Update app.py

app.py

@@ -6,7 +6,11 @@ from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "Lykon/dreamshaper-8" #Replace to the model you would like to use
+all_model_id = {
+ "DreamShaper-8": "Lykon/dreamshaper-8",
+ "SD-v1.4": "CompVis/stable-diffusion-v1-4",
+ "RealisticVision-v4.0": "SG161222/Realistic_Vision_V4.0_noVAE"
+}
 
 if torch.cuda.is_available():
  torch_dtype = torch.float16
@@ -17,7 +21,7 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 #@spaces.GPU #[uncomment to use ZeroGPU]
-def infer_t2i(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+def infer_t2i(model_repo_id, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
  if randomize_seed:
  seed = random.randint(0, MAX_SEED)
@@ -40,7 +44,7 @@ def infer_t2i(prompt, negative_prompt, seed, randomize_seed, width, height, guid
  return image, seed
 
 #@spaces.GPU #[uncomment to use ZeroGPU]
-def infer_i2i(prompt, image, strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+def infer_i2i(model_repo_id, prompt, image, strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
 
  if randomize_seed:
  seed = random.randint(0, MAX_SEED)
@@ -64,6 +68,34 @@ def infer_i2i(prompt, image, strength, negative_prompt, seed, randomize_seed, wi
  
  return image, seed
 
+#@spaces.GPU #[uncomment to use ZeroGPU]
+def infer_ip_adapter(model_repo_id, prompt, image, scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+
+ if randomize_seed:
+ seed = random.randint(0, MAX_SEED)
+ 
+ generator = torch.Generator().manual_seed(seed)
+
+ pipe = StableDiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+ pipe = pipe.to(device)
+ pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-plus_sd15.bin")
+ pipeline.set_ip_adapter_scale(scale)
+ 
+ image = pipe(
+ prompt = prompt, 
+ image = image.resize((width, height)),
+ strength = strength,
+ negative_prompt = negative_prompt,
+ guidance_scale = guidance_scale, 
+ num_inference_steps = num_inference_steps, 
+ ip_adapter_image = image,
+ width = width, 
+ height = height,
+ generator = generator
+ ).images[0] 
+ 
+ return image, seed
+
 examples = [
  "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
  "An astronaut riding a green horse",
@@ -116,6 +148,8 @@ with gr.Blocks(css=css) as demo:
  step=1,
  value=0,
  )
+
+ model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.key()), value=list(all_model_id.key())[0])
  
  randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
  
@@ -126,7 +160,7 @@ with gr.Blocks(css=css) as demo:
  minimum=256,
  maximum=MAX_IMAGE_SIZE,
  step=32,
- value=1024, #Replace with defaults that work for your model
+ value=512, #Replace with defaults that work for your model
  )
  
  height = gr.Slider(
@@ -134,7 +168,7 @@ with gr.Blocks(css=css) as demo:
  minimum=256,
  maximum=MAX_IMAGE_SIZE,
  step=32,
- value=1024, #Replace with defaults that work for your model
+ value=512, #Replace with defaults that work for your model
  )
  
  with gr.Row():
@@ -162,7 +196,7 @@ with gr.Blocks(css=css) as demo:
  
  run_button.click(
  fn=infer_t2i,
- inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+ inputs = [all_model_id[model_choice], prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
  outputs = [result, seed]
  )
 
@@ -207,6 +241,8 @@ with gr.Blocks(css=css) as demo:
  )
  
  randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+ model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.key()), value=list(all_model_id.key())[0])
  
  with gr.Row():
  
@@ -215,7 +251,7 @@ with gr.Blocks(css=css) as demo:
  minimum=256,
  maximum=MAX_IMAGE_SIZE,
  step=32,
- value=1024, #Replace with defaults that work for your model
+ value=512, #Replace with defaults that work for your model
  )
  
  height = gr.Slider(
@@ -223,7 +259,7 @@ with gr.Blocks(css=css) as demo:
  minimum=256,
  maximum=MAX_IMAGE_SIZE,
  step=32,
- value=1024, #Replace with defaults that work for your model
+ value=512, #Replace with defaults that work for your model
  )
  
  with gr.Row():
@@ -259,7 +295,106 @@ with gr.Blocks(css=css) as demo:
  
  run_button.click(
  fn=infer_i2i,
- inputs = [prompt, image_upload_input, editing_strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+ inputs = [all_model_id[model_choice], prompt, image_upload_input, editing_strength, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+ outputs = [result, seed]
+ )
+
+ with gr.Tab("IP-Adapter"):
+
+ with gr.Column(elem_id="col-container"):
+ gr.Markdown(f"""
+ # Text-to-Image Gradio Template
+ """)
+ 
+ with gr.Row():
+ 
+ prompt = gr.Text(
+ label="Prompt",
+ show_label=False,
+ max_lines=1,
+ placeholder="Enter your prompt",
+ container=False,
+ )
+ 
+ run_button = gr.Button("Run", scale=0)
+
+ image_upload_input = gr.Image(label="Upload an Image", type="pil")
+ 
+ result = gr.Image(label="Result", show_label=False)
+ 
+ with gr.Accordion("Advanced Settings", open=False):
+ 
+ negative_prompt = gr.Text(
+ label="Negative prompt",
+ max_lines=1,
+ placeholder="Enter a negative prompt",
+ visible=False,
+ )
+ 
+ seed = gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=MAX_SEED,
+ step=1,
+ value=0,
+ )
+ 
+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+ model_choice = gr.Dropdown(label="Choose Model", choices=list(all_model_id.key()), value=list(all_model_id.key())[0])
+ 
+ with gr.Row():
+ 
+ width = gr.Slider(
+ label="Width",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=512, #Replace with defaults that work for your model
+ )
+ 
+ height = gr.Slider(
+ label="Height",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=512, #Replace with defaults that work for your model
+ )
+ 
+ with gr.Row():
+ 
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ minimum=0.0,
+ maximum=10.0,
+ step=0.1,
+ value=7.5, #Replace with defaults that work for your model
+ )
+ 
+ num_inference_steps = gr.Slider(
+ label="Number of inference steps",
+ minimum=1,
+ maximum=50,
+ step=1,
+ value=25, #Replace with defaults that work for your model
+ )
+
+ ip_adapter_scale = gr.Slider(
+ label="Strength of image condition",
+ minimum=0,
+ maximum=1,
+ step=0.01,
+ value=0.4, #Replace with defaults that work for your model
+ )
+ 
+ gr.Examples(
+ examples = examples,
+ inputs = [prompt]
+ )
+ 
+ run_button.click(
+ fn=infer_ip_adapter,
+ inputs = [all_model_id[model_choice], prompt, image_upload_input, ip_adapter_scale, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
  outputs = [result, seed]
  )