Update app.py

app.py

@@ -72,15 +72,169 @@ API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-
 headers = {"Authorization": "Bearer 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"}
 
 
+#@title Computer ko aang lagani ho to hi show code click karke ched chad karna
+
+!pip install git+https://github.com/openai/glide-text2im
+
+from PIL import Image
+from IPython.display import display
+import torch as th
+
+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')
+
+# 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()))
+
 def query_model_with_image(image_description):
-    payload = {
-        "inputs": image_description
-    }
-    response = requests.post(API_URL, headers=headers, json=payload)
-    image_bytes = response.content
+    # Sampling parameters
+  # image_description = "dog in the field" #@param {type:"string"}
+  # image_description = ""
+  batch_size = 1 #@param {type:"integer"}
+  guidance_scale = 8.0
+
+  # 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
+
+  ##############################
+  # Sample from the base model #
+  ##############################
+
+  # Create the text tokens to feed to the model.
+  tokens = model.tokenizer.encode(image_description)
+  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,
+      ),
+  )
+
+  # 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)
+
+  # 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
+  show_images(samples)
+
+
+  ##############################
+  # Upsample the 64x64 samples #
+  ##############################
+
+  tokens = model_up.tokenizer.encode(image_description)
+  tokens, mask = model_up.tokenizer.padded_tokens_and_mask(
+      tokens, options_up['text_ctx']
+  )
+
+  # 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.
+  model_up.del_cache()
+  up_shape = (batch_size, 3, options_up["image_size"], options_up["image_size"])
+  image = 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
+  show_images(image)
+  return image
 
-    image = Image.open(io.BytesIO(image_bytes))
-    return image
 
 def upload_to_cloudinary(image, prompt_text):
     image_data = io.BytesIO()