Merge pull request #38 from borisdayma/app-ui

Gradio app

Former-commit-id: 267ca13571b577deef87cd3000f8a96fef485f23

README.md

@@ -3,8 +3,8 @@ title: Dalle Mini
 emoji: 🎨
 colorFrom: red
 colorTo: blue
-sdk: streamlit
-app_file: app/app.py
+sdk: gradio
+app_file: app/app_gradio.py
 pinned: false
 ---
 
@@ -18,7 +18,7 @@ TODO: add some cool example
 
 ## How does it work?
 
-Refer to [our report](TODO).
+Refer to [our report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA).
 
 ## Development
 

app/app_gradio.py

@@ -163,7 +163,7 @@ def clip_top_k(prompt, images, k=8):
     scores = np.array(logits[0]).argsort()[-k:][::-1]
     return [images[score] for score in scores]
 
-def captioned_strip(images, caption):
+def compose_predictions(images, caption=None):
     increased_h = 0 if caption is None else 48
     w, h = images[0].size[0], images[0].size[1]
     img = Image.new("RGB", (len(images)*w, h + increased_h))
@@ -176,19 +176,55 @@ def captioned_strip(images, caption):
         draw.text((20, 3), caption, (255,255,255), font=font)
     return img
 
-def run_inference(prompt, num_images=64, num_preds=8):
-    images = hallucinate(prompt, num_images=num_images)
-    images = clip_top_k(prompt, images, k=num_preds)
-    predictions_strip = captioned_strip(images, None)
-    return predictions_strip
-
+def top_k_predictions(prompt, num_candidates=32, k=8):
+    images = hallucinate(prompt, num_images=num_candidates)
+    images = clip_top_k(prompt, images, k=k)
+    return images
+
+def run_inference(prompt, num_images=32, num_preds=8):
+    images = top_k_predictions(prompt, num_candidates=num_images, k=num_preds)
+    predictions = compose_predictions(images)
+    output_title = f"""
+    <p style="font-size:22px; font-style:bold">Best predictions</p>
+    <p>We asked our model to generate 32 candidates for your prompt:</p>
+
+    <pre>
+
+    <b>{prompt}</b>
+    </pre>
+    <p>We then used a pre-trained <a href="https://huggingface.co/openai/clip-vit-base-patch32">CLIP model</a> to score them according to the
+    similarity of the text and the image representations.</p>
+
+    <p>This is the result:</p>
+    """
+    output_description = """
+    <p>Read more about the process <a href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">in our report</a>.<p>
+    <p style='text-align: center'>Created with <a href="https://github.com/borisdayma/dalle-mini">DALLE·mini</a></p>
+    """
+    return (output_title, predictions, output_description)
+
+outputs = [
+    gr.outputs.HTML(label=""),      # To be used as title
+    gr.outputs.Image(label=''),
+    gr.outputs.HTML(label=""),      # Additional text that appears in the screenshot
+]
+
+description = """
+Welcome to our demo of DALL·E-mini. This project was created on TPU v3-8s during the 🤗 Flax / JAX Community Week.
+It reproduces the essential characteristics of OpenAI's DALL·E, at a fraction of the size.
+
+Please, write what you would like the model to generate, or select one of the examples below.
+"""
 gr.Interface(run_inference, 
     inputs=[gr.inputs.Textbox(label='Prompt')], #, gr.inputs.Slider(1,64,1,8, label='Candidates to generate'), gr.inputs.Slider(1,8,1,1, label='Best predictions to show')], 
-    outputs=gr.outputs.Image(label='Generated image'), 
-    title='DALLE-mini - HuggingFace Community Week',
-    description='This is a demo of the DALLE-mini model trained with Jax/Flax on TPU v3-8s during the HuggingFace Community Week',
-    article="<p style='text-align: center'> DALLE-mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>",
+    outputs=outputs, 
+    title='DALL·E mini',
+    description=description,
+    article="<p style='text-align: center'> DALLE·mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>",
     layout='vertical',
     theme='huggingface',
-    examples=[['an armchair in the shape of an avocado']],
-    server_port=8999).launch(share=True)
+    examples=[['an armchair in the shape of an avocado'], ['snowy mountains by the sea']],
+    allow_flagging=False,
+    live=False,
+    # server_port=8999
+).launch()

app/sample_images/readme.txt

@@ -0,0 +1 @@
+These images were generated by one of our checkpoints, as responses to the prompt "snowy mountains by the sea".

