Update app.py

app.py

@@ -1,9 +1,7 @@
-import warnings
-warnings.filterwarnings("ignore", category=FutureWarning)
-
-import torch
-import gradio as gr
+import sys
+sys.path.append('./')
 from PIL import Image
+import gradio as gr
 from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
 from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
 from src.unet_hacked_tryon import UNet2DConditionModel
@@ -13,44 +11,46 @@ from transformers import (
     CLIPTextModel,
     CLIPTextModelWithProjection,
 )
-from diffusers import DDPMScheduler, AutoencoderKL
+from diffusers import DDPMScheduler,AutoencoderKL
 from typing import List
 
-import numpy as np
+import torch
 import os
+from transformers import AutoTokenizer
+import numpy as np
 from utils_mask import get_mask_location
 from torchvision import transforms
 import apply_net
 from preprocess.humanparsing.run_parsing import Parsing
 from preprocess.openpose.run_openpose import OpenPose
-from detectron2.data.detection_utils import convert_PIL_to_numpy, _apply_exif_orientation
+from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation
 from torchvision.transforms.functional import to_pil_image
 
-# Function to convert PIL image to binary mask
+device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+
 def pil_to_binary_mask(pil_image, threshold=0):
     np_image = np.array(pil_image)
     grayscale_image = Image.fromarray(np_image).convert("L")
     binary_mask = np.array(grayscale_image) > threshold
     mask = np.zeros(binary_mask.shape, dtype=np.uint8)
-    for i in range(binary_mask.shape):
-        for j in range(binary_mask.shape):
-            if binary_mask[i, j] == True:
-                mask[i, j] = 1
-    mask = (mask * 255).astype(np.uint8)
+    for i in range(binary_mask.shape[0]):
+        for j in range(binary_mask.shape[1]):
+            if binary_mask[i,j] == True :
+                mask[i,j] = 1
+    mask = (mask*255).astype(np.uint8)
     output_mask = Image.fromarray(mask)
     return output_mask
 
+
 base_path = 'yisol/IDM-VTON'
 example_path = os.path.join(os.path.dirname(__file__), 'example')
 
-# Load models with lower precision (float16) to reduce memory usage
 unet = UNet2DConditionModel.from_pretrained(
     base_path,
     subfolder="unet",
     torch_dtype=torch.float16,
 )
 unet.requires_grad_(False)
-
 tokenizer_one = AutoTokenizer.from_pretrained(
     base_path,
     subfolder="tokenizer",
@@ -63,7 +63,6 @@ tokenizer_two = AutoTokenizer.from_pretrained(
     revision=None,
     use_fast=False,
 )
-
 noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
 
 text_encoder_one = CLIPTextModel.from_pretrained(
@@ -80,10 +79,13 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(
     base_path,
     subfolder="image_encoder",
     torch_dtype=torch.float16,
+    )
+vae = AutoencoderKL.from_pretrained(base_path,
+                                    subfolder="vae",
+                                    torch_dtype=torch.float16,
 )
 
-vae = AutoencoderKL.from_pretrained(base_path, subfolder="vae", torch_dtype=torch.float16)
-
+# "stabilityai/stable-diffusion-xl-base-1.0",
 UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
     base_path,
     subfolder="unet_encoder",
@@ -99,40 +101,37 @@ vae.requires_grad_(False)
 unet.requires_grad_(False)
 text_encoder_one.requires_grad_(False)
 text_encoder_two.requires_grad_(False)
-
-tensor_transform = transforms.Compose(
-    [
-        transforms.ToTensor(),
-        transforms.Normalize([0.5], [0.5]),
-    ]
-)
+tensor_transfrom = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+    )
 
 pipe = TryonPipeline.from_pretrained(
-    base_path,
-    unet=unet,
-    vae=vae,
-    feature_extractor=CLIPImageProcessor(),
-    text_encoder=text_encoder_one,
-    text_encoder_2=text_encoder_two,
-    tokenizer=tokenizer_one,
-    tokenizer_2=tokenizer_two,
-    scheduler=noise_scheduler,
-    image_encoder=image_encoder,
-    torch_dtype=torch.float16,
+        base_path,
+        unet=unet,
+        vae=vae,
+        feature_extractor= CLIPImageProcessor(),
+        text_encoder = text_encoder_one,
+        text_encoder_2 = text_encoder_two,
+        tokenizer = tokenizer_one,
+        tokenizer_2 = tokenizer_two,
+        scheduler = noise_scheduler,
+        image_encoder=image_encoder,
+        torch_dtype=torch.float16,
 )
 pipe.unet_encoder = UNet_Encoder
 
