fix

README.md~

@@ -1,37 +0,0 @@
----
-title: Glide Text2im
-emoji: 📊
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

app.py~

@@ -1,201 +0,0 @@
-
-import os
-os.system('pip install -e .')
-import gradio as gr
-
-import base64
-from io import BytesIO
-# from fastapi import FastAPI
-
-from PIL import Image
-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
-)
-
-"""
-credit: follows the gradio glide example by valhalla https://huggingface.co/spaces/valhalla/glide-text2im
-"""
-
-
-# print("Loading models...")
-# app = FastAPI()
-
-# 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 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])
-    return Image.fromarray(reshaped.numpy())
-
-
-# Create a classifier-free guidance sampling function
-guidance_scale = 3.0
-
-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)
-
-
-# @app.get("/")
-def read_root():
-    return {"glide!"}
-
-# @app.get("/{generate}")
-def sample(prompt):
-    # Sampling parameters
-    batch_size = 1
-
-    # 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(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,
-        ),
-    )
-
-    # 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()
-
-
-    ##############################
-    # 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.
-    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"])
-    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
-    image = get_images(up_samples)
-    # image = to_base64(image)
-    # return {"image": image}
-    return image
-
-
-def to_base64(pil_image):
-    buffered = BytesIO()
-    pil_image.save(buffered, format="JPEG")
-    return base64.b64encode(buffered.getvalue())
-
-title = "Interactive demo: glide-text2im"
-description = "Demo for OpenAI's GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10741'>GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models</a> | <a href='https://github.com/openai/glide-text2im/'>Official Repo</a></p>"
-examples =["an oil painting of a corgi"]
-
-iface = gr.Interface(fn=sample, 
-                     inputs=gr.inputs.Textbox(label='What would you like to see?'), 
-                     outputs=gr.outputs.Image(type="pil", label="Model input + completions"),
-                     title=title,
-                     description=description,
-                     article=article,
-                     examples=examples,
-                     enable_queue=True)
-iface.launch(debug=True)

server.py~

@@ -1,246 +0,0 @@
-import base64
-from io import BytesIO
-from fastapi import FastAPI
-<<<<<<< HEAD
-
-from PIL import Image
-import torch as th
-
-=======
-from PIL import Image
-import torch as th
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-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
-)
-<<<<<<< HEAD
-
-print("Loading models...")
-app = FastAPI()
-
-# 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("Loading models...")
-app = FastAPI()
-# 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')
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-# 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()))
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-# 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()))
-<<<<<<< HEAD
-
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-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])
-    Image.fromarray(reshaped.numpy())
-<<<<<<< HEAD
-
-
-# Create a classifier-free guidance sampling function
-guidance_scale = 3.0
-
-=======
-# Create a classifier-free guidance sampling function
-guidance_scale = 3.0
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-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)
-<<<<<<< HEAD
-
-
-@app.get("/")
-def read_root():
-    return {"glide!"}
-
-=======
-@app.get("/")
-def read_root():
-    return {"glide!"}
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-@app.get("/{generate}")
-def sample(prompt):
-    # Sampling parameters
-    batch_size = 1
-<<<<<<< HEAD
-
-    # 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 #
-    ##############################
-
-=======
-    # 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 #
-    ##############################
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # 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']
-    )
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # 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']
-    )
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # 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,
-        ),
-    )
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # 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()
-<<<<<<< HEAD
-
-
-    ##############################
-    # Upsample the 64x64 samples #
-    ##############################
-
-=======
-    ##############################
-    # Upsample the 64x64 samples #
-    ##############################
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    tokens = model_up.tokenizer.encode(prompt)
-    tokens, mask = model_up.tokenizer.padded_tokens_and_mask(
-        tokens, options_up['text_ctx']
-    )
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # Create the model conditioning dict.
-    model_kwargs = dict(
-        # Low-res image to upsample.
-        low_res=((samples+1)*127.5).round()/127.5 - 1,
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-        # Text tokens
-        tokens=th.tensor(
-            [tokens] * batch_size, device=device
-        ),
-        mask=th.tensor(
-            [mask] * batch_size,
-            dtype=th.bool,
-            device=device,
-        ),
-    )
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # 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()
-<<<<<<< HEAD
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-    # Show the output
-    image = get_images(up_samples)
-    image = to_base64(image)
-    return {"image": image}
-<<<<<<< HEAD
-
-
-=======
->>>>>>> 8c239b8a9cdaf13e28c145e788b984c129547a37
-def to_base64(pil_image):
-    buffered = BytesIO()
-    pil_image.save(buffered, format="JPEG")
-    return base64.b64encode(buffered.getvalue())