up

control_net_canny.py

@@ -10,6 +10,7 @@ import numpy as np
 
 from diffusers import (
     ControlNetModel,
+    EulerDiscreteScheduler,
     StableDiffusionControlNetPipeline,
     UniPCMultistepScheduler,
 )
@@ -35,7 +36,7 @@ pipe = StableDiffusionControlNetPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
 )
 
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
 
 generator = torch.manual_seed(33)

control_net_shuffle.py

@@ -8,6 +8,8 @@ from controlnet_aux import ContentShuffleDetector
 
 from diffusers import (
     ControlNetModel,
+    EulerDiscreteScheduler,
+    EulerDiscreteScheduler,
     StableDiffusionControlNetPipeline,
     UniPCMultistepScheduler,
 )
@@ -29,7 +31,8 @@ pipe = StableDiffusionControlNetPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
 )
 
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+# pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
 
 generator = torch.manual_seed(33)

mass_open_controlnet_pr.sh

@@ -3,4 +3,4 @@ while read p; do
 	echo "-------------------------------"
 	echo "Open PR for $p"
 	python convert_flax_to_pt.py $p
-done
+done <model_ids.txt

run_local.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-from diffusers import StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler
+from diffusers import StableDiffusionControlNetPipeline
 import time
 import os
 from huggingface_hub import HfApi
@@ -17,7 +17,7 @@ api = HfApi()
 start_time = time.time()
 #pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16, device_map="auto")
 #pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(path, torch_dtype=torch.float16, safety_checker=None)
+pipe = StableDiffusionControlNetPipeline.from_pretrained(path, torch_dtype=torch.float16, safety_checker=None)
 pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
 
 # compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)