1

app.py

@@ -1,3 +1,191 @@
+## **** below codelines are borrowed from multimodalart space
+from pydoc import describe
+import gradio as gr
+import torch
+from omegaconf import OmegaConf
+import sys 
+sys.path.append(".")
+sys.path.append('./taming-transformers')
+sys.path.append('./latent-diffusion')
+from taming.models import vqgan 
+from ldm.util import instantiate_from_config
+from huggingface_hub import hf_hub_download
+
+model_path_e = hf_hub_download(repo_id="multimodalart/compvis-latent-diffusion-text2img-large", filename="txt2img-f8-large.ckpt")
+
+#@title Import stuff
+import argparse, os, sys, glob
+import numpy as np
+from PIL import Image
+from einops import rearrange
+from torchvision.utils import make_grid
+import transformers
+import gc
+from ldm.util import instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.plms import PLMSSampler
+from open_clip import tokenizer
+import open_clip
+
+def load_model_from_config(config, ckpt, verbose=False):
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cuda")
+    sd = pl_sd["state_dict"]
+    model = instantiate_from_config(config.model)
+    m, u = model.load_state_dict(sd, strict=False)
+    if len(m) > 0 and verbose:
+        print("missing keys:")
+        print(m)
+    if len(u) > 0 and verbose:
+        print("unexpected keys:")
+        print(u)
+
+    model = model.half().cuda()
+    model.eval()
+    return model
+
+def load_safety_model(clip_model):
+    """load the safety model"""
+    import autokeras as ak  # pylint: disable=import-outside-toplevel
+    from tensorflow.keras.models import load_model  # pylint: disable=import-outside-toplevel
+    from os.path import expanduser  # pylint: disable=import-outside-toplevel
+
+    home = expanduser("~")
+
+    cache_folder = home + "/.cache/clip_retrieval/" + clip_model.replace("/", "_")
+    if clip_model == "ViT-L/14":
+        model_dir = cache_folder + "/clip_autokeras_binary_nsfw"
+        dim = 768
+    elif clip_model == "ViT-B/32":
+        model_dir = cache_folder + "/clip_autokeras_nsfw_b32"
+        dim = 512
+    else:
+        raise ValueError("Unknown clip model")
+    if not os.path.exists(model_dir):
+        os.makedirs(cache_folder, exist_ok=True)
+
+        from urllib.request import urlretrieve  # pylint: disable=import-outside-toplevel
+
+        path_to_zip_file = cache_folder + "/clip_autokeras_binary_nsfw.zip"
+        if clip_model == "ViT-L/14":
+            url_model = "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_binary_nsfw.zip"
+        elif clip_model == "ViT-B/32":
+            url_model = (
+                "https://raw.githubusercontent.com/LAION-AI/CLIP-based-NSFW-Detector/main/clip_autokeras_nsfw_b32.zip"
+            )
+        else:
+            raise ValueError("Unknown model {}".format(clip_model))
+        urlretrieve(url_model, path_to_zip_file)
+        import zipfile  # pylint: disable=import-outside-toplevel
+
+        with zipfile.ZipFile(path_to_zip_file, "r") as zip_ref:
+            zip_ref.extractall(cache_folder)
+
+    loaded_model = load_model(model_dir, custom_objects=ak.CUSTOM_OBJECTS)
+    loaded_model.predict(np.random.rand(10 ** 3, dim).astype("float32"), batch_size=10 ** 3)
+
+    return loaded_model
+    
+def is_unsafe(safety_model, embeddings, threshold=0.5):
+    """find unsafe embeddings"""
+    nsfw_values = safety_model.predict(embeddings, batch_size=embeddings.shape[0])
+    x = np.array([e[0] for e in nsfw_values])
+    return True if x > threshold else False
+
+config = OmegaConf.load("latent-diffusion/configs/latent-diffusion/txt2img-1p4B-eval.yaml")
+model = load_model_from_config(config,model_path_e)
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+model = model.to(device)
+
+#NSFW CLIP Filter
+safety_model = load_safety_model("ViT-B/32")
+clip_model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='openai')
+
+
+def run(prompt, steps, width, height, images, scale):
+    opt = argparse.Namespace(
+        prompt = prompt, 
+        outdir='latent-diffusion/outputs',
+        ddim_steps = int(steps),
+        ddim_eta = 0,
+        n_iter = 1,
+        W=int(width),
+        H=int(height),
+        n_samples=int(images),
+        scale=scale,
+        plms=True
+    )
+
+    if opt.plms:
+        opt.ddim_eta = 0
+        sampler = PLMSSampler(model)
+    else:
+        sampler = DDIMSampler(model)
+    
+    os.makedirs(opt.outdir, exist_ok=True)
+    outpath = opt.outdir
+
+    prompt = opt.prompt
+
+
+    sample_path = os.path.join(outpath, "samples")
+    os.makedirs(sample_path, exist_ok=True)
+    base_count = len(os.listdir(sample_path))
+
+    all_samples=list()
+    all_samples_images=list()
+    with torch.no_grad():
+        with torch.cuda.amp.autocast():
+            with model.ema_scope():
+                uc = None
+                if opt.scale > 0:
+                    uc = model.get_learned_conditioning(opt.n_samples * [""])
+                for n in range(opt.n_iter):
+                    c = model.get_learned_conditioning(opt.n_samples * [prompt])
+                    shape = [4, opt.H//8, opt.W//8]
+                    samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
+                                                    conditioning=c,
+                                                    batch_size=opt.n_samples,
+                                                    shape=shape,
+                                                    verbose=False,
+                                                    unconditional_guidance_scale=opt.scale,
+                                                    unconditional_conditioning=uc,
+                                                    eta=opt.ddim_eta)
+
+                    x_samples_ddim = model.decode_first_stage(samples_ddim)
+                    x_samples_ddim = torch.clamp((x_samples_ddim+1.0)/2.0, min=0.0, max=1.0)
+
+                    for x_sample in x_samples_ddim:
+                        x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
+                        image_vector = Image.fromarray(x_sample.astype(np.uint8))
+                        image_preprocess = preprocess(image_vector).unsqueeze(0)
+                        with torch.no_grad():
+                          image_features = clip_model.encode_image(image_preprocess)
+                        image_features /= image_features.norm(dim=-1, keepdim=True)
+                        query = image_features.cpu().detach().numpy().astype("float32")
+                        unsafe = is_unsafe(safety_model,query,0.5)
+                        if(not unsafe):
+                            all_samples_images.append(image_vector)
+                        else:
+                            return(None,None,"Sorry, potential NSFW content was detected on your outputs by our NSFW detection model. Try again with different prompts. If you feel your prompt was not supposed to give NSFW outputs, this may be due to a bias in the model. Read more about biases in the Biases Acknowledgment section below.")
+                        #Image.fromarray(x_sample.astype(np.uint8)).save(os.path.join(sample_path, f"{base_count:04}.png"))
+                        base_count += 1
+                    all_samples.append(x_samples_ddim)
+                    
+    
+    # additionally, save as grid
+    grid = torch.stack(all_samples, 0)
+    grid = rearrange(grid, 'n b c h w -> (n b) c h w')
+    grid = make_grid(grid, nrow=2)
+    # to image
+    grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
+    
+    Image.fromarray(grid.astype(np.uint8)).save(os.path.join(outpath, f'{prompt.replace(" ", "-")}.png'))
+    #return(Image.fromarray(grid.astype(np.uint8)),all_samples_images,None)    
+    return Image.fromarray(grid.astype(np.uint8))
+        
+## **** above codelines are borrowed from multimodalart space
+
 import gradio as gr
 
