fix

app.py

@@ -5,24 +5,18 @@ import gradio as gr
 import os, json
 import numpy as np
 from PIL import Image
-
-from pipelines.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline
 from pipelines.pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
-from diffusers import ControlNetModel, AutoencoderKL
-from transformers import DPTFeatureExtractor, DPTForDepthEstimation
 from random import randint
 from utils import init_latent
 from transformers import Blip2Processor, Blip2ForConditionalGeneration
 from diffusers import DDIMScheduler
 
-# device = 'cuda' if torch.cuda.is_available() else 'cpu'
-# if device == 'cpu':
-#     torch_dtype = torch.float32
-# else:
-#     torch_dtype = torch.float16
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+if device == 'cpu':
+    torch_dtype = torch.float32
+else:
+    torch_dtype = torch.float16
 
-device = 'cuda'
-torch_dtype = torch.float16
 def memory_efficient(model):
     try:
         model.to(device)
@@ -37,33 +31,16 @@ def memory_efficient(model):
         model.enable_vae_slicing()
     except AttributeError:
         print("enable_vae_slicing is not supported.")
-    # if device == 'cuda':
-    #     try:
-    #         model.enable_xformers_memory_efficient_attention()
-    #     except AttributeError:
-    #         print("enable_xformers_memory_efficient_attention is not supported.")
-
-controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch_dtype)
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype)
-
-model_controlnet = StableDiffusionXLControlNetPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, vae=vae, torch_dtype=torch_dtype
-)
+    if device == 'cuda':
+        try:
+            model.enable_xformers_memory_efficient_attention()
+        except AttributeError:
+            print("enable_xformers_memory_efficient_attention is not supported.")
 
 model = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch_dtype)
-
-print("vae")
-memory_efficient(vae)
-print("control")
-memory_efficient(controlnet)
-print("ControlNet-SDXL")
-memory_efficient(model_controlnet)
 print("SDXL")
 memory_efficient(model)
 
-depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
-feature_extractor = DPTFeatureExtractor.from_pretrained("Intel/dpt-hybrid-midas")
-
 blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
 blip_model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch_dtype).to(device)
 
@@ -75,44 +52,8 @@ def parse_config(config):
         config = json.load(f)
     return config
 
-def get_depth_map(image):
-    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
-    with torch.no_grad(), torch.autocast(device):
-        depth_map = depth_estimator(image).predicted_depth
-
-    depth_map = torch.nn.functional.interpolate(
-        depth_map.unsqueeze(1),
-        size=(1024, 1024),
-        mode="bicubic",
-        align_corners=False,
-    )
-    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
-    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
-    image = torch.cat([depth_map] * 3, dim=1)
-
-    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
-    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
-    return image
-
-
-def get_depth_edge_array(depth_img_path):
-    depth_image_tmp = Image.fromarray(depth_img_path)
-
-    # get depth map
-    depth_map = get_depth_map(depth_image_tmp)
-
-    return depth_map
-
-def blip_inf_prompt(image):
-    inputs = blip_processor(images=image, return_tensors="pt").to(device, torch.float16)
 
-    generated_ids = blip_model.generate(**inputs)
-    generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-
-    return generated_text
-
-def load_example_controlnet():
+def load_example_style():
     folder_path = 'assets/ref'
     examples = []
     for filename in os.listdir(folder_path):
@@ -125,33 +66,22 @@ def load_example_controlnet():
             config = parse_config(config_path)
             inf_object_name = config["inference_info"]["inf_object_list"][0]
 
-            canny_path = './assets/depth_dir/gundam.png'
-            image_info = [image_path, canny_path, style_name, inf_object_name, 1, 0.5, 50]
-
+            image_info = [image_path, style_name, inf_object_name, 1, 50]
             examples.append(image_info)
 
     return examples
 
-def load_example_style():
-    folder_path = 'assets/ref'
-    examples = []
-    for filename in os.listdir(folder_path):
-        if filename.endswith((".png")):
-            image_path = os.path.join(folder_path, filename)
-            image_name = os.path.basename(image_path)
-            style_name = image_name.split('_')[1]
-
-            config_path = './config/{}.json'.format(style_name)
-            config = parse_config(config_path)
-            inf_object_name = config["inference_info"]["inf_object_list"][0]
+def blip_inf_prompt(image):
+    inputs = blip_processor(images=image, return_tensors="pt").to(device, torch.float16)
 