app/ui_gradio.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+from PIL import Image
+import gradio as gr
+
+def compose_predictions(images, caption=None):
+    increased_h = 0 if caption is None else 48
+    w, h = images[0].size[0], images[0].size[1]
+    img = Image.new("RGB", (len(images)*w, h + increased_h))
+    for i, img_ in enumerate(images):
+        img.paste(img_, (i*w, increased_h))
+
+    if caption is not None:
+        draw = ImageDraw.Draw(img)
+        font = ImageFont.truetype("/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40)
+        draw.text((20, 3), caption, (255,255,255), font=font)
+    return img
+
+def compose_predictions_grid(images):
+    cols = 4
+    rows = len(images) // cols
+    w, h = images[0].size[0], images[0].size[1]
+    img = Image.new("RGB", (w * cols, h * rows))
+    for i, img_ in enumerate(images):
+        row = i // cols
+        col = i % cols
+        img.paste(img_, (w * col, h * row))
+    return img
+
+def top_k_predictions_real(prompt, num_candidates=32, k=8):
+    images = hallucinate(prompt, num_images=num_candidates)
+    images = clip_top_k(prompt, images, k=num_preds)
+    return images
+
+def top_k_predictions(prompt, num_candidates=32, k=8):
+    images = []
+    for i in range(k):
+        image = Image.open(f"sample_images/image_{i}.jpg")
+        images.append(image)
+    return images
+
+def run_inference(prompt, num_images=32, num_preds=8):
+    images = top_k_predictions(prompt, num_candidates=num_images, k=num_preds)
+    predictions = compose_predictions(images)
+    output_title = f"""
+    <p style="font-size:22px; font-style:bold">Best predictions</p>
+    <p>We asked our model to generate 32 candidates for your prompt:</p>
+
+    <pre>
+
+    <b>{prompt}</b>
+    </pre>
+    <p>We then used a pre-trained <a href="https://huggingface.co/openai/clip-vit-base-patch32">CLIP model</a> to score them according to the
+    similarity of the text and the image representations.</p>
+
+    <p>This is the result:</p>
+    """
+    output_description = """
+    <p>Read more about the process <a href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA">in our report</a>.<p>
+    <p style='text-align: center'>Created with <a href="https://github.com/borisdayma/dalle-mini">DALLE·mini</a></p>
+    """
+    return (output_title, predictions, output_description)
+
+outputs = [
+    gr.outputs.HTML(label=""),      # To be used as title
+    gr.outputs.Image(label=''),
+    gr.outputs.HTML(label=""),      # Additional text that appears in the screenshot
+]
+
+description = """
+Welcome to our demo of DALL·E-mini. This project was created on TPU v3-8s during the 🤗 Flax / JAX Community Week.
+It reproduces the essential characteristics of OpenAI's DALL·E, at a fraction of the size.
+
+Please, write what you would like the model to generate, or select one of the examples below.
+"""
+gr.Interface(run_inference, 
+    inputs=[gr.inputs.Textbox(label='Prompt')], #, gr.inputs.Slider(1,64,1,8, label='Candidates to generate'), gr.inputs.Slider(1,8,1,1, label='Best predictions to show')], 
+    outputs=outputs, 
+    title='DALL·E mini',
+    description=description,
+    article="<p style='text-align: center'> DALLE·mini by Boris Dayma et al. | <a href='https://github.com/borisdayma/dalle-mini'>GitHub</a></p>",
+    layout='vertical',
+    theme='huggingface',
+    examples=[['an armchair in the shape of an avocado'], ['snowy mountains by the sea']],
+    allow_flagging=False,
+    live=False,
+    server_port=8999
+).launch(
+    share=True     # Creates temporary public link if true
+)

