Spaces:
Running
on
Zero
Running
on
Zero
DecoderWQH666
commited on
Commit
•
7e2c35e
1
Parent(s):
92b6ce4
Update app.py
Browse files
app.py
CHANGED
@@ -18,7 +18,7 @@ from utils import text_encoder_forward
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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from utils import latents_to_images, downsampling, merge_and_save_images
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from omegaconf import OmegaConf
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from accelerate.utils import set_seed
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from tqdm import tqdm
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
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from PIL import Image
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@@ -133,7 +133,7 @@ woman_Embedding_Manager = models.embedding_manager.EmbeddingManagerId_adain(
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text_encoder.text_model.embeddings.forward = original_forward
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DEFAULT_STYLE_NAME = "Watercolor"
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MAX_SEED = np.iinfo(np.int32).max
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def replace_phrases(prompt):
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@@ -185,6 +185,7 @@ def generate_image(chose_emb, choice, gender_GAN, prompts_array):
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os.makedirs(save_dir, exist_ok=True)
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random_embedding = torch.randn(1, 1, input_dim).to(device)
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if choice == "Create a new character":
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_, emb_dict = Embedding_Manager(tokenized_text=None, embedded_text=None, name_batch=None, random_embeddings = random_embedding, timesteps = None,)
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test_emb = emb_dict["adained_total_embedding"].to(device)
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from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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from utils import latents_to_images, downsampling, merge_and_save_images
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from omegaconf import OmegaConf
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# from accelerate.utils import set_seed
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from tqdm import tqdm
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
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from PIL import Image
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text_encoder.text_model.embeddings.forward = original_forward
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DEFAULT_STYLE_NAME = "Watercolor"
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# MAX_SEED = np.iinfo(np.int32).max
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def replace_phrases(prompt):
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os.makedirs(save_dir, exist_ok=True)
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random_embedding = torch.randn(1, 1, input_dim).to(device)
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print(random_embedding)
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if choice == "Create a new character":
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_, emb_dict = Embedding_Manager(tokenized_text=None, embedded_text=None, name_batch=None, random_embeddings = random_embedding, timesteps = None,)
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test_emb = emb_dict["adained_total_embedding"].to(device)
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