-            image_info = [image_path, style_name, inf_object_name, 1, 50]
-            examples.append(image_info)
+    generated_ids = blip_model.generate(**inputs)
+    generated_text = blip_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
 
-    return examples
+    return generated_text
 
 @spaces.GPU
-def style_fn(image_path, style_name, content_text, output_number, diffusion_step=50):
+def style_fn(image_path, style_name, content_text, output_number=1, diffusion_step=50):
+
     user_image_flag = not style_name.strip() # empty
 
     if not user_image_flag:
@@ -272,131 +202,23 @@ def style_fn(image_path, style_name, content_text, output_number, diffusion_step
                 )[0][1:]
 
                 n_row = 1
-                n_col = len(inf_seeds)
+                n_col = len(inf_seeds) + 1 # 원본추가하려면 + 1
 
                 # make grid
                 grid = create_image_grid(images, n_row, n_col, padding=10)
 
         return grid
 
-@spaces.GPU
-def controlnet_fn(image_path, depth_image_path, style_name, content_text, output_number, controlnet_scale=0.5, diffusion_step=50):
-    config_path = './config/{}.json'.format(style_name)
-    config = parse_config(config_path)
-
-    inf_object = content_text
-    inf_seeds = [randint(0, 10**10) for _ in range(int(output_number))]
-    # inf_seeds = [i for i in range(int(output_number))]
-
-    activate_layer_indices_list = config['inference_info']['activate_layer_indices_list']
-    activate_step_indices_list = config['inference_info']['activate_step_indices_list']
-    ref_seed = config['reference_info']['ref_seeds'][0]
-
-    attn_map_save_steps = config['inference_info']['attn_map_save_steps']
-    guidance_scale = config['guidance_scale']
-    use_inf_negative_prompt = config['inference_info']['use_negative_prompt']
-
-    style_name = config["style_name_list"][0]
-
-    ref_object = config["reference_info"]["ref_object_list"][0]
-    ref_with_style_description = config['reference_info']['with_style_description']
-    inf_with_style_description = config['inference_info']['with_style_description']
-
-    use_shared_attention = config['inference_info']['use_shared_attention']
-    adain_queries = config['inference_info']['adain_queries']
-    adain_keys = config['inference_info']['adain_keys']
-    adain_values = config['inference_info']['adain_values']
-
-    use_advanced_sampling = config['inference_info']['use_advanced_sampling']
-
-    #get canny edge array
-    depth_image = get_depth_edge_array(depth_image_path)
-
-    style_description_pos, style_description_neg = STYLE_DESCRIPTION_DICT[style_name][0], \
-                                                   STYLE_DESCRIPTION_DICT[style_name][1]
-
-    # Inference
-    with torch.inference_mode():
-        grid = None
-        if ref_with_style_description:
-            ref_prompt = style_description_pos.replace("{object}", ref_object)
-        else:
-            ref_prompt = ref_object
-
-        if inf_with_style_description:
-            inf_prompt = style_description_pos.replace("{object}", inf_object)
-        else:
-            inf_prompt = inf_object
-
-        for activate_layer_indices in activate_layer_indices_list:
-
-            for activate_step_indices in activate_step_indices_list:
-
-                str_activate_layer, str_activate_step = model_controlnet.activate_layer(
-                    activate_layer_indices=activate_layer_indices,
-                    attn_map_save_steps=attn_map_save_steps,
-                    activate_step_indices=activate_step_indices,
-                    use_shared_attention=use_shared_attention,
-                    adain_queries=adain_queries,
-                    adain_keys=adain_keys,
-                    adain_values=adain_values,
-                )
-
-                # ref_latent = model_controlnet.get_init_latent(ref_seed, precomputed_path=None)
-                ref_latent = init_latent(model_controlnet, device_name=device, dtype=torch_dtype, seed=ref_seed)
-                latents = [ref_latent]
-
-                for inf_seed in inf_seeds:
-                    # latents.append(model_controlnet.get_init_latent(inf_seed, precomputed_path=None))
-                    inf_latent = init_latent(model_controlnet, device_name=device, dtype=torch_dtype, seed=inf_seed)
-                    latents.append(inf_latent)
-
-
-                latents = torch.cat(latents, dim=0)
-                latents.to(device)
-
-                images = model_controlnet.generated_ve_inference(
-                    prompt=ref_prompt,
-                    negative_prompt=style_description_neg,
-                    guidance_scale=guidance_scale,
-                    num_inference_steps=diffusion_step,
-                    controlnet_conditioning_scale=controlnet_scale,
-                    latents=latents,
-                    num_images_per_prompt=len(inf_seeds) + 1,
-                    target_prompt=inf_prompt,
-                    image=depth_image,
-                    use_inf_negative_prompt=use_inf_negative_prompt,
-                    use_advanced_sampling=use_advanced_sampling
-                )[0][1:]
-
-                n_row = 1
-                n_col = len(inf_seeds)  # 원본추가하려면 + 1
-
-                # make grid
-                grid = create_image_grid(images, n_row, n_col)
-
-        return grid
-
-
 description_md = """
 
