Update app.py

app.py

@@ -2,17 +2,20 @@ import spaces
 import gradio as gr
 import torch
 from PIL import Image
-from diffusers import DiffusionPipeline
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
 import random
 import uuid
-from typing import Tuple
+from typing import Tuple, Union, List, Optional, Any, Dict
 import numpy as np
 import time
 import zipfile
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
-DESCRIPTION = """## flux realism hpc/.
-"""
+# Description for the app
+DESCRIPTION = """## Flux Realism HPC with Krea Integration
+Choose between 'flux.1-dev-realism' for hyper-realistic images or 'flux.1-krea' for creative outputs."""
 
+# Helper functions
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
@@ -24,37 +27,188 @@ def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     return seed
 
 MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
-base_model = "black-forest-labs/FLUX.1-dev"
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
-
+# Load pipelines for both models
+# Flux.1-dev-realism
+base_model_dev = "black-forest-labs/FLUX.1-dev"
+pipe_dev = DiffusionPipeline.from_pretrained(base_model_dev, torch_dtype=torch.bfloat16)
 lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
 trigger_word = "Super Realism"
+pipe_dev.load_lora_weights(lora_repo)
+pipe_dev.to("cuda")
+
+# Flux.1-krea
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-Krea-dev", subfolder="vae", torch_dtype=dtype).to(device)
+pipe_krea = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Krea-dev", torch_dtype=dtype, vae=taef1).to(device)
+
+# Define the flux_pipe_call_that_returns_an_iterable_of_images for flux.1-krea
+@torch.inference_mode()
+def flux_pipe_call_that_returns_an_iterable_of_images(
+    self,
+    prompt: Union[str, List[str]] = None,
+    prompt_2: Optional[Union[str, List[str]]] = None,
+    height: Optional[int] = None,
+    width: Optional[int] = None,
+    num_inference_steps: int = 28,
+    timesteps: List[int] = None,
+    guidance_scale: float = 3.5,
+    num_images_per_prompt: Optional[int] = 1,
+    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    latents: Optional[torch.FloatTensor] = None,
+    prompt_embeds: Optional[torch.FloatTensor] = None,
+    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    max_sequence_length: int = 512,
+    good_vae: Optional[Any] = None,
+):
+    height = height or self.default_sample_size * self.vae_scale_factor
+    width = width or self.default_sample_size * self.vae_scale_factor
+
+    self.check_inputs(
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        max_sequence_length=max_sequence_length,
+    )
+
+    self._guidance_scale = guidance_scale
+    self._joint_attention_kwargs = joint_attention_kwargs
+    self._interrupt = False
+
+    batch_size = 1 if isinstance(prompt, str) else len(prompt)
+    device = self._execution_device
+
+    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
+    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
+        prompt=prompt,
+        prompt_2=prompt_2,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        device=device,
+        num_images_per_prompt=num_images_per_prompt,
+        max_sequence_length=max_sequence_length,
+        lora_scale=lora_scale,
+    )
+
+    num_channels_latents = self.transformer.config.in_channels // 4
+    latents, latent_image_ids = self.prepare_latents(
+        batch_size * num_images_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        prompt_embeds.dtype,
+        device,
+        generator,
+        latents,
+    )
+
+    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+    image_seq_len = latents.shape[1]
+    mu = calculate_shift(
+        image_seq_len,
+        self.scheduler.config.base_image_seq_len,
+        self.scheduler.config.max_image_seq_len,
+        self.scheduler.config.base_shift,
+        self.scheduler.config.max_shift,
+    )
+    timesteps, num_inference_steps = retrieve_timesteps(
+        self.scheduler,
+        num_inference_steps,
+        device,
+        timesteps,
+        sigmas,
+        mu=mu,
+    )
+    self._num_timesteps = len(timesteps)
+
+    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
+
+    for i, t in enumerate(timesteps):
+        if self.interrupt:
+            continue
+
+        timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+        noise_pred = self.transformer(
+            hidden_states=latents,
+            timestep=timestep / 1000,
+            guidance=guidance,
+            pooled_projections=pooled_prompt_embeds,
+            encoder_hidden_states=prompt_embeds,
+            txt_ids=text_ids,
+            img_ids=latent_image_ids,
+            joint_attention_kwargs=self.joint_attention_kwargs,
+            return_dict=False,
+        )[0]
+
+        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+        image = self.vae.decode(latents_for_image, return_dict=False)[0]
+        yield self.image_processor.postprocess(image, output_type=output_type)[0]
+        
+        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+        torch.cuda.empty_cache()
 
-pipe.load_lora_weights(lora_repo)
-pipe.to("cuda")
+    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
+    image = good_vae.decode(latents, return_dict=False)[0]
+    self.maybe_free_model_hooks()
+    torch.cuda.empty_cache()
+    yield self.image_processor.postprocess(image, output_type=output_type)[0]
 
+pipe_krea.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe_krea)
+
+# Helper functions for flux.1-krea
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed.")
+    if timesteps is not None:
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+# Styles for flux.1-dev-realism
 style_list = [
-    {
-        "name": "3840 x 2160",
-        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "",
-    },
-    {
-        "name": "2560 x 1440",
-        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "",
-    },
-    {
-        "name": "HD+",
-        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-        "negative_prompt": "",
-    },
-    {
-        "name": "Style Zero",
-        "prompt": "{prompt}",
-        "negative_prompt": "",
-    },
+    {"name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
+    {"name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
+    {"name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
+    {"name": "Style Zero", "prompt": "{prompt}", "negative_prompt": ""},
 ]
 
 styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
@@ -65,8 +219,9 @@ def apply_style(style_name: str, positive: str) -> Tuple[str, str]:
     p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
     return p.replace("{prompt}", positive), n
 