-@grSpaces.GPU
-def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
-    device = "cuda"
+def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed):
     
     openpose_model.preprocessor.body_estimation.model.to(device)
     pipe.to(device)
     pipe.unet_encoder.to(device)
 
-    garm_img = garm_img.convert("RGB").resize((768, 1024))
-    human_img_orig = dict["background"].convert("RGB")
-
+    garm_img= garm_img.convert("RGB").resize((768,1024))
+    human_img_orig = dict["background"].convert("RGB")    
+    
     if is_checked_crop:
         width, height = human_img_orig.size
         target_width = int(min(width, height * (3 / 4)))
@@ -143,112 +142,121 @@ def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denois
         bottom = (height + target_height) / 2
         cropped_img = human_img_orig.crop((left, top, right, bottom))
         crop_size = cropped_img.size
-        human_img = cropped_img.resize((768, 1024))
+        human_img = cropped_img.resize((768,1024))
     else:
-        human_img = human_img_orig.resize((768, 1024))
+        human_img = human_img_orig.resize((768,1024))
+
 
     if is_checked:
-        keypoints = openpose_model(human_img.resize((384, 512)))
-        model_parse, _ = parsing_model(human_img.resize((384, 512)))
+        keypoints = openpose_model(human_img.resize((384,512)))
+        model_parse, _ = parsing_model(human_img.resize((384,512)))
         mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints)
-        mask = mask.resize((768, 1024))
+        mask = mask.resize((768,1024))
     else:
-        mask = pil_to_binary_mask(dict['layers'].convert("RGB").resize((768, 1024)))
+        mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
+        # mask = transforms.ToTensor()(mask)
+        # mask = mask.unsqueeze(0)
+    mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
+    mask_gray = to_pil_image((mask_gray+1.0)/2.0)
 
-    mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transform(human_img)
-    mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
 
-    human_img_arg = _apply_exif_orientation(human_img.resize((384, 512)))
+    human_img_arg = _apply_exif_orientation(human_img.resize((384,512)))
     human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")
+     
+    
 
     args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
-    pose_img = args.func(args, human_img_arg)
-    pose_img = pose_img[:, :, ::-1]
-    pose_img = Image.fromarray(pose_img).resize((768, 1024))
-
-    with torch.cuda.amp.autocast():
-        with torch.no_grad():
-            prompt = "model is wearing " + garment_des
-            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-            with torch.inference_mode():
-                (
-                    prompt_embeds,
-                    negative_prompt_embeds,
-                    pooled_prompt_embeds,
-                    negative_pooled_prompt_embeds,
-                ) = pipe.encode_prompt(
-                    prompt,
-                    num_images_per_prompt=1,
-                    do_classifier_free_guidance=True,
-                    negative_prompt=negative_prompt,
-                )
-
-                prompt = "a photo of " + garment_des
+    # verbosity = getattr(args, "verbosity", None)
+    pose_img = args.func(args,human_img_arg)    
+    pose_img = pose_img[:,:,::-1]    
+    pose_img = Image.fromarray(pose_img).resize((768,1024))
+    
+    with torch.no_grad():
+        # Extract the images
+        with torch.cuda.amp.autocast():
+            with torch.no_grad():
+                prompt = "model is wearing " + garment_des
                 negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-                if not isinstance(prompt, List):
-                    prompt = [prompt] * 1
-                if not isinstance(negative_prompt, List):
-                    negative_prompt = [negative_prompt] * 1
                 with torch.inference_mode():
                     (
-                        prompt_embeds_c,
-                        _,
-                        _,
-                        _,
+                        prompt_embeds,
+                        negative_prompt_embeds,
+                        pooled_prompt_embeds,
+                        negative_pooled_prompt_embeds,
                     ) = pipe.encode_prompt(
                         prompt,
                         num_images_per_prompt=1,
-                        do_classifier_free_guidance=False,
+                        do_classifier_free_guidance=True,
                         negative_prompt=negative_prompt,
                     )
-
-            pose_img = tensor_transform(pose_img).unsqueeze(0).to(device, torch.float16)
-            garm_tensor = tensor_transform(garm_img).unsqueeze(0).to(device, torch.float16)
-            generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
-            images = pipe(
-                prompt_embeds=prompt_embeds.to(device, torch.float16),
-                negative_prompt_embeds=negative_prompt_embeds.to(device, torch.float16),
-                pooled_prompt_embeds=pooled_prompt_embeds.to(device, torch.float16),
-                negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device, torch.float16),
-                num_inference_steps=denoise_steps,
-                generator=generator,
-                strength=1.0,
-                pose_img=pose_img.to(device, torch.float16),
-                text_embeds_cloth=prompt_embeds_c.to(device, torch.float16),
-                cloth=garm_tensor.to(device, torch.float16),
-                mask_image=mask,
-                image=human_img,
-                height=1024,
-                width=768,
-                ip_adapter_image=garm_img.resize((768, 1024)),
-                guidance_scale=2.0,
-            )
-
-    # Clear GPU memory after inference
-    torch.cuda.empty_cache()
+                                    
+                    prompt = "a photo of " + garment_des
+                    negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
+                    if not isinstance(prompt, List):
+                        prompt = [prompt] * 1
+                    if not isinstance(negative_prompt, List):
+                        negative_prompt = [negative_prompt] * 1
+                    with torch.inference_mode():
+                        (
+                            prompt_embeds_c,
+                            _,
+                            _,
+                            _,
+                        ) = pipe.encode_prompt(
+                            prompt,
+                            num_images_per_prompt=1,
+                            do_classifier_free_guidance=False,
+                            negative_prompt=negative_prompt,
+                        )
+
+
+
+                    pose_img =  tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16)
+                    garm_tensor =  tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16)
+                    generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
+                    images = pipe(
+                        prompt_embeds=prompt_embeds.to(device,torch.float16),
+                        negative_prompt_embeds=negative_prompt_embeds.to(device,torch.float16),
+                        pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.float16),
+                        negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,torch.float16),
+                        num_inference_steps=denoise_steps,
+                        generator=generator,
+                        strength = 1.0,
+                        pose_img = pose_img.to(device,torch.float16),
+                        text_embeds_cloth=prompt_embeds_c.to(device,torch.float16),
+                        cloth = garm_tensor.to(device,torch.float16),
+                        mask_image=mask,
+                        image=human_img, 
+                        height=1024,
+                        width=768,
+                        ip_adapter_image = garm_img.resize((768,1024)),
+                        guidance_scale=2.0,
+                    )[0]
 