demo/model-sweep.py

@@ -0,0 +1,220 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import random
+
+import jax
+import flax.linen as nn
+from flax.training.common_utils import shard
+from flax.jax_utils import replicate, unreplicate
+
+from transformers.models.bart.modeling_flax_bart import *
+from transformers import BartTokenizer, FlaxBartForConditionalGeneration
+
+import io
+
+import requests
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+from torchvision.transforms import InterpolationMode
+
+from dalle_mini.vqgan_jax.modeling_flax_vqgan import VQModel
+
+# TODO: set those args in a config file
+OUTPUT_VOCAB_SIZE = 16384 + 1  # encoded image token space + 1 for bos
+OUTPUT_LENGTH = 256 + 1  # number of encoded tokens + 1 for bos
+BOS_TOKEN_ID = 16384
+BASE_MODEL = 'facebook/bart-large-cnn'
+WANDB_MODEL = '3iwhu4w6'
+
+class CustomFlaxBartModule(FlaxBartModule):
+    def setup(self):
+        # we keep shared to easily load pre-trained weights
+        self.shared = nn.Embed(
+            self.config.vocab_size,
+            self.config.d_model,
+            embedding_init=jax.nn.initializers.normal(self.config.init_std, self.dtype),
+            dtype=self.dtype,
+        )
+        # a separate embedding is used for the decoder
+        self.decoder_embed = nn.Embed(
+            OUTPUT_VOCAB_SIZE,
+            self.config.d_model,
+            embedding_init=jax.nn.initializers.normal(self.config.init_std, self.dtype),
+            dtype=self.dtype,
+        )
+        self.encoder = FlaxBartEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared)
+
+        # the decoder has a different config
+        decoder_config = BartConfig(self.config.to_dict())
+        decoder_config.max_position_embeddings = OUTPUT_LENGTH
+        decoder_config.vocab_size = OUTPUT_VOCAB_SIZE
+        self.decoder = FlaxBartDecoder(decoder_config, dtype=self.dtype, embed_tokens=self.decoder_embed)
+
+class CustomFlaxBartForConditionalGenerationModule(FlaxBartForConditionalGenerationModule):
+    def setup(self):
+        self.model = CustomFlaxBartModule(config=self.config, dtype=self.dtype)
+        self.lm_head = nn.Dense(
+            OUTPUT_VOCAB_SIZE,
+            use_bias=False,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.normal(self.config.init_std, self.dtype),
+        )
+        self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, OUTPUT_VOCAB_SIZE))
+
+class CustomFlaxBartForConditionalGeneration(FlaxBartForConditionalGeneration):
+    module_class = CustomFlaxBartForConditionalGenerationModule
+
+tokenizer = BartTokenizer.from_pretrained(BASE_MODEL)
+vqgan = VQModel.from_pretrained("flax-community/vqgan_f16_16384")
+
+def custom_to_pil(x):
+    x = np.clip(x, 0., 1.)
+    x = (255*x).astype(np.uint8)
+    x = Image.fromarray(x)
+    if not x.mode == "RGB":
+        x = x.convert("RGB")
+    return x
+
+def generate(input, rng, params):
+    return model.generate(
+        **input,
+        max_length=257,
+        num_beams=1,
+        do_sample=True,
+        prng_key=rng,
+        eos_token_id=50000,
+        pad_token_id=50000,
+        params=params,
+    )
+
+def get_images(indices, params):
+    return vqgan.decode_code(indices, params=params)
+
+def plot_images(images):
+    fig = plt.figure(figsize=(40, 20))
+    columns = 4
+    rows = 2
+    plt.subplots_adjust(hspace=0, wspace=0)
+
+    for i in range(1, columns*rows +1):
+        fig.add_subplot(rows, columns, i)
+        plt.imshow(images[i-1])
+    plt.gca().axes.get_yaxis().set_visible(False)
+    plt.show()
+    
+def stack_reconstructions(images):
+    w, h = images[0].size[0], images[0].size[1]
+    img = Image.new("RGB", (len(images)*w, h))
+    for i, img_ in enumerate(images):
+        img.paste(img_, (i*w,0))
+    return img
+
+p_generate = jax.pmap(generate, "batch")
+p_get_images = jax.pmap(get_images, "batch")
+
+# ## CLIP Scoring
