added app file

app.py

@@ -0,0 +1,287 @@
+from base64 import b64encode
+
+import gradio as gr
+import numpy
+import torch
+import torchvision.transforms as T
+
+from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
+from PIL import Image
+from torch import autocast
+from torchvision import transforms as tfms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer, logging
+
+torch.manual_seed(1)
+logging.set_verbosity_error()
+
+torch_device = "cpu"
+
+# Load the autoencoder model which will be used to decode the latents into image space.
+vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae")
+
+# Load the tokenizer and text encoder to tokenize and encode the text.
+tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
+
+# The UNet model for generating the latents.
+unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet")
+
+# The noise scheduler
+scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
+
+# To the GPU we go!
+vae = vae.to(torch_device)
+text_encoder = text_encoder.to(torch_device)
+unet = unet.to(torch_device);
+
+token_emb_layer = text_encoder.text_model.embeddings.token_embedding
+pos_emb_layer = text_encoder.text_model.embeddings.position_embedding
+position_ids = text_encoder.text_model.embeddings.position_ids[:, :77]
+position_embeddings = pos_emb_layer(position_ids)
+
+def pil_to_latent(input_im):
+    # Single image -> single latent in a batch (so size 1, 4, 64, 64)
+    with torch.no_grad():
+        latent = vae.encode(tfms.ToTensor()(input_im).unsqueeze(0).to(torch_device)*2-1) # Note scaling
+    return 0.18215 * latent.latent_dist.sample()
+
+def latents_to_pil(latents):
+    # bath of latents -> list of images
+    latents = (1 / 0.18215) * latents
+    with torch.no_grad():
+        image = vae.decode(latents).sample
+    image = (image / 2 + 0.5).clamp(0, 1)
+    image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
+    images = (image * 255).round().astype("uint8")
+    pil_images = [Image.fromarray(image) for image in images]
+    return pil_images
+
+def get_output_embeds(input_embeddings):
+    # CLIP's text model uses causal mask, so we prepare it here:
+    bsz, seq_len = input_embeddings.shape[:2]
+    causal_attention_mask = text_encoder.text_model._build_causal_attention_mask(bsz, seq_len, dtype=input_embeddings.dtype)
+
+    # Getting the output embeddings involves calling the model with passing output_hidden_states=True
+    # so that it doesn't just return the pooled final predictions:
+    encoder_outputs = text_encoder.text_model.encoder(
+        inputs_embeds=input_embeddings,
+        attention_mask=None, # We aren't using an attention mask so that can be None
+        causal_attention_mask=causal_attention_mask.to(torch_device),
+        output_attentions=None,
+        output_hidden_states=True, # We want the output embs not the final output
+        return_dict=None,
+    )
+
+    # We're interested in the output hidden state only
+    output = encoder_outputs[0]
+
+    # There is a final layer norm we need to pass these through
+    output = text_encoder.text_model.final_layer_norm(output)
+
+    # And now they're ready!
+    return output
+
+def generate_with_embs(text_embeddings, seed, max_length):
+    height = 512                        # default height of Stable Diffusion
+    width = 512                         # default width of Stable Diffusion
+    num_inference_steps = 30            # Number of denoising steps
+    guidance_scale = 7.5                # Scale for classifier-free guidance
+    generator = torch.manual_seed(seed)   # Seed generator to create the inital latent noise
+    batch_size = 1
+
+    # max_length = text_input.input_ids.shape[-1]
+    uncond_input = tokenizer(
+      [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+    )
+    with torch.no_grad():
+        uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
+    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    # Prep Scheduler
+    set_timesteps(scheduler, num_inference_steps)
+
+    # Prep latents
+    latents = torch.randn(
+      (batch_size, unet.in_channels, height // 8, width // 8),
+      generator=generator,
+    )
+    latents = latents.to(torch_device)
+    latents = latents * scheduler.init_noise_sigma
+
+    # Loop
+    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
+        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+        latent_model_input = torch.cat([latents] * 2)
+        sigma = scheduler.sigmas[i]
+        latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+
+        # predict the noise residual
+        with torch.no_grad():
+            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
+
+        # perform guidance
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+        # compute the previous noisy sample x_t -> x_t-1
+        latents = scheduler.step(noise_pred, t, latents).prev_sample
+
+    return latents_to_pil(latents)[0]
+
+# Prep Scheduler
+def set_timesteps(scheduler, num_inference_steps):
+    scheduler.set_timesteps(num_inference_steps)
+    scheduler.timesteps = scheduler.timesteps.to(torch.float32) # minor fix to ensure MPS compatibility, fixed in diffusers PR 3925
+
+def eos_pos(prompt):
+  return len(prompt.split()) + 1
+
+def embed_style(prompt, style_embed, style_seed):
+    # Tokenize
