Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
5.54k
python retrieve.py --class_prompt {} --class_data_dir {} --num_class_images 200 Then you can launch the script: Copied export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_custom_diffusion.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--output_dir=$OUTPUT_DIR \
--concepts_list=./concept_list.json \
--with_prior_preservation \
--real_prior \
--prior_loss_weight=1.0 \
--resolution=512 \
--train_batch_size=2 \
--learning_rate=1e-5 \
--lr_warmup_steps=0 \
--max_train_steps=500 \
--num_class_images=200 \
--scale_lr \
--hflip \
--modifier_token "<new1>+<new2>" \
--push_to_hub
</hfoption>
</hfoptions>
Once training is finished, you can use your new Custom Diffusion model for inference.
<hfoptions id="training-inference">
<hfoption id="single concept">
Copied import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16,
).to("cuda")
pipeline.unet.load_attn_procs("path-to-save-model", weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.load_textual_inversion("path-to-save-model", weight_name="<new1>.bin")
image = pipeline(
"<new1> cat sitting in a bucket",
num_inference_steps=100,
guidance_scale=6.0,
eta=1.0,
).images[0]
image.save("cat.png")
</hfoption>
<hfoption id="multiple concepts">
Copied import torch
from huggingface_hub.repocard import RepoCard
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("sayakpaul/custom-diffusion-cat-wooden-pot", torch_dtype=torch.float16).to("cuda")
pipeline.unet.load_attn_procs(model_id, weight_name="pytorch_custom_diffusion_weights.bin")
pipeline.load_textual_inversion(model_id, weight_name="<new1>.bin")
pipeline.load_textual_inversion(model_id, weight_name="<new2>.bin")
image = pipeline(
"the <new1> cat sculpture in the style of a <new2> wooden pot",
num_inference_steps=100,
guidance_scale=6.0,
eta=1.0,
).images[0]
image.save("multi-subject.png")
</hfoption>
</hfoptions>
Next steps Congratulations on training a model with Custom Diffusion! 🎉 To learn more: Read the Multi-Concept Customization of Text-to-Image Diffusion blog post to learn more details about the experimental results from the Custom Diffusion team.
Text-to-Video Generation with AnimateDiff Overview AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai. The abstract of the paper is the following: With the advance of text-to-image models (e.g., Stab...
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers.utils import export_to_gif
# Load the motion adapter
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
# load SD 1.5 based finetuned model
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
pipe.scheduler = scheduler
# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()
output = pipe(
prompt=(
"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
"orange sky, warm lighting, fishing boats, ocean waves seagulls, "
"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
"golden hour, coastal landscape, seaside scenery"
),
negative_prompt="bad quality, worse quality",
num_frames=16,
guidance_scale=7.5,
num_inference_steps=25,
generator=torch.Generator("cpu").manual_seed(42),
)