Request to upload 5.31.22

app.py

@@ -1,184 +1,23 @@
+import torch
+import streamlit as st
 from PIL import Image
-from IPython.display import display
-import torch as th
-import gradio as gr
+from megatron import MegatronLM, download_pretrained_model
+ 
+#@st.cache(allow_output_mutation=True) # this line is important! without it, the app will crash on first run after a restart. 
+def load_model():
+    model = MegatronLM.from_pretrained('google/megatron-lm-1b')    
+    return model.cuda()
 
-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')
+     #@st.cache(allow_output_mutation=True) # this line is important! without it, the app will crash on first run after a restart. 
 
+     def load_image(filename):        
 
-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
+          image = Image.open(filename).convert("RGB")        
 
-# 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()
+          return image