler.from_config(pipe_high_noise.scheduler.config)
+pipe_high_noise.scheduler = scheduler
 pipe_high_noise.to("cuda")
 
 pipe_low_noise = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
-pipe_low_noise.scheduler = DEISMultistepScheduler.from_config(pipe_low_noise.scheduler.config)
+# pipe_low_noise.scheduler = EulerDiscreteScheduler.from_config(pipe_low_noise.scheduler.config)
+pipe_low_noise.scheduler = scheduler
 pipe_low_noise.to("cuda")
 
-prompt = "A majestic lion jumping from a big stone at night"
+prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
 
 num_inference_steps = 40
 high_noise_frac = 0.8