 ### We introduce `Visual Style Prompting`, which reflects the style of a reference image to the images generated by a pretrained text-to-image diffusion model without finetuning or optimization (e.g., Figure N).
 ### 📖 [[Paper](https://arxiv.org/abs/2402.12974)] | ✨ [[Project page](https://curryjung.github.io/VisualStylePrompt)] | ✨ [[Code](https://github.com/naver-ai/Visual-Style-Prompting)]
+### 🔥 [[w/ Controlnet ver](https://huggingface.co/spaces/naver-ai/VisualStylePrompting_Controlnet)]
 ---
-### 👉 To better reflect the style of a user's image, the higher the resolution, the better.
 ### 🔥 To try out our vanilla demo,
 1. Choose a `style reference` from the collection of images below.
 2. Enter the `text prompt`.
 3. Choose the `number of outputs`.
----
-### ✨ Visual Style Prompting also works on `ControlNet` which specifies the shape of the results by depthmap or keypoints.
-### ‼️ w/ ControlNet ver does not support user style images.
-### 🔥 To try out our demo with ControlNet,
-1. Upload an `image for depth control`. An off-the-shelf model will produce the depthmap from it.
-2. Choose `ControlNet scale` which determines the alignment to the depthmap.
-3. Choose a `style reference` from the collection of images below.
-4. Enter the `text prompt`. (`Empty text` is okay, but a depthmap description helps.)
-5. Choose the `number of outputs`.
 
 ### 👉 To achieve faster results, we recommend lowering the diffusion steps to 30.
 ### Enjoy ! 😄
@@ -417,22 +239,4 @@ iface_style = gr.Interface(
     examples=load_example_style(),
 )
 
-iface_controlnet = gr.Interface(
-    fn=controlnet_fn,
-    inputs=[
-        gr.components.Image(label="Style image"),
-        gr.components.Image(label="Depth image"),
-        gr.components.Textbox(label='Style name', visible=False),
-        gr.components.Textbox(label="Text prompt", placeholder="Enter Text prompt"),
-        gr.components.Textbox(label="Number of outputs", placeholder="Enter Number of outputs"),
-        gr.components.Slider(minimum=0.5, maximum=10, step=0.5, value=0.5, label="Controlnet scale"),
-        gr.components.Slider(minimum=10, maximum=50, step=10, value=50, label="Diffusion steps")
-    ],
-    outputs=gr.components.Image(label="Generated Image"),
-    title="🎨 Visual Style Prompting (w/ ControlNet)",
-    description=description_md,
-    examples=load_example_controlnet(),
-)
-
-iface = gr.TabbedInterface([iface_style, iface_controlnet], ["Vanilla", "w/ ControlNet"])
-iface.launch(debug=True)
+iface_style.launch(debug=True)