all app and sentencepiecesentencepiece

app-dev.py

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+import gradio as gr
+import PIL
+import spaces
+import torch
+from hi_diffusers import HiDreamImagePipeline, HiDreamImageTransformer2DModel
+from hi_diffusers.schedulers.flash_flow_match import (
+    FlashFlowMatchEulerDiscreteScheduler,
+)
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+# Constants
+MODEL_PREFIX: str = "HiDream-ai"
+LLAMA_MODEL_NAME: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+MODEL_PATH = "HiDream-ai/HiDream-I1-Dev"
+MODEL_CONFIGS = {
+    "guidance_scale": 0.0,
+    "num_inference_steps": 28,
+    "shift": 6.0,
+    "scheduler": FlashFlowMatchEulerDiscreteScheduler,
+}
+
+
+# Supported image sizes
+RESOLUTION_OPTIONS: list[str] = [
+    "1024 x 1024 (Square)",
+    "768 x 1360 (Portrait)",
+    "1360 x 768 (Landscape)",
+    "880 x 1168 (Portrait)",
+    "1168 x 880 (Landscape)",
+    "1248 x 832 (Landscape)",
+    "832 x 1248 (Portrait)",
+]
+
+
+tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL_NAME, use_fast=False)
+text_encoder = LlamaForCausalLM.from_pretrained(
+    LLAMA_MODEL_NAME,
+    output_hidden_states=True,
+    output_attentions=True,
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+transformer = HiDreamImageTransformer2DModel.from_pretrained(
+    MODEL_PATH,
+    subfolder="transformer",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+scheduler = MODEL_CONFIGS["scheduler"](
+    num_train_timesteps=1000,
+    shift=MODEL_CONFIGS["shift"],
+    use_dynamic_shifting=False,
+)
+
+pipe = HiDreamImagePipeline.from_pretrained(
+    MODEL_PATH,
+    scheduler=scheduler,
+    tokenizer_4=tokenizer,
+    text_encoder_4=text_encoder,
+    torch_dtype=torch.bfloat16,
+).to("cuda", torch.bfloat16)
+
+pipe.transformer = transformer
+
+
+@spaces.GPU(duration=90)
+def generate_image(
+    prompt: str,
+    resolution: str,
+    seed: int,
+) -> tuple[PIL.Image.Image, int]:
+    if seed == -1:
+        seed = torch.randint(0, 1_000_000, (1,)).item()
+
+    height, width = tuple(map(int, resolution.replace(" ", "").split("x")))
+    generator = torch.Generator("cuda").manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        height=height,
+        width=width,
+        guidance_scale=MODEL_CONFIGS["guidance_scale"],
+        num_inference_steps=MODEL_CONFIGS["num_inference_steps"],
+        generator=generator,
+    ).images[0]
+
+    torch.cuda.empty_cache()
+    return image, seed
+
+
+# Gradio UI
+with gr.Blocks(title="HiDream Image Generator") as demo:
+    gr.Markdown("## 🌈 HiDream Image Generator")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="e.g. A futuristic city with floating cars at sunset",
+                lines=3,
+            )
+
+            resolution = gr.Radio(
+                choices=RESOLUTION_OPTIONS,
+                value=RESOLUTION_OPTIONS[0],
+                label="Resolution",
+            )
+
+            seed = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
+            generate_btn = gr.Button("Generate Image", variant="primary")
+            seed_used = gr.Number(label="Seed Used", interactive=False)
+
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image", type="pil")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, resolution, seed],
+        outputs=[output_image, seed_used],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

app-fast.py

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+import gradio as gr
+import PIL
+import spaces
+import torch
+from hi_diffusers import HiDreamImagePipeline, HiDreamImageTransformer2DModel
+from hi_diffusers.schedulers.flash_flow_match import (
+    FlashFlowMatchEulerDiscreteScheduler,
+)
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+# Constants
+MODEL_PREFIX: str = "HiDream-ai"
+LLAMA_MODEL_NAME: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+MODEL_PATH = "HiDream-ai/HiDream-I1-Fast"
+MODEL_CONFIGS = {
+    "guidance_scale": 0.0,
+    "num_inference_steps": 16,
+    "shift": 3.0,
+    "scheduler": FlashFlowMatchEulerDiscreteScheduler,
+}
+
+
+# Supported image sizes
+RESOLUTION_OPTIONS: list[str] = [
+    "1024 x 1024 (Square)",
+    "768 x 1360 (Portrait)",
+    "1360 x 768 (Landscape)",
+    "880 x 1168 (Portrait)",
+    "1168 x 880 (Landscape)",
+    "1248 x 832 (Landscape)",
+    "832 x 1248 (Portrait)",
+]
+
+
+tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL_NAME, use_fast=False)
+text_encoder = LlamaForCausalLM.from_pretrained(
+    LLAMA_MODEL_NAME,
+    output_hidden_states=True,
+    output_attentions=True,
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+transformer = HiDreamImageTransformer2DModel.from_pretrained(
+    MODEL_PATH,
+    subfolder="transformer",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+scheduler = MODEL_CONFIGS["scheduler"](
+    num_train_timesteps=1000,
+    shift=MODEL_CONFIGS["shift"],
+    use_dynamic_shifting=False,
+)
+
+pipe = HiDreamImagePipeline.from_pretrained(
+    MODEL_PATH,
+    scheduler=scheduler,
+    tokenizer_4=tokenizer,
+    text_encoder_4=text_encoder,
+    torch_dtype=torch.bfloat16,
+).to("cuda", torch.bfloat16)
+
+pipe.transformer = transformer
+
+
+@spaces.GPU(duration=90)
+def generate_image(
+    prompt: str,
+    resolution: str,
+    seed: int,
+) -> tuple[PIL.Image.Image, int]:
+    if seed == -1:
+        seed = torch.randint(0, 1_000_000, (1,)).item()
+
+    height, width = tuple(map(int, resolution.replace(" ", "").split("x")))
+    generator = torch.Generator("cuda").manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        height=height,
+        width=width,
+        guidance_scale=MODEL_CONFIGS["guidance_scale"],
+        num_inference_steps=MODEL_CONFIGS["num_inference_steps"],
+        generator=generator,
+    ).images[0]
+
+    torch.cuda.empty_cache()
+    return image, seed
+
+
+# Gradio UI
+with gr.Blocks(title="HiDream Image Generator") as demo:
+    gr.Markdown("## 🌈 HiDream Image Generator")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="e.g. A futuristic city with floating cars at sunset",
+                lines=3,
+            )
+
+            resolution = gr.Radio(
+                choices=RESOLUTION_OPTIONS,
+                value=RESOLUTION_OPTIONS[0],
+                label="Resolution",
+            )
+
+            seed = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
+            generate_btn = gr.Button("Generate Image", variant="primary")
+            seed_used = gr.Number(label="Seed Used", interactive=False)
+
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image", type="pil")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, resolution, seed],
+        outputs=[output_image, seed_used],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

