test gradio

app.py

@@ -1,12 +1,10 @@
 import gradio as gr
-import torch
-# from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel, UniPCMultistepScheduler
-from diffusers import StableDiffusionXLPipeline,T2IAdapter
 from huggingface_hub import login
 import os
 import spaces
+import torch
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionPipeline
 from diffusers.utils import load_image, make_image_grid
-from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
@@ -27,16 +25,24 @@ login(token=token)
 # controlnet = SD3ControlNetModel.from_pretrained("alimama-creative/SD3-Controlnet-Softedge", torch_dtype=torch.float16)
 
 # pipe = StableDiffusion3ControlNetPipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet)
-adapter = T2IAdapter.from_pretrained(
-  "TencentARC/t2iadapter_color_sd14v1", torch_dtype=torch.float16, varient="fp16"
-)
+# adapter = T2IAdapter.from_pretrained(
+#   "TencentARC/t2iadapter_color_sd14v1", torch_dtype=torch.float16, varient="fp16"
+# )
+#
+# model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
+# euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
+# vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+# pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
+#     model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16",
+# )
 
-model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
-euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
-    model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16",
-)
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    "Yntec/AbsoluteReality",
+    torch_dtype=torch.float16)
+pipe.load_ip_adapter("h94/IP-Adapter",
+                     subfolder="models",
+                     weight_name="ip-adapter_sd15.bin")
 
 pipe.to("cuda", torch.float16)
 
@@ -45,11 +51,15 @@ pipe.to("cuda", torch.float16)
 def generate_image(prompt, reference_image, controlnet_conditioning_scale):
 
     # Generate the image with ControlNet conditioning
-    generated_image = pipe(
-        prompt=prompt,
-        image=load_image(reference_image),
-        adapter_conditioning_scale=controlnet_conditioning_scale,
-    ).images[0]
+    pipe.set_ip_adapter_scale(controlnet_conditioning_scale)
+    generated_image = pipe(prompt=prompt,
+                  negative_prompt="low quality",
+                  height=768,
+                  width=768,
+                  ip_adapter_image=load_image(reference_image),
+                  guidance_scale=6,
+                  num_inference_steps=20,
+                  num_images_per_prompt=3).images[0]
     return generated_image
 
 # Set up Gradio interface
@@ -63,6 +73,7 @@ interface = gr.Interface(
     outputs="image",
     title="Image Generation with Stable Diffusion 3 medium and ControlNet",
     description="Generates an image based on a text prompt and a reference image using Stable Diffusion 3 medium with ControlNet."
+
 )
 
 interface.launch()