add description

README.md

@@ -18,4 +18,13 @@ pinned: false
 
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Text-to-Image Denoising Diffusion GANs is a text-to-image model
+based on Denoising Diffusion GANs <https://arxiv.org/abs/2112.07804>.
+The code is based on their official code <<https://nvlabs.github.io/denoising-diffusion-gan/>,
+which is updated to support text conditioning. Many thanks to the authors of DDGAN for releasing
+the code.
+
+The provided models are trained on DiffusionDB <https://arxiv.org/abs/2210.14896>, which is a dataset that was synthetically
+generated with Stable Diffusion, many thanks to the authors for releasing the dataset.
+
+Models were trained on JURECA-DC supercomputer at Jülich Supercomputing Centre (JSC), many thanks for the compute provided to train the models.

app.py

@@ -21,15 +21,16 @@ def load(name):
     if name in cache:
         return cache[name]
     else:
-        model_config, model_path = models[name]
-        print(model_config, model_path)
+        cfg_name = models[name]
+        model_config = get_model_config(cfg_name)
+        model_path = download(name + ".th")
         model = load_model(model_config, model_path, device=device)
         cache[name] = model
         return model
 
 models = {
-    "diffusion_db_128ch_1timesteps_openclip_vith14": (get_model_config('ddgan_ddb_v2'), download('diffusion_db_128ch_1timesteps_openclip_vith14.th')),
-    "diffusion_db_192ch_2timesteps_openclip_vith14": (get_model_config('ddgan_ddb_v3'), download('diffusion_db_192ch_2timesteps_openclip_vith14.th')),
+    "diffusion_db_128ch_1timesteps_openclip_vith14": "ddgan_ddb_v2",
+    "diffusion_db_192ch_2timesteps_openclip_vith14": 'ddgan_ddb_v3',
 }
 default = "diffusion_db_128ch_1timesteps_openclip_vith14"
 
@@ -62,7 +63,16 @@ def gen(md, model_name, md2, text, seed, nb_samples, width, height):
     return Image.fromarray(grid)
 
 text = """
-DDGAN
+Text-to-Image Denoising Diffusion GANs is a text-to-image model
+based on Denoising Diffusion GANs <https://arxiv.org/abs/2112.07804>.
+The code is based on their official code <<https://nvlabs.github.io/denoising-diffusion-gan/>,
+which is updated to support text conditioning. Many thanks to the authors of DDGAN for releasing
+the code.
+
+The provided models are trained on DiffusionDB <https://arxiv.org/abs/2210.14896>, which is a dataset that was synthetically
+generated with Stable Diffusion, many thanks to the authors for releasing the dataset.
+
+Models were trained on JURECA-DC supercomputer at Jülich Supercomputing Centre (JSC), many thanks for the compute provided to train the models.
 """
 iface = gr.Interface(
     fn=gen,