+# Generation function for flux.1-dev-realism
 @spaces.GPU
-def generate(
+def generate_dev(
     prompt: str,
     negative_prompt: str = "",
     use_negative_prompt: bool = False,
@@ -98,7 +253,7 @@ def generate(
     
     start_time = time.time()
     
-    images = pipe(
+    images = pipe_dev(
         prompt=positive_prompt,
         negative_prompt=final_negative_prompt if final_negative_prompt else None,
         width=width,
@@ -125,11 +280,111 @@ def generate(
     
     return image_paths, seed, f"{duration:.2f}", zip_path
 
+# Generation function for flux.1-krea
+@spaces.GPU
+def generate_krea(
+    prompt: str,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 4.5,
+    randomize_seed: bool = False,
+    num_inference_steps: int = 28,
+    num_images: int = 1,
+    zip_images: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    start_time = time.time()
+    
+    images = []
+    for _ in range(num_images):
+        final_img = list(pipe_krea.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil",
+            good_vae=good_vae,
+        ))[-1]  # Take the final image only
+        images.append(final_img)
+    
+    end_time = time.time()
+    duration = end_time - start_time
+    
+    image_paths = [save_image(img) for img in images]
+    
+    zip_path = None
+    if zip_images:
+        zip_name = str(uuid.uuid4()) + ".zip"
+        with zipfile.ZipFile(zip_name, 'w') as zipf:
+            for i, img_path in enumerate(image_paths):
+                zipf.write(img_path, arcname=f"Img_{i}.png")
+        zip_path = zip_name
+    
+    return image_paths, seed, f"{duration:.2f}", zip_path
+
+# Main generation function to handle model choice
+@spaces.GPU
+def generate(
+    model_choice: str,
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    style_name: str = DEFAULT_STYLE_NAME,
+    num_inference_steps: int = 30,
+    num_images: int = 1,
+    zip_images: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if model_choice == "flux.1-dev-realism":
+        return generate_dev(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            use_negative_prompt=use_negative_prompt,
+            seed=seed,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            randomize_seed=randomize_seed,
+            style_name=style_name,
+            num_inference_steps=num_inference_steps,
+            num_images=num_images,
+            zip_images=zip_images,
+            progress=progress,
+        )
+    elif model_choice == "flux.1-krea":
+        return generate_krea(
+            prompt=prompt,
+            seed=seed,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            randomize_seed=randomize_seed,
+            num_inference_steps=num_inference_steps,
+            num_images=num_images,
+            zip_images=zip_images,
+            progress=progress,
+        )
+    else:
+        raise ValueError("Invalid model choice")
+
+# Examples (tailored for flux.1-dev-realism)
 examples = [
     "Super Realism, High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250",
     "Woman in a red jacket, snowy, in the style of hyper-realistic portraiture, caninecore, mountainous vistas, timeless beauty, palewave, iconic, distinctive noses --ar 72:101 --stylize 750 --v 6",
     "Super Realism, Headshot of handsome young man, wearing dark gray sweater with buttons and big shawl collar, brown hair and short beard, serious look on his face, black background, soft studio lighting, portrait photography --ar 85:128 --v 6.0 --style",
-    "Super-realism, Purple Dreamy, a medium-angle shot of a young woman with long brown hair, wearing a pair of eye-level glasses, stands in front of a backdrop of purple and white lights. The womans eyes are closed, her lips are slightly parted, as if she is looking up at the sky. Her hair is cascading over her shoulders, framing her face. She is wearing a sleeveless top, adorned with tiny white dots, and a gold chain necklace around her neck. Her left earrings are dangling from her ears, adding a pop of color to the scene."
+    "Super-realism, Purple Dreamy, a medium-angle shot of a young woman with long brown hair, wearing a pair of eye-level glasses, stands in front of a backdrop of purple and white lights."
 ]
 
 css = '''
@@ -145,6 +400,7 @@ footer {
 }
 '''
 
+# Gradio interface
 with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
     gr.Markdown(DESCRIPTION)
     with gr.Row():
@@ -157,15 +413,22 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         )
         run_button = gr.Button("Run", scale=0, variant="primary")
     result = gr.Gallery(label="Result", columns=1, show_label=False, preview=True)
-
+    
+    # Model choice radio button above additional options
+    model_choice = gr.Radio(
+        choices=["flux.1-krea", "flux.1-dev-realism"],
+        label="Select Model",
+        value="flux.1-krea"
+    )
+    
     with gr.Accordion("Additional Options", open=False):
         style_selection = gr.Dropdown(
-            label="Quality Style",
+            label="Quality Style (for flux.1-dev-realism only)",
             choices=STYLE_NAMES,
             value=DEFAULT_STYLE_NAME,
             interactive=True,
         )
-        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt (for flux.1-dev-realism only)", value=False)
         negative_prompt = gr.Text(
             label="Negative prompt",
             max_lines=1,
@@ -245,6 +508,7 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
         ],
         fn=generate,
         inputs=[
+            model_choice,
             prompt,
             negative_prompt,
             use_negative_prompt,