finish

run_kandinsky.py

@@ -17,8 +17,7 @@ negative_prompt = "low quality, bad quality"
 generator = torch.Generator(device="cuda").manual_seed(10)
 image_embeds, negative_image_embeds = pipe_prior(prompt, negative_prompt, guidance_scale=1.0, generator=generator).to_tuple()
 
-scheduler = DDPMScheduler.from_pretrained("../kandinsky-2-1/", subfolder="ddpm_scheduler")
-t2i_pipe = DiffusionPipeline.from_pretrained("../kandinsky-2-1/", scheduler=scheduler, torch_dtype=torch.float16)
+t2i_pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
 t2i_pipe.to("cuda")
 print(t2i_pipe.scheduler.config)
 

run_local_xl.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
-from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
+from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline, AutoencoderKL
 import time
 from pytorch_lightning import seed_everything
 import os
@@ -19,15 +19,15 @@ start_time = time.time()
 use_refiner = bool(int(sys.argv[1]))
 use_diffusers = True
 
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, force_upcast=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 = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", vae=vae, 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")
+        refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
         print(time.time() - start_time)
         refiner.to("cuda")
         # refiner.enable_sequential_cpu_offload()
@@ -45,13 +45,15 @@ else:
 
 
 prompt = "An astronaut riding a green horse on Mars"
-steps = 30
+steps = 20
 seed = 0
 seed_everything(seed)
-image = pipe(prompt=prompt, num_inference_steps=steps, output_type="latent" if use_refiner else "pil").images[0]
+start_time = time.time()
+image = pipe(prompt=prompt, num_inference_steps=steps, output_type="pil").images[0]
+print(time.time() - start_time)
 
 if use_refiner:
-    image = refiner(prompt=prompt, num_inference_steps=steps - 10, image=image[None, :]).images[0]
+    image = refiner(prompt=prompt, num_inference_steps=steps - 10, image=image).images[0]
 
 file_name = f"aaa"
 path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.png")

run_xl_ediffi.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
-from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
+from diffusers import DiffusionPipeline
 import time
 from pytorch_lightning import seed_everything
 import os
@@ -18,22 +18,26 @@ 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)
+model_id = "stabilityai/stable-diffusion-xl-base-0.9"
+model_id = "runwayml/stable-diffusion-v1-5"
+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 = EulerDiscreteScheduler.from_config(pipe_high_noise.scheduler.config)
 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 = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+pipe_low_noise.scheduler = EulerDiscreteScheduler.from_config(pipe_low_noise.scheduler.config)
 pipe_low_noise.to("cuda")
 
 prompt = "A majestic lion jumping from a big stone at night"
 
 num_inference_steps = 40
-high_noise_frac = None
+high_noise_frac = 0.8
 
-seed = 0
-seed_everything(seed)
+# seed = 0
+# seed_everything(seed)
 
-image = pipe_high_noise(prompt=prompt, num_inference_steps=num_inference_steps, denoising_end=high_noise_frac).images[0]
-# image = pipe_low_noise(prompt=prompt, num_inference_steps=num_inference_steps, denoising_start=high_noise_frac, image=image).images[0]
+image = pipe_high_noise(prompt=prompt, num_inference_steps=num_inference_steps, denoising_end=high_noise_frac, output_type="pt").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_1"
 path = os.path.join(Path.home(), "images", "ediffi_sdxl", f"{file_name}.png")