Upload wuerstchen_app.py

wuerstchen_app.py

@@ -0,0 +1,259 @@
+import os
+import random
+import gradio as gr
+import numpy as np
+import PIL.Image
+import torch
+from typing import List
+from diffusers.utils import numpy_to_pil
+from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
+from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
+from previewer.modules import Previewer
+os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+
+DESCRIPTION = "# Würstchen"
+DESCRIPTION += "\n<p style=\"text-align: center\"><a href='https://huggingface.co/warp-ai/wuerstchen' target='_blank'>Würstchen</a> is a new fast and efficient high resolution text-to-image architecture and model</p>"
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
+USE_TORCH_COMPILE = False
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
+PREVIEW_IMAGES = True
+
+dtype = torch.float16
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    prior_pipeline = WuerstchenPriorPipeline.from_pretrained("warp-ai/wuerstchen-prior", torch_dtype=dtype)
+    decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained("warp-ai/wuerstchen", torch_dtype=dtype)
+    if ENABLE_CPU_OFFLOAD:
+        prior_pipeline.enable_model_cpu_offload()
+        decoder_pipeline.enable_model_cpu_offload()
+    else:
+        prior_pipeline.to(device)
+        decoder_pipeline.to(device)
+
+    if USE_TORCH_COMPILE:
+        prior_pipeline.prior = torch.compile(prior_pipeline.prior, mode="reduce-overhead", fullgraph=True)
+        decoder_pipeline.decoder = torch.compile(decoder_pipeline.decoder, mode="reduce-overhead", fullgraph=True)
+    
+    if PREVIEW_IMAGES:
+        previewer = Previewer()
+        previewer.load_state_dict(torch.load("previewer/text2img_wurstchen_b_v1_previewer_100k.pt")["state_dict"])
+        previewer.eval().requires_grad_(False).to(device).to(dtype)
+
+        def callback_prior(i, t, latents):
+            output = previewer(latents)
+            output = numpy_to_pil(output.clamp(0, 1).permute(0, 2, 3, 1).cpu().numpy())
+            return output
+    else:
+        previewer = None
+        callback_prior = None
+else:
+    prior_pipeline = None
+    decoder_pipeline = None
+
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    prior_num_inference_steps: int = 60,
+    # prior_timesteps: List[float] = None,
+    prior_guidance_scale: float = 4.0,
+    decoder_num_inference_steps: int = 12,
+    # decoder_timesteps: List[float] = None,
+    decoder_guidance_scale: float = 0.0,
+    num_images_per_prompt: int = 1,
+) -> PIL.Image.Image:
+    generator = torch.Generator().manual_seed(seed)
+
+    prior_output = prior_pipeline(
+        prompt=prompt,
+        height=height,
+        width=width,
+		num_inference_steps  = prior_num_inference_steps,
+        # timesteps=DEFAULT_STAGE_C_TIMESTEPS,
+        negative_prompt=negative_prompt,
+        guidance_scale=prior_guidance_scale,
+        num_images_per_prompt=num_images_per_prompt,
+        generator=generator,
+        callback=callback_prior,
+    )
+
+   
+    decoder_output = decoder_pipeline(
+        image_embeddings=prior_output.image_embeddings,
+        prompt=prompt,
+		num_inference_steps = decoder_num_inference_steps,
+        # timesteps=decoder_timesteps,
+        guidance_scale=decoder_guidance_scale,
+        negative_prompt=negative_prompt,
+        generator=generator,
+        output_type="pil",
+    ).images
+    yield decoder_output
+
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+]
+
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a Negative Prompt",
+        )
+
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row():
+            width = gr.Slider(
+                label="Width",
+                minimum=1024,
+                maximum=MAX_IMAGE_SIZE,
+                step=512,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=1024,
+                maximum=MAX_IMAGE_SIZE,
+                step=512,
+                value=1024,
+            )
+            num_images_per_prompt = gr.Slider(
+                label="Number of Images",
+                minimum=1,
+                maximum=20,
+                step=1,
+                value=1,
+            )
+        with gr.Row():
+            prior_guidance_scale = gr.Slider(
+                label="Prior Guidance Scale",
+                minimum=0,
+                maximum=40,
+                step=0.1,
+                value=4.0,
+            )
+            prior_num_inference_steps = gr.Slider(
+                label="Prior Inference Steps",
+                minimum=30,
+                maximum=240,
+                step=1,
+                value=30,
+            )
+
+            decoder_guidance_scale = gr.Slider(
+                label="Decoder Guidance Scale",
+                minimum=0,
+                maximum=20,
+                step=0.1,
+                value=0.0,
+            )
+            decoder_num_inference_steps = gr.Slider(
+                label="Decoder Inference Steps",
+                minimum=4,
+                maximum=240,
+                step=1,
+                value=12,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=result,
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    inputs = [
+            prompt,
+            negative_prompt,
+            seed,
+            width,
+            height,
+            prior_num_inference_steps,
+            # prior_timesteps,
+            prior_guidance_scale,
+            decoder_num_inference_steps,
+            # decoder_timesteps,
+            decoder_guidance_scale,
+            num_images_per_prompt,
+    ]
+    prompt.submit(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name="run",
+    )
+    negative_prompt.submit(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+    run_button.click(
+        fn=randomize_seed_fn,
+        inputs=[seed, randomize_seed],
+        outputs=seed,
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=generate,
+        inputs=inputs,
+        outputs=result,
+        api_name=False,
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()