+from transformers import CLIPProcessor, FlaxCLIPModel
+
+clip = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+def hallucinate(prompt, num_images=64):
+    prompt = [prompt] * jax.device_count()
+    inputs = tokenizer(prompt, return_tensors='jax', padding="max_length", truncation=True, max_length=128).data
+    inputs = shard(inputs)
+
+    all_images = []
+    for i in range(num_images // jax.device_count()):
+        key = random.randint(0, 1e7)
+        rng = jax.random.PRNGKey(key)
+        rngs = jax.random.split(rng, jax.local_device_count())
+        indices = p_generate(inputs, rngs, bart_params).sequences
+        indices = indices[:, :, 1:]
+
+        images = p_get_images(indices, vqgan_params)
+        images = np.squeeze(np.asarray(images), 1)
+        for image in images:
+            all_images.append(custom_to_pil(image))
+    return all_images
+
+def clip_top_k(prompt, images, k=8):
+    inputs = processor(text=prompt, images=images, return_tensors="np", padding=True)
+    outputs = clip(**inputs)
+    logits = outputs.logits_per_text
+    scores = np.array(logits[0]).argsort()[-k:][::-1]
+    return [images[score] for score in scores]
+
+from PIL import ImageDraw, ImageFont
+
+def captioned_strip(images, caption):
+    w, h = images[0].size[0], images[0].size[1]
+    img = Image.new("RGB", (len(images)*w, h + 48))
+    for i, img_ in enumerate(images):
+        img.paste(img_, (i*w, 48))
+    draw = ImageDraw.Draw(img)
+    font = ImageFont.truetype("/usr/share/fonts/truetype/liberation2/LiberationMono-Bold.ttf", 40)
+    draw.text((20, 3), caption, (255,255,255), font=font)
+    return img
+    
+def log_to_wandb(prompts):
+    strips = []
+    for prompt in prompts:
+        print(f"Generating candidates for: {prompt}")
+        images = hallucinate(prompt, num_images=32)
+        selected = clip_top_k(prompt, images, k=8)
+        strip = captioned_strip(selected, prompt)
+        strips.append(wandb.Image(strip))
+    wandb.log({"images": strips})
+
+## Artifact loop
+
+import wandb
+import os
+os.environ["WANDB_SILENT"] = "true"
+os.environ["WANDB_CONSOLE"] = "off"
+
+id = wandb.util.generate_id()
+print(f"Logging images to wandb run id: {id}")
+
+run = wandb.init(id=id,
+        entity='wandb',
+        project="hf-flax-dalle-mini",
+        job_type="predictions",
+        resume="allow"
+)
+
+artifact = run.use_artifact('wandb/hf-flax-dalle-mini/model-3iwhu4w6:v0', type='bart_model')
+producer_run = artifact.logged_by()
+logged_artifacts = producer_run.logged_artifacts()
+
+for artifact in logged_artifacts:
+    print(f"Generating predictions with version {artifact.version}")
+    artifact_dir = artifact.download()
+
+    # create our model
+    model = CustomFlaxBartForConditionalGeneration.from_pretrained(artifact_dir)
+    model.config.force_bos_token_to_be_generated = False
+    model.config.forced_bos_token_id = None
+    model.config.forced_eos_token_id = None
+
+    bart_params = replicate(model.params)
+    vqgan_params = replicate(vqgan.params)
+
+    prompts = prompts = [
+        "white snow covered mountain under blue sky during daytime",
+        "aerial view of beach during daytime",
+        "aerial view of beach at night",
+        "an armchair in the shape of an avocado",
+        "young woman riding her bike trough a forest",
+        "rice fields by the mediterranean coast",
+        "white houses on the hill of a greek coastline",
+        "illustration of a shark with a baby shark",
+    ]
+
+    log_to_wandb(prompts)

demo/wandb-examples.py

@@ -83,7 +83,7 @@ run = wandb.init(id=id,
         job_type="predictions",
         resume="allow"
 )
-artifact = run.use_artifact('wandb/hf-flax-dalle-mini/model-3iwhu4w6:latest', type='bart_model')
+artifact = run.use_artifact('wandb/hf-flax-dalle-mini/model-4oh3u7ca:latest', type='bart_model')
 artifact_dir = artifact.download()
 
 # create our model