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import gradio as gr | |
import numpy as np | |
import random | |
from diffusers import DiffusionPipeline | |
import torch | |
from huggingface_hub import hf_hub_download | |
from safetensors.torch import load_file | |
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler | |
from cog_sdxl.no_init import no_init_or_tensor | |
from diffusers.models.attention_processor import LoRAAttnProcessor2_0 | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
pipe = DiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-xl-base-1.0", | |
torch_dtype=torch.float16, | |
use_safetensors=True, | |
variant="fp16", | |
).to(device) | |
unet = pipe.unet | |
lora_path = hf_hub_download(repo_id="SvenN/sdxl-emoji", filename="lora.safetensors", repo_type="model") | |
embeddings_path = hf_hub_download(repo_id="SvenN/sdxl-emoji", filename="embeddings.pti", repo_type="model") | |
tensors = load_file(lora_path) | |
unet_lora_attn_procs = {} | |
name_rank_map = {} | |
for tk, tv in tensors.items(): | |
# up is N, d | |
tensors[tk] = tv.half() | |
if tk.endswith("up.weight"): | |
proc_name = ".".join(tk.split(".")[:-3]) | |
r = tv.shape[1] | |
name_rank_map[proc_name] = r | |
for name, attn_processor in unet.attn_processors.items(): | |
cross_attention_dim = ( | |
None | |
if name.endswith("attn1.processor") | |
else unet.config.cross_attention_dim | |
) | |
if name.startswith("mid_block"): | |
hidden_size = unet.config.block_out_channels[-1] | |
elif name.startswith("up_blocks"): | |
block_id = int(name[len("up_blocks.")]) | |
hidden_size = list(reversed(unet.config.block_out_channels))[ | |
block_id | |
] | |
elif name.startswith("down_blocks"): | |
block_id = int(name[len("down_blocks.")]) | |
hidden_size = unet.config.block_out_channels[block_id] | |
with no_init_or_tensor(): | |
module = LoRAAttnProcessor2_0( | |
hidden_size=hidden_size, | |
cross_attention_dim=cross_attention_dim, | |
rank=name_rank_map[name], | |
).half() | |
unet_lora_attn_procs[name] = module.to("cuda", non_blocking=True) | |
unet.set_attn_processor(unet_lora_attn_procs) | |
unet.load_state_dict(tensors, strict=False) | |
handler = TokenEmbeddingsHandler( | |
[pipe.text_encoder, pipe.text_encoder_2], [pipe.tokenizer, pipe.tokenizer_2] | |
) | |
handler.load_embeddings(embeddings_path) | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 1024 | |
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator().manual_seed(seed) | |
image = pipe( | |
prompt = prompt, | |
negative_prompt = negative_prompt, | |
guidance_scale = guidance_scale, | |
num_inference_steps = num_inference_steps, | |
width = width, | |
height = height, | |
generator = generator, | |
cross_attention_kwargs={"scale": 0.6}, | |
).images[0] | |
return image | |
examples = [ | |
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
"An astronaut riding a green horse", | |
"A delicious ceviche cheesecake slice", | |
] | |
css=""" | |
#col-container { | |
margin: 0 auto; | |
max-width: 520px; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown(f""" | |
# SDXL Emoji running on diffusers 0.25.0 | |
""") | |
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.Image(label="Result", show_label=False) | |
with gr.Accordion("Advanced Settings", open=False): | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
max_lines=1, | |
placeholder="Enter a negative prompt", | |
visible=False, | |
) | |
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=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=256, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.0, | |
maximum=10.0, | |
step=0.1, | |
value=7.5, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=50, | |
) | |
gr.Examples( | |
examples = examples, | |
inputs = [prompt] | |
) | |
run_button.click( | |
fn = infer, | |
inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], | |
outputs = [result] | |
) | |
demo.queue().launch() |