+    text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
+    input_ids = text_input.input_ids.to(torch_device)
+
+    # Get token embeddings
+    token_embeddings = token_emb_layer(input_ids)
+
+    # The new embedding - our special birb word
+    replacement_token_embedding = style_embed.to(torch_device)
+
+    # Insert this into the token embeddings
+    token_embeddings[0, torch.tensor(eos_pos(prompt))] = replacement_token_embedding.to(torch_device)
+
+    # Combine with pos embs
+    input_embeddings = token_embeddings + position_embeddings
+
+    #  Feed through to get final output embs
+    modified_output_embeddings = get_output_embeds(input_embeddings)
+
+    # And generate an image with this:
+    max_length = text_input.input_ids.shape[-1]
+    return generate_with_embs(modified_output_embeddings, style_seed, max_length)
+
+def custom_loss(image):
+    # Calculate colorfulness metric (standard deviation of RGB channels)
+    std_dev = torch.std(image, dim=(1, 2))
+    loss = torch.mean(std_dev)
+    return loss
+    
+def generate_image_on_loss(prompt, seed):
+
+    height = 64                        # default height of Stable Diffusion
+    width = 64                         # default width of Stable Diffusion
+    num_inference_steps = 50            # Number of denoising steps
+    guidance_scale = 8               # Scale for classifier-free guidance
+    generator = torch.manual_seed(64)   # Seed generator to create the inital latent noise
+    batch_size = 1
+    loss_scale = 200
+
+    # Prep text
+    text_input = tokenizer([prompt], padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
+    with torch.no_grad():
+        text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]
+
+    # And the uncond. input as before:
+    max_length = text_input.input_ids.shape[-1]
+    uncond_input = tokenizer(
+        [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
+    )
+    with torch.no_grad():
+        uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]
+    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+    # Prep Scheduler
+    set_timesteps(scheduler, num_inference_steps+1)
+
+    # Prep latents
+    latents = torch.randn(
+      (batch_size, unet.in_channels, height // 8, width // 8),
+      generator=generator,
+    )
+    latents = latents.to(torch_device)
+    latents = latents * scheduler.init_noise_sigma
+
+    sched_out = None
+
+    # Loop
+    for i, t in tqdm(enumerate(scheduler.timesteps), total=len(scheduler.timesteps)):
+        # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
+        latent_model_input = torch.cat([latents] * 2)
+        sigma = scheduler.sigmas[i]
+        latent_model_input = scheduler.scale_model_input(latent_model_input, t)
+
+        # predict the noise residual
+        with torch.no_grad():
+            noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
+
+        # perform CFG
+        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+        ### ADDITIONAL GUIDANCE ###
+        if i%5 == 0 and i>0:
+            # Requires grad on the latents
+            latents = latents.detach().requires_grad_()
+
+            # Get the predicted x0:
+            scheduler._step_index -= 1
+            latents_x0 = scheduler.step(noise_pred, t, latents).pred_original_sample
+
+            # Decode to image space
+            denoised_images = vae.decode((1 / 0.18215) * latents_x0).sample / 2 + 0.5 # range (0, 1)
+
+            # Calculate loss
+            loss = custom_loss(denoised_images) * loss_scale
+
+            # Occasionally print it out
+            # if i%10==0:
+            print(i, 'loss:', loss)
+
+            # Get gradient
+            cond_grad = torch.autograd.grad(loss, latents)[0]
+
+            # Modify the latents based on this gradient
+            latents = latents.detach() - cond_grad * sigma**2
+            # To PIL Images
+            im_t0 = latents_to_pil(latents_x0)[0]
+            im_next = latents_to_pil(latents)[0]
+
+        # Now step with scheduler
+        latents = scheduler.step(noise_pred, t, latents).prev_sample
+        
+    return latents_to_pil(latents)[0]
+
+def generate_image_from_prompt(prompt, style):
+    style_list = ['dreamy_painting.bin', 'egorey.bin', 'fairy_tale_painting.bin', 'matrix.bin', 'pjablonski_style.bin']
+    style_seeds = [16, 64, 32, 128, 8]
+
+    style_file = style + '.bin'
+    idx = style_list.index(style_file)
+    
+    style_seed = style_seeds[idx]
+    style_dict = torch.load(style_file)
+    style_embed = [val for val in style_dict.values()]
+
+    generated_image = embed_style(prompt, style_embed[0], style_seed)
+    loss_generated_img = generate_image_on_loss(prompt, style_seed)
+
+    return [generated_image, loss_generated_img]
+
+demo = gr.Interface(
+    fn = generate_image_from_prompt,
+    inputs = [
+        gr.Textbox(label="Prompt", value="Enter your prompt"),
+        gr.Dropdown(
+          ["dreamy_painting", "egorey", "fairy_tale_painting", "matrix", "pjablonski_style"], value="dreamy_painting", label="Pretrained Styles"
+        )
+    ],
+    outputs = [
+        gr.Gallery(label="Generated images", show_label=False, elem_id="gallery", columns=[2], rows=[1], object_fit="contain", height="512")
+    ],
+    examples = [
+        ["a cat climbing a tree", "dreamy_painting"]
+    ]
+)
+
+demo.launch(debug=True)