Removed control net because something didn’t work

app.py

@@ -1,45 +1,15 @@
-from diffusers import AutoencoderKL, StableDiffusionXLControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
+from diffusers import StableDiffusionXLPipeline
 import torch
-from controlnet_aux import OpenposeDetector
-from diffusers.utils import load_image
+#from controlnet_aux import OpenposeDetector
+#from diffusers.utils import load_image
 import gradio as gr
 
-#sd1.5 bases
-#model_base = "SG161222/Realistic_Vision_V5.1_noVAE" #fantasy people
-#model_base = "Justin-Choo/epiCRealism-Natural_Sin_RC1_VAE" #cartoon people
-#model_base = "Lykon/DreamShaper" #unrealistic people
-#model_base = "runwayml/stable-diffusion-v1-5" #base
-#model_base = "Krebzonide/LazyMixPlus" #nsfw people
-#model_base = "Krebzonide/Humans" #boring people
-#model_base = "aufahr/unofficial_aom3" #anime people
-
-#lora_model_path = "Krebzonide/LoRA-CH-0" #mecjh - Corey H, traind on epiCRealism
-#lora_model_path = "Krebzonide/LoRA-CH-1" #mecjh - Corey H, traind on epiCRealism
-#lora_model_path = "Krebzonide/LoRA-EM1" #exgfem - Emily M, trained on LizyMixPlus
-#lora_model_path = "Krebzonide/LoRA-EM-2-0" #exgfem - Emily M, trained on Humans
-#lora_model_path = "Krebzonide/LoRA-YX1" #uwspyx - Professor Xing, trained on Realistic_Vision
-
-#pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16, use_safetensors=True, use_auth_token="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")
-#pipe.unet.load_attn_procs(lora_model_path, use_auth_token="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")
-#pipe.to("cuda")
-
-
 model_base = "stabilityai/stable-diffusion-xl-base-1.0"
 
-openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
-
-#image is a random guy. openpose_image is the pose of that guy.
-image = load_image(
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
-)
-openpose_image = openpose(image)
-
-controlnet = ControlNetModel.from_pretrained("thibaud/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16)
-
-pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-    model_base, controlnet=controlnet, torch_dtype=torch.float16
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    model_base, torch_dtype=torch.float16
 )
-pipe.enable_model_cpu_offload()
+pipe = pipe.to("cuda")
 
 css = """
 .btn-green {
@@ -61,7 +31,6 @@ def generate(prompt, neg_prompt, samp_steps, guide_scale, lora_scale, progress=g
         #cross_attention_kwargs={"scale": lora_scale},
         num_images_per_prompt=4,
         #generator=torch.manual_seed(97),
-        image=openpose_image.resize((1024, 1024)), #THIS IS THE OPENPOSE IMAGE
     ).images
     return [(img, f"Image {i+1}") for i, img in enumerate(images)]
         
@@ -71,7 +40,7 @@ with gr.Blocks(css=css) as demo:
         prompt = gr.Textbox(label="Prompt")
         negative_prompt = gr.Textbox(label="Negative Prompt", value="lowres, bad anatomy, bad hands, cropped, worst quality, disfigured, deformed, extra limbs, asian, filter, render")
         submit_btn = gr.Button("Generate", elem_classes="btn-green")
-        gallery = gr.Gallery(label="Generated images", height=700)
+        gallery = gr.Gallery(label="Generated images", height=1100)
         with gr.Row():
             samp_steps = gr.Slider(1, 100, value=25, step=1, label="Sampling steps")
             guide_scale = gr.Slider(1, 10, value=6, step=0.5, label="Guidance scale")