Update app.py

app.py

@@ -1,5 +1,184 @@
-## make a streamlit app gui that says hello world
-pip3 install "streamlit"
-import streamlit as st 
-st.streamlit_version()
-st.title("FLAMESTOPIA.AI SYSTEMS (C) 2022-20XX - Version 0.0.1")
+from PIL import Image
+from IPython.display import display
+import torch as th
+import gradio as gr
+
+from glide_text2im.download import load_checkpoint
+from glide_text2im.model_creation import (
+    create_model_and_diffusion,
+    model_and_diffusion_defaults,
+    model_and_diffusion_defaults_upsampler
+)
+# This notebook supports both CPU and GPU.
+# On CPU, generating one sample may take on the order of 20 minutes.
+# On a GPU, it should be under a minute.
+
+has_cuda = th.cuda.is_available()
+device = th.device('cpu' if not has_cuda else 'cuda')
+print('Using device:', device)
+
+# Create base model.
+options = model_and_diffusion_defaults()
+options['use_fp16'] = has_cuda
+options['timestep_respacing'] = '100' # use 100 diffusion steps for fast sampling
+model, diffusion = create_model_and_diffusion(**options)
+model.eval()
+if has_cuda:
+    model.convert_to_fp16()
+model.to(device)
+model.load_state_dict(load_checkpoint('base', device))
+print('total base parameters', sum(x.numel() for x in model.parameters()))
+# Create upsampler model.
+options_up = model_and_diffusion_defaults_upsampler()
+options_up['use_fp16'] = has_cuda
+options_up['timestep_respacing'] = 'fast27' # use 27 diffusion steps for very fast sampling
+model_up, diffusion_up = create_model_and_diffusion(**options_up)
+model_up.eval()
+if has_cuda:
+    model_up.convert_to_fp16()
+model_up.to(device)
+model_up.load_state_dict(load_checkpoint('upsample', device))
+print('total upsampler parameters', sum(x.numel() for x in model_up.parameters()))
+
+def show_images(batch: th.Tensor):
+    """ Display a batch of images inline. """
+    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()
+    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])
+    #display(Image.fromarray(reshaped.numpy()))
+    #Image.fromarray(reshaped.numpy()).save('image.png')
+
+
+def get_images(batch: th.Tensor):
+    """ Display a batch of images inline. """
+    scaled = ((batch + 1)*127.5).round().clamp(0,255).to(th.uint8).cpu()
+    reshaped = scaled.permute(2, 0, 3, 1).reshape([batch.shape[2], -1, 3])
+    img = Image.fromarray(reshaped.numpy())
+    #img.save('img.png')
+    return img
+
+# Sampling parameters
+batch_size = 1
+guidance_scale = 3.0
+
+# Tune this parameter to control the sharpness of 256x256 images.
+# A value of 1.0 is sharper, but sometimes results in grainy artifacts.
+upsample_temp = 0.997
+
+
+# Create a classifier-free guidance sampling function
+def model_fn(x_t, ts, **kwargs):
+    half = x_t[: len(x_t) // 2]
+    combined = th.cat([half, half], dim=0)
+    model_out = model(combined, ts, **kwargs)
+    eps, rest = model_out[:, :3], model_out[:, 3:]
+    cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)
+    half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
+    eps = th.cat([half_eps, half_eps], dim=0)
+    return th.cat([eps, rest], dim=1)
+    
+def run(prompt):    
+
+    ##############################
+    # Sample from the base model #
+    ##############################
+    
+    # Create the text tokens to feed to the model.
+    tokens = model.tokenizer.encode(prompt)
+    tokens, mask = model.tokenizer.padded_tokens_and_mask(
+        tokens, options['text_ctx']
+    )
+    
+    # Create the classifier-free guidance tokens (empty)
+    full_batch_size = batch_size * 2
+    uncond_tokens, uncond_mask = model.tokenizer.padded_tokens_and_mask(
+        [], options['text_ctx']
+    )
+    
+    # Pack the tokens together into model kwargs.
+    model_kwargs = dict(
+        tokens=th.tensor(
+            [tokens] * batch_size + [uncond_tokens] * batch_size, device=device
+        ),
+        mask=th.tensor(
+            [mask] * batch_size + [uncond_mask] * batch_size,
+            dtype=th.bool,
+            device=device,
+        ),
+)
+
+
+    print('run():')
+    
+    # Sample from the base model.
+    print('    # Sample from the base model.')
+    model.del_cache()
+    samples = diffusion.p_sample_loop(
+        model_fn,
+        (full_batch_size, 3, options["image_size"], options["image_size"]),
+        device=device,
+        clip_denoised=True,
+        progress=True,
+        model_kwargs=model_kwargs,
+        cond_fn=None,
+    )[:batch_size]
+    model.del_cache()
+
+    # Show the output
+    print('    # Show the output')
+    #show_images(samples)
+    ##############################
+    # Upsample the 64x64 samples #
+    ##############################
+    
+    tokens = model_up.tokenizer.encode(prompt)
+    tokens, mask = model_up.tokenizer.padded_tokens_and_mask(
+        tokens, options_up['text_ctx']
+    )
+
+    # Create the model conditioning dict.
+    print('    # Create the model conditioning dict.')
+    model_kwargs = dict(
+        # Low-res image to upsample.
+        low_res=((samples+1)*127.5).round()/127.5 - 1,
+    
+        # Text tokens
+        tokens=th.tensor(
+            [tokens] * batch_size, device=device
+        ),
+        mask=th.tensor(
+            [mask] * batch_size,
+            dtype=th.bool,
+            device=device,
+        ),
+    )
+    
+    # Sample from the base model.
+    print('    # Sample from the base model.')
+    model_up.del_cache()
+    up_shape = (batch_size, 3, options_up["image_size"], options_up["image_size"])
+    up_samples = diffusion_up.ddim_sample_loop(
+        model_up,
+        up_shape,
+        noise=th.randn(up_shape, device=device) * upsample_temp,
+        device=device,
+        clip_denoised=True,
+        progress=True,
+        model_kwargs=model_kwargs,
+        cond_fn=None,
+    )[:batch_size]
+    model_up.del_cache()
+    
+    # Show the output
+    print('# Show the output')
+    out_images = get_images(up_samples)
+    
+    return out_images
+
+
+  
+
+iface = gr.Interface(
+  fn=run, 
+  inputs=["text"],
+  outputs=["image"])
+iface.launch()