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Browse files- model.py +3 -0
- multit2i.py +13 -11
model.py
CHANGED
@@ -2,6 +2,7 @@ from multit2i import find_model_list
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models = [
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'yodayo-ai/kivotos-xl-2.0',
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'yodayo-ai/holodayo-xl-2.1',
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'cagliostrolab/animagine-xl-3.1',
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@@ -18,6 +19,8 @@ models = [
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'Raelina/Raemu-XL-V4',
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]
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# Examples:
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#models = ['yodayo-ai/kivotos-xl-2.0', 'yodayo-ai/holodayo-xl-2.1'] # specific models
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models = [
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'yodayo-ai/clandestine-xl-1.0',
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'yodayo-ai/kivotos-xl-2.0',
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'yodayo-ai/holodayo-xl-2.1',
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'cagliostrolab/animagine-xl-3.1',
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'Raelina/Raemu-XL-V4',
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]
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#models = find_model_list("Disty0", [], "", "last_modified", 100)
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# Examples:
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#models = ['yodayo-ai/kivotos-xl-2.0', 'yodayo-ai/holodayo-xl-2.1'] # specific models
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multit2i.py
CHANGED
@@ -124,7 +124,7 @@ def load_from_model(model_name: str, hf_token: str = None):
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f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
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)
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headers["X-Wait-For-Model"] = "true"
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client = huggingface_hub.InferenceClient(model=model_name, headers=headers, token=hf_token)
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inputs = gr.components.Textbox(label="Input")
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outputs = gr.components.Image(label="Output")
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fn = client.text_to_image
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@@ -325,19 +325,20 @@ async def infer_multi(prompt: str, neg_prompt: str, results: list, image_num: fl
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images = results if results else []
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image_num_offset = len(images)
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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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tasks = [asyncio.to_thread(infer, prompt, neg_prompt, model_name) for i in range(image_num)]
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for task in tasks:
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progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
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try:
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result = await task
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except Exception as e:
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print(e)
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task.cancel()
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result = None
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image_num_offset += 1
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with lock:
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if result and len(result) == 2 and result[1]: images.append(result)
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await asyncio.sleep(0
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yield images
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@@ -351,17 +352,18 @@ async def infer_multi_random(prompt: str, neg_prompt: str, results: list, image_
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random.seed()
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model_names = random.choices(list(loaded_models.keys()), k = image_num)
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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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tasks = [asyncio.to_thread(infer, prompt, neg_prompt, model_name) for model_name in model_names]
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for task in tasks:
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progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
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try:
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result = await task
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except Exception as e:
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print(e)
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task.cancel()
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result = None
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image_num_offset += 1
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with lock:
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if result and len(result) == 2 and result[1]: images.append(result)
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await asyncio.sleep(0
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yield images
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f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
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)
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headers["X-Wait-For-Model"] = "true"
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client = huggingface_hub.InferenceClient(model=model_name, headers=headers, token=hf_token, timeout=120)
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inputs = gr.components.Textbox(label="Input")
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outputs = gr.components.Image(label="Output")
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fn = client.text_to_image
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images = results if results else []
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image_num_offset = len(images)
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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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tasks = [asyncio.create_task(asyncio.to_thread(infer, prompt, neg_prompt, model_name)) for i in range(image_num)]
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await asyncio.sleep(0)
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for task in tasks:
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progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
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try:
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result = await asyncio.wait_for(task, timeout=120)
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except (Exception, asyncio.TimeoutError) as e:
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print(e)
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if not task.done(): task.cancel()
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result = None
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image_num_offset += 1
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with lock:
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if result and len(result) == 2 and result[1]: images.append(result)
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await asyncio.sleep(0)
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yield images
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random.seed()
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model_names = random.choices(list(loaded_models.keys()), k = image_num)
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prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
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tasks = [asyncio.create_task(asyncio.to_thread(infer, prompt, neg_prompt, model_name)) for model_name in model_names]
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await asyncio.sleep(0)
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for task in tasks:
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progress(float(len(images) - image_num_offset) / float(image_num), desc="Running inference.")
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try:
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result = await asyncio.wait_for(task, timeout=120)
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except (Exception, asyncio.TimeoutError) as e:
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print(e)
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if not task.done(): task.cancel()
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result = None
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image_num_offset += 1
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with lock:
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if result and len(result) == 2 and result[1]: images.append(result)
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await asyncio.sleep(0)
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yield images
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