app-full.py

@@ -0,0 +1,120 @@
+import gradio as gr
+import PIL
+import spaces
+import torch
+from hi_diffusers import HiDreamImagePipeline, HiDreamImageTransformer2DModel
+from hi_diffusers.schedulers.fm_solvers_unipc import FlowUniPCMultistepScheduler
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+# Constants
+MODEL_PREFIX: str = "HiDream-ai"
+LLAMA_MODEL_NAME: str = "meta-llama/Meta-Llama-3.1-8B-Instruct"
+MODEL_PATH = "HiDream-ai/HiDream-I1-full"
+MODEL_CONFIGS = {
+    "guidance_scale": 5.0,
+    "num_inference_steps": 50,
+    "shift": 3.0,
+    "scheduler": FlowUniPCMultistepScheduler,
+}
+
+# Supported image sizes
+RESOLUTION_OPTIONS: list[str] = [
+    "1024 x 1024 (Square)",
+    "768 x 1360 (Portrait)",
+    "1360 x 768 (Landscape)",
+    "880 x 1168 (Portrait)",
+    "1168 x 880 (Landscape)",
+    "1248 x 832 (Landscape)",
+    "832 x 1248 (Portrait)",
+]
+
+
+tokenizer = AutoTokenizer.from_pretrained(LLAMA_MODEL_NAME, use_fast=False)
+text_encoder = LlamaForCausalLM.from_pretrained(
+    LLAMA_MODEL_NAME,
+    output_hidden_states=True,
+    output_attentions=True,
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+transformer = HiDreamImageTransformer2DModel.from_pretrained(
+    MODEL_PATH,
+    subfolder="transformer",
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+scheduler = MODEL_CONFIGS["scheduler"](
+    num_train_timesteps=1000,
+    shift=MODEL_CONFIGS["shift"],
+    use_dynamic_shifting=False,
+)
+
+pipe = HiDreamImagePipeline.from_pretrained(
+    MODEL_PATH,
+    scheduler=scheduler,
+    tokenizer_4=tokenizer,
+    text_encoder_4=text_encoder,
+    torch_dtype=torch.bfloat16,
+).to("cuda", torch.bfloat16)
+
+pipe.transformer = transformer
+
+
+@spaces.GPU(duration=90)
+def generate_image(
+    prompt: str,
+    resolution: str,
+    seed: int,
+) -> tuple[PIL.Image.Image, int]:
+    if seed == -1:
+        seed = torch.randint(0, 1_000_000, (1,)).item()
+
+    height, width = tuple(map(int, resolution.replace(" ", "").split("x")))
+    generator = torch.Generator("cuda").manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        height=height,
+        width=width,
+        guidance_scale=MODEL_CONFIGS["guidance_scale"],
+        num_inference_steps=MODEL_CONFIGS["num_inference_steps"],
+        generator=generator,
+    ).images[0]
+
+    torch.cuda.empty_cache()
+    return image, seed
+
+
+# Gradio UI
+with gr.Blocks(title="HiDream Image Generator") as demo:
+    gr.Markdown("## 🌈 HiDream Image Generator")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(
+                label="Prompt",
+                placeholder="e.g. A futuristic city with floating cars at sunset",
+                lines=3,
+            )
+
+            resolution = gr.Radio(
+                choices=RESOLUTION_OPTIONS,
+                value=RESOLUTION_OPTIONS[0],
+                label="Resolution",
+            )
+
+            seed = gr.Number(label="Seed (-1 for random)", value=-1, precision=0)
+            generate_btn = gr.Button("Generate Image", variant="primary")
+            seed_used = gr.Number(label="Seed Used", interactive=False)
+
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image", type="pil")
+
+    generate_btn.click(
+        fn=generate_image,
+        inputs=[prompt, resolution, seed],
+        outputs=[output_image, seed_used],
+    )
+
+if __name__ == "__main__":
+    demo.launch()