finish

control_net_canny.py

@@ -33,14 +33,14 @@ canny_image = Image.fromarray(image)
 
 controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
 pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
+    "runwayml/stable-diffusion-v1-5", controlnet=[controlnet, controlnet], torch_dtype=torch.float16
 )
 
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
 
 generator = torch.manual_seed(33)
-out_image = pipe("a blue paradise bird in the jungle", num_inference_steps=20, generator=generator, image=canny_image).images[0]
+out_image = pipe("a blue paradise bird in the jungle", control_guidance_start=[0.2, 0.2], num_inference_steps=20, generator=generator, image=[canny_image, canny_image]).images[0]
 
 path = os.path.join(Path.home(), "images", "aa.png")
 out_image.save(path)

run_better_trans_clip.py

@@ -0,0 +1,14 @@
+#!/usr/bin/env python3
+from transformers import CLIPTextConfig, CLIPModel
+import torch
+
+config = CLIPTextConfig.from_pretrained("openMUSE/CLIP-ViT-L-14-DataComp.XL-s13B-b90K-penultimate")
+
+model = CLIPModel.from_pretrained("laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K", text_config=config)
+
+model.to_bettertransformer()
+
+text_encoder = model.text_model
+text_encoder = torch.compile(text_encoder)
+
+

run_consistency.py

@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+import torch
+
+from diffusers import ConsistencyModelPipeline, UNet2DModel
+
+device = "cpu"
+# Load the cd_bedroom256_lpips checkpoint.
+model_id_or_path = "openai/diffusers-cd_bedroom256_lpips"
+pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path)
+pipe.to(device)
+
+# Multistep sampling
+# Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
+# https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L83
+for _ in range(10):
+    image = pipe(timesteps=[17, 0]).images[0]
+    image.show()
+

run_lora

@@ -12,7 +12,6 @@ from PIL import Image
 from io import BytesIO
 
 # path = sys.argv[1]
-path = "runwayml/stable-diffusion-v1-5"
 path = "gsdf/Counterfeit-V2.5"
 # path = "stabilityai/stable-diffusion-2-1"
 

run_no_grad.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+from transformers import BertModel
+import torch
+import time
+
+
+model = BertModel.from_pretrained("bert-base-uncased")
+model.to("cuda")
+
+input_ids = torch.ones((16, 256), dtype=torch.long)
+input_ids = input_ids.to("cuda")
+
+model.requires_grad_(False)
+
+start_time = time.time()
+
+for _ in range(5):
+    with torch.no_grad():
+        logits = model(input_ids)
+
+print(time.time() - start_time)
+

run_safety.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python3
+import PIL
+from transformers import CLIPImageProcessor
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+
+feature_extractor = CLIPImageProcessor.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="feature_extractor")
+safety_checker = StableDiffusionSafetyChecker.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="safety_checker")
+device = "cpu"
+
+image = PIL.Image.open("/home/patrick/images/0.png")
+
+safety_checker_input = feature_extractor(image, return_tensors="pt").to(device)
+image, has_nsfw_concept = safety_checker(images=image, clip_input=safety_checker_input.pixel_values)

run_sd_xl.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python3
+from diffusers import StableDiffusionXLPipeline

sd_2_1_API.py

@@ -0,0 +1,17 @@
+#!/usr/bin/env python3
+import requests
+import io
+from PIL import Image
+
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
+headers = {"Authorization": "Bearer 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"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+image_bytes = query({
+	"inputs": "Astronaut riding a horse",
+})
+# You can access the image with PIL.Image for example
+image = Image.open(io.BytesIO(image_bytes))
+import ipdb; ipdb.set_trace()