 fastspeech = gr.Interface.load("huggingface/facebook/fastspeech2-en-ljspeech")
@@ -12,13 +200,14 @@ def engine(text_input):
     entities = [tupl for tupl in entities if None not in tupl]
     entities_num = len(entities)
     
+    img = run(entities[0],'50','256','256','1','10')
     #img_intfc = gr.Interface.load("spaces/multimodalart/latentdiffusion")
-    img_intfc = gr.Interface.load("spaces/multimodalart/latentdiffusion", inputs=[gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text")],
-    outputs=[gr.outputs.Image(type="pil", label="output image"),gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")], )
+    #img_intfc = gr.Interface.load("spaces/multimodalart/latentdiffusion", inputs=[gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text"), gr.inputs.Textbox(lines=1, label="Input Text")],
+    #outputs=[gr.outputs.Image(type="pil", label="output image"),gr.outputs.Carousel(label="Individual images",components=["image"]),gr.outputs.Textbox(label="Error")], )
     #title="Convert text to image")
     #img = img_intfc[0]
-    img = img_intfc(('George','50','256','256','1','10'))
-    img = img[0]
+    #img = img_intfc('George','50','256','256','1','10')
+    #img = img[0]
     #inputs=['George',50,256,256,1,10]
     #run(prompt, steps, width, height, images, scale)