Create app.py

app.py

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+
+import gradio as gr
+from PIL import Image
+
+from tld.denoiser import Denoiser
+from tld.diffusion import DiffusionGenerator
+
+from diffusers import AutoencoderKL, AutoencoderTiny
+from tqdm import tqdm
+import clip
+import torch
+import numpy as np
+import torchvision.utils as vutils
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, TensorDataset
+from PIL import Image
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+to_pil = transforms.ToPILImage()
+
+
+###config:
+vae_scale_factor = 8
+img_size = 32
+model_dtype = torch.float32
+
+@torch.no_grad()
+def encode_text(label, model):
+    text_tokens = clip.tokenize(label, truncate=True).to(device)
+    text_encoding = model.encode_text(text_tokens)
+    return text_encoding.cpu()
+
+def generate_image_from_text(prompt, class_guidance=6, seed=11, num_imgs=1, img_size = 32):
+
+    n_iter = 15
+    nrow = int(np.sqrt(num_imgs))
+
+    cur_prompts = [prompt]*num_imgs
+    labels = encode_text(cur_prompts, clip_model)
+    out, out_latent = diffuser.generate(labels=labels,
+                                        num_imgs=num_imgs,
+                                        class_guidance=class_guidance,
+                                        seed=seed,
+                                        n_iter=n_iter,
+                                        exponent=1,
+                                        scale_factor=8,
+                                        sharp_f=0,
+                                        bright_f=0
+                                            )
+
+    out = to_pil((vutils.make_grid((out+1)/2, nrow=nrow, padding=4)).float().clip(0, 1))
+
+    out.save(f'{prompt}_cfg:{class_guidance}_seed:{seed}.png')
+
+    print("Images Generated and Saved. They will shortly output below.")
+    return out
+
+
+
+denoiser = Denoiser(image_size=32, noise_embed_dims=256, patch_size=2,
+                 embed_dim=768, dropout=0, n_layers=12)
+
+
+state_dict = torch.load('state_dict_378000.pth', map_location=torch.device('cpu'))
+
+denoiser = denoiser.to(model_dtype)
+denoiser.load_state_dict(state_dict)
+denoiser = denoiser.to(device)
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix",
+                                    torch_dtype=model_dtype).to(device)
+
+clip_model, preprocess = clip.load("ViT-L/14")
+clip_model = clip_model.to(device)
+
+diffuser = DiffusionGenerator(denoiser, vae, device, model_dtype)
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=generate_image_from_text,  # The function to generate the image
+    inputs=["text", "slider"],
+    outputs="image",
+    title="Text-to-Image Generator",
+    description="Enter a text prompt to generate an image."
+)
+
+# Launch the app
+iface.launch()