     if is_checked_crop:
-        out_img = images.resize(crop_size)
-        human_img_orig.paste(out_img, (int(left), int(top)))
+        out_img = images[0].resize(crop_size)        
+        human_img_orig.paste(out_img, (int(left), int(top)))    
         return human_img_orig, mask_gray
     else:
-        return images, mask_gray
+        return images[0], mask_gray
+    # return images[0], mask_gray
 
-garm_list = os.listdir(os.path.join(example_path, "cloth"))
-garm_list_path = [os.path.join(example_path, "cloth", garm) for garm in garm_list]
+garm_list = os.listdir(os.path.join(example_path,"cloth"))
+garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list]
 
-human_list = os.listdir(os.path.join(example_path, "human"))
-human_list_path = [os.path.join(example_path, "human", human) for human in human_list]
+human_list = os.listdir(os.path.join(example_path,"human"))
+human_list_path = [os.path.join(example_path,"human",human) for human in human_list]
 
 human_ex_list = []
 for ex_human in human_list_path:
-    ex_dict = {}
+    ex_dict= {}
     ex_dict['background'] = ex_human
     ex_dict['layers'] = None
     ex_dict['composite'] = None
     human_ex_list.append(ex_dict)
 
-# Default human
+##default human
+
 
 image_blocks = gr.Blocks().queue()
 with image_blocks as demo:
@@ -258,9 +266,9 @@ with image_blocks as demo:
         with gr.Column():
             imgs = gr.ImageEditor(sources='upload', type="pil", label='Human. Mask with pen or use auto-masking', interactive=True)
             with gr.Row():
-                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)", value=True)
+                is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask (Takes 5 seconds)",value=True)
             with gr.Row():
-                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing", value=False)
+                is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False)
 
             example = gr.Examples(
                 inputs=imgs,
@@ -279,10 +287,13 @@ with image_blocks as demo:
                 examples=garm_list_path)
         with gr.Column():
             # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            masked_img = gr.Image(label="Masked image output", elem_id="masked-img", show_share_button=False)
+            masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False)
         with gr.Column():
             # image_out = gr.Image(label="Output", elem_id="output-img", height=400)
-            image_out = gr.Image(label="Output", elem_id="output-img", show_share_button=False)
+            image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False)
+
+
+
 
     with gr.Column():
         try_button = gr.Button(value="Try-on")
@@ -291,6 +302,11 @@ with image_blocks as demo:
                 denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1)
                 seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)
 
-    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked, is_checked_crop, denoise_steps, seed], outputs=[image_out, masked_img], api_name='tryon')
 
-image_blocks.launch()
+
+    try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out,masked_img], api_name='tryon')
+
+            
+
+
+image